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train · 44.6k rows

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5f1005f9-f2a0-4e94-803f-ba478b40ccac	wish-i-can-feel-what-you-feel-a-neural	2212.02000	null	https://arxiv.org/abs/2212.02000v1	https://arxiv.org/pdf/2212.02000v1.pdf	Wish I Can Feel What You Feel: A Neural Approach for Empathetic Response Generation	Expressing empathy is important in everyday conversations, and exploring how empathy arises is crucial in automatic response generation. Most previous approaches consider only a single factor that affects empathy. However, in practice, empathy generation and expression is a very complex and dynamic psychological process. A listener needs to find out events which cause a speaker's emotions (emotion cause extraction), project the events into some experience (knowledge extension), and express empathy in the most appropriate way (communication mechanism). To this end, we propose a novel approach, which integrates the three components - emotion cause, knowledge graph, and communication mechanism for empathetic response generation. Experimental results on the benchmark dataset demonstrate the effectiveness of our method and show that incorporating the key components generates more informative and empathetic responses.	['Chunfeng Liang', 'Yangbin Chen']	2022-12-05	null	null	null	null	['response-generation', 'empathetic-response-generation', 'emotion-cause-extraction']	['natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[-1.28826186e-01 1.57644302e-01 -3.30497362e-02 -3.83627683e-01 -3.55877243e-02 -2.18915388e-01 5.25389493e-01 1.77813917e-01 -1.88914806e-01 9.68161821e-01 8.74731123e-01 2.76169658e-01 -1.77134767e-01 -9.12085772e-01 1.36815265e-01 -3.68136436e-01 4.78450298e-01 2.46534556e-01 -3.32728326e-01 -5.59081256e-01 5.12007952e-01 4.14673448e-01 -9.17605698e-01 3.52461398e-01 9.58435833e-01 4.99846280e-01 -3.52845371e-01 5.95405817e-01 -4.65404928e-01 1.69369650e+00 -9.17705297e-01 -1.03253531e+00 -3.84483159e-01 -1.26417005e+00 -1.25559950e+00 -2.53993720e-01 -7.68143415e-01 -3.08670342e-01 1.54252961e-01 8.87507081e-01 5.57912290e-01 5.39598107e-01 7.71387875e-01 -1.36790144e+00 -5.93985856e-01 9.66740251e-01 -4.45860445e-01 -2.18665361e-01 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ec95ef12-68bd-4972-a61a-689e4725da1b	multi-frame-quality-enhancement-on-compressed	2201.11389	null	https://arxiv.org/abs/2201.11389v1	https://arxiv.org/pdf/2201.11389v1.pdf	Multi-Frame Quality Enhancement On Compressed Video Using Quantised Data of Deep Belief Networks	In the age of streaming and surveillance compressed video enhancement has become a problem in need of constant improvement. Here, we investigate a way of improving the Multi-Frame Quality Enhancement approach. This approach consists of making use of the frames that have the peak quality in the region to improve those that have a lower quality in that region. This approach consists of obtaining quantized data from the videos using a deep belief network. The quantized data is then fed into the MF-CNN architecture to improve the compressed video. We further investigate the impact of using a Bi-LSTM for detecting the peak quality frames. Our approach obtains better results than the first approach of the MFQE which uses an SVM for PQF detection. On the other hand, our MFQE approach does not outperform the latest version of the MQFE approach that uses a Bi-LSTM for PQF detection.	['Mkhuseli Ngxande', 'Dionne Takudzwa Chasi']	2022-01-27	null	null	null	null	['video-enhancement']	['computer-vision']	[ 2.93603420e-01 -1.23783581e-01 1.15203097e-01 -2.58734733e-01 -6.06225550e-01 2.93719769e-03 3.97340655e-01 6.46577403e-02 -6.11514390e-01 4.37352777e-01 1.77823514e-01 -2.31937632e-01 4.95831929e-02 -1.00483716e+00 -8.49975049e-01 -8.14961433e-01 -2.08532110e-01 -3.38145047e-01 6.50821447e-01 -3.42258960e-01 4.36202735e-01 2.85856456e-01 -1.62435067e+00 7.87283838e-01 3.50469887e-01 1.38811922e+00 4.01060700e-01 1.20235348e+00 2.49197483e-01 9.93820012e-01 -8.97700369e-01 -1.46139085e-01 3.45816672e-01 -4.33624476e-01 -6.51713312e-01 4.48150039e-02 6.34365499e-01 -1.03449500e+00 -3.58222276e-01 1.14604723e+00 5.80196261e-01 4.08710120e-03 1.90670833e-01 -1.04875696e+00 -2.95833290e-01 2.42748588e-01 -3.74998271e-01 9.40669477e-01 6.04573071e-01 -4.84153964e-02 6.29807472e-01 -4.41067874e-01 3.73381048e-01 1.20927060e+00 5.81447065e-01 4.43775296e-01 -6.80210710e-01 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5e015f94-4e04-4075-8eb2-d7e338c7a07c	domain-expanded-aste-rethinking	2305.14434	null	https://arxiv.org/abs/2305.14434v1	https://arxiv.org/pdf/2305.14434v1.pdf	Domain-Expanded ASTE: Rethinking Generalization in Aspect Sentiment Triplet Extraction	Aspect Sentiment Triplet Extraction (ASTE) is a subtask of Aspect-Based Sentiment Analysis (ABSA) that considers each opinion term, their expressed sentiment, and the corresponding aspect targets. However, existing methods are limited to the in-domain setting with two domains. Hence, we propose a domain-expanded benchmark to address the in-domain, out-of-domain and cross-domain settings. We support the new benchmark by annotating more than 4000 data samples for two new domains based on hotel and cosmetics reviews. Our analysis of five existing methods shows that while there is a significant gap between in-domain and out-of-domain performance, generative methods have a strong potential for domain generalization. Our datasets, code implementation and models are available at https://github.com/DAMO-NLP-SG/domain-expanded-aste .	['Lidong Bing', 'Soujanya Poria', 'Sharifah Mahani Aljunied', 'Guizhen Chen', 'Wei Han', 'Hui Chen', 'Yew Ken Chia']	2023-05-23	null	null	null	null	['sentiment-analysis', 'aspect-based-sentiment-analysis', 'aspect-sentiment-triplet-extraction']	['natural-language-processing', 'natural-language-processing', 'natural-language-processing']	[-1.36432618e-01 -1.24272585e-01 -3.57206970e-01 -7.10818529e-01 -1.17615449e+00 -1.17840052e+00 8.42586339e-01 2.60439166e-03 7.44599849e-04 6.75754786e-01 3.78907144e-01 -2.97719359e-01 1.17933132e-01 -8.04790497e-01 -3.68366897e-01 -3.75735998e-01 4.61917549e-01 7.13417470e-01 -1.01087920e-01 -5.78255355e-01 7.21004829e-02 -1.54489130e-01 -1.34189606e+00 5.71735978e-01 8.69081140e-01 9.41679776e-01 -4.46770042e-01 2.55439967e-01 -3.81031275e-01 2.22201899e-01 -7.66500831e-01 -9.84130263e-01 3.58546644e-01 -4.21102434e-01 -7.05165923e-01 1.14018701e-01 1.40582785e-01 2.32417449e-01 1.48711279e-01 1.04237914e+00 5.77500761e-01 -1.06189795e-01 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1fe7c8e5-d8c4-438f-8226-eab24b59326a	action-quality-assessment-using-siamese	2002.12096	null	https://arxiv.org/abs/2002.12096v1	https://arxiv.org/pdf/2002.12096v1.pdf	Action Quality Assessment using Siamese Network-Based Deep Metric Learning	Automated vision-based score estimation models can be used as an alternate opinion to avoid judgment bias. In the past works the score estimation models were learned by regressing the video representations to the ground truth score provided by the judges. However such regression-based solutions lack interpretability in terms of giving reasons for the awarded score. One solution to make the scores more explicable is to compare the given action video with a reference video. This would capture the temporal variations w.r.t. the reference video and map those variations to the final score. In this work, we propose a new action scoring system as a two-phase system: (1) A Deep Metric Learning Module that learns similarity between any two action videos based on their ground truth scores given by the judges; (2) A Score Estimation Module that uses the first module to find the resemblance of a video to a reference video in order to give the assessment score. The proposed scoring model has been tested for Olympics Diving and Gymnastic vaults and the model outperforms the existing state-of-the-art scoring models.	['Hiteshi Jain', 'Avinash Sharma', 'Gaurav Harit']	2020-02-27	null	null	null	null	['action-quality-assessment']	['computer-vision']	[ 3.68907489e-02 -7.23593161e-02 -4.78847325e-02 -7.62447059e-01 -6.92331076e-01 -3.85201335e-01 4.11756307e-01 1.31483704e-01 -4.88766700e-01 4.52605247e-01 2.68020868e-01 3.26264113e-01 -2.46357575e-01 -5.87901235e-01 -4.21518594e-01 -4.46536422e-01 1.48038685e-01 3.56800377e-01 5.89640498e-01 -3.02814335e-01 7.94422805e-01 7.32250288e-02 -1.87964785e+00 7.74480104e-01 7.39922345e-01 1.00141931e+00 9.30595845e-02 8.34226072e-01 9.30599198e-02 1.41387403e+00 -7.94804811e-01 -3.00636917e-01 4.83875722e-01 -9.14514780e-01 -6.91895008e-01 4.52318601e-02 7.73140192e-01 -4.52115744e-01 -3.78193259e-01 1.16889882e+00 1.43131837e-01 3.85459870e-01 7.20968127e-01 -1.23303831e+00 -4.91266012e-01 2.77835906e-01 -4.29138631e-01 2.66031235e-01 9.54882920e-01 8.73608515e-02 1.04274583e+00 -6.82054460e-01 5.65727293e-01 1.02052367e+00 4.65444624e-01 5.67961097e-01 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9.70551968e-01 -3.25827524e-02 -7.58906901e-01 -3.04195195e-01 3.80276054e-01]	[8.03217887878418, 0.5767263174057007]
d3c5a102-0dfd-46bf-b204-b17a29ba9729	trans4trans-efficient-transformer-for-1	2108.09174	null	https://arxiv.org/abs/2108.09174v1	https://arxiv.org/pdf/2108.09174v1.pdf	Trans4Trans: Efficient Transformer for Transparent Object and Semantic Scene Segmentation in Real-World Navigation Assistance	Transparent objects, such as glass walls and doors, constitute architectural obstacles hindering the mobility of people with low vision or blindness. For instance, the open space behind glass doors is inaccessible, unless it is correctly perceived and interacted with. However, traditional assistive technologies rarely cover the segmentation of these safety-critical transparent objects. In this paper, we build a wearable system with a novel dual-head Transformer for Transparency (Trans4Trans) perception model, which can segment general- and transparent objects. The two dense segmentation results are further combined with depth information in the system to help users navigate safely and assist them to negotiate transparent obstacles. We propose a lightweight Transformer Parsing Module (TPM) to perform multi-scale feature interpretation in the transformer-based decoder. Benefiting from TPM, the double decoders can perform joint learning from corresponding datasets to pursue robustness, meanwhile maintain efficiency on a portable GPU, with negligible calculation increase. The entire Trans4Trans model is constructed in a symmetrical encoder-decoder architecture, which outperforms state-of-the-art methods on the test sets of Stanford2D3D and Trans10K-v2 datasets, obtaining mIoU of 45.13% and 75.14%, respectively. Through a user study and various pre-tests conducted in indoor and outdoor scenes, the usability and reliability of our assistive system have been extensively verified. Meanwhile, the Tran4Trans model has outstanding performances on driving scene datasets. On Cityscapes, ACDC, and DADA-seg datasets corresponding to common environments, adverse weather, and traffic accident scenarios, mIoU scores of 81.5%, 76.3%, and 39.2% are obtained, demonstrating its high efficiency and robustness for real-world transportation applications.	['Rainer Stiefelhagen', 'Karin Müller', 'Kunyu Peng', 'Angela Constantinescu', 'Kailun Yang', 'Jiaming Zhang']	2021-08-20	null	null	null	null	['transparent-objects', 'scene-segmentation']	['computer-vision', 'computer-vision']	[-3.66911255e-02 -5.42878360e-03 1.86380312e-01 -3.09151828e-01 -5.54311335e-01 -1.84123173e-01 -4.38672379e-02 -3.77963334e-01 -3.93528730e-01 4.11988229e-01 1.12460636e-01 -5.35415530e-01 2.99781114e-01 -7.29785502e-01 -5.65315723e-01 -6.09789491e-01 2.71757394e-01 -8.19621086e-02 5.17609000e-01 -2.45102182e-01 -2.03021467e-01 -1.80028379e-02 -1.73963499e+00 3.10217768e-01 1.46468782e+00 1.23201418e+00 6.00842893e-01 4.26555276e-01 2.47793123e-01 3.24072093e-01 -3.36662322e-01 -7.28927493e-01 1.89282492e-01 3.97539020e-01 -2.29364753e-01 8.41607526e-02 4.90541339e-01 -9.09371614e-01 -5.77041864e-01 6.98051155e-01 8.56395960e-01 -2.44307667e-01 4.10858572e-01 -1.03885925e+00 -5.04363358e-01 -2.05616578e-01 -6.45067751e-01 -2.06056118e-01 6.03338003e-01 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49a7a831-5c6c-4ec6-a1b8-be862a2a2cdc	video-face-super-resolution-with-motion	2002.06378	null	https://arxiv.org/abs/2002.06378v1	https://arxiv.org/pdf/2002.06378v1.pdf	Video Face Super-Resolution with Motion-Adaptive Feedback Cell	Video super-resolution (VSR) methods have recently achieved a remarkable success due to the development of deep convolutional neural networks (CNN). Current state-of-the-art CNN methods usually treat the VSR problem as a large number of separate multi-frame super-resolution tasks, at which a batch of low resolution (LR) frames is utilized to generate a single high resolution (HR) frame, and running a slide window to select LR frames over the entire video would obtain a series of HR frames. However, duo to the complex temporal dependency between frames, with the number of LR input frames increase, the performance of the reconstructed HR frames become worse. The reason is in that these methods lack the ability to model complex temporal dependencies and hard to give an accurate motion estimation and compensation for VSR process. Which makes the performance degrade drastically when the motion in frames is complex. In this paper, we propose a Motion-Adaptive Feedback Cell (MAFC), a simple but effective block, which can efficiently capture the motion compensation and feed it back to the network in an adaptive way. Our approach efficiently utilizes the information of the inter-frame motion, the dependence of the network on motion estimation and compensation method can be avoid. In addition, benefiting from the excellent nature of MAFC, the network can achieve better performance in the case of extremely complex motion scenarios. Extensive evaluations and comparisons validate the strengths of our approach, and the experimental results demonstrated that the proposed framework is outperform the state-of-the-art methods.	['Zhifeng Li', 'Nannan Wang', 'Jie Li', 'Xinbo Gao', 'Jingwei Xin']	2020-02-15	null	null	null	null	['multi-frame-super-resolution']	['computer-vision']	[ 2.98600227e-01 -5.21733642e-01 -1.32483974e-01 -3.36880200e-02 -3.73154491e-01 -4.31682020e-02 3.08859348e-01 -6.21236861e-01 -3.99136662e-01 7.63273180e-01 2.39287004e-01 2.78046548e-01 8.61756206e-02 -6.83905840e-01 -5.29058039e-01 -7.69111395e-01 5.89479953e-02 -4.47011948e-01 7.61056602e-01 -4.02419209e-01 1.18669845e-01 5.53939342e-01 -1.72729254e+00 4.60297465e-01 6.84872806e-01 1.04427171e+00 7.15144157e-01 6.90073907e-01 -1.99405476e-02 1.03914261e+00 -4.32775676e-01 1.31053418e-01 3.00032467e-01 -5.46194494e-01 -6.04704142e-01 6.09932914e-02 3.06423783e-01 -8.64855945e-01 -6.42242849e-01 9.62826848e-01 5.74342549e-01 3.49283487e-01 -1.96867608e-04 -5.75442791e-01 -4.13311571e-01 3.77448529e-01 -8.12456369e-01 7.14150965e-01 3.51193666e-01 1.12000622e-01 5.16202748e-01 -1.17931211e+00 8.04394245e-01 1.43783748e+00 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c6221aff-2064-4884-a033-4d1286c98380	deep-triplet-hashing-network-for-case-based	2101.12346	null	https://arxiv.org/abs/2101.12346v1	https://arxiv.org/pdf/2101.12346v1.pdf	Deep Triplet Hashing Network for Case-based Medical Image Retrieval	Deep hashing methods have been shown to be the most efficient approximate nearest neighbor search techniques for large-scale image retrieval. However, existing deep hashing methods have a poor small-sample ranking performance for case-based medical image retrieval. The top-ranked images in the returned query results may be as a different class than the query image. This ranking problem is caused by classification, regions of interest (ROI), and small-sample information loss in the hashing space. To address the ranking problem, we propose an end-to-end framework, called Attention-based Triplet Hashing (ATH) network, to learn low-dimensional hash codes that preserve the classification, ROI, and small-sample information. We embed a spatial-attention module into the network structure of our ATH to focus on ROI information. The spatial-attention module aggregates the spatial information of feature maps by utilizing max-pooling, element-wise maximum, and element-wise mean operations jointly along the channel axis. The triplet cross-entropy loss can help to map the classification information of images and similarity between images into the hash codes. Extensive experiments on two case-based medical datasets demonstrate that our proposed ATH can further improve the retrieval performance compared to the state-of-the-art deep hashing methods and boost the ranking performance for small samples. Compared to the other loss methods, the triplet cross-entropy loss can enhance the classification performance and hash code-discriminability	['Jiang Liu', 'Huazhu Fu', 'Jiansheng Fang']	2021-01-29	null	null	null	null	['medical-image-retrieval', 'medical-image-retrieval']	['computer-vision', 'medical']	[-2.27607071e-01 -3.01321447e-01 -4.43169564e-01 -5.34371376e-01 -1.49914193e+00 6.34248108e-02 7.84427822e-02 5.12651622e-01 -4.70638812e-01 2.75953442e-01 4.66023177e-01 2.42105350e-01 -3.59457225e-01 -8.46525371e-01 -5.26626468e-01 -9.52761829e-01 -5.16360402e-01 2.54831284e-01 2.78285950e-01 3.83203439e-02 1.90696642e-01 3.81332725e-01 -1.45334065e+00 7.82385290e-01 4.44486678e-01 1.47781217e+00 1.16806716e-01 1.87467486e-01 2.35862285e-01 7.17074990e-01 -3.41305703e-01 -2.00282782e-01 2.49264836e-01 -1.61055535e-01 -6.05571926e-01 -6.62127316e-01 3.36296380e-01 -9.63925898e-01 -9.74238932e-01 1.03917193e+00 1.14712322e+00 2.42382158e-02 5.39429367e-01 -1.17326081e+00 -8.84758055e-01 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51d62790-7dff-4c27-9b46-2c7bfde3871c	is-an-object-centric-video-representation	2207.10075	null	https://arxiv.org/abs/2207.10075v2	https://arxiv.org/pdf/2207.10075v2.pdf	Is an Object-Centric Video Representation Beneficial for Transfer?	The objective of this work is to learn an object-centric video representation, with the aim of improving transferability to novel tasks, i.e., tasks different from the pre-training task of action classification. To this end, we introduce a new object-centric video recognition model based on a transformer architecture. The model learns a set of object-centric summary vectors for the video, and uses these vectors to fuse the visual and spatio-temporal trajectory 'modalities' of the video clip. We also introduce a novel trajectory contrast loss to further enhance objectness in these summary vectors. With experiments on four datasets -- SomethingSomething-V2, SomethingElse, Action Genome and EpicKitchens -- we show that the object-centric model outperforms prior video representations (both object-agnostic and object-aware), when: (1) classifying actions on unseen objects and unseen environments; (2) low-shot learning of novel classes; (3) linear probe to other downstream tasks; as well as (4) for standard action classification.	['Andrew Zisserman', 'Ankush Gupta', 'Chuhan Zhang']	2022-07-20	null	null	null	null	['action-classification']	['computer-vision']	[ 6.46230221e-01 -2.77945846e-01 -3.03546727e-01 -4.20014560e-01 -7.16405094e-01 -4.47170496e-01 9.31323171e-01 -2.14412883e-01 -3.39529872e-01 4.76587772e-01 6.82568431e-01 4.77626622e-02 -1.23920217e-01 -2.73567021e-01 -9.33855653e-01 -7.96493351e-01 -2.66764522e-01 1.09569535e-01 6.27294302e-01 -5.70143722e-02 3.08580875e-01 3.82016420e-01 -1.91727543e+00 9.22180533e-01 1.25279889e-01 1.15675843e+00 1.70684472e-01 8.36087286e-01 3.36879879e-01 1.45893323e+00 -2.44070202e-01 2.88609955e-02 3.18213940e-01 -6.20468974e-01 -8.97083223e-01 5.45072973e-01 5.40766358e-01 -5.57613552e-01 -6.70264184e-01 7.19035983e-01 1.03934966e-01 7.03175664e-01 8.56726885e-01 -1.51258922e+00 -9.07266200e-01 3.41616184e-01 -3.63030136e-01 8.10466945e-01 4.09770787e-01 5.95602453e-01 8.56222034e-01 -8.81813586e-01 8.33134413e-01 1.35262382e+00 4.46032703e-01 6.74674630e-01 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-7.46571600e-01 9.63016987e-01 4.58411038e-01 -1.15819073e+00 -4.20527041e-01 5.56836069e-01]	[8.680416107177734, 0.8629804849624634]
ea34bc69-a504-4298-af86-0421ec4b09ae	a-large-scale-film-style-dataset-for-learning	2301.08880	null	https://arxiv.org/abs/2301.08880v2	https://arxiv.org/pdf/2301.08880v2.pdf	A Large-scale Film Style Dataset for Learning Multi-frequency Driven Film Enhancement	Film, a classic image style, is culturally significant to the whole photographic industry since it marks the birth of photography. However, film photography is time-consuming and expensive, necessitating a more efficient method for collecting film-style photographs. Numerous datasets that have emerged in the field of image enhancement so far are not film-specific. In order to facilitate film-based image stylization research, we construct FilmSet, a large-scale and high-quality film style dataset. Our dataset includes three different film types and more than 5000 in-the-wild high resolution images. Inspired by the features of FilmSet images, we propose a novel framework called FilmNet based on Laplacian Pyramid for stylizing images across frequency bands and achieving film style outcomes. Experiments reveal that the performance of our model is superior than state-of-the-art techniques. The link of code and data is \url{https://github.com/CXH-Research/FilmNet}.	['Zinuo Li', 'Shuqiang Wang', 'Chi-Man Pun', 'Xuhang Chen']	2023-01-21	null	null	null	null	['film-simulation', 'image-stylization']	['computer-vision', 'computer-vision']	[ 4.39036697e-01 -4.62865829e-01 -2.06481189e-01 -3.90383482e-01 -4.67341065e-01 -3.07345301e-01 3.10152054e-01 -2.25877464e-01 -1.59839913e-01 5.02326310e-01 4.50289041e-01 -5.50621673e-02 2.67950017e-02 -8.62950921e-01 -7.90042996e-01 -3.83812517e-01 4.72071141e-01 -4.25671518e-01 1.94920614e-01 -3.42555016e-01 3.73176813e-01 4.38163847e-01 -1.37015963e+00 4.09872383e-01 7.30105698e-01 1.00799370e+00 2.66848564e-01 6.85382843e-01 -1.24101125e-01 6.70130312e-01 -6.32132173e-01 -1.07787669e+00 4.57460403e-01 -8.66235435e-01 -6.06082380e-01 6.65281534e-01 9.59527433e-01 -5.68077564e-01 -6.79145455e-01 1.47109425e+00 4.90797371e-01 -2.79921889e-02 1.63716286e-01 -9.46316361e-01 -1.48288071e+00 4.40189600e-01 -8.65274191e-01 4.48442727e-01 4.94945437e-01 2.48909265e-01 8.04902911e-01 -6.64759278e-01 9.35664117e-01 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-1.03878200e+00 5.16350754e-03 -8.48355174e-01 -9.64786649e-01 9.01486337e-01 6.75498992e-02 -4.66026187e-01 1.36369437e-01 7.90810168e-01]	[11.347829818725586, -0.9423348307609558]
383e3e93-6649-4ece-bd66-16108793ba8b	deep-optimized-priors-for-3d-shape-modeling	2012.07241	null	https://arxiv.org/abs/2012.07241v1	https://arxiv.org/pdf/2012.07241v1.pdf	Deep Optimized Priors for 3D Shape Modeling and Reconstruction	Many learning-based approaches have difficulty scaling to unseen data, as the generality of its learned prior is limited to the scale and variations of the training samples. This holds particularly true with 3D learning tasks, given the sparsity of 3D datasets available. We introduce a new learning framework for 3D modeling and reconstruction that greatly improves the generalization ability of a deep generator. Our approach strives to connect the good ends of both learning-based and optimization-based methods. In particular, unlike the common practice that fixes the pre-trained priors at test time, we propose to further optimize the learned prior and latent code according to the input physical measurements after the training. We show that the proposed strategy effectively breaks the barriers constrained by the pre-trained priors and could lead to high-quality adaptation to unseen data. We realize our framework using the implicit surface representation and validate the efficacy of our approach in a variety of challenging tasks that take highly sparse or collapsed observations as input. Experimental results show that our approach compares favorably with the state-of-the-art methods in terms of both generality and accuracy.	['Kui Jia', 'Yongwei Chen', 'Weikai Chen', 'Yuxin Wen', 'Mingyue Yang']	2020-12-14	null	http://openaccess.thecvf.com//content/CVPR2021/html/Yang_Deep_Optimized_Priors_for_3D_Shape_Modeling_and_Reconstruction_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Yang_Deep_Optimized_Priors_for_3D_Shape_Modeling_and_Reconstruction_CVPR_2021_paper.pdf	cvpr-2021-1	['3d-shape-modeling']	['computer-vision']	[ 2.35206597e-02 2.20762968e-01 4.60981615e-02 -2.51723409e-01 -6.90670192e-01 -4.10516530e-01 7.05114603e-01 -2.48851672e-01 -8.85700285e-02 5.24554729e-01 9.25405622e-02 -8.25150385e-02 -2.17534691e-01 -7.04027295e-01 -9.53191042e-01 -7.53982961e-01 4.26113233e-03 5.10604501e-01 3.15754473e-01 -2.51129037e-03 8.47830772e-02 7.94728041e-01 -1.66291368e+00 -1.86007116e-02 7.98308134e-01 1.10002255e+00 2.96091616e-01 2.10977852e-01 4.48796041e-02 3.26718271e-01 -3.32600415e-01 -4.64753732e-02 4.57586288e-01 -2.25603774e-01 -3.75187546e-01 3.23253751e-01 4.22345102e-01 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f9f46d69-1afd-4f55-b7fc-c332ff48c94f	deep-laparoscopic-stereo-matching-with	2207.12152	null	https://arxiv.org/abs/2207.12152v1	https://arxiv.org/pdf/2207.12152v1.pdf	Deep Laparoscopic Stereo Matching with Transformers	The self-attention mechanism, successfully employed with the transformer structure is shown promise in many computer vision tasks including image recognition, and object detection. Despite the surge, the use of the transformer for the problem of stereo matching remains relatively unexplored. In this paper, we comprehensively investigate the use of the transformer for the problem of stereo matching, especially for laparoscopic videos, and propose a new hybrid deep stereo matching framework (HybridStereoNet) that combines the best of the CNN and the transformer in a unified design. To be specific, we investigate several ways to introduce transformers to volumetric stereo matching pipelines by analyzing the loss landscape of the designs and in-domain/cross-domain accuracy. Our analysis suggests that employing transformers for feature representation learning, while using CNNs for cost aggregation will lead to faster convergence, higher accuracy and better generalization than other options. Our extensive experiments on Sceneflow, SCARED2019 and dVPN datasets demonstrate the superior performance of our HybridStereoNet.	['ZongYuan Ge', 'Zhiyong Wang', 'Tom Drummond', 'Mehrtash Harandi', 'Yiran Zhong', 'Xuelian Cheng']	2022-07-25	null	null	null	null	['stereo-matching-1']	['computer-vision']	[ 1.24807693e-01 -2.74258256e-02 6.90357238e-02 -3.11506152e-01 -4.44822490e-01 -2.94934034e-01 4.70659405e-01 -8.22339952e-02 -2.96863139e-01 1.66246772e-01 2.36083105e-01 -3.15286517e-01 -2.63772488e-01 -7.69684732e-01 -9.01644349e-01 -5.09824574e-01 1.24841452e-01 2.52305150e-01 1.53968394e-01 -2.55108654e-01 2.42924601e-01 5.89413464e-01 -1.85549867e+00 3.43890399e-01 9.00729477e-01 1.55630994e+00 2.93737143e-01 2.20632344e-01 -1.45666108e-01 8.74022305e-01 -1.60166532e-01 -7.33238161e-01 8.72858703e-01 -2.11490840e-02 -8.65845799e-01 -1.21132568e-01 8.17615926e-01 -5.19558549e-01 -5.22324860e-01 7.98008442e-01 7.80368805e-01 -1.09358449e-02 3.68306160e-01 -1.13502562e+00 -2.76823819e-01 5.02014458e-01 -4.52725738e-01 3.17108244e-01 3.03386629e-01 3.37391615e-01 8.47905159e-01 -7.16283917e-01 6.48596466e-01 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a8979c2a-0018-462d-a826-36996807594c	test-positive-at-w-nut-2020-shared-task-3-1	null	null	https://aclanthology.org/2020.wnut-1.76	https://aclanthology.org/2020.wnut-1.76.pdf	TEST_POSITIVE at W-NUT 2020 Shared Task-3: Cross-task modeling	The competition of extracting COVID-19 events from Twitter is to develop systems that can automatically extract related events from tweets. The built system should identify different pre-defined slots for each event, in order to answer important questions (e.g., Who is tested positive? What is the age of the person? Where is he/she?). To tackle these challenges, we propose the Joint Event Multi-task Learning (JOELIN) model. Through a unified global learning framework, we make use of all the training data across different events to learn and fine-tune the language model. Moreover, we implement a type-aware post-processing procedure using named entity recognition (NER) to further filter the predictions. JOELIN outperforms the BERT baseline by 17.2% in micro F1.	['Jiaqi Wang', 'Enyan Dai', 'Yang Shi', 'Yaqi Hou', 'Chieh-Yang Huang', 'Chacha Chen']	null	null	null	null	emnlp-wnut-2020-11	['extracting-covid-19-events-from-twitter']	['natural-language-processing']	[-3.44452858e-02 -9.51137319e-02 -1.79236457e-01 -6.07772529e-01 -1.10899103e+00 -4.70800608e-01 6.77391827e-01 6.64580941e-01 -9.74343598e-01 8.18702579e-01 3.80033851e-01 -3.57404910e-02 1.86628729e-01 -1.10697877e+00 -6.83704078e-01 -3.31660479e-01 2.50340819e-01 6.43059790e-01 3.60129744e-01 -2.11138785e-01 -1.16669357e-01 2.97140807e-01 -1.41852129e+00 4.96034116e-01 5.67529023e-01 7.69570231e-01 2.34019496e-02 5.78523636e-01 -3.88125986e-01 8.60442340e-01 -7.40836561e-01 -6.26777351e-01 -3.09000194e-01 -1.59328401e-01 -9.17002916e-01 -3.09828401e-01 4.46820743e-02 -5.91858663e-03 1.75574988e-01 7.38139510e-01 3.74736279e-01 2.17005745e-01 7.36320496e-01 -1.08569527e+00 1.04807608e-01 9.15732324e-01 -4.71521497e-01 5.03835797e-01 1.98505893e-01 -3.45636606e-01 1.07326984e+00 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5c6a4072-be66-41a5-bc51-825b7eacb71d	a-study-of-situational-reasoning-for-traffic	2306.02520	null	https://arxiv.org/abs/2306.02520v1	https://arxiv.org/pdf/2306.02520v1.pdf	A Study of Situational Reasoning for Traffic Understanding	Intelligent Traffic Monitoring (ITMo) technologies hold the potential for improving road safety/security and for enabling smart city infrastructure. Understanding traffic situations requires a complex fusion of perceptual information with domain-specific and causal commonsense knowledge. Whereas prior work has provided benchmarks and methods for traffic monitoring, it remains unclear whether models can effectively align these information sources and reason in novel scenarios. To address this assessment gap, we devise three novel text-based tasks for situational reasoning in the traffic domain: i) BDD-QA, which evaluates the ability of Language Models (LMs) to perform situational decision-making, ii) TV-QA, which assesses LMs' abilities to reason about complex event causality, and iii) HDT-QA, which evaluates the ability of models to solve human driving exams. We adopt four knowledge-enhanced methods that have shown generalization capability across language reasoning tasks in prior work, based on natural language inference, commonsense knowledge-graph self-supervision, multi-QA joint training, and dense retrieval of domain information. We associate each method with a relevant knowledge source, including knowledge graphs, relevant benchmarks, and driving manuals. In extensive experiments, we benchmark various knowledge-aware methods against the three datasets, under zero-shot evaluation; we provide in-depth analyses of model performance on data partitions and examine model predictions categorically, to yield useful insights on traffic understanding, given different background knowledge and reasoning strategies.	['Alessandro Oltramari', 'Jonathan Francis', 'Aravinda Kollaa', 'Kaixin Ma', 'Filip Ilievski', 'Jiarui Zhang']	2023-06-05	null	null	null	null	['knowledge-graphs', 'natural-language-inference']	['knowledge-base', 'natural-language-processing']	[ 3.99187028e-01 3.76626700e-01 -5.67184031e-01 -4.21064019e-01 -7.12231278e-01 -2.94643044e-01 7.72287965e-01 1.78971946e-01 8.96408781e-03 8.42286944e-01 4.41330492e-01 -9.29671049e-01 -7.21660137e-01 -9.89252508e-01 -5.62437356e-01 -8.16034377e-02 2.14033946e-01 6.93457663e-01 5.00676870e-01 -7.31010199e-01 2.11499378e-01 5.12113810e-01 -1.82369232e+00 4.42573756e-01 1.20925093e+00 9.88908112e-01 -1.83252171e-01 6.95172310e-01 -1.08990699e-01 1.76138401e+00 -4.16823924e-01 -9.25079048e-01 -1.09224111e-01 -1.01914130e-01 -1.04082501e+00 -2.48022497e-01 7.08885431e-01 -9.07568634e-02 -7.68605351e-01 8.78553450e-01 1.90737426e-01 3.51245880e-01 7.61485338e-01 -1.83597100e+00 -8.76657188e-01 5.84804952e-01 6.36492446e-02 8.40490103e-01 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21c31612-a872-43c2-ac08-536631db44a5	finnwoodlands-dataset	2304.00793	null	https://arxiv.org/abs/2304.00793v1	https://arxiv.org/pdf/2304.00793v1.pdf	FinnWoodlands Dataset	While the availability of large and diverse datasets has contributed to significant breakthroughs in autonomous driving and indoor applications, forestry applications are still lagging behind and new forest datasets would most certainly contribute to achieving significant progress in the development of data-driven methods for forest-like scenarios. This paper introduces a forest dataset called \textit{FinnWoodlands}, which consists of RGB stereo images, point clouds, and sparse depth maps, as well as ground truth manual annotations for semantic, instance, and panoptic segmentation. \textit{FinnWoodlands} comprises a total of 4226 objects manually annotated, out of which 2562 objects (60.6\%) correspond to tree trunks classified into three different instance categories, namely "Spruce Tree", "Birch Tree", and "Pine Tree". Besides tree trunks, we also annotated "Obstacles" objects as instances as well as the semantic stuff classes "Lake", "Ground", and "Track". Our dataset can be used in forestry applications where a holistic representation of the environment is relevant. We provide an initial benchmark using three models for instance segmentation, panoptic segmentation, and depth completion, and illustrate the challenges that such unstructured scenarios introduce.	['Esa Rahtu', 'Urho Lempiö', 'Juan Lagos']	2023-04-03	null	null	null	null	['panoptic-segmentation', 'depth-completion']	['computer-vision', 'computer-vision']	[ 4.70114708e-01 1.69898495e-01 -3.64317633e-02 -6.06237769e-01 -3.71158689e-01 -6.78321719e-01 6.73198342e-01 3.65078390e-01 -8.48757699e-02 9.50231671e-01 2.29522921e-02 -4.00850981e-01 -3.13276321e-01 -1.30198109e+00 -4.87815380e-01 -7.67169237e-01 -1.56436905e-01 8.05108428e-01 4.65887964e-01 -3.92094940e-01 1.77372158e-01 5.23297668e-01 -2.19244790e+00 -7.56446272e-02 9.89549518e-01 1.12623823e+00 9.42967653e-01 6.74827933e-01 -5.04460394e-01 5.02871275e-01 -4.29586291e-01 -2.11581260e-01 3.98782134e-01 2.40707532e-01 -8.94745171e-01 3.66794795e-01 6.89069152e-01 -5.03712237e-01 -1.00608803e-02 1.17356825e+00 1.34175852e-01 7.06842169e-02 5.16146362e-01 -1.16720366e+00 -1.71430960e-01 3.56870651e-01 -6.50496542e-01 -1.58339575e-01 -1.17813654e-01 3.13824594e-01 9.77174938e-01 -5.53908169e-01 7.95086741e-01 1.17151868e+00 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90deb841-1ffd-4eeb-a202-9410150c21e9	enhancing-cross-lingual-transfer-via-phonemic	2307.04361	null	https://arxiv.org/abs/2307.04361v1	https://arxiv.org/pdf/2307.04361v1.pdf	Enhancing Cross-lingual Transfer via Phonemic Transcription Integration	Previous cross-lingual transfer methods are restricted to orthographic representation learning via textual scripts. This limitation hampers cross-lingual transfer and is biased towards languages sharing similar well-known scripts. To alleviate the gap between languages from different writing scripts, we propose PhoneXL, a framework incorporating phonemic transcriptions as an additional linguistic modality beyond the traditional orthographic transcriptions for cross-lingual transfer. Particularly, we propose unsupervised alignment objectives to capture (1) local one-to-one alignment between the two different modalities, (2) alignment via multi-modality contexts to leverage information from additional modalities, and (3) alignment via multilingual contexts where additional bilingual dictionaries are incorporated. We also release the first phonemic-orthographic alignment dataset on two token-level tasks (Named Entity Recognition and Part-of-Speech Tagging) among the understudied but interconnected Chinese-Japanese-Korean-Vietnamese (CJKV) languages. Our pilot study reveals phonemic transcription provides essential information beyond the orthography to enhance cross-lingual transfer and bridge the gap among CJKV languages, leading to consistent improvements on cross-lingual token-level tasks over orthographic-based multilingual PLMs.	['Philip S. Yu', 'Eugene Rohrbaugh', 'Tao Zhang', 'Chenwei Zhang', 'Hoang H. Nguyen']	2023-07-10	null	null	null	null	['representation-learning', 'part-of-speech-tagging', 'cross-lingual-transfer']	['methodology', 'natural-language-processing', 'natural-language-processing']	[-3.99105949e-03 -4.43623334e-01 -6.33055568e-01 -4.43289727e-01 -1.27307963e+00 -1.11869335e+00 6.20994329e-01 -1.07967265e-01 -8.79254758e-01 7.31825590e-01 8.58745098e-01 -3.90594065e-01 4.53832954e-01 -3.41648519e-01 -8.26252937e-01 -2.88575470e-01 5.56794167e-01 7.17794359e-01 -3.83971244e-01 -2.80596852e-01 -1.39246181e-01 -1.01084575e-01 -7.00952649e-01 7.53888667e-01 1.23058915e+00 1.88001409e-01 4.38888371e-01 5.56567274e-02 -3.77870679e-01 1.93667665e-01 -2.21435189e-01 -8.23146760e-01 2.16112942e-01 -6.62374616e-01 -7.98609257e-01 -1.78307950e-01 9.79911089e-01 -7.85160363e-02 -6.07144870e-02 8.71516824e-01 4.96541619e-01 -1.33773476e-01 6.71307623e-01 -6.82631493e-01 -1.28539026e+00 1.05883670e+00 -3.89805436e-01 -2.47814208e-02 3.06972623e-01 1.38863549e-02 1.18564832e+00 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a09d34bc-8de1-4974-a3d7-5d167b84bbaa	stt4sg-350-a-speech-corpus-for-all-swiss	2305.18855	null	https://arxiv.org/abs/2305.18855v1	https://arxiv.org/pdf/2305.18855v1.pdf	STT4SG-350: A Speech Corpus for All Swiss German Dialect Regions	We present STT4SG-350 (Speech-to-Text for Swiss German), a corpus of Swiss German speech, annotated with Standard German text at the sentence level. The data is collected using a web app in which the speakers are shown Standard German sentences, which they translate to Swiss German and record. We make the corpus publicly available. It contains 343 hours of speech from all dialect regions and is the largest public speech corpus for Swiss German to date. Application areas include automatic speech recognition (ASR), text-to-speech, dialect identification, and speaker recognition. Dialect information, age group, and gender of the 316 speakers are provided. Genders are equally represented and the corpus includes speakers of all ages. Roughly the same amount of speech is provided per dialect region, which makes the corpus ideally suited for experiments with speech technology for different dialects. We provide training, validation, and test splits of the data. The test set consists of the same spoken sentences for each dialect region and allows a fair evaluation of the quality of speech technologies in different dialects. We train an ASR model on the training set and achieve an average BLEU score of 74.7 on the test set. The model beats the best published BLEU scores on 2 other Swiss German ASR test sets, demonstrating the quality of the corpus.	['Mark Cieliebak', 'Manfred Vogel', 'Tanja Samardžić', 'Manuela Hürlimann', 'Christian Scheller', 'Larissa Schmidt', 'Julia Hartmann', 'Claudio Paonessa', 'Yanick Schraner', 'Jan Deriu', 'Michel Plüss']	2023-05-30	null	null	null	null	['dialect-identification', 'speaker-recognition', 'automatic-speech-recognition']	['natural-language-processing', 'speech', 'speech']	[-1.83218971e-01 2.62742579e-01 -8.92390236e-02 -7.86766171e-01 -1.21240270e+00 -7.44863629e-01 7.09908307e-01 2.09819511e-01 -4.01408315e-01 3.88560086e-01 7.46649384e-01 -4.85903859e-01 3.36173356e-01 -4.35317993e-01 -1.78980559e-01 -3.12304080e-01 2.16067776e-01 8.23017538e-01 4.47772592e-02 -6.80743515e-01 -2.70659566e-01 2.15210617e-01 -1.26373887e+00 3.91169220e-01 7.67775953e-01 5.49119830e-01 5.32676339e-01 9.53528583e-01 -5.40670455e-02 4.70950693e-01 -1.16307819e+00 -7.69643128e-01 -4.39029012e-04 -3.15846443e-01 -1.02975667e+00 3.34049255e-01 4.70505029e-01 -1.64434865e-01 -5.25203228e-01 7.94062734e-01 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d21ff505-409c-4af7-9302-d458d0044257	a-graph-based-analysis-of-medical-queries-of	null	null	https://aclanthology.org/W14-1102	https://aclanthology.org/W14-1102.pdf	A Graph-Based Analysis of Medical Queries of a Swedish Health Care Portal	null	['Philippas Tsigas', 'Ann-Marie Eklund', 'Farnaz Moradi', 'Tomas Olovsson', 'Dimitrios Kokkinakis']	2014-04-01	null	null	null	ws-2014-4	['local-community-detection']	['graphs']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 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d47a207a-6877-4fae-a9a1-8da7601a7304	mask3d-for-3d-semantic-instance-segmentation	2210.03105	null	https://arxiv.org/abs/2210.03105v2	https://arxiv.org/pdf/2210.03105v2.pdf	Mask3D: Mask Transformer for 3D Semantic Instance Segmentation	Modern 3D semantic instance segmentation approaches predominantly rely on specialized voting mechanisms followed by carefully designed geometric clustering techniques. Building on the successes of recent Transformer-based methods for object detection and image segmentation, we propose the first Transformer-based approach for 3D semantic instance segmentation. We show that we can leverage generic Transformer building blocks to directly predict instance masks from 3D point clouds. In our model called Mask3D each object instance is represented as an instance query. Using Transformer decoders, the instance queries are learned by iteratively attending to point cloud features at multiple scales. Combined with point features, the instance queries directly yield all instance masks in parallel. Mask3D has several advantages over current state-of-the-art approaches, since it neither relies on (1) voting schemes which require hand-selected geometric properties (such as centers) nor (2) geometric grouping mechanisms requiring manually-tuned hyper-parameters (e.g. radii) and (3) enables a loss that directly optimizes instance masks. Mask3D sets a new state-of-the-art on ScanNet test (+6.2 mAP), S3DIS 6-fold (+10.1 mAP), STPLS3D (+11.2 mAP) and ScanNet200 test (+12.4 mAP).	['Bastian Leibe', 'Siyu Tang', 'Or Litany', 'Alexander Hermans', 'Francis Engelmann', 'Jonas Schult']	2022-10-06	null	null	null	null	['3d-instance-segmentation-1', '3d-semantic-instance-segmentation']	['computer-vision', 'computer-vision']	[ 1.54748514e-01 3.09726268e-01 -4.22390103e-02 -3.91843617e-01 -1.16666436e+00 -1.01382852e+00 8.63794923e-01 1.87975094e-01 -1.55955479e-01 3.01348921e-02 -6.07808411e-01 -4.94963735e-01 -4.87729125e-02 -8.97078872e-01 -9.59282935e-01 -3.38380784e-01 -2.52054900e-01 1.33904636e+00 1.15561521e+00 -1.21194430e-01 5.65212786e-01 1.04205072e+00 -1.71114635e+00 1.43751577e-01 7.80878067e-01 1.38800621e+00 3.06906968e-01 6.90897882e-01 -6.16580904e-01 4.61026244e-02 -5.36593914e-01 -2.17116430e-01 7.92081833e-01 -1.80352014e-02 -8.13270807e-01 3.22462589e-01 7.00130820e-01 -2.28462033e-02 1.14584841e-01 8.01717401e-01 3.27878535e-01 -5.16608320e-02 7.95975387e-01 -1.10515916e+00 -3.48979048e-02 1.74377769e-01 -7.81897485e-01 9.06107351e-02 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45614828-6cee-4448-bd76-31e1fe922f12	pedestrian-3d-bounding-box-prediction	2206.14195	null	https://arxiv.org/abs/2206.14195v1	https://arxiv.org/pdf/2206.14195v1.pdf	Pedestrian 3D Bounding Box Prediction	Safety is still the main issue of autonomous driving, and in order to be globally deployed, they need to predict pedestrians' motions sufficiently in advance. While there is a lot of research on coarse-grained (human center prediction) and fine-grained predictions (human body keypoints prediction), we focus on 3D bounding boxes, which are reasonable estimates of humans without modeling complex motion details for autonomous vehicles. This gives the flexibility to predict in longer horizons in real-world settings. We suggest this new problem and present a simple yet effective model for pedestrians' 3D bounding box prediction. This method follows an encoder-decoder architecture based on recurrent neural networks, and our experiments show its effectiveness in both the synthetic (JTA) and real-world (NuScenes) datasets. The learned representation has useful information to enhance the performance of other tasks, such as action anticipation. Our code is available online: https://github.com/vita-epfl/bounding-box-prediction	['Alexandre Alahi', 'Yi Zhou Ju', 'Saeed Saadatnejad']	2022-06-28	null	null	null	null	['action-anticipation']	['computer-vision']	[-2.40308180e-01 8.25964287e-02 -4.06598747e-01 -6.10952616e-01 -5.07660449e-01 -1.35270625e-01 6.64072871e-01 -2.55850822e-01 -3.86246026e-01 6.58062220e-01 4.76860344e-01 -4.20258254e-01 4.23506379e-01 -7.87241280e-01 -8.01847816e-01 -5.57251751e-01 -2.60063857e-01 3.11972499e-01 7.08691299e-01 -5.80490053e-01 4.64300327e-02 3.90272588e-01 -1.72939050e+00 1.88066065e-01 6.45729959e-01 7.22501695e-01 1.97696760e-01 9.56304193e-01 4.89735574e-01 9.54359412e-01 -2.20414862e-01 -5.96116126e-01 4.28281844e-01 -9.02270600e-02 -4.95695084e-01 -1.67280763e-01 2.69788533e-01 -5.89539826e-01 -7.62765169e-01 7.57080734e-01 3.85664940e-01 3.56930792e-01 4.80806351e-01 -1.53712535e+00 -1.79003820e-01 2.52501696e-01 -3.03435266e-01 1.88693374e-01 1.96559101e-01 5.51015019e-01 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9d79716e-dea9-4f5e-a39e-464dd8a89d54	efficient-multilingual-text-classification	null	null	https://aclanthology.org/2021.ranlp-main.3	https://aclanthology.org/2021.ranlp-main.3.pdf	Efficient Multilingual Text Classification for Indian Languages	India is one of the richest language hubs on the earth and is very diverse and multilingual. But apart from a few Indian languages, most of them are still considered to be resource poor. Since most of the NLP techniques either require linguistic knowledge that can only be developed by experts and native speakers of that language or they require a lot of labelled data which is again expensive to generate, the task of text classification becomes challenging for most of the Indian languages. The main objective of this paper is to see how one can benefit from the lexical similarity found in Indian languages in a multilingual scenario. Can a classification model trained on one Indian language be reused for other Indian languages? So, we performed zero-shot text classification via exploiting lexical similarity and we observed that our model performs best in those cases where the vocabulary overlap between the language datasets is maximum. Our experiments also confirm that a single multilingual model trained via exploiting language relatedness outperforms the baselines by significant margins.	['Radhika Mamidi', 'Sourav Kumar', 'Salil Aggarwal']	null	null	https://aclanthology.org/2021.ranlp-1.3	https://aclanthology.org/2021.ranlp-1.3.pdf	ranlp-2021-9	['multilingual-text-classification']	['miscellaneous']	[-1.68845221e-01 -1.58654422e-01 -3.75837982e-01 -1.77349716e-01 -9.10062850e-01 -9.04349327e-01 9.78251398e-01 6.28971398e-01 -7.18445778e-01 1.04227281e+00 3.13116103e-01 -5.77512920e-01 1.49426028e-01 -7.65343308e-01 -3.61214936e-01 -5.32682598e-01 1.97806999e-01 8.97480428e-01 2.39572287e-01 -7.90370107e-01 3.52657408e-01 2.75792152e-01 -1.38438249e+00 1.47315681e-01 1.18020284e+00 3.39522839e-01 3.89376432e-01 3.58471304e-01 -8.42415094e-01 7.72590876e-01 -5.35607398e-01 -6.42008960e-01 2.12336168e-01 -3.15988034e-01 -1.05500269e+00 -2.90222555e-01 2.43528426e-01 3.51394564e-01 4.17320468e-02 1.04393089e+00 5.90854049e-01 4.05213945e-02 7.09384382e-01 -8.34382653e-01 -5.55162370e-01 9.07752693e-01 -4.65349406e-01 2.46752992e-01 4.92297977e-01 -4.86537218e-01 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4008310a-3cd8-4642-9a92-758fa947283b	multi-agent-deep-reinforcement-learning-for-12	2306.14683	null	https://arxiv.org/abs/2306.14683v1	https://arxiv.org/pdf/2306.14683v1.pdf	Multi-Agent Deep Reinforcement Learning for Dynamic Avatar Migration in AIoT-enabled Vehicular Metaverses with Trajectory Prediction	Avatars, as promising digital assistants in Vehicular Metaverses, can enable drivers and passengers to immerse in 3D virtual spaces, serving as a practical emerging example of Artificial Intelligence of Things (AIoT) in intelligent vehicular environments. The immersive experience is achieved through seamless human-avatar interaction, e.g., augmented reality navigation, which requires intensive resources that are inefficient and impractical to process on intelligent vehicles locally. Fortunately, offloading avatar tasks to RoadSide Units (RSUs) or cloud servers for remote execution can effectively reduce resource consumption. However, the high mobility of vehicles, the dynamic workload of RSUs, and the heterogeneity of RSUs pose novel challenges to making avatar migration decisions. To address these challenges, in this paper, we propose a dynamic migration framework for avatar tasks based on real-time trajectory prediction and Multi-Agent Deep Reinforcement Learning (MADRL). Specifically, we propose a model to predict the future trajectories of intelligent vehicles based on their historical data, indicating the future workloads of RSUs.Based on the expected workloads of RSUs, we formulate the avatar task migration problem as a long-term mixed integer programming problem. To tackle this problem efficiently, the problem is transformed into a Partially Observable Markov Decision Process (POMDP) and solved by multiple DRL agents with hybrid continuous and discrete actions in decentralized. Numerical results demonstrate that our proposed algorithm can effectively reduce the latency of executing avatar tasks by around 25% without prediction and 30% with prediction and enhance user immersive experiences in the AIoT-enabled Vehicular Metaverse (AeVeM).	['Shengli Xie', 'Abbas Jamalipour', 'Chuan Chen', 'Dusit Niyato', 'Zehui Xiong', 'Minrui Xu', 'Jiawen Kang', 'Junlong Chen']	2023-06-26	null	null	null	null	['trajectory-prediction']	['computer-vision']	[-6.27728999e-01 1.80574149e-01 -3.64664972e-01 1.91612262e-02 -3.35399449e-01 -4.02095854e-01 4.83577698e-01 -5.07839620e-01 -6.38153732e-01 8.26665878e-01 -1.78634897e-01 -6.59577549e-01 -1.52013481e-01 -8.16687107e-01 -7.70883381e-01 -6.80556595e-01 -1.62440270e-01 8.63106012e-01 4.17239517e-01 -4.19713676e-01 -3.46542716e-01 5.55217147e-01 -1.88607168e+00 -2.34052479e-01 1.21616328e+00 9.87539291e-01 7.78936923e-01 8.75312388e-01 -2.56350666e-01 1.08302200e+00 -3.20348144e-01 -2.04959914e-01 1.68414220e-01 3.67413491e-01 -6.16190076e-01 6.33773655e-02 -6.95941806e-01 -5.90526164e-01 -7.04834938e-01 5.37259161e-01 2.35297129e-01 6.34594977e-01 5.19555449e-01 -2.29579568e+00 -2.09659696e-01 3.36114705e-01 -4.54433978e-01 2.39043206e-01 -1.07260784e-02 4.45787162e-01 4.68293309e-01 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1095b25b-50fa-4878-8941-9b59aedcf923	implicit-occupancy-flow-fields-for-perception	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Agro_Implicit_Occupancy_Flow_Fields_for_Perception_and_Prediction_in_Self-Driving_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Agro_Implicit_Occupancy_Flow_Fields_for_Perception_and_Prediction_in_Self-Driving_CVPR_2023_paper.pdf	Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving	A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory forecasting of the detected objects, or predict dense occupancy and flow grids for the whole scene. The former poses a safety concern as the number of detections needs to be kept low for efficiency reasons, sacrificing object recall. The latter is computationally expensive due to the high-dimensionality of the output grid, and suffers from the limited receptive field inherent to fully convolutional networks. Furthermore, both approaches employ many computational resources predicting areas or objects that might never be queried by the motion planner. This motivates our unified approach to perception and future prediction that implicitly represents occupancy and flow over time with a single neural network. Our method avoids unnecessary computation, as it can be directly queried by the motion planner at continuous spatio-temporal locations. Moreover, we design an architecture that overcomes the limited receptive field of previous explicit occupancy prediction methods by adding an efficient yet effective global attention mechanism. Through extensive experiments in both urban and highway settings, we demonstrate that our implicit model outperforms the current state-of-the-art. For more information, visit the project website: https://waabi.ai/research/implicito.	['Raquel Urtasun', 'Sergio Casas', 'Quinlan Sykora', 'Ben Agro']	2023-01-01	null	null	null	cvpr-2023-1	['trajectory-forecasting']	['computer-vision']	[ 3.40160392e-02 9.49454978e-02 -2.36948803e-01 -2.85617292e-01 -3.84441704e-01 -2.63671458e-01 6.31990314e-01 4.14614156e-02 -6.12986386e-01 6.64668620e-01 1.64677426e-01 -4.05675441e-01 8.86690468e-02 -1.06404626e+00 -6.93264782e-01 -5.65666676e-01 -8.24304521e-02 4.64132428e-01 7.91845441e-01 -7.92440400e-02 2.60891140e-01 6.70955718e-01 -2.02945137e+00 9.61164981e-02 5.52824736e-01 1.21891344e+00 6.33361936e-01 6.92712426e-01 6.24897443e-02 1.07078636e+00 -1.46582186e-01 1.06913559e-01 2.33416021e-01 -3.55989672e-02 -5.28422952e-01 1.28622865e-02 5.14980376e-01 -7.08848596e-01 -8.48994732e-01 6.78161621e-01 2.24424064e-01 4.65547800e-01 4.78782117e-01 -1.51748681e+00 -2.54342020e-01 1.20759100e-01 -3.28137904e-01 5.68653584e-01 -1.67124197e-01 6.65024102e-01 7.08685637e-01 -7.83358157e-01 4.95152295e-01 1.04580939e+00 4.60026771e-01 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b1bafe3a-4064-4ab7-ae8e-b62f557278a5	a-novel-tsk-fuzzy-system-incorporating-multi	2111.08457	null	https://arxiv.org/abs/2111.08457v1	https://arxiv.org/pdf/2111.08457v1.pdf	A Novel TSK Fuzzy System Incorporating Multi-view Collaborative Transfer Learning for Personalized Epileptic EEG Detection	In clinical practice, electroencephalography (EEG) plays an important role in the diagnosis of epilepsy. EEG-based computer-aided diagnosis of epilepsy can greatly improve the ac-curacy of epilepsy detection while reducing the workload of physicians. However, there are many challenges in practical applications for personalized epileptic EEG detection (i.e., training of detection model for a specific person), including the difficulty in extracting effective features from one single view, the undesirable but common scenario of lacking sufficient training data in practice, and the no guarantee of identically distributed training and test data. To solve these problems, we propose a TSK fuzzy system-based epilepsy detection algorithm that integrates multi-view collaborative transfer learning. To address the challenge due to the limitation of single-view features, multi-view learning ensures the diversity of features by extracting them from different views. The lack of training data for building a personalized detection model is tackled by leveraging the knowledge from the source domain (reference scene) to enhance the performance of the target domain (current scene of interest), where mismatch of data distributions between the two domains is resolved with adaption technique based on maximum mean discrepancy. Notably, the transfer learning and multi-view feature extraction are performed at the same time. Furthermore, the fuzzy rules of the TSK fuzzy system equip the model with strong fuzzy logic inference capability. Hence, the proposed method has the potential to detect epileptic EEG signals effectively, which is demonstrated with the positive results from a large number of experiments on the CHB-MIT dataset.	['Shitong Wang', 'Hongbin Shen', 'Kup-Sze Choi', 'Qiongdan Lou', 'Zhaohong Deng', 'Andong Li']	2021-11-11	null	null	null	null	['multi-view-learning']	['computer-vision']	[ 2.24001594e-02 -3.44854027e-01 2.47326553e-01 -1.06322072e-01 -7.75450945e-01 -3.56760472e-01 1.14322796e-01 -8.13475400e-02 -2.87831724e-01 6.14943802e-01 -1.84944123e-01 2.44819745e-01 -7.42747188e-01 -5.35633683e-01 -2.85668850e-01 -9.82894063e-01 -2.94603109e-02 3.82545680e-01 6.05734736e-02 -2.49609634e-01 1.65905461e-01 3.92180473e-01 -1.46195281e+00 5.08231699e-01 1.13733494e+00 1.09938788e+00 6.32988572e-01 4.82105203e-02 3.50376517e-01 2.47477829e-01 -6.26112938e-01 -8.99185985e-02 8.68933722e-02 -2.21303508e-01 -1.96729615e-01 2.28624731e-01 -1.23204194e-01 -2.08540753e-01 -1.01992875e-01 1.16698849e+00 8.98368359e-01 -1.01653315e-01 7.00426102e-01 -1.18152809e+00 -3.47854853e-01 9.24329758e-02 -6.19767010e-01 4.73071843e-01 2.71220863e-01 -1.68978661e-01 3.59080255e-01 -1.01383233e+00 2.59675801e-01 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27502aed-f297-497c-93ab-279a147089a9	you-only-align-once-bidirectional-interaction	2207.06345	null	https://arxiv.org/abs/2207.06345v1	https://arxiv.org/pdf/2207.06345v1.pdf	You Only Align Once: Bidirectional Interaction for Spatial-Temporal Video Super-Resolution	Spatial-Temporal Video Super-Resolution (ST-VSR) technology generates high-quality videos with higher resolution and higher frame rates. Existing advanced methods accomplish ST-VSR tasks through the association of Spatial and Temporal video super-resolution (S-VSR and T-VSR). These methods require two alignments and fusions in S-VSR and T-VSR, which is obviously redundant and fails to sufficiently explore the information flow of consecutive spatial LR frames. Although bidirectional learning (future-to-past and past-to-future) was introduced to cover all input frames, the direct fusion of final predictions fails to sufficiently exploit intrinsic correlations of bidirectional motion learning and spatial information from all frames. We propose an effective yet efficient recurrent network with bidirectional interaction for ST-VSR, where only one alignment and fusion is needed. Specifically, it first performs backward inference from future to past, and then follows forward inference to super-resolve intermediate frames. The backward and forward inferences are assigned to learn structures and details to simplify the learning task with joint optimizations. Furthermore, a Hybrid Fusion Module (HFM) is designed to aggregate and distill information to refine spatial information and reconstruct high-quality video frames. Extensive experiments on two public datasets demonstrate that our method outperforms state-of-the-art methods in efficiency, and reduces calculation cost by about 22%.	['Zheng Wang', 'Zhixiang Nie', 'Kui Jiang', 'Mengshun Hu']	2022-07-13	null	null	null	null	['video-super-resolution']	['computer-vision']	[ 2.65757531e-01 -2.56102532e-01 -4.28889960e-01 -2.96924204e-01 -9.62263763e-01 -1.20712928e-01 4.08143580e-01 -6.84460580e-01 -1.11147821e-01 8.82480979e-01 6.49047077e-01 -1.82117894e-01 -1.13589540e-01 -7.35043943e-01 -7.68783092e-01 -5.37024975e-01 -1.81176439e-01 -2.16129869e-01 7.93712735e-01 -3.45986009e-01 4.80227284e-02 3.54312658e-01 -1.51435864e+00 8.83935273e-01 6.90119863e-01 8.66494060e-01 8.38295817e-01 9.27480340e-01 -4.06375341e-03 1.31947327e+00 -2.24715859e-01 -9.45556164e-02 3.13872039e-01 -6.19379938e-01 -8.39720070e-01 -1.12889528e-01 2.71336138e-01 -7.45017052e-01 -7.54382074e-01 8.03647399e-01 4.64493185e-01 3.57429206e-01 -4.23751883e-02 -6.14949167e-01 -8.03156137e-01 6.43517554e-01 -1.01743042e+00 7.38323390e-01 6.85366869e-01 2.98231453e-01 8.66406739e-01 -1.02451730e+00 8.85021925e-01 1.38249707e+00 4.41974670e-01 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-3.27792495e-01 2.17875883e-01 6.51990652e-01 -5.11547983e-01 -1.82908341e-01 3.99031132e-01]	[11.04642105102539, -1.8814469575881958]
ba81cf55-e803-4c4b-8b72-fc2fc6cf08ff	utilizing-temporal-information-in	1909.02406	null	https://arxiv.org/abs/1909.02406v2	https://arxiv.org/pdf/1909.02406v2.pdf	Utilizing Temporal Information in Deep Convolutional Network for Efficient Soccer Ball Detection and Tracking	Soccer ball detection is identified as one of the critical challenges in the RoboCup competition. It requires an efficient vision system capable of handling the task of detection with high precision and recall and providing robust and low inference time. In this work, we present a novel convolutional neural network (CNN) approach to detect the soccer ball in an image sequence. In contrast to the existing methods where only the current frame or an image is used for the detection, we make use of the history of frames. Using history allows to efficiently track the ball in situations where the ball disappears or gets partially occluded in some of the frames. Our approach exploits spatio-temporal correlation and detects the ball based on the trajectory of its movements. We present our results with three convolutional methods, namely temporal convolutional networks (TCN), ConvLSTM, and ConvGRU. We first solve the detection task for an image using fully convolutional encoder-decoder architecture, and later, we use it as an input to our temporal models and jointly learn the detection task in sequences of images. We evaluate all our experiments on a novel dataset prepared as a part of this work. Furthermore, we present empirical results to support the effectiveness of using the history of the ball in challenging scenarios.	['Hafez Farazi', 'Anna Kukleva', 'Sven Behnke', 'Mohammad Asif Khan']	2019-09-05	null	null	null	null	['game-of-football']	['playing-games']	[-9.18893889e-02 -6.56282306e-01 -9.91231129e-02 -5.84571548e-02 -4.37050879e-01 -3.24554682e-01 4.07095253e-01 -1.34611905e-01 -1.03725553e+00 5.57561278e-01 -2.00417176e-01 4.51461002e-02 9.46823657e-02 -5.70096910e-01 -9.87793088e-01 -4.78770018e-01 -1.82001397e-01 2.12742984e-01 1.10356104e+00 -3.98013353e-01 2.89253742e-01 4.21412498e-01 -1.59000766e+00 6.84292495e-01 2.81152278e-02 1.26235688e+00 4.50804293e-01 9.94900525e-01 4.63406086e-01 1.39748764e+00 -7.09395826e-01 -6.39584735e-02 3.73837471e-01 -2.38682389e-01 -7.32949674e-01 -5.90035319e-02 2.10999131e-01 -5.96961379e-01 -8.12190354e-01 7.49862731e-01 3.41553390e-01 5.19348502e-01 1.04377046e-01 -1.13837838e+00 -1.46866394e-02 4.24397022e-01 -6.39727712e-01 9.46029842e-01 5.16057909e-01 3.47930580e-01 6.69590592e-01 -8.14499438e-01 8.39353740e-01 9.93495047e-01 5.40003717e-01 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1d63004a-6015-4485-b0b0-e34c88737875	project-level-encoding-for-neural-source-code	2103.11599	null	https://arxiv.org/abs/2103.11599v1	https://arxiv.org/pdf/2103.11599v1.pdf	Project-Level Encoding for Neural Source Code Summarization of Subroutines	Source code summarization of a subroutine is the task of writing a short, natural language description of that subroutine. The description usually serves in documentation aimed at programmers, where even brief phrase (e.g. "compresses data to a zip file") can help readers rapidly comprehend what a subroutine does without resorting to reading the code itself. Techniques based on neural networks (and encoder-decoder model designs in particular) have established themselves as the state-of-the-art. Yet a problem widely recognized with these models is that they assume the information needed to create a summary is present within the code being summarized itself - an assumption which is at odds with program comprehension literature. Thus a current research frontier lies in the question of encoding source code context into neural models of summarization. In this paper, we present a project-level encoder to improve models of code summarization. By project-level, we mean that we create a vectorized representation of selected code files in a software project, and use that representation to augment the encoder of state-of-the-art neural code summarization techniques. We demonstrate how our encoder improves several existing models, and provide guidelines for maximizing improvement while controlling time and resource costs in model size.	['Collin McMillan', 'Sakib Haque', 'Aakash Bansal']	2021-03-22	null	null	null	null	['code-summarization']	['computer-code']	[ 6.13032818e-01 3.96313488e-01 -3.24285805e-01 -5.16397834e-01 -6.90932751e-01 -4.41454530e-01 3.00334573e-01 6.85896516e-01 -1.04858264e-01 2.70888925e-01 8.37326527e-01 -5.80214322e-01 8.78105164e-02 -5.76605499e-01 -8.02807033e-01 8.15146789e-02 1.93817198e-01 -6.92249909e-02 -2.84026802e-01 -2.09343940e-01 9.96497571e-01 1.21790692e-01 -1.61203718e+00 6.58818364e-01 1.00987422e+00 2.29772627e-01 6.83026850e-01 9.16526854e-01 -7.00596750e-01 1.26427722e+00 -9.23337996e-01 -4.52535957e-01 -2.53909737e-01 -8.00264001e-01 -1.13516223e+00 9.23795067e-03 5.79802454e-01 -3.95756185e-01 -3.97436529e-01 1.11842179e+00 2.17452217e-02 -1.75884888e-01 7.02297986e-01 -7.86835194e-01 -9.23523009e-01 1.21925700e+00 -6.19296730e-01 3.17301124e-01 4.09631521e-01 -4.15133834e-02 1.10461771e+00 -6.36927485e-01 6.87400222e-01 6.04066610e-01 8.48885655e-01 5.78962862e-01 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2784c0f4-314b-4c28-bbb4-cb73e4bef0ca	on-utilizing-relationships-for-transferable	2212.00770	null	https://arxiv.org/abs/2212.00770v1	https://arxiv.org/pdf/2212.00770v1.pdf	On Utilizing Relationships for Transferable Few-Shot Fine-Grained Object Detection	State-of-the-art object detectors are fast and accurate, but they require a large amount of well annotated training data to obtain good performance. However, obtaining a large amount of training annotations specific to a particular task, i.e., fine-grained annotations, is costly in practice. In contrast, obtaining common-sense relationships from text, e.g., "a table-lamp is a lamp that sits on top of a table", is much easier. Additionally, common-sense relationships like "on-top-of" are easy to annotate in a task-agnostic fashion. In this paper, we propose a probabilistic model that uses such relational knowledge to transform an off-the-shelf detector of coarse object categories (e.g., "table", "lamp") into a detector of fine-grained categories (e.g., "table-lamp"). We demonstrate that our method, RelDetect, achieves performance competitive to finetuning based state-of-the-art object detector baselines when an extremely low amount of fine-grained annotations is available ($0.2\%$ of entire dataset). We also demonstrate that RelDetect is able to utilize the inherent transferability of relationship information to obtain a better performance ($+5$ mAP points) than the above baselines on an unseen dataset (zero-shot transfer). In summary, we demonstrate the power of using relationships for object detection on datasets where fine-grained object categories can be linked to coarse-grained categories via suitable relationships.	['René Vidal', 'Amit Kumar K C', 'Arnau Ramisa', 'Ambar Pal']	2022-12-01	null	null	null	null	['common-sense-reasoning']	['reasoning']	[-3.86393219e-02 3.65326293e-02 -1.90480918e-01 -6.82661891e-01 -9.68035221e-01 -8.35665047e-01 5.52978098e-01 2.92558491e-01 -3.61781657e-01 3.72571051e-01 -1.64245635e-01 -9.98497158e-02 1.84931025e-01 -1.00985944e+00 -1.17136610e+00 -3.72755766e-01 2.64035434e-01 5.89660823e-01 7.62734532e-01 -1.73983455e-01 8.45169127e-02 4.07666057e-01 -1.67057168e+00 5.18138170e-01 4.01889920e-01 1.16487777e+00 3.43708664e-01 4.20297742e-01 -1.36937778e-02 4.29523975e-01 -5.98819375e-01 -4.19918060e-01 2.80210018e-01 2.51553148e-01 -8.34381223e-01 -4.00789976e-02 8.88379574e-01 -3.79664123e-01 -3.12047303e-01 1.13537860e+00 7.76476786e-02 1.01581447e-01 7.44489610e-01 -1.19333768e+00 -8.80959392e-01 6.44128382e-01 -8.55260849e-01 3.02924901e-01 1.21450178e-01 1.30737454e-01 1.43298066e+00 -1.21824265e+00 4.62521344e-01 1.45994866e+00 7.26318717e-01 4.96183723e-01 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5acfac70-786a-4ddb-80ac-c8a806f5845e	all-in-focus-imaging-from-event-focal-stack	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Lou_All-in-Focus_Imaging_From_Event_Focal_Stack_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Lou_All-in-Focus_Imaging_From_Event_Focal_Stack_CVPR_2023_paper.pdf	All-in-Focus Imaging From Event Focal Stack	Traditional focal stack methods require multiple shots to capture images focused at different distances of the same scene, which cannot be applied to dynamic scenes well. Generating a high-quality all-in-focus image from a single shot is challenging, due to the highly ill-posed nature of the single-image defocus and deblurring problem. In this paper, to restore an all-in-focus image, we propose the event focal stack which is defined as event streams captured during a continuous focal sweep. Given an RGB image focused at an arbitrary distance, we explore the high temporal resolution of event streams, from which we automatically select refocusing timestamps and reconstruct corresponding refocused images with events to form a focal stack. Guided by the neighbouring events around the selected timestamps, we can merge the focal stack with proper weights and restore a sharp all-in-focus image. Experimental results on both synthetic and real datasets show superior performance over state-of-the-art methods.	['Boxin Shi', 'Yixin Yang', 'Minggui Teng', 'Hanyue Lou']	2023-01-01	null	null	null	cvpr-2023-1	['deblurring']	['computer-vision']	[ 8.25305223e-01 -6.66592419e-01 4.51461494e-01 -3.21957409e-01 -5.88726759e-01 -5.48980117e-01 4.08397198e-01 -3.19376171e-01 -3.67769510e-01 7.74753571e-01 4.04185057e-01 4.36201572e-01 -5.32770455e-01 -6.52548552e-01 -6.24594986e-01 -1.01995885e+00 1.19127966e-01 9.92989913e-03 5.78495562e-01 2.74722368e-01 4.75691259e-01 4.49443698e-01 -1.75875294e+00 2.44979635e-01 9.44411397e-01 7.33832240e-01 8.21000814e-01 8.05292368e-01 -5.67508154e-02 9.08546805e-01 -4.89275396e-01 2.61675358e-01 6.44968301e-02 -7.35779345e-01 -2.76444763e-01 3.17175299e-01 5.92308462e-01 -7.37334609e-01 -7.19586909e-01 1.10318947e+00 4.59353119e-01 3.43054324e-01 1.53445005e-01 -1.03385329e+00 -5.26600927e-02 1.40270427e-01 -6.89488947e-01 5.84432065e-01 8.64547968e-01 4.84226257e-01 3.27072382e-01 -8.80507290e-01 8.42634201e-01 8.55677664e-01 2.53825128e-01 2.13956803e-01 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e364215f-ab23-4f1e-998e-7be994fb3b48	aed-net-an-abnormal-event-detection-network	1903.11891	null	http://arxiv.org/abs/1903.11891v1	http://arxiv.org/pdf/1903.11891v1.pdf	AED-Net: An Abnormal Event Detection Network	It is challenging to detect the anomaly in crowded scenes for quite a long time. In this paper, a self-supervised framework, abnormal event detection network (AED-Net), which is composed of PCAnet and kernel principal component analysis (kPCA), is proposed to address this problem. Using surveillance video sequences of different scenes as raw data, PCAnet is trained to extract high-level semantics of crowd's situation. Next, kPCA,a one-class classifier, is trained to determine anomaly of the scene. In contrast to some prevailing deep learning methods,the framework is completely self-supervised because it utilizes only video sequences in a normal situation. Experiments of global and local abnormal event detection are carried out on UMN and UCSD datasets, and competitive results with higher EER and AUC compared to other state-of-the-art methods are observed. Furthermore, by adding local response normalization (LRN) layer, we propose an improvement to original AED-Net. And it is proved to perform better by promoting the framework's generalization capacity according to the experiments.	['Zichen Miao', 'Yuxin Chen', 'Tian Wang', 'Hichem Snoussi', 'Guangcun Shan', 'Yi Zhou']	2019-03-28	null	null	null	null	['one-class-classifier']	['methodology']	[-4.16937005e-03 -4.49958563e-01 5.80725312e-01 -2.27910101e-01 -7.56939277e-02 -2.58916039e-02 7.55781710e-01 1.27300113e-01 -5.26389360e-01 4.46755201e-01 3.57264638e-01 1.02390639e-01 9.36659500e-02 -7.06219733e-01 -4.66490924e-01 -9.87134337e-01 -2.20977411e-01 -1.59204558e-01 6.19067550e-01 -2.46655121e-02 1.07674468e-02 6.26778901e-01 -1.65218925e+00 3.75801325e-01 7.42152631e-01 1.09978878e+00 -1.08653523e-01 6.26832545e-01 -1.15292389e-02 1.39140821e+00 -6.59289777e-01 -3.87049168e-01 2.23531663e-01 -2.13119119e-01 -4.18542057e-01 4.24323112e-01 1.15956791e-01 -4.75763172e-01 -7.19176829e-01 1.22699869e+00 5.98713577e-01 1.69869468e-01 6.64840341e-01 -1.26476645e+00 -4.56478447e-01 -2.04688415e-01 -5.12474418e-01 9.05138373e-01 3.59555572e-01 2.96770811e-01 4.21289623e-01 -8.63498271e-01 2.41735309e-01 1.10623658e+00 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ac5d5d73-8b56-4f3e-a5b4-a63a4ab3c2a5	heterogeneous-domain-adaptation-and-equipment	2301.01038	null	https://arxiv.org/abs/2301.01038v1	https://arxiv.org/pdf/2301.01038v1.pdf	Heterogeneous Domain Adaptation and Equipment Matching: DANN-based Alignment with Cyclic Supervision (DBACS)	Process monitoring and control are essential in modern industries for ensuring high quality standards and optimizing production performance. These technologies have a long history of application in production and have had numerous positive impacts, but also hold great potential when integrated with Industry 4.0 and advanced machine learning, particularly deep learning, solutions. However, in order to implement these solutions in production and enable widespread adoption, the scalability and transferability of deep learning methods have become a focus of research. While transfer learning has proven successful in many cases, particularly with computer vision and homogenous data inputs, it can be challenging to apply to heterogeneous data. Motivated by the need to transfer and standardize established processes to different, non-identical environments and by the challenge of adapting to heterogeneous data representations, this work introduces the Domain Adaptation Neural Network with Cyclic Supervision (DBACS) approach. DBACS addresses the issue of model generalization through domain adaptation, specifically for heterogeneous data, and enables the transfer and scalability of deep learning-based statistical control methods in a general manner. Additionally, the cyclic interactions between the different parts of the model enable DBACS to not only adapt to the domains, but also match them. To the best of our knowledge, DBACS is the first deep learning approach to combine adaptation and matching for heterogeneous data settings. For comparison, this work also includes subspace alignment and a multi-view learning that deals with heterogeneous representations by mapping data into correlated latent feature spaces. Finally, DBACS with its ability to adapt and match, is applied to a virtual metrology use case for an etching process run on different machine types in semiconductor manufacturing.	['Gian Antonio Susto', 'Natalie Gentner']	2023-01-03	null	null	null	null	['multi-view-learning']	['computer-vision']	[ 2.08448783e-01 -2.03912809e-01 1.70741715e-02 -1.96181014e-01 -3.73160124e-01 -5.50581336e-01 5.51272452e-01 2.76248506e-03 9.20669958e-02 4.39230412e-01 -3.24643224e-01 -1.64162982e-02 -4.61987376e-01 -6.64398372e-01 -5.53953171e-01 -8.67808938e-01 2.29468822e-01 9.66083229e-01 -2.12571532e-01 -2.81377763e-01 1.20292708e-01 9.34327304e-01 -1.49049270e+00 2.96508998e-01 4.97825593e-01 1.03949594e+00 3.85888010e-01 3.99985552e-01 -5.43157160e-02 3.35382968e-01 -4.19431299e-01 -5.14278300e-02 4.99335974e-01 -2.22729370e-01 -5.21104693e-01 3.26141238e-01 3.83556396e-01 4.31386903e-02 -3.64576019e-02 6.92716122e-01 6.48803890e-01 1.24603942e-01 6.03368223e-01 -1.24053764e+00 -5.14079869e-01 2.62353599e-01 -3.62996727e-01 -2.36310348e-01 2.53891181e-02 2.57432550e-01 5.76421082e-01 -5.56869864e-01 7.02957392e-01 1.20254517e+00 5.49585819e-01 5.02039015e-01 -1.59007394e+00 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975432be-2a59-47a8-a97b-0a8a9d04b62a	machine-reading-comprehension-using-case	2305.14815	null	https://arxiv.org/abs/2305.14815v1	https://arxiv.org/pdf/2305.14815v1.pdf	Machine Reading Comprehension using Case-based Reasoning	We present an accurate and interpretable method for answer extraction in machine reading comprehension that is reminiscent of case-based reasoning (CBR) from classical AI. Our method (CBR-MRC) builds on the hypothesis that contextualized answers to similar questions share semantic similarities with each other. Given a target question, CBR-MRC retrieves a set of similar questions from a memory of observed cases and predicts an answer by selecting the span in the target context that is most similar to the contextualized representations of answers in the retrieved cases. The semi-parametric nature of our approach allows CBR-MRC to attribute a prediction to the specific set of cases used during inference, making it a desirable choice for building reliable and debuggable QA systems. We show that CBR-MRC achieves high test accuracy comparable with large reader models, outperforming baselines by 11.5 and 8.4 EM on NaturalQuestions and NewsQA, respectively. Further, we also demonstrate the ability of CBR-MRC in identifying not just the correct answer tokens but also the span with the most relevant supporting evidence. Lastly, we observe that contexts for certain question types show higher lexical diversity than others and find CBR-MRC to be robust to these variations while performance using fully-parametric methods drops.	['Andrew McCallum', 'Hannaneh Hajishirzi', 'Jay-Yoon Lee', 'Manzil Zaheer', 'Rajarshi Das', 'Mudit Chaudhary', 'Dhruv Agarwal', 'Dung Thai']	2023-05-24	null	null	null	null	['reading-comprehension', 'machine-reading-comprehension']	['natural-language-processing', 'natural-language-processing']	[ 2.59169817e-01 4.81314063e-01 -1.57724589e-01 -4.82445866e-01 -1.62324834e+00 -9.48232591e-01 7.31164992e-01 6.12482011e-01 -3.18425983e-01 9.25335646e-01 6.84453189e-01 -5.75734317e-01 -5.91969013e-01 -8.83735180e-01 -8.71028960e-01 -1.51316561e-02 2.94111550e-01 8.55246007e-01 8.33079696e-01 -6.81531370e-01 7.88094044e-01 1.63653731e-01 -1.78962743e+00 1.06867397e+00 1.21167481e+00 8.09334040e-01 2.94053346e-01 8.62880766e-01 -4.17904615e-01 1.43125021e+00 -8.95724237e-01 -6.70655966e-01 -4.70935047e-01 -7.38690853e-01 -1.76041293e+00 -5.52982271e-01 8.25392723e-01 -1.29961342e-01 -1.24533683e-01 4.50533211e-01 3.11846286e-01 4.08870876e-01 8.94183576e-01 -8.61481667e-01 -7.59228885e-01 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f68a77c1-f9ae-414a-86fa-bfef771f2fa0	l3i-at-semeval-2022-task-11-straightforward	null	null	https://aclanthology.org/2022.semeval-1.225	https://aclanthology.org/2022.semeval-1.225.pdf	L3i at SemEval-2022 Task 11: Straightforward Additional Context for Multilingual Named Entity Recognition	This paper summarizes the participation of the L3i laboratory of the University of La Rochelle in the SemEval-2022 Task 11, Multilingual Complex Named Entity Recognition (MultiCoNER). The task focuses on detecting semantically ambiguous and complex entities in short and low-context monolingual and multilingual settings. We argue that using a language-specific and a multilingual language model could improve the performance of multilingual and mixed NER. Also, we consider that using additional contexts from the training set could improve the performance of a NER on short texts. Thus, we propose a straightforward technique for generating additional contexts with and without the presence of entities. Our findings suggest that, in our internal experimental setup, this approach is promising. However, we ranked above average for the high-resource languages and lower than average for low-resource and multilingual models.	['Antoine Doucet', 'Jose Moreno', 'Carlos-Emiliano González-Gallardo', 'Emanuela Boros']	null	null	null	null	semeval-naacl-2022-7	['multilingual-named-entity-recognition']	['natural-language-processing']	[-5.45823276e-01 1.30826473e-01 1.12789743e-01 -2.58912027e-01 -9.94814932e-01 -1.00512242e+00 9.21778083e-01 4.01267052e-01 -1.10235775e+00 1.08787823e+00 3.93138230e-01 -4.76925194e-01 1.66623175e-01 -5.19009233e-01 -6.91324592e-01 -1.06773237e-02 1.94129989e-01 7.46764898e-01 1.03782102e-01 -4.15179074e-01 -1.23335414e-01 6.04029596e-01 -1.03412414e+00 3.66395146e-01 1.22057068e+00 1.38442606e-01 5.26760697e-01 4.49224770e-01 -1.52715996e-01 6.11388028e-01 -6.49624825e-01 -7.88542986e-01 1.35657474e-01 -1.04882076e-01 -1.08780289e+00 -5.89262843e-01 3.91073108e-01 3.79017144e-01 9.08033028e-02 9.17031884e-01 7.07333267e-01 1.46287233e-01 5.68964541e-01 -7.21156299e-01 -2.58552432e-01 1.22903633e+00 1.06537886e-01 3.78538847e-01 6.70562327e-01 -4.67952192e-02 1.10703480e+00 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e3771c46-4c01-4853-a820-cbd98082276b	detecting-finger-vein-presentation-attacks	1912.01408	null	https://arxiv.org/abs/1912.01408v1	https://arxiv.org/pdf/1912.01408v1.pdf	Detecting Finger-Vein Presentation Attacks Using 3D Shape & Diffuse Reflectance Decomposition	Despite the high biometric performance, finger-vein recognition systems are vulnerable to presentation attacks (aka., spoofing attacks). In this paper, we present a new and robust approach for detecting presentation attacks on finger-vein biometric systems exploiting the 3D Shape (normal-map) and material properties (diffuse-map) of the finger. Observing the normal-map and diffuse-map exhibiting enhanced textural differences in comparison with the original finger-vein image, especially in the presence of varying illumination intensity, we propose to employ textural feature-descriptors on both of them independently. The features are subsequently used to compute a separating hyper-plane using Support Vector Machine (SVM) classifiers for the features computed from normal-maps and diffuse-maps independently. Given the scores from each classifier for normal-map and diffuse-map, we propose sum-rule based score level fusion to make detection of such presentation attack more robust. To this end, we construct a new database of finger-vein images acquired using a custom capture device with three inbuilt illuminations and validate the applicability of the proposed approach. The newly collected database consists of 936 images, which corresponds to 468 bona fide images and 468 artefact images. We establish the superiority of the proposed approach by benchmarking it with classical textural feature-descriptor applied directly on finger-vein images. The proposed approach outperforms the classical approaches by providing the Attack Presentation Classification Error Rate (APCER) & Bona fide Presentation Classification Error Rate (BPCER) of 0% compared to comparable traditional methods.	['Jag Mohan Singh', 'Sushma Venkatesh', 'Kiran B. Raja', 'Christoph Busch', 'Raghavendra Ramachandra']	2019-12-03	null	null	null	null	['finger-vein-recognition']	['computer-vision']	[ 5.87676823e-01 -3.75492811e-01 2.33941719e-01 -6.69692680e-02 -5.01954138e-01 -8.96536827e-01 8.76190841e-01 2.91965514e-01 -4.82028514e-01 4.27617788e-01 -1.24639191e-01 -3.26817632e-02 -5.04432082e-01 -6.99507177e-01 -1.71322748e-01 -7.96049476e-01 -1.40293211e-01 1.35528073e-01 1.93228140e-01 8.72087013e-03 6.20576322e-01 1.01603246e+00 -1.41822827e+00 2.60453373e-01 4.00303811e-01 1.18820298e+00 -6.03080153e-01 9.29434776e-01 2.30522230e-01 -2.42763922e-01 -8.79256487e-01 -8.44677866e-01 5.61355054e-01 -2.93461740e-01 -2.61673152e-01 -3.51052284e-02 6.20689988e-01 -4.79334414e-01 -9.97185707e-02 1.01115561e+00 7.32509553e-01 -3.31494123e-01 1.10907567e+00 -9.52230453e-01 -1.83521494e-01 -2.71165699e-01 -6.95976019e-01 2.13540405e-01 6.33230686e-01 3.28256011e-01 5.22588909e-01 -7.06617475e-01 5.66700459e-01 1.04851699e+00 6.34990215e-01 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-1.06903696e+00 -9.05283451e-01 -8.87574375e-01 7.95743644e-01 3.29136848e-01 -7.39391744e-01 -3.97331119e-01 7.54767582e-02]	[13.020110130310059, 1.0315479040145874]
5c9e4513-8d4c-417a-9426-5779d62450ec	explaining-classes-through-word-attribution	2108.13653	null	https://arxiv.org/abs/2108.13653v1	https://arxiv.org/pdf/2108.13653v1.pdf	Explaining Classes through Word Attribution	In recent years, several methods have been proposed for explaining individual predictions of deep learning models, yet there has been little study of how to aggregate these predictions to explain how such models view classes as a whole in text classification tasks. In this work, we propose a method for explaining classes using deep learning models and the Integrated Gradients feature attribution technique by aggregating explanations of individual examples in text classification to general descriptions of the classes. We demonstrate the approach on Web register (genre) classification using the XML-R model and the Corpus of Online Registers of English (CORE), finding that the method identifies plausible and discriminative keywords characterizing all but the smallest class.	['Filip Ginter', 'Veronika Laippala', 'Sampo Pyysalo', 'Aki-Juhani Kyröläinen', 'Amanda Myntti', 'Samuel Rönnqvist']	2021-08-31	null	null	null	null	['genre-classification']	['computer-vision']	[ 3.10883850e-01 8.90941262e-01 -7.63034403e-01 -8.51355791e-01 -6.97307765e-01 -3.89191002e-01 1.14532185e+00 4.65667278e-01 -4.12724689e-02 5.13591051e-01 9.62176502e-01 -5.91488004e-01 -6.31588757e-01 -4.62408096e-01 -7.01044142e-01 -1.51353061e-01 1.79847226e-01 8.60165536e-01 2.57556308e-02 -2.56665707e-01 5.28372467e-01 2.78692365e-01 -1.74219310e+00 1.23884761e+00 6.50584877e-01 8.68688464e-01 7.77796935e-03 5.34987569e-01 -5.65362036e-01 9.49753284e-01 -5.90652704e-01 -9.59948182e-01 -3.32162917e-01 -3.71580034e-01 -1.23857713e+00 2.23123766e-02 6.28112555e-01 -1.83451474e-01 -6.11733079e-01 6.61456525e-01 -2.59574354e-02 -2.48841345e-01 1.19280922e+00 -1.08451092e+00 -9.77181375e-01 1.69615448e+00 -1.40049076e-02 1.62679419e-01 3.28785658e-01 -5.58391511e-01 1.74170136e+00 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91f2befe-0101-424b-b0a1-a3792d4ae8de	retromae-2-duplex-masked-auto-encoder-for-pre	2305.02564	null	https://arxiv.org/abs/2305.02564v1	https://arxiv.org/pdf/2305.02564v1.pdf	RetroMAE-2: Duplex Masked Auto-Encoder For Pre-Training Retrieval-Oriented Language Models	To better support information retrieval tasks such as web search and open-domain question answering, growing effort is made to develop retrieval-oriented language models, e.g., RetroMAE and many others. Most of the existing works focus on improving the semantic representation capability for the contextualized embedding of the [CLS] token. However, recent study shows that the ordinary tokens besides [CLS] may provide extra information, which help to produce a better representation effect. As such, it's necessary to extend the current methods where all contextualized embeddings can be jointly pre-trained for the retrieval tasks. In this work, we propose a novel pre-training method called Duplex Masked Auto-Encoder, a.k.a. DupMAE. It is designed to improve the quality of semantic representation where all contextualized embeddings of the pre-trained model can be leveraged. It takes advantage of two complementary auto-encoding tasks: one reconstructs the input sentence on top of the [CLS] embedding; the other one predicts the bag-of-words feature of the input sentence based on the ordinary tokens' embeddings. The two tasks are jointly conducted to train a unified encoder, where the whole contextualized embeddings are aggregated in a compact way to produce the final semantic representation. DupMAE is simple but empirically competitive: it substantially improves the pre-trained model's representation capability and transferability, where superior retrieval performances can be achieved on popular benchmarks, like MS MARCO and BEIR.	['Zhao Cao', 'Yingxia Shao', 'Zheng Liu', 'Shitao Xiao']	2023-05-04	null	null	null	null	['open-domain-question-answering']	['natural-language-processing']	[ 1.27245814e-01 -2.14217491e-02 -3.53259385e-01 -3.66628855e-01 -9.13564682e-01 -2.61990041e-01 7.28457153e-01 3.65156323e-01 -4.11972791e-01 3.40276241e-01 5.45575857e-01 -2.66815811e-01 -1.27210781e-01 -8.73416066e-01 -6.37801170e-01 -6.13859117e-01 2.39992589e-01 9.95979533e-02 3.05366963e-01 -5.81537604e-01 3.12723428e-01 -6.98911101e-02 -1.66468382e+00 5.48141479e-01 1.02627575e+00 1.24219227e+00 5.26426971e-01 3.06056231e-01 -5.60124576e-01 4.66470808e-01 -5.07274926e-01 -3.74870151e-01 2.57386342e-02 -2.06422284e-01 -7.88059890e-01 -2.93952763e-01 -6.45871684e-02 -3.88647348e-01 -5.27680397e-01 7.69246221e-01 5.40924191e-01 4.03236568e-01 6.25830829e-01 -7.35696316e-01 -1.24485171e+00 6.79562092e-01 -2.88907588e-01 1.02325529e-01 4.16351140e-01 -1.67189687e-01 1.53038490e+00 -1.16104555e+00 4.79555756e-01 1.31409657e+00 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-9.57171500e-01 -1.27172577e+00 -4.08864528e-01 5.33905745e-01 2.48065785e-01 -7.75399745e-01 -4.81495798e-01 -4.03975606e-01]	[11.188477516174316, 8.155590057373047]
ac7e69f1-7abc-47e0-8537-f9d18bb8d810	sketchgan-joint-sketch-completion-and	null	null	http://openaccess.thecvf.com/content_CVPR_2019/html/Liu_SketchGAN_Joint_Sketch_Completion_and_Recognition_With_Generative_Adversarial_Network_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_SketchGAN_Joint_Sketch_Completion_and_Recognition_With_Generative_Adversarial_Network_CVPR_2019_paper.pdf	SketchGAN: Joint Sketch Completion and Recognition With Generative Adversarial Network	Hand-drawn sketch recognition is a fundamental problem in computer vision, widely used in sketch-based image and video retrieval, editing, and reorganization. Previous methods often assume that a complete sketch is used as input; however, hand-drawn sketches in common application scenarios are often incomplete, which makes sketch recognition a challenging problem. In this paper, we propose SketchGAN, a new generative adversarial network (GAN) based approach that jointly completes and recognizes a sketch, boosting the performance of both tasks. Specifically, we use a cascade Encode-Decoder network to complete the input sketch in an iterative manner, and employ an auxiliary sketch recognition task to recognize the completed sketch. Experiments on the Sketchy database benchmark demonstrate that our joint learning approach achieves competitive sketch completion and recognition performance compared with the state-of-the-art methods. Further experiments using several sketch-based applications also validate the performance of our method.	[' Hongan Wang', ' Cuixia Ma', ' Yong-Jin Liu', ' Yu-Kun Lai', ' Xiaoming Deng', 'Fang Liu']	2019-06-01	null	null	null	cvpr-2019-6	['sketch-recognition']	['computer-vision']	[ 2.73404866e-01 -4.53200459e-01 -1.72107741e-01 -2.77203619e-01 -6.31193876e-01 -7.44551003e-01 9.23203766e-01 -7.15536714e-01 -1.22280912e-02 3.63575846e-01 -8.27318281e-02 -3.41208816e-01 3.30632538e-01 -7.77883887e-01 -7.17563152e-01 -5.09534061e-01 5.98957658e-01 3.73059958e-01 -2.54986525e-01 -8.67221728e-02 3.78631681e-01 8.93051922e-01 -9.34588253e-01 2.67208219e-01 4.61575240e-01 8.39404643e-01 1.58216298e-01 7.63403714e-01 -4.66834217e-01 7.15306520e-01 -6.98712111e-01 -7.21403360e-01 4.45189685e-01 -4.79588151e-01 -2.45241940e-01 4.15691108e-01 7.66921103e-01 -9.42262769e-01 -1.02342570e+00 8.52224231e-01 3.76653194e-01 -8.01972076e-02 7.30777621e-01 -1.35869193e+00 -9.84860599e-01 3.50983411e-01 -5.32799542e-01 -4.30144936e-01 2.94855893e-01 2.48147249e-01 8.56855035e-01 -1.38993073e+00 7.17337608e-01 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18e9ecb7-d96a-4e45-afda-91ab940b63ce	approximating-poker-probabilities-with-deep	1808.07220	null	http://arxiv.org/abs/1808.07220v2	http://arxiv.org/pdf/1808.07220v2.pdf	Approximating Poker Probabilities with Deep Learning	Many poker systems, whether created with heuristics or machine learning, rely on the probability of winning as a key input. However calculating the precise probability using combinatorics is an intractable problem, so instead we approximate it. Monte Carlo simulation is an effective technique that can be used to approximate the probability that a player will win and/or tie a hand. However, without the use of a memory-intensive lookup table or a supercomputer, it becomes infeasible to run millions of times when training an agent with self-play. To combat the space-time tradeoff, we use deep learning to approximate the probabilities obtained from the Monte Carlo simulation with high accuracy. The learned model proves to be a lightweight alternative to Monte Carlo simulation, which ultimately allows us to use the probabilities as inputs during self-play efficiently. The source code and optimized neural network can be found at https://github.com/brandinho/Poker-Probability-Approximation	['Brandon Da Silva']	2018-08-22	null	null	null	null	['game-of-poker', 'card-games']	['playing-games', 'playing-games']	[-4.81583893e-01 -1.53768778e-01 -2.93524861e-01 1.20704472e-01 -7.86416173e-01 -6.73752785e-01 5.97960472e-01 2.51323432e-01 -5.97022355e-01 1.14891613e+00 -3.21077198e-01 -8.19330454e-01 -8.27328488e-02 -1.36651230e+00 -8.88679445e-01 -5.28348804e-01 -1.98120624e-01 1.08157480e+00 1.40011892e-01 -7.87869543e-02 2.79380709e-01 3.51585239e-01 -1.62491190e+00 2.88802665e-02 6.68421745e-01 7.77264595e-01 2.38305867e-01 9.62211907e-01 -1.64301142e-01 6.62219465e-01 -5.08386612e-01 -6.07110500e-01 5.22659123e-01 -5.86300015e-01 -6.90717816e-01 -7.13940084e-01 -9.53328162e-02 -6.80983067e-01 -5.30892253e-01 8.28962684e-01 4.00874257e-01 1.36709899e-01 4.42011714e-01 -1.26614416e+00 1.88553348e-01 9.90206838e-01 -2.34520584e-01 1.79865614e-01 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8c6f0a03-41ca-4a81-9ad7-f49161a9c35a	learning-deep-features-via-congenerous-cosine	1702.06890	null	http://arxiv.org/abs/1702.06890v2	http://arxiv.org/pdf/1702.06890v2.pdf	Learning Deep Features via Congenerous Cosine Loss for Person Recognition	Person recognition aims at recognizing the same identity across time and space with complicated scenes and similar appearance. In this paper, we propose a novel method to address this task by training a network to obtain robust and representative features. The intuition is that we directly compare and optimize the cosine distance between two features - enlarging inter-class distinction as well as alleviating inner-class variance. We propose a congenerous cosine loss by minimizing the cosine distance between samples and their cluster centroid in a cooperative way. Such a design reduces the complexity and could be implemented via softmax with normalized inputs. Our method also differs from previous work in person recognition that we do not conduct a second training on the test subset. The identity of a person is determined by measuring the similarity from several body regions in the reference set. Experimental results show that the proposed approach achieves better classification accuracy against previous state-of-the-arts.	['Yu Liu', 'Hongyang Li', 'Xiaogang Wang']	2017-02-22	null	null	null	null	['person-recognition']	['computer-vision']	[ 2.63034284e-01 -2.20550537e-01 -1.00204751e-01 -8.94168317e-01 -4.31248665e-01 -3.45401585e-01 6.39037430e-01 -1.01401046e-01 -7.37601340e-01 5.48042476e-01 1.25299627e-02 4.93961960e-01 -2.45784342e-01 -5.71495175e-01 -4.76056904e-01 -8.11584234e-01 1.86801776e-02 1.70589373e-01 -2.46350020e-01 1.62883818e-01 1.47203147e-01 4.30560946e-01 -1.57620847e+00 5.41240945e-02 7.57434070e-01 1.25183690e+00 -1.52267978e-01 3.89648765e-01 3.33041936e-01 3.59301925e-01 -8.15263093e-01 -6.26102746e-01 6.26762927e-01 -5.60519338e-01 -5.46749651e-01 4.18381035e-01 1.00098193e+00 -2.28768419e-02 -2.70167232e-01 1.03444064e+00 7.95424581e-01 6.00347281e-01 6.65213764e-01 -1.31322956e+00 -8.23030233e-01 2.05926031e-01 -5.41435063e-01 -7.41427615e-02 4.34141904e-01 -7.50722289e-02 6.19588554e-01 -6.84070230e-01 3.30297858e-01 1.25577641e+00 9.46801603e-01 7.87413716e-01 -1.14655936e+00 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8.13295484e-01 2.38873854e-01 -6.85864985e-01 -9.24380794e-02 3.53486329e-01]	[14.634811401367188, 1.0105292797088623]
ff25d5cb-ac0d-485c-878a-c0251f8f461a	ba-sot-boundary-aware-serialized-output	2305.13716	null	https://arxiv.org/abs/2305.13716v2	https://arxiv.org/pdf/2305.13716v2.pdf	BA-SOT: Boundary-Aware Serialized Output Training for Multi-Talker ASR	The recently proposed serialized output training (SOT) simplifies multi-talker automatic speech recognition (ASR) by generating speaker transcriptions separated by a special token. However, frequent speaker changes can make speaker change prediction difficult. To address this, we propose boundary-aware serialized output training (BA-SOT), which explicitly incorporates boundary knowledge into the decoder via a speaker change detection task and boundary constraint loss. We also introduce a two-stage connectionist temporal classification (CTC) strategy that incorporates token-level SOT CTC to restore temporal context information. Besides typical character error rate (CER), we introduce utterance-dependent character error rate (UD-CER) to further measure the precision of speaker change prediction. Compared to original SOT, BA-SOT reduces CER/UD-CER by 5.1%/14.0%, and leveraging a pre-trained ASR model for BA-SOT model initialization further reduces CER/UD-CER by 8.4%/19.9%.	['Lei Xie', 'Qian Chen', 'Shiliang Zhang', 'Pengcheng Guo', 'Yangze Li', 'Fan Yu', 'Yuhao Liang']	2023-05-23	null	null	null	null	['change-detection']	['computer-vision']	[ 5.28537035e-01 8.06312561e-02 -2.27536839e-02 -5.53002298e-01 -1.41963851e+00 -4.49944973e-01 4.10392910e-01 8.77380818e-02 -4.86671597e-01 4.91247594e-01 3.93917561e-01 -5.60732365e-01 4.89802033e-01 -1.33359442e-02 -5.78328669e-01 -4.85787302e-01 2.72419274e-01 1.12937264e-01 2.54435223e-02 -5.73018156e-02 -6.81706220e-02 1.42988622e-01 -1.02138495e+00 4.07392889e-01 8.88462901e-01 7.01370895e-01 1.91309243e-01 9.56628144e-01 -6.80294856e-02 4.85090703e-01 -1.02803218e+00 -1.15892716e-01 -1.23795345e-01 -6.25723839e-01 -7.31572032e-01 1.75315123e-02 1.37015000e-01 -6.88157156e-02 -1.11360393e-01 6.69405103e-01 7.29553759e-01 3.63818347e-01 3.94511163e-01 -9.06713128e-01 -4.68422532e-01 9.84666586e-01 -5.43163538e-01 5.69923103e-01 2.89415300e-01 2.63596535e-01 8.49626243e-01 -9.30135846e-01 3.36709380e-01 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8b44ba06-cefd-417b-a183-dff84f094603	cold-start-based-multi-scenario-ranking-model	2304.07858	null	https://arxiv.org/abs/2304.07858v1	https://arxiv.org/pdf/2304.07858v1.pdf	Cold-Start based Multi-Scenario Ranking Model for Click-Through Rate Prediction	Online travel platforms (OTPs), e.g., Ctrip.com or Fliggy.com, can effectively provide travel-related products or services to users. In this paper, we focus on the multi-scenario click-through rate (CTR) prediction, i.e., training a unified model to serve all scenarios. Existing multi-scenario based CTR methods struggle in the context of OTP setting due to the ignorance of the cold-start users who have very limited data. To fill this gap, we propose a novel method named Cold-Start based Multi-scenario Network (CSMN). Specifically, it consists of two basic components including: 1) User Interest Projection Network (UIPN), which firstly purifies users' behaviors by eliminating the scenario-irrelevant information in behaviors with respect to the visiting scenario, followed by obtaining users' scenario-specific interests by summarizing the purified behaviors with respect to the target item via an attention mechanism; and 2) User Representation Memory Network (URMN), which benefits cold-start users from users with rich behaviors through a memory read and write mechanism. CSMN seamlessly integrates both components in an end-to-end learning framework. Extensive experiments on real-world offline dataset and online A/B test demonstrate the superiority of CSMN over state-of-the-art methods.	['Chao Zhang', 'Ying Zhou', 'Wanjie Tao', 'Qijie Shen', 'Zhao Li', 'Fuyu Lv', 'Jing Zhang', 'Hong Wen', 'Peilin Chen']	2023-04-16	null	null	null	null	['click-through-rate-prediction']	['miscellaneous']	[-1.10620983e-01 -4.53260362e-01 -5.79453468e-01 -7.28563666e-01 -6.92248106e-01 -2.68303126e-01 5.15289545e-01 -1.29350662e-01 -4.60979640e-01 2.82395303e-01 5.48870027e-01 -2.41537720e-01 -3.73154849e-01 -8.95306826e-01 -4.66903865e-01 -6.44885004e-01 1.76541522e-01 6.37487233e-01 1.38570398e-01 -5.99426210e-01 3.30617726e-01 1.52858511e-01 -1.66019380e+00 4.48941469e-01 1.19328487e+00 1.07702017e+00 5.63031673e-01 4.34722751e-02 -3.43302459e-01 7.70852268e-01 -1.10599868e-01 -2.42932573e-01 2.12138623e-01 1.35102883e-01 -4.88981813e-01 -2.15861797e-01 -1.19921947e-02 -6.06565833e-01 -8.48743796e-01 3.81062746e-01 4.18686569e-01 1.06296563e+00 1.59463361e-01 -1.25009501e+00 -9.96504366e-01 6.71123683e-01 -5.15637457e-01 2.10935175e-01 4.32584167e-01 2.60776766e-02 9.92447853e-01 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86c70782-6274-400a-ba27-7d02f5375d41	scalable-bottom-up-hierarchical-clustering	2010.11821	null	https://arxiv.org/abs/2010.11821v3	https://arxiv.org/pdf/2010.11821v3.pdf	Scalable Hierarchical Agglomerative Clustering	The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability. Existing scalable hierarchical clustering methods sacrifice quality for speed and often lead to over-merging of clusters. In this paper, we present a scalable, agglomerative method for hierarchical clustering that does not sacrifice quality and scales to billions of data points. We perform a detailed theoretical analysis, showing that under mild separability conditions our algorithm can not only recover the optimal flat partition, but also provide a two-approximation to non-parametric DP-Means objective. This introduces a novel application of hierarchical clustering as an approximation algorithm for the non-parametric clustering objective. We additionally relate our algorithm to the classic hierarchical agglomerative clustering method. We perform extensive empirical experiments in both hierarchical and flat clustering settings and show that our proposed approach achieves state-of-the-art results on publicly available clustering benchmarks. Finally, we demonstrate our method's scalability by applying it to a dataset of 30 billion queries. Human evaluation of the discovered clusters show that our method finds better quality of clusters than the current state-of-the-art.	['Yuchen Wu', 'YuAn Wang', 'Bryon Tjanaka', 'Mert Terzihan', 'Marc Najork', 'Gokhan Mergen', 'Andrew McCallum', 'Amr Ahmed', 'Manzil Zaheer', 'Guru Guruganesh', 'Avinava Dubey', 'Nicholas Monath']	2020-10-22	null	null	null	null	['2d-human-pose-estimation']	['computer-vision']	[-5.52701354e-01 -8.78472850e-02 1.33861959e-01 -3.40141565e-01 -1.30469835e+00 -9.13695633e-01 1.14287600e-01 5.39425611e-01 -2.82986045e-01 1.62924364e-01 2.98100024e-01 -1.90702692e-01 -4.55515355e-01 -6.39832854e-01 -6.38827264e-01 -9.38568890e-01 -5.62568307e-01 1.23892653e+00 7.40345359e-01 4.23338890e-01 3.50923002e-01 2.95148909e-01 -1.68465877e+00 5.18410027e-01 8.74688625e-01 6.38779223e-01 9.31019802e-03 7.16996014e-01 1.30694404e-01 1.49083272e-01 -7.14957774e-01 -1.33022130e-01 3.42502207e-01 -1.69709906e-01 -1.10585272e+00 4.29249495e-01 1.23983994e-01 1.98256597e-02 1.47923306e-01 7.49722004e-01 5.58037400e-01 1.16576925e-01 7.63513982e-01 -1.53968370e+00 -3.47821951e-01 9.38419998e-01 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c1fa9d88-c702-4748-858a-75238ce0d383	jbnu-at-mrp-2019-multi-level-biaffine	null	null	https://aclanthology.org/K19-2009	https://aclanthology.org/K19-2009.pdf	JBNU at MRP 2019: Multi-level Biaffine Attention for Semantic Dependency Parsing	This paper describes Jeonbuk National University (JBNU){'}s system for the 2019 shared task on Cross-Framework Meaning Representation Parsing (MRP 2019) at the Conference on Computational Natural Language Learning. Of the five frameworks, we address only the DELPH-IN MRS Bi-Lexical Dependencies (DP), Prague Semantic Dependencies (PSD), and Universal Conceptual Cognitive Annotation (UCCA) frameworks. We propose a unified parsing model using biaffine attention (Dozat and Manning, 2017), consisting of 1) a BERT-BiLSTM encoder and 2) a biaffine attention decoder. First, the BERT-BiLSTM for sentence encoder uses BERT to compose a sentence{'}s wordpieces into word-level embeddings and subsequently applies BiLSTM to word-level representations. Second, the biaffine attention decoder determines the scores for an edge{'}s existence and its labels based on biaffine attention functions between roledependent representations. We also present multi-level biaffine attention models by combining all the role-dependent representations that appear at multiple intermediate layers.	['Young-Kil Kim', 'Jong-Hun Shin', 'Kwanghyeon Park', 'Jinwoon Min', 'Seung-Hoon Na']	2019-11-01	null	null	null	conll-2019-11	['semantic-dependency-parsing']	['natural-language-processing']	[ 2.11049154e-01 4.81109917e-01 -4.62777652e-02 -5.85383594e-01 -1.03964329e+00 -6.02627277e-01 3.75640750e-01 4.26677555e-01 -6.34267986e-01 4.85088378e-01 7.01294065e-01 -5.36459088e-01 1.79306403e-01 -7.26816952e-01 -6.58566535e-01 -4.03995931e-01 1.14835747e-01 2.33167306e-01 3.54776084e-02 -2.88320154e-01 2.17822582e-01 1.10767744e-01 -1.09369314e+00 5.87650597e-01 4.47063148e-01 7.41210401e-01 5.57049394e-01 1.20341706e+00 -2.07789555e-01 1.12914097e+00 -5.49516797e-01 -5.34142375e-01 -3.04952562e-01 -4.67666268e-01 -1.22654569e+00 -5.64130664e-01 -5.40425405e-02 -2.20399886e-01 -2.93457671e-03 1.04933953e+00 1.65736333e-01 2.38383502e-01 5.47239900e-01 -1.04955745e+00 -1.46407974e+00 1.03278220e+00 -3.17730844e-01 4.19299662e-01 2.40620703e-01 -1.90884009e-01 1.71730030e+00 -1.00421643e+00 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94eec152-5513-44fa-ad24-dafb726139d5	openvis-open-vocabulary-video-instance	2305.16835	null	https://arxiv.org/abs/2305.16835v1	https://arxiv.org/pdf/2305.16835v1.pdf	OpenVIS: Open-vocabulary Video Instance Segmentation	We propose and study a new computer vision task named open-vocabulary video instance segmentation (OpenVIS), which aims to simultaneously segment, detect, and track arbitrary objects in a video according to corresponding text descriptions. Compared to the original video instance segmentation, OpenVIS enables users to identify objects of desired categories, regardless of whether those categories were included in the training dataset. To achieve this goal, we propose a two-stage pipeline for proposing high-quality class-agnostic object masks and predicting their corresponding categories via pre-trained VLM. Specifically, we first employ a query-based mask proposal network to generate masks of all potential objects, where we replace the original class head with an instance head trained with a binary object loss, thereby enhancing the class-agnostic mask proposal ability. Then, we introduce a proposal post-processing approach to adapt the proposals better to the pre-trained VLMs, avoiding distortion and unnatural proposal inputs. Meanwhile, to facilitate research on this new task, we also propose an evaluation benchmark that utilizes off-the-shelf datasets to comprehensively assess its performance. Experimentally, the proposed OpenVIS exhibits a remarkable 148\% improvement compared to the full-supervised baselines on BURST, which have been trained on all categories.	['Wenqiang Zhang', 'Zhaoyu Chen', 'Tianjun Xiao', 'Xuefeng Liu', 'Peiyang He', 'Tony Huang', 'Pinxue Guo']	2023-05-26	null	null	null	null	['video-instance-segmentation']	['computer-vision']	[ 3.77305388e-01 6.60093576e-02 -3.46150249e-01 -4.70288008e-01 -7.99938083e-01 -6.98906362e-01 5.43150961e-01 -2.16752440e-01 -5.91325879e-01 2.92526931e-01 -1.26898825e-01 1.55371148e-02 3.01572680e-01 -4.75134581e-01 -9.44217622e-01 -4.91931051e-01 4.06678379e-01 5.32136321e-01 6.72258615e-01 2.97982872e-01 2.72778541e-01 1.70110896e-01 -1.78204846e+00 5.83202779e-01 8.87409687e-01 1.24465954e+00 4.72875893e-01 3.62905413e-01 -2.08658293e-01 6.64792955e-01 -5.47713697e-01 -6.68153763e-01 3.62575263e-01 -1.13740489e-01 -9.25577641e-01 7.37102985e-01 9.95788515e-01 -4.12418395e-01 -4.35385287e-01 1.23032308e+00 7.54257217e-02 2.51135409e-01 8.54789138e-01 -1.18591595e+00 -5.53251386e-01 5.93147814e-01 -7.02099323e-01 2.90821731e-01 1.43122643e-01 6.54861867e-01 1.09967053e+00 -1.10700274e+00 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52971906-ed98-450a-ac37-74726b3f9c82	deep-differentiable-reinforcement-learning	2112.02944	null	https://arxiv.org/abs/2112.02944v2	https://arxiv.org/pdf/2112.02944v2.pdf	Deep differentiable reinforcement learning and optimal trading	In many reinforcement learning applications, the underlying environment reward and transition functions are explicitly known differentiable functions. This enables us to use recent research which applies machine learning tools to stochastic control to find optimal action functions. In this paper, we define differentiable reinforcement learning as a particular case of this research. We find that incorporating deep learning in this framework leads to more accurate and stable solutions than those obtained from more generic actor critic algorithms. We apply this deep differentiable reinforcement learning (DDRL) algorithm to the problem of one asset optimal trading strategies in various environments where the market dynamics are known. Thanks to the stability of this method, we are able to efficiently find optimal strategies for complex multi-scale market models. We also extend these methods to simultaneously find optimal action functions for a wide range of environment parameters. This makes it applicable to real life financial signals and portfolio optimization where the expected return has multiple time scales. In the case of a slow and a fast alpha signal, we find that the optimal trading strategy consists in using the fast signal to time the trades associated to the slow signal.	['Thibault Jaisson']	2021-12-06	null	null	null	null	['portfolio-optimization']	['time-series']	[-2.90738732e-01 -1.74020797e-01 -1.94484398e-01 1.05302706e-01 -5.37709475e-01 -7.41997600e-01 6.59655988e-01 -2.81225126e-02 -6.56236768e-01 9.45233107e-01 -2.68834919e-01 -2.82097250e-01 -4.25471753e-01 -9.08084035e-01 -7.24262178e-01 -7.93084025e-01 -2.83897430e-01 5.13011336e-01 -1.14889368e-02 -6.37757838e-01 4.05627131e-01 5.36680162e-01 -1.12855613e+00 -4.31933999e-01 3.87254715e-01 1.17466831e+00 8.56413841e-02 9.82243478e-01 -3.60782221e-02 7.91898072e-01 -6.75172150e-01 -3.05960506e-01 7.45178938e-01 -4.89549071e-01 -3.26130331e-01 5.38853705e-02 -1.18255280e-01 -3.47429127e-01 -6.91128895e-02 9.86302912e-01 6.11810207e-01 3.05238575e-01 4.53065723e-01 -1.10579240e+00 -3.36449236e-01 5.68707168e-01 -4.73464906e-01 3.62752497e-01 -1.02523454e-01 2.76678830e-01 1.29836071e+00 -2.35336438e-01 2.86190957e-01 1.16406083e+00 5.84486902e-01 4.30806696e-01 -1.31800866e+00 -3.44895959e-01 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2793a273-a21f-4ae3-9518-89e447ed03a0	self-contained-stylization-via-steganography	1812.03910	null	https://arxiv.org/abs/1812.03910v3	https://arxiv.org/pdf/1812.03910v3.pdf	Self-Contained Stylization via Steganography for Reverse and Serial Style Transfer	Style transfer has been widely applied to give real-world images a new artistic look. However, given a stylized image, the attempts to use typical style transfer methods for de-stylization or transferring it again into another style usually lead to artifacts or undesired results. We realize that these issues are originated from the content inconsistency between the original image and its stylized output. Therefore, in this paper we advance to keep the content information of the input image during the process of style transfer by the power of steganography, with two approaches proposed: a two-stage model and an end-to-end model. We conduct extensive experiments to successfully verify the capacity of our models, in which both of them are able to not only generate stylized images of quality comparable with the ones produced by typical style transfer methods, but also effectively eliminate the artifacts introduced in reconstructing original input from a stylized image as well as performing multiple times of style transfer in series.	['Wei-Chen Chiu', 'I-Sheng Fang', 'Hung-Yu Chen']	2018-12-10	null	null	null	null	['reverse-style-transfer', 'serial-style-transfer']	['computer-vision', 'computer-vision']	[ 8.31672668e-01 1.05921015e-01 4.84511614e-01 1.16688907e-01 -2.92262048e-01 -7.60481775e-01 7.73761213e-01 -5.09636104e-01 -1.58828616e-01 8.78767490e-01 -8.00072998e-02 -2.88068861e-01 4.76536095e-01 -8.96893501e-01 -6.57203138e-01 -5.89753330e-01 3.88318419e-01 1.06908292e-01 2.62542844e-01 -1.57477498e-01 4.27071154e-01 3.12494367e-01 -1.38264966e+00 5.28953746e-02 9.12634850e-01 6.94603682e-01 1.48971185e-01 6.17823720e-01 -5.94251156e-01 7.01482713e-01 -9.35624480e-01 -7.08936393e-01 4.18971002e-01 -1.20395875e+00 -8.20630133e-01 6.10573232e-01 -1.05285738e-02 -3.74703586e-01 -2.44057328e-01 1.40259087e+00 2.08520249e-01 -3.48518223e-01 5.90407133e-01 -1.19395030e+00 -8.82106781e-01 2.58939654e-01 -8.43487501e-01 -3.22837472e-01 4.48302031e-01 2.16733307e-01 3.24571013e-01 -5.17091572e-01 9.28560138e-01 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4c98a899-13c0-40dd-8cb2-683b41c71367	understanding-grounded-language-learning	1710.09867	null	https://arxiv.org/abs/1710.09867v2	https://arxiv.org/pdf/1710.09867v2.pdf	Understanding Early Word Learning in Situated Artificial Agents	Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and execute symbolic instructions as first-person actors in partially-observable worlds. To achieve this so-called grounded language learning, models must overcome challenges that infants face when learning their first words. While it is notable that models with no meaningful prior knowledge overcome these obstacles, researchers currently lack a clear understanding of how they do so, a problem that we attempt to address in this paper. For maximum control and generality, we focus on a simple neural network-based language learning agent, trained via policy-gradient methods, which can interpret single-word instructions in a simulated 3D world. Whilst the goal is not to explicitly model infant word learning, we take inspiration from experimental paradigms in developmental psychology and apply some of these to the artificial agent, exploring the conditions under which established human biases and learning effects emerge. We further propose a novel method for visualising semantic representations in the agent.	['Felix Hill', 'Stephen Clark', 'Karl Moritz Hermann', 'Phil Blunsom']	2017-10-26	null	null	null	iclr-2018-1	['grounded-language-learning']	['natural-language-processing']	[ 3.95020217e-01 6.86932325e-01 -1.11282719e-02 -4.64193493e-01 -5.35067208e-02 -4.75643069e-01 8.63589525e-01 2.75559753e-01 -8.43327284e-01 3.78898889e-01 4.70563471e-01 -4.27202016e-01 -7.50916004e-02 -5.28732955e-01 -9.20389891e-01 -4.47710276e-01 -1.04083084e-01 3.72030914e-01 -3.03056035e-02 -1.60805270e-01 5.65037906e-01 5.37609935e-01 -1.50351167e+00 3.14542413e-01 4.77971882e-01 -1.51392743e-02 4.88426328e-01 6.54381514e-01 -1.89055234e-01 1.21290553e+00 -3.05674106e-01 -3.17384720e-01 -5.15414067e-02 -7.84591615e-01 -1.05343735e+00 -7.99389184e-02 3.81492287e-01 -7.81271279e-01 -1.07593596e-01 9.66624796e-01 4.56180066e-01 2.66381919e-01 9.13790047e-01 -8.75632882e-01 -9.95613575e-01 5.68733037e-01 -1.72313780e-01 3.53302032e-01 6.31123185e-01 1.57096773e-01 7.96977699e-01 -7.86230385e-01 6.52488947e-01 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0cf6ef4a-c461-4a36-bac7-a9a99b74dd00	4d-stop-panoptic-segmentation-of-4d-lidar	2209.14858	null	https://arxiv.org/abs/2209.14858v1	https://arxiv.org/pdf/2209.14858v1.pdf	4D-StOP: Panoptic Segmentation of 4D LiDAR using Spatio-temporal Object Proposal Generation and Aggregation	In this work, we present a new paradigm, called 4D-StOP, to tackle the task of 4D Panoptic LiDAR Segmentation. 4D-StOP first generates spatio-temporal proposals using voting-based center predictions, where each point in the 4D volume votes for a corresponding center. These tracklet proposals are further aggregated using learned geometric features. The tracklet aggregation method effectively generates a video-level 4D scene representation over the entire space-time volume. This is in contrast to existing end-to-end trainable state-of-the-art approaches which use spatio-temporal embeddings that are represented by Gaussian probability distributions. Our voting-based tracklet generation method followed by geometric feature-based aggregation generates significantly improved panoptic LiDAR segmentation quality when compared to modeling the entire 4D volume using Gaussian probability distributions. 4D-StOP achieves a new state-of-the-art when applied to the SemanticKITTI test dataset with a score of 63.9 LSTQ, which is a large (+7%) improvement compared to current best-performing end-to-end trainable methods. The code and pre-trained models are available at: https://github.com/LarsKreuzberg/4D-StOP.	['Bastian Leibe', 'Francis Engelmann', 'Sabarinath Mahadevan', 'Idil Esen Zulfikar', 'Lars Kreuzberg']	2022-09-29	null	null	null	null	['panoptic-segmentation', 'object-proposal-generation']	['computer-vision', 'computer-vision']	[-1.75239772e-01 -2.93246180e-01 -2.03957692e-01 -2.78761864e-01 -1.20017040e+00 -7.18714654e-01 7.88721502e-01 1.64248824e-01 -3.03777516e-01 1.65280879e-01 -1.83498971e-02 -3.26258719e-01 -3.11669707e-02 -8.70272994e-01 -7.09587991e-01 -3.76064330e-01 -2.27735445e-01 1.18956876e+00 6.42727256e-01 3.09638083e-01 2.09262207e-01 7.64195502e-01 -1.55846107e+00 1.35434091e-01 8.93345177e-01 1.10449457e+00 1.03632182e-01 8.85482848e-01 -4.39336151e-01 2.32446596e-01 -1.31245509e-01 -2.07167014e-01 6.02378547e-01 1.35085359e-01 -4.37375784e-01 -1.00314930e-01 1.09101892e+00 -4.33840603e-01 -2.51633793e-01 6.04676127e-01 3.17625523e-01 2.26563349e-01 9.68889356e-01 -1.05078483e+00 -5.27500451e-01 5.58754615e-02 -7.33226657e-01 1.80451125e-01 1.05195856e-02 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4d862f98-ff05-4bae-bfd3-77582bc6514c	limsis-participation-to-the-2013-shared-task	null	null	https://aclanthology.org/W13-1733	https://aclanthology.org/W13-1733.pdf	LIMSI's participation to the 2013 shared task on Native Language Identification	null	["Aur{\\'e}lien Max", 'Ryo Nagata', 'Gabriel Illouz', 'Thomas Lavergne']	2013-06-01	null	null	null	ws-2013-6	['native-language-identification']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 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d71dbc10-ed09-4d78-956e-18a64c719170	a-deep-learning-approach-using-masked-image	2208.11472	null	https://arxiv.org/abs/2208.11472v1	https://arxiv.org/pdf/2208.11472v1.pdf	A Deep Learning Approach Using Masked Image Modeling for Reconstruction of Undersampled K-spaces	Magnetic Resonance Imaging (MRI) scans are time consuming and precarious, since the patients remain still in a confined space for extended periods of time. To reduce scanning time, some experts have experimented with undersampled k spaces, trying to use deep learning to predict the fully sampled result. These studies report that as many as 20 to 30 minutes could be saved off a scan that takes an hour or more. However, none of these studies have explored the possibility of using masked image modeling (MIM) to predict the missing parts of MRI k spaces. This study makes use of 11161 reconstructed MRI and k spaces of knee MRI images from Facebook's fastmri dataset. This tests a modified version of an existing model using baseline shifted window (Swin) and vision transformer architectures that makes use of MIM on undersampled k spaces to predict the full k space and consequently the full MRI image. Modifications were made using pytorch and numpy libraries, and were published to a github repository. After the model reconstructed the k space images, the basic Fourier transform was applied to determine the actual MRI image. Once the model reached a steady state, experimentation with hyperparameters helped to achieve pinpoint accuracy for the reconstructed images. The model was evaluated through L1 loss, gradient normalization, and structural similarity values. The model produced reconstructed images with L1 loss values averaging to <0.01 and gradient normalization values <0.1 after training finished. The reconstructed k spaces yielded structural similarity values of over 99% for both training and validation with the fully sampled k spaces, while validation loss continually decreased under 0.01. These data strongly support the idea that the algorithm works for MRI reconstruction, as they indicate the model's reconstructed image aligns extremely well with the original, fully sampled k space.	['Yogesh Rathi', 'Arghya Pal', 'Kyler Larsen']	2022-08-24	null	null	null	null	['mri-reconstruction']	['computer-vision']	[ 2.13853881e-01 2.47397453e-01 -7.17305169e-02 -4.70853448e-01 -9.43433166e-01 -8.58751684e-02 3.01699072e-01 -5.37132099e-02 -6.85654879e-01 6.27342582e-01 1.74220905e-01 -2.81354219e-01 -2.86777675e-01 -3.66108537e-01 -5.79765677e-01 -9.06507909e-01 -4.24452692e-01 3.96941841e-01 4.02797252e-01 2.43977472e-01 2.33063713e-01 5.67580521e-01 -1.16761911e+00 6.85324192e-01 3.99226904e-01 8.99295032e-01 4.51705635e-01 7.06131160e-01 2.58742273e-01 7.70033121e-01 -2.65048534e-01 4.56131175e-02 3.40535134e-01 -2.16950119e-01 -1.03644431e+00 -4.70176376e-02 3.94255340e-01 -6.64414883e-01 -4.81049925e-01 6.76002979e-01 6.89922392e-01 1.07011713e-01 2.91957885e-01 -9.23763931e-01 -2.50783056e-01 4.37232137e-01 -4.23313588e-01 5.12156367e-01 -2.70164534e-02 5.16133904e-01 1.79192990e-01 -8.73271286e-01 7.43211031e-01 6.11020207e-01 1.05536187e+00 2.98056930e-01 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-8.61160338e-01 -7.20531225e-01 3.83764505e-01 2.17055768e-01 -4.28263664e-01 -2.27574229e-01 -5.11263646e-02]	[13.704834938049316, -2.415963888168335]
2e3dc80a-0b17-4437-a341-1bc4b2984659	multi-domain-multi-definition-landmark	2203.10358	null	https://arxiv.org/abs/2203.10358v3	https://arxiv.org/pdf/2203.10358v3.pdf	Multi-Domain Multi-Definition Landmark Localization for Small Datasets	We present a novel method for multi image domain and multi-landmark definition learning for small dataset facial localization. Training a small dataset alongside a large(r) dataset helps with robust learning for the former, and provides a universal mechanism for facial landmark localization for new and/or smaller standard datasets. To this end, we propose a Vision Transformer encoder with a novel decoder with a definition agnostic shared landmark semantic group structured prior, that is learnt, as we train on more than one dataset concurrently. Due to our novel definition agnostic group prior the datasets may vary in landmark definitions and domains. During the decoder stage we use cross- and self-attention, whose output is later fed into domain/definition specific heads that minimize a Laplacian-log-likelihood loss. We achieve state-of-the-art performance on standard landmark localization datasets such as COFW and WFLW, when trained with a bigger dataset. We also show state-of-the-art performance on several varied image domain small datasets for animals, caricatures, and facial portrait paintings. Further, we contribute a small dataset (150 images) of pareidolias to show efficacy of our method. Finally, we provide several analysis and ablation studies to justify our claims.	['Gaurav Bharaj', 'David Ferman']	2022-03-19	null	null	null	null	['face-alignment']	['computer-vision']	[ 2.33843192e-01 3.97027507e-02 -3.11236829e-01 -6.93609834e-01 -1.18138754e+00 -5.07315874e-01 6.92294896e-01 -3.70131969e-01 -4.33557898e-01 4.76050258e-01 8.58732089e-02 3.53077799e-01 6.81765750e-03 -4.18723494e-01 -9.06353116e-01 -5.19196033e-01 -6.62463456e-02 6.12034023e-01 5.52569740e-02 -7.54948035e-02 -3.52632143e-02 4.68987584e-01 -1.38274419e+00 4.49794799e-01 4.20836031e-01 1.17090392e+00 3.33708525e-01 2.24653229e-01 1.96202412e-01 6.10869050e-01 -4.46748078e-01 -5.75844347e-01 4.30128932e-01 -3.09601486e-01 -8.32295656e-01 1.84000447e-01 1.22337258e+00 -2.01081574e-01 -1.68075070e-01 9.73595917e-01 7.51028836e-01 -8.54278356e-02 6.41629100e-01 -1.29567420e+00 -1.03852522e+00 5.54891407e-01 -9.77339923e-01 6.14806376e-02 2.90112823e-01 6.51337877e-02 1.04514050e+00 -1.13721752e+00 7.80853391e-01 1.40793109e+00 8.93855631e-01 8.88179183e-01 -1.40649581e+00 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a24fc2c3-e7cd-4974-b4f1-725cfa0cf401	matting-anything	2306.05399	null	https://arxiv.org/abs/2306.05399v1	https://arxiv.org/pdf/2306.05399v1.pdf	Matting Anything	In this paper, we propose the Matting Anything Model (MAM), an efficient and versatile framework for estimating the alpha matte of any instance in an image with flexible and interactive visual or linguistic user prompt guidance. MAM offers several significant advantages over previous specialized image matting networks: (i) MAM is capable of dealing with various types of image matting, including semantic, instance, and referring image matting with only a single model; (ii) MAM leverages the feature maps from the Segment Anything Model (SAM) and adopts a lightweight Mask-to-Matte (M2M) module to predict the alpha matte through iterative refinement, which has only 2.7 million trainable parameters. (iii) By incorporating SAM, MAM simplifies the user intervention required for the interactive use of image matting from the trimap to the box, point, or text prompt. We evaluate the performance of MAM on various image matting benchmarks, and the experimental results demonstrate that MAM achieves comparable performance to the state-of-the-art specialized image matting models under different metrics on each benchmark. Overall, MAM shows superior generalization ability and can effectively handle various image matting tasks with fewer parameters, making it a practical solution for unified image matting. Our code and models are open-sourced at https://github.com/SHI-Labs/Matting-Anything.	['Humphrey Shi', 'Jitesh Jain', 'Jiachen Li']	2023-06-08	null	null	null	null	['image-matting', 'referring-image-matting']	['computer-vision', 'computer-vision']	[ 2.18097836e-01 -5.20388484e-02 -2.49238029e-01 -4.19916958e-01 -8.11086357e-01 -4.35832739e-01 3.32746536e-01 -3.32345784e-01 -2.26716921e-01 1.97045386e-01 -1.03654869e-01 -5.07735074e-01 3.29213679e-01 -5.87563753e-01 -1.01556242e+00 -5.58227599e-01 4.34715956e-01 5.22512972e-01 6.61976589e-03 -1.92897469e-01 1.91663560e-02 2.99200356e-01 -1.30765450e+00 6.27423823e-01 1.08183074e+00 1.11562693e+00 5.86298883e-01 5.93722701e-01 -3.14381003e-01 7.87828386e-01 -4.23521578e-01 -6.95888996e-01 4.07212377e-01 -3.07949096e-01 -7.42613137e-01 4.80039299e-01 9.42952633e-01 -6.47683501e-01 -3.68596792e-01 9.70880389e-01 7.62654617e-02 2.21521445e-02 4.82927799e-01 -1.31816018e+00 -8.08089733e-01 6.94458306e-01 -8.62291157e-01 7.52538368e-02 1.56892404e-01 6.25728071e-01 8.00442338e-01 -1.26296353e+00 4.34989780e-01 1.25599134e+00 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-1.79855675e-01 -1.04385090e+00 -2.02455148e-01 5.34468889e-01 2.28150755e-01 -5.36944270e-01 -1.57442719e-01 3.72261077e-01]	[10.68653392791748, -0.8145278692245483]
521a576f-4d30-4a74-9231-33cf20c9c594	variational-learning-across-domains-with	1806.08672	null	http://arxiv.org/abs/1806.08672v2	http://arxiv.org/pdf/1806.08672v2.pdf	Variational learning across domains with triplet information	The work investigates deep generative models, which allow us to use training data from one domain to build a model for another domain. We propose the Variational Bi-domain Triplet Autoencoder (VBTA) that learns a joint distribution of objects from different domains. We extend the VBTAs objective function by the relative constraints or triplets that sampled from the shared latent space across domains. In other words, we combine the deep generative models with a metric learning ideas in order to improve the final objective with the triplets information. The performance of the VBTA model is demonstrated on different tasks: image-to-image translation, bi-directional image generation and cross-lingual document classification.	['Alexandr Ogaltsov', 'Rita Kuznetsova', 'Oleg Bakhteev']	2018-06-22	null	null	null	null	['cross-lingual-document-classification']	['natural-language-processing']	[ 1.62170902e-02 -1.27881914e-01 -5.30292392e-02 -6.56424165e-01 -8.14458132e-01 -4.20535207e-01 1.22574115e+00 -8.91388118e-01 6.62974790e-02 9.78994608e-01 2.74445742e-01 1.28060356e-01 2.79514045e-02 -8.83150339e-01 -9.56065953e-01 -9.30988014e-01 8.45202446e-01 9.99903560e-01 -2.79213101e-01 -8.12655017e-02 -1.43478932e-02 2.45169684e-01 -1.28839183e+00 4.96659547e-01 9.15674150e-01 8.31058145e-01 4.27937537e-01 3.08558643e-01 -3.52210552e-01 6.72633648e-01 -6.39900625e-01 -4.91395265e-01 2.34036267e-01 -8.68574917e-01 -5.05117118e-01 4.71452564e-01 3.49323243e-01 -3.22679967e-01 -1.05160795e-01 1.02888691e+00 3.91558200e-01 1.43480897e-01 1.30006170e+00 -1.53276634e+00 -1.58026516e+00 3.55227917e-01 -3.67996573e-01 -3.16362917e-01 1.15472209e-02 -3.65861468e-02 9.32838976e-01 -8.92959833e-01 9.60890174e-01 1.46015620e+00 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0ee1ca96-aca8-45df-8b66-1c1617603925	compositional-embeddings-joint-perception-and	null	null	https://openreview.net/forum?id=BJx-ZeSKDB	https://openreview.net/pdf?id=BJx-ZeSKDB	Compositional Embeddings: Joint Perception and Comparison of Class Label Sets	We explore the idea of compositional set embeddings that can be used to infer not just a single class, but the set of classes associated with the input data (e.g., image, video, audio signal). This can be useful, for example, in multi-object detection in images, or multi-speaker diarization (one-shot learning) in audio. In particular, we devise and implement two novel models consisting of (1) an embedding function f trained jointly with a “composite” function g that computes set union opera- tions between the classes encoded in two embedding vectors; and (2) embedding f trained jointly with a “query” function h that computes whether the classes en- coded in one embedding subsume the classes encoded in another embedding. In contrast to prior work, these models must both perceive the classes associated with the input examples, and also encode the relationships between different class label sets. In experiments conducted on simulated data, OmniGlot, and COCO datasets, the proposed composite embedding models outperform baselines based on traditional embedding approaches.	['Jacob Whitehill', 'Zeqian Li']	2019-09-25	null	null	null	null	['one-shot-learning']	['methodology']	[ 4.47134763e-01 4.63302247e-02 1.61969848e-02 -5.81502974e-01 -8.78272951e-01 -5.94206393e-01 8.16014767e-01 4.71930802e-01 -3.75779688e-01 2.48226553e-01 3.47552747e-01 -9.24783759e-03 -8.23707432e-02 -9.15474653e-01 -6.84770167e-01 -7.35685706e-01 -1.69487298e-01 3.79200637e-01 2.32902214e-01 1.71839729e-01 6.43055290e-02 1.92825601e-01 -2.04380298e+00 6.20401323e-01 2.55755484e-01 1.07738853e+00 1.23179905e-01 7.17714071e-01 -4.30234000e-02 6.28188252e-01 -5.63465595e-01 -2.32817560e-01 9.12390575e-02 -3.27375472e-01 -5.79854846e-01 4.15152013e-01 5.12512863e-01 -3.07909429e-01 -3.48287642e-01 1.08261991e+00 2.87488371e-01 2.55593091e-01 9.76332545e-01 -1.48136008e+00 -8.34530532e-01 5.05692244e-01 -2.28007153e-01 1.06441930e-01 4.05352384e-01 -2.96210982e-02 1.44624734e+00 -1.08745837e+00 5.05507410e-01 1.52936172e+00 1.31325066e-01 5.45895457e-01 -1.34442544e+00 -6.14180028e-01 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c42bd61b-bbba-435f-bc3f-6471d93c6b32	twin-net-descriptor-twin-negative-mining-with	null	null	https://ieeexplore.ieee.org/abstract/document/8835028	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8835028	Twin-Net Descriptor: Twin Negative Mining With Quad Loss for Patch-Based Matching	Local keypoint matching is an important step for computer vision based tasks. In recent years, Deep Convolutional Neural Network (CNN) based strategies have been employed to learn descriptor generation to enhance keypoint matching accuracy. Recent state-of-art works in this direction primarily rely upon a triplet based loss function (and its variations) utilizing three samples: an anchor, a positive and a negative. In this work we propose a novel “Twin Negative Mining” based sampling strategy coupled with a Quad loss function to train a deep neural network based pipeline (Twin-Net) for generating a robust descriptor that provides an increased discriminatory power to differentiate between patches that do not correspond to each other. Our sampling strategy and choice of loss function is aimed at placing an upper bound that descriptors of two patches representing same location could be at worst no more dissimilar than the descriptors of two similar looking patches that do-not belong to same 3D location. This results in an increase in the generalization capability of the network and outperforms its existing counterparts when trained over the same datasets. Twin-Net outputs a 128-dimensional descriptor and uses L 2 Distance as the similarity metric, and hence conforms to the classical descriptor matching pipelines such as that of SIFT. Our results on Brown and HPatches datasets demonstrate Twin-Net's consistently better performance as well as better discriminatory and generalization capability as compared to the state-of-art.	['Yongju Cho', 'Muhammad Faisal', 'Rehan Hafiz', 'Mohsen Ali', 'Jeongil Seo', 'Aman Irshad']	2019-09-19	null	null	null	ieee-access-2019-9	['patch-matching']	['computer-vision']	[ 1.12370566e-01 -2.89520860e-01 -1.29456669e-01 -3.02880198e-01 -8.81385267e-01 -3.88013273e-01 8.14251244e-01 4.73774135e-01 -5.38052619e-01 2.60153681e-01 -1.62906498e-01 1.30007580e-01 -4.33939725e-01 -9.24199939e-01 -6.40034258e-01 -7.89000869e-01 -2.13188648e-01 2.82905400e-01 4.26609963e-01 -4.12729740e-01 4.03415650e-01 1.05253744e+00 -1.85617208e+00 1.64486945e-01 4.90795225e-01 1.53756702e+00 -1.91736281e-01 2.33716339e-01 3.84037681e-02 2.30828270e-01 -6.74123287e-01 -3.09308261e-01 8.29340816e-01 -1.15990050e-01 -4.22243744e-01 -3.16559047e-01 1.10117924e+00 -1.33170038e-01 -3.75212759e-01 8.89472425e-01 6.70984626e-01 2.07990363e-01 7.70038486e-01 -1.31925058e+00 -5.38099170e-01 -7.24430308e-02 -4.82672215e-01 6.53483942e-02 3.75412673e-01 2.12601339e-03 9.96236444e-01 -8.09888303e-01 6.30739868e-01 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8a1f7ce4-850a-498a-a9a0-e117dbe3ab42	unsupervised-haze-removal-from-underwater	2306.02912	null	https://arxiv.org/abs/2306.02912v1	https://arxiv.org/pdf/2306.02912v1.pdf	Unsupervised haze removal from underwater images	Several supervised networks exist that remove haze information from underwater images using paired datasets and pixel-wise loss functions. However, training these networks requires large amounts of paired data which is cumbersome, complex and time-consuming. Also, directly using adversarial and cycle consistency loss functions for unsupervised learning is inaccurate as the underlying mapping from clean to underwater images is one-to-many, resulting in an inaccurate constraint on the cycle consistency loss. To address these issues, we propose a new method to remove haze from underwater images using unpaired data. Our model disentangles haze and content information from underwater images using a Haze Disentanglement Network (HDN). The disentangled content is used by a restoration network to generate a clean image using adversarial losses. The disentangled haze is then used as a guide for underwater image regeneration resulting in a strong constraint on cycle consistency loss and improved performance gains. Different ablation studies show that the haze and content from underwater images are effectively separated. Exhaustive experiments reveal that accurate cycle consistency constraint and the proposed network architecture play an important role in yielding enhanced results. Experiments on UFO-120, UWNet, UWScenes, and UIEB underwater datasets indicate that the results of our method outperform prior art both visually and quantitatively.	['A. N. Rajagopalan', 'Praveen Kandula']	2023-06-05	null	null	null	null	['disentanglement']	['methodology']	[ 3.31912637e-01 -4.71137986e-02 7.31333733e-01 -2.19346702e-01 -6.96217775e-01 -6.67266250e-01 1.79581642e-01 -4.98532921e-01 -6.08499587e-01 1.04708755e+00 2.39038095e-01 -2.23135762e-02 -3.53805870e-01 -8.95334780e-01 -9.15123820e-01 -1.44854724e+00 -2.66617119e-01 -2.80284315e-01 -1.38700055e-02 -5.69299519e-01 1.41294628e-01 1.94739386e-01 -1.58357441e+00 -1.68758720e-01 1.38804579e+00 6.79419100e-01 2.52113551e-01 7.63482094e-01 8.75726789e-02 7.46834517e-01 -6.13745391e-01 -4.03518289e-01 9.90729213e-01 -7.80707479e-01 -2.02071041e-01 -3.06565493e-01 9.22864914e-01 -7.70518661e-01 -8.15696239e-01 1.43793929e+00 7.94527352e-01 2.70718962e-01 4.24418151e-01 -1.23381174e+00 -7.69513190e-01 5.17016590e-01 -4.15318549e-01 2.65595056e-02 -3.48604769e-01 1.23811886e-01 7.20228374e-01 -8.38395417e-01 2.33643994e-01 9.76593494e-01 7.40482032e-01 6.95811331e-01 -9.38983858e-01 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4.13475573e-01 1.07071793e+00 -4.79568899e-01 -1.11645794e+00 3.50992739e-01]	[10.709670066833496, -3.516563653945923]
29062bf8-d925-4483-8028-707c0fbd0bc9	dipping-plms-sauce-bridging-structure-and	2307.01709	null	https://arxiv.org/abs/2307.01709v1	https://arxiv.org/pdf/2307.01709v1.pdf	Dipping PLMs Sauce: Bridging Structure and Text for Effective Knowledge Graph Completion via Conditional Soft Prompting	Knowledge Graph Completion (KGC) often requires both KG structural and textual information to be effective. Pre-trained Language Models (PLMs) have been used to learn the textual information, usually under the fine-tune paradigm for the KGC task. However, the fine-tuned PLMs often overwhelmingly focus on the textual information and overlook structural knowledge. To tackle this issue, this paper proposes CSProm-KG (Conditional Soft Prompts for KGC) which maintains a balance between structural information and textual knowledge. CSProm-KG only tunes the parameters of Conditional Soft Prompts that are generated by the entities and relations representations. We verify the effectiveness of CSProm-KG on three popular static KGC benchmarks WN18RR, FB15K-237 and Wikidata5M, and two temporal KGC benchmarks ICEWS14 and ICEWS05-15. CSProm-KG outperforms competitive baseline models and sets new state-of-the-art on these benchmarks. We conduct further analysis to show (i) the effectiveness of our proposed components, (ii) the efficiency of CSProm-KG, and (iii) the flexibility of CSProm-KG.	['Kwok-Yan Lam', 'Bing Li', 'Aixin Sun', 'YuFei Wang', 'Chen Chen']	2023-07-04	null	null	null	null	['knowledge-graph-completion']	['knowledge-base']	[-1.85381860e-01 4.03490067e-01 -3.72446418e-01 -3.49789470e-01 -7.16418147e-01 -4.18460041e-01 7.54226625e-01 3.22884887e-01 -5.74455500e-01 6.02065802e-01 5.46119153e-01 -2.62688220e-01 -2.74178624e-01 -9.05424416e-01 -9.67422426e-01 -3.77951652e-01 -3.07596296e-01 5.67333400e-01 4.52125400e-01 -1.89753339e-01 -2.95356780e-01 -1.77765653e-01 -1.18188179e+00 3.98662388e-01 1.12528706e+00 6.67353690e-01 4.18695420e-01 3.84152859e-01 -2.95919478e-01 9.87201869e-01 -2.35367045e-01 -9.12132084e-01 1.73441600e-02 1.04621343e-01 -9.89699304e-01 -3.45816374e-01 3.66092265e-01 -2.30159983e-02 -4.06485915e-01 8.78291309e-01 2.83990562e-01 3.63566838e-02 6.66873991e-01 -1.30780387e+00 -7.07835257e-01 1.40896595e+00 -5.00444114e-01 -2.60841787e-01 2.34561950e-01 8.01260620e-02 1.41932988e+00 -1.05535233e+00 9.96677756e-01 1.25841522e+00 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5b4e6339-1633-4633-b9fe-8f70e0c70af8	hierarchical-joint-scene-coordinate	1909.06216	null	https://arxiv.org/abs/1909.06216v3	https://arxiv.org/pdf/1909.06216v3.pdf	Hierarchical Scene Coordinate Classification and Regression for Visual Localization	Visual localization is critical to many applications in computer vision and robotics. To address single-image RGB localization, state-of-the-art feature-based methods match local descriptors between a query image and a pre-built 3D model. Recently, deep neural networks have been exploited to regress the mapping between raw pixels and 3D coordinates in the scene, and thus the matching is implicitly performed by the forward pass through the network. However, in a large and ambiguous environment, learning such a regression task directly can be difficult for a single network. In this work, we present a new hierarchical scene coordinate network to predict pixel scene coordinates in a coarse-to-fine manner from a single RGB image. The network consists of a series of output layers, each of them conditioned on the previous ones. The final output layer predicts the 3D coordinates and the others produce progressively finer discrete location labels. The proposed method outperforms the baseline regression-only network and allows us to train compact models which scale robustly to large environments. It sets a new state-of-the-art for single-image RGB localization performance on the 7-Scenes, 12-Scenes, Cambridge Landmarks datasets, and three combined scenes. Moreover, for large-scale outdoor localization on the Aachen Day-Night dataset, we present a hybrid approach which outperforms existing scene coordinate regression methods, and reduces significantly the performance gap w.r.t. explicit feature matching methods.	['Yi Zhao', 'Xiaotian Li', 'Shuzhe Wang', 'Juho Kannala', 'Jakob Verbeek']	2019-09-13	hierarchical-scene-coordinate-classification	http://openaccess.thecvf.com/content_CVPR_2020/html/Li_Hierarchical_Scene_Coordinate_Classification_and_Regression_for_Visual_Localization_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Hierarchical_Scene_Coordinate_Classification_and_Regression_for_Visual_Localization_CVPR_2020_paper.pdf	cvpr-2020-6	['outdoor-localization']	['robots']	[ 1.88736945e-01 -3.45475644e-01 1.29118143e-02 -5.99620044e-01 -1.08253205e+00 -4.37880576e-01 5.48542023e-01 -1.41336378e-02 -8.72722566e-01 3.94749612e-01 -2.64380544e-01 -1.32475570e-01 2.74258312e-02 -6.64487183e-01 -1.11924875e+00 -7.25911796e-01 2.35646024e-01 4.08848852e-01 3.58156681e-01 -6.51752874e-02 2.19453603e-01 8.92458379e-01 -1.60989070e+00 -5.22844270e-02 4.35144126e-01 1.52223516e+00 4.30878252e-01 6.42149091e-01 2.14739400e-03 7.28279591e-01 -2.12290391e-01 9.81498733e-02 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01775eda-d46f-4984-b731-607ee2def3d8	visual-question-answering-in-remote-sensing	2306.14264	null	https://arxiv.org/abs/2306.14264v1	https://arxiv.org/pdf/2306.14264v1.pdf	Visual Question Answering in Remote Sensing with Cross-Attention and Multimodal Information Bottleneck	In this research, we deal with the problem of visual question answering (VQA) in remote sensing. While remotely sensed images contain information significant for the task of identification and object detection, they pose a great challenge in their processing because of high dimensionality, volume and redundancy. Furthermore, processing image information jointly with language features adds additional constraints, such as mapping the corresponding image and language features. To handle this problem, we propose a cross attention based approach combined with information maximization. The CNN-LSTM based cross-attention highlights the information in the image and language modalities and establishes a connection between the two, while information maximization learns a low dimensional bottleneck layer, that has all the relevant information required to carry out the VQA task. We evaluate our method on two VQA remote sensing datasets of different resolutions. For the high resolution dataset, we achieve an overall accuracy of 79.11% and 73.87% for the two test sets while for the low resolution dataset, we achieve an overall accuracy of 85.98%.	['Rajbabu Velmurugan', 'Biplab Banerjee', 'Shabnam Choudhury', 'Shivam Pande', 'Jayesh Songara']	2023-06-25	null	null	null	null	['visual-question-answering-1', 'question-answering']	['computer-vision', 'natural-language-processing']	[ 3.34560275e-01 -3.23935658e-01 1.31574616e-01 -3.95324111e-01 -9.94096875e-01 -4.95523542e-01 5.66496134e-01 3.04326147e-01 -7.33421445e-01 4.06867683e-01 -2.15616710e-02 -3.70394081e-01 -2.52626449e-01 -9.31242883e-01 -4.40226644e-01 -6.02670491e-01 -1.14091046e-01 1.06222317e-01 -1.52298599e-01 -2.39983462e-02 8.04363713e-02 4.85404611e-01 -1.63720727e+00 4.64272290e-01 9.71587837e-01 1.25835502e+00 7.18407214e-01 6.87865615e-01 -4.70645785e-01 8.89489353e-01 -3.92139703e-01 -2.74682879e-01 3.35142702e-01 -5.85981347e-02 -1.06643903e+00 1.81705490e-01 6.36880398e-01 -4.61857021e-01 -1.68380335e-01 1.09670067e+00 4.64506507e-01 2.79402792e-01 6.87820435e-01 -1.07955289e+00 -9.96103108e-01 2.76078522e-01 -8.50464404e-01 4.46035206e-01 -7.08017200e-02 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7cfb2435-075e-4bf0-9867-61de05adc55b	the-discriminative-kalman-filter-for-bayesian	null	null	https://doi.org/10.1162/neco_a_01275	https://direct.mit.edu/neco/article-pdf/32/5/969/1865334/neco_a_01275.pdf	The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models	The Kalman filter provides a simple and efficient algorithm to compute the posterior distribution for state-space models where both the latent state and measurement models are linear and gaussian. Extensions to the Kalman filter, including the extended and unscented Kalman filters, incorporate linearizations for models where the observation model p(observation∣state) is nonlinear. We argue that in many cases, a model for p(state∣observation) proves both easier to learn and more accurate for latent state estimation. Approximating p(state∣observation) as gaussian leads to a new filtering algorithm, the discriminative Kalman filter (DKF), which can perform well even when p(observation∣state) is highly nonlinear and/or nongaussian. The approximation, motivated by the Bernstein–von Mises theorem, improves as the dimensionality of the observations increases. The DKF has computational complexity similar to the Kalman filter, allowing it in some cases to perform much faster than particle filters with similar precision, while better accounting for nonlinear and nongaussian observation models than Kalman-based extensions. When the observation model must be learned from training data prior to filtering, off-the-shelf nonlinear and nonparametric regression techniques can provide a gaussian model for p(observation∣state) that cleanly integrates with the DKF. As part of the BrainGate2 clinical trial, we successfully implemented gaussian process regression with the DKF framework in a brain-computer interface to provide real-time, closed-loop cursor control to a person with a complete spinal cord injury. In this letter, we explore the theory underlying the DKF, exhibit some illustrative examples, and outline potential extensions.	['Matthew T. Harrison', 'Leigh R. Hochberg', 'Brian Franco', 'David M. Brandman', 'Michael C. Burkhart']	2020-05-01	null	null	null	null	['sequential-bayesian-inference']	['time-series']	[-1.60136819e-01 -1.06934614e-01 -1.28669232e-01 -4.80505824e-02 -6.39635623e-01 -5.00340760e-01 7.37271130e-01 -1.60483524e-01 -4.15642887e-01 8.75797391e-01 3.07465255e-01 -6.44922376e-01 -3.20551068e-01 -2.85396665e-01 -4.82475609e-01 -1.11144960e+00 -2.34564647e-01 5.33360302e-01 1.74461119e-02 3.61865401e-01 -8.07180852e-02 6.64895773e-01 -1.33341348e+00 -4.46917385e-01 7.72893190e-01 6.91315234e-01 4.06060010e-01 9.53212082e-01 2.85469502e-01 3.33141237e-01 -4.79742944e-01 2.39312783e-01 9.06585809e-03 -1.85748816e-01 -1.40783921e-01 -1.00631610e-01 1.65972300e-02 -5.05710065e-01 -6.23312652e-01 1.03372478e+00 4.28787798e-01 3.90372962e-01 8.92530382e-01 -1.16070712e+00 -2.86260456e-01 1.02576679e-02 -3.16433273e-02 1.84460193e-01 1.15884289e-01 1.78080305e-01 2.10117519e-01 -5.72411120e-01 1.54081360e-01 1.24809659e+00 8.05077910e-01 5.50606191e-01 -1.61389291e+00 -6.50434792e-01 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-1.03187680e-01 5.06522655e-01 -1.04462492e+00 -5.10822475e-01 -1.58199385e-01]	[6.705418109893799, 3.7241439819335938]
3c6e42cb-5bf2-4cd8-9d0f-08325492ff77	enhancing-quality-of-pose-varied-face	2205.14377	null	https://arxiv.org/abs/2205.14377v3	https://arxiv.org/pdf/2205.14377v3.pdf	Enhancing Quality of Pose-varied Face Restoration with Local Weak Feature Sensing and GAN Prior	Facial semantic guidance (including facial landmarks, facial heatmaps, and facial parsing maps) and facial generative adversarial networks (GAN) prior have been widely used in blind face restoration (BFR) in recent years. Although existing BFR methods have achieved good performance in ordinary cases, these solutions have limited resilience when applied to face images with serious degradation and pose-varied (e.g., looking right, looking left, laughing, etc.) in real-world scenarios. In this work, we propose a well-designed blind face restoration network with generative facial prior. The proposed network is mainly comprised of an asymmetric codec and a StyleGAN2 prior network. In the asymmetric codec, we adopt a mixed multi-path residual block (MMRB) to gradually extract weak texture features of input images, which can better preserve the original facial features and avoid excessive fantasy. The MMRB can also be plug-and-play in other networks. Furthermore, thanks to the affluent and diverse facial priors of the StyleGAN2 model, we adopt it as the primary generator network in our proposed method and specially design a novel self-supervised training strategy to fit the distribution closer to the target and flexibly restore natural and realistic facial details. Extensive experiments on synthetic and real-world datasets demonstrate that our model performs superior to the prior art for face restoration and face super-resolution tasks.	['Bin Fu', 'Gang Yu', 'Wei Lu', 'Renhe Liu', 'Yu Liu', 'Kai Hu']	2022-05-28	null	null	null	null	['blind-face-restoration']	['computer-vision']	[ 1.64729461e-01 1.07120149e-01 2.19033852e-01 -3.58738154e-01 -2.87055701e-01 -2.06744939e-01 5.46143115e-01 -1.27265382e+00 1.37497514e-01 6.46508276e-01 4.35914576e-01 1.25619411e-01 1.44389078e-01 -8.25732470e-01 -6.50398791e-01 -9.53865707e-01 4.88510787e-01 4.69100103e-02 -1.37356639e-01 -5.31038404e-01 -1.33249745e-01 7.03135848e-01 -1.56634283e+00 7.53294677e-02 9.68164146e-01 7.72346616e-01 1.10145941e-01 1.23528764e-01 -4.21797074e-02 7.41469800e-01 -5.48430562e-01 -6.91069901e-01 4.20897812e-01 -6.34782374e-01 -4.21707302e-01 4.28491652e-01 2.25359172e-01 -6.02439761e-01 -6.97660387e-01 1.39384961e+00 7.93106675e-01 6.99071139e-02 5.65962672e-01 -1.13362229e+00 -1.09145522e+00 9.54244509e-02 -9.69349265e-01 -1.15082435e-01 4.23103064e-01 4.03926969e-01 1.87733367e-01 -9.12673712e-01 4.44871992e-01 1.80845964e+00 5.51515400e-01 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-6.99204326e-01 3.98260146e-01 5.15545428e-01 -7.63183594e-01 -5.03293335e-01 2.78303832e-01]	[12.811284065246582, -0.04115390405058861]
29347d47-c0f4-4b2b-a512-4a7b7645aeeb	data-driven-representations-for-testing	2110.14122	null	https://arxiv.org/abs/2110.14122v1	https://arxiv.org/pdf/2110.14122v1.pdf	Data-Driven Representations for Testing Independence: Modeling, Analysis and Connection with Mutual Information Estimation	This work addresses testing the independence of two continuous and finite-dimensional random variables from the design of a data-driven partition. The empirical log-likelihood statistic is adopted to approximate the sufficient statistics of an oracle test against independence (that knows the two hypotheses). It is shown that approximating the sufficient statistics of the oracle test offers a learning criterion for designing a data-driven partition that connects with the problem of mutual information estimation. Applying these ideas in the context of a data-dependent tree-structured partition (TSP), we derive conditions on the TSP's parameters to achieve a strongly consistent distribution-free test of independence over the family of probabilities equipped with a density. Complementing this result, we present finite-length results that show our TSP scheme's capacity to detect the scenario of independence structurally with the data-driven partition as well as new sampling complexity bounds for this detection. Finally, some experimental analyses provide evidence regarding our scheme's advantage for testing independence compared with some strategies that do not use data-driven representations.	['Marcos E. Orchard', 'Miguel Videla', 'Jorge F. Silva', 'Mauricio E. Gonzalez']	2021-10-27	null	null	null	null	['mutual-information-estimation']	['methodology']	[ 4.49988097e-01 4.47573662e-01 -3.48075598e-01 -5.19574881e-01 -1.10532200e+00 -6.05197012e-01 1.19243547e-01 6.96771815e-02 -1.34850129e-01 1.10017717e+00 -8.78528319e-03 -9.07303810e-01 -6.78394973e-01 -8.70486915e-01 -6.51652932e-01 -9.70696211e-01 -4.97563809e-01 1.11019182e+00 8.11748207e-02 3.10848743e-01 2.43227184e-01 3.69821072e-01 -1.71006036e+00 -1.58565149e-01 8.56083572e-01 9.11032736e-01 4.40402403e-02 8.35271418e-01 3.14284682e-01 3.42450619e-01 -6.66868210e-01 -1.51124626e-01 3.51770997e-01 -6.83106124e-01 -1.03568733e+00 3.34781438e-01 1.95087232e-02 -2.79261410e-01 -8.98882151e-02 1.01749027e+00 3.26689214e-01 -2.76453793e-01 1.13748288e+00 -1.40094590e+00 -2.74975359e-01 7.86913514e-01 -4.35034603e-01 3.36061984e-01 5.09699404e-01 -3.60207289e-01 1.23154092e+00 -4.68990475e-01 3.88421059e-01 1.04889226e+00 4.04554725e-01 -5.49663082e-02 -1.47527266e+00 -4.45133746e-01 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-1.13576211e-01 1.77947298e-01 -5.51899552e-01 -6.87448800e-01 -3.35477412e-01]	[7.393171787261963, 4.566883563995361]
866b0215-cef5-4018-95cc-a9f241be5001	meta-learning-one-class-classifiers-with	2103.00684	null	https://arxiv.org/abs/2103.00684v1	https://arxiv.org/pdf/2103.00684v1.pdf	Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection	Neural network-based anomaly detection methods have shown to achieve high performance. However, they require a large amount of training data for each task. We propose a neural network-based meta-learning method for supervised anomaly detection. The proposed method improves the anomaly detection performance on unseen tasks, which contains a few labeled normal and anomalous instances, by meta-training with various datasets. With a meta-learning framework, quick adaptation to each task and its effective backpropagation are important since the model is trained by the adaptation for each epoch. Our model enables them by formulating adaptation as a generalized eigenvalue problem with one-class classification; its global optimum solution is obtained, and the solver is differentiable. We experimentally demonstrate that the proposed method achieves better performance than existing anomaly detection and few-shot learning methods on various datasets.	['Atsutoshi Kumagai', 'Tomoharu Iwata']	2021-03-01	null	null	null	null	['supervised-anomaly-detection']	['computer-vision']	[ 1.98660031e-01 -1.35961860e-01 -8.98706540e-02 -3.48798245e-01 -5.37590742e-01 2.96458900e-01 3.94221842e-01 2.79380620e-01 -4.85923409e-01 2.54080147e-01 -3.11372101e-01 -1.16063990e-01 -1.21202826e-01 -6.44874454e-01 -4.44375187e-01 -6.07919395e-01 -2.00650081e-01 5.05850136e-01 2.60939121e-01 -2.79345065e-01 4.82126564e-01 2.65671939e-01 -1.60350633e+00 2.06875637e-01 1.25483882e+00 1.26331198e+00 -3.90656918e-01 6.94587588e-01 -7.13365018e-01 8.70291531e-01 -5.89709103e-01 -2.79232681e-01 2.33103782e-01 -4.04195726e-01 -6.91540956e-01 7.46934563e-02 4.42143381e-01 -4.45480198e-01 -1.82572782e-01 1.23229718e+00 2.62512267e-01 6.62512541e-01 8.52356613e-01 -1.66395867e+00 -5.26557386e-01 5.28467447e-02 -7.41223097e-01 7.79316962e-01 -1.42258242e-01 1.10056484e-02 9.16754007e-01 -1.14535105e+00 -3.52173820e-02 8.70411932e-01 8.22424710e-01 7.93638289e-01 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9f41a319-b608-44fe-9fc3-096520c8914b	jsi-at-semeval-2022-task-1-codwoe-reverse	null	null	https://aclanthology.org/2022.semeval-1.12	https://aclanthology.org/2022.semeval-1.12.pdf	JSI at SemEval-2022 Task 1: CODWOE - Reverse Dictionary: Monolingual and cross-lingual approaches	The reverse dictionary task is a sequence-to-vector task in which a gloss is provided as input, and the output must be a semantically matching word vector. The reverse dictionary is useful in practical applications such as solving the tip-of-the-tongue problem, helping new language learners, etc. In this paper, we evaluate the effect of a Transformer-based model with cross-lingual zero-shot learning to improve the reverse dictionary performance. Our experiments are conducted in five languages in the CODWOE dataset, including English, French, Italian, Spanish, and Russian. Even if we did not achieve a good ranking in the CODWOE competition, we show that our work partially improves the current baseline from the organizers with a hypothesis on the impact of LSTM in monolingual, multilingual, and zero-shot learning. All the codes are available at https://github.com/honghanhh/codwoe2021.	['Senja Pollak', 'Matthew Purver', 'Matej Martinc', 'Thi Hong Hanh Tran']	null	null	null	null	semeval-naacl-2022-7	['reverse-dictionary']	['natural-language-processing']	[-2.90008694e-01 -2.23205447e-01 -4.55936313e-01 -1.26336664e-01 -1.06848645e+00 -6.57506645e-01 5.26360035e-01 4.70337570e-02 -7.75775731e-01 5.62045813e-01 5.01322508e-01 -7.09814668e-01 2.83448756e-01 -5.46219647e-01 -6.36566222e-01 -4.29846227e-01 3.61167252e-01 5.61443508e-01 9.45212319e-02 -6.28034115e-01 -2.15968788e-02 -2.99446881e-02 -1.10925281e+00 5.04519820e-01 8.49814057e-01 4.72408026e-01 6.16869271e-01 2.71682233e-01 -2.19730884e-01 6.34697974e-01 -2.59395331e-01 -5.53840339e-01 2.74306774e-01 -4.12836671e-01 -6.62829518e-01 -3.48334253e-01 3.90660495e-01 -1.02961797e-03 -3.77436817e-01 1.12132978e+00 6.53635979e-01 1.76351622e-01 5.52053869e-01 -9.08389568e-01 -9.55388308e-01 9.97251391e-01 -3.15515399e-01 3.39272857e-01 1.88317358e-01 7.43769705e-02 1.45252275e+00 -1.49280655e+00 9.61284935e-01 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e43ba32f-9e4b-4d5e-8a2a-5029a7153edc	streaming-video-temporal-action-segmentation	2209.13808	null	https://arxiv.org/abs/2209.13808v2	https://arxiv.org/pdf/2209.13808v2.pdf	Streaming Video Temporal Action Segmentation In Real Time	Temporal action segmentation (TAS) is a critical step toward long-term video understanding. Recent studies follow a pattern that builds models based on features instead of raw video picture information. However, we claim those models are trained complicatedly and limit application scenarios. It is hard for them to segment human actions of video in real time because they must work after the full video features are extracted. As the real-time action segmentation task is different from TAS task, we define it as streaming video real-time temporal action segmentation (SVTAS) task. In this paper, we propose a real-time end-to-end multi-modality model for SVTAS task. More specifically, under the circumstances that we cannot get any future information, we segment the current human action of streaming video chunk in real time. Furthermore, the model we propose combines the last steaming video chunk feature extracted by language model with the current image feature extracted by image model to improve the quantity of real-time temporal action segmentation. To the best of our knowledge, it is the first multi-modality real-time temporal action segmentation model. Under the same evaluation criteria as full video temporal action segmentation, our model segments human action in real time with less than 40% of state-of-the-art model computation and achieves 90% of the accuracy of the full video state-of-the-art model.	['Shenlan Liu', 'Wanxiao Yang', 'Lin Feng', 'Zhuben Dong', 'Yunheng Li', 'Wujun Wen']	2022-09-28	null	null	null	null	['action-segmentation']	['computer-vision']	[ 6.01460278e-01 -2.77563725e-02 -4.87874746e-01 -1.63527265e-01 -5.76758027e-01 -4.25840020e-01 5.19527555e-01 -3.33483398e-01 -6.66037261e-01 2.66427428e-01 2.22139448e-01 -1.35266557e-01 1.65116861e-01 -4.46438581e-01 -8.09239864e-01 -4.44750071e-01 -3.14396881e-02 2.42651358e-01 8.83545101e-01 -4.66138907e-02 2.29287937e-01 1.56570107e-01 -1.52475178e+00 8.66482437e-01 5.75671613e-01 1.18123114e+00 2.10359886e-01 9.17587638e-01 -2.16197193e-01 1.13831997e+00 -3.21834564e-01 -1.09078757e-01 4.25676584e-01 -6.89349473e-01 -1.13499367e+00 4.99142200e-01 4.93905932e-01 -6.92123055e-01 -5.69522381e-01 6.71975672e-01 1.27379850e-01 1.60831422e-01 1.86768711e-01 -1.41565025e+00 -4.63937819e-02 6.04600191e-01 -4.63833004e-01 4.27653134e-01 7.74433136e-01 3.48301083e-01 6.66095912e-01 -6.49785340e-01 8.91954124e-01 9.88564909e-01 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2c859515-fc67-450e-ab60-ff4f3a30b292	the-color-out-of-space-learning-self	2006.12119	null	https://arxiv.org/abs/2006.12119v1	https://arxiv.org/pdf/2006.12119v1.pdf	The color out of space: learning self-supervised representations for Earth Observation imagery	The recent growth in the number of satellite images fosters the development of effective deep-learning techniques for Remote Sensing (RS). However, their full potential is untapped due to the lack of large annotated datasets. Such a problem is usually countered by fine-tuning a feature extractor that is previously trained on the ImageNet dataset. Unfortunately, the domain of natural images differs from the RS one, which hinders the final performance. In this work, we propose to learn meaningful representations from satellite imagery, leveraging its high-dimensionality spectral bands to reconstruct the visible colors. We conduct experiments on land cover classification (BigEarthNet) and West Nile Virus detection, showing that colorization is a solid pretext task for training a feature extractor. Furthermore, we qualitatively observe that guesses based on natural images and colorization rely on different parts of the input. This paves the way to an ensemble model that eventually outperforms both the above-mentioned techniques.	['Angelo Porrello', 'Marco Cipriano', 'Carla Ippoliti', 'Stefano Vincenzi', 'Pietro Fronte', 'Pietro Buzzega', 'Annamaria Conte', 'Roberto Cuccu', 'Simone Calderara']	2020-06-22	null	null	null	null	['remote-sensing-image-classification']	['miscellaneous']	[ 3.71967852e-01 -1.76569641e-01 1.70916915e-01 -4.49877292e-01 -5.78689873e-01 -7.80745924e-01 6.41006112e-01 5.26498817e-02 -6.03468299e-01 6.92603409e-01 -1.71692044e-01 -5.74507296e-01 -1.08552635e-01 -1.03628218e+00 -7.25035012e-01 -1.00212145e+00 -3.89193684e-01 1.83834597e-01 -1.07275710e-01 -5.26497006e-01 -4.91909757e-02 7.32224762e-01 -1.77093458e+00 1.98446706e-01 9.53135073e-01 8.37147951e-01 3.17493647e-01 6.73088133e-01 -2.68868655e-01 4.92361903e-01 -2.51723439e-01 -1.64546847e-01 5.12238503e-01 -2.42456228e-01 -6.72185898e-01 1.38163313e-01 5.11059225e-01 -3.20722252e-01 4.33669649e-02 1.24119210e+00 1.53977633e-01 -4.88001816e-02 6.18216634e-01 -9.86534417e-01 -5.28863788e-01 4.87523586e-01 -5.83624184e-01 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8419c32b-fbc3-4302-920c-130a7151be9a	rainformer-features-extraction-balanced	null	null	https://ieeexplore.ieee.org/document/9743916	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9743916	Rainformer: Features Extraction Balanced Network for Radar-Based Precipitation Nowcasting	Precipitation nowcasting is one of the fundamental challenges in natural hazard research. High-intensity rainfall, especially the rainstorm, will lead to the enormous loss of people’s property. Existing methods usually utilize convolution operation to extract rainfall features and increase the network depth to expand the receptive field to obtain fake global features. Although this scheme is simple, only local rainfall features can be extracted leading to insensitivity to high-intensity rainfall. This letter proposes a novel precipitation nowcasting framework named Rainformer, in which, two practical components are proposed: the global features extraction unit and the gate fusion unit (GFU). The former provides robust global features learning ability depending on the window-based multi-head self-attention (W-MSA) mechanism, while the latter provides a balanced fusion of local and global features. Rainformer has a simple yet efficient architecture and significantly improves the accuracy of rainfall prediction, especially on high-intensity rainfall. It offers a potential solution for real-world applications. The experimental results show that Rainformer outperforms seven state of the arts methods on the benchmark database and provides more insights into the high-intensity rainfall prediction task.	['ShengYong Chen', 'Yi Song', 'Jinglin Zhang', 'Feng Sun', 'Cong Bai']	2022-03-28	null	null	null	ieee-geoscience-and-remote-sensing-letters-8	['weather-forecasting']	['miscellaneous']	[-1.85729489e-02 -5.00265062e-01 2.57797092e-01 -6.34133697e-01 -6.08392477e-01 1.65354073e-01 4.47034240e-01 9.15217493e-03 -4.88005668e-01 8.71251822e-01 1.83817297e-01 -3.16354096e-01 3.29638600e-01 -1.23495090e+00 -4.45360929e-01 -1.40009534e+00 -2.83936411e-01 -1.12057187e-01 1.02418996e-01 -4.14439172e-01 2.11483657e-01 5.96804261e-01 -1.61998940e+00 1.44997776e-01 1.09697783e+00 8.91782522e-01 7.17994511e-01 5.72592556e-01 -2.73217052e-01 7.63778508e-01 -6.50477648e-01 1.42976180e-01 1.29661620e-01 -2.36121476e-01 -2.19809577e-01 -3.02112997e-01 3.40721846e-01 -5.66376567e-01 -2.46545374e-01 8.24593961e-01 8.27009141e-01 1.60388798e-01 3.70766044e-01 -6.79771245e-01 -6.07336402e-01 5.17745137e-01 -1.03647184e+00 8.62139106e-01 -2.45122194e-01 8.17387998e-02 8.01735520e-01 -1.28913903e+00 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1634cbe9-70ec-43fd-b466-7ddcbecbb355	analysing-the-robustness-of-dual-encoders-for	2205.02303	null	https://arxiv.org/abs/2205.02303v1	https://arxiv.org/pdf/2205.02303v1.pdf	Analysing the Robustness of Dual Encoders for Dense Retrieval Against Misspellings	Dense retrieval is becoming one of the standard approaches for document and passage ranking. The dual-encoder architecture is widely adopted for scoring question-passage pairs due to its efficiency and high performance. Typically, dense retrieval models are evaluated on clean and curated datasets. However, when deployed in real-life applications, these models encounter noisy user-generated text. That said, the performance of state-of-the-art dense retrievers can substantially deteriorate when exposed to noisy text. In this work, we study the robustness of dense retrievers against typos in the user question. We observe a significant drop in the performance of the dual-encoder model when encountering typos and explore ways to improve its robustness by combining data augmentation with contrastive learning. Our experiments on two large-scale passage ranking and open-domain question answering datasets show that our proposed approach outperforms competing approaches. Additionally, we perform a thorough analysis on robustness. Finally, we provide insights on how different typos affect the robustness of embeddings differently and how our method alleviates the effect of some typos but not of others.	['Evangelos Kanoulas', 'Georgios Sidiropoulos']	2022-05-04	null	null	null	null	['passage-ranking']	['natural-language-processing']	[-2.20686093e-01 -4.61947680e-01 9.00881290e-02 -1.65994763e-01 -1.51646101e+00 -6.94941998e-01 6.21476412e-01 4.47573125e-01 -6.47429287e-01 5.94821215e-01 7.84520924e-01 -2.42080307e-03 -2.90969133e-01 -6.84874058e-01 -7.58950472e-01 -4.31917846e-01 1.34896085e-01 3.76640558e-01 3.44901085e-01 -7.06129968e-01 3.72062385e-01 -1.39361694e-01 -1.58694601e+00 4.86561000e-01 1.03617501e+00 7.42141664e-01 9.77534503e-02 7.39718318e-01 -4.62660119e-02 7.83522069e-01 -8.94625127e-01 -6.52145028e-01 1.41934603e-01 -1.84260651e-01 -9.17074502e-01 -6.19504333e-01 7.98028171e-01 -6.05877578e-01 -7.45860219e-01 8.71375203e-01 9.65902209e-01 3.95151347e-01 7.10175931e-01 -6.62736833e-01 -1.25033605e+00 6.61541343e-01 -1.16258971e-01 6.43975675e-01 6.88147724e-01 -1.48530155e-01 1.55264342e+00 -9.72578466e-01 5.22146642e-01 1.02084053e+00 5.86578488e-01 3.33794773e-01 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d37d256e-d5ad-4f99-9790-609c94ec6f17	hyhtm-hyperbolic-geometry-based-hierarchical	2305.09258	null	https://arxiv.org/abs/2305.09258v1	https://arxiv.org/pdf/2305.09258v1.pdf	HyHTM: Hyperbolic Geometry based Hierarchical Topic Models	Hierarchical Topic Models (HTMs) are useful for discovering topic hierarchies in a collection of documents. However, traditional HTMs often produce hierarchies where lowerlevel topics are unrelated and not specific enough to their higher-level topics. Additionally, these methods can be computationally expensive. We present HyHTM - a Hyperbolic geometry based Hierarchical Topic Models - that addresses these limitations by incorporating hierarchical information from hyperbolic geometry to explicitly model hierarchies in topic models. Experimental results with four baselines show that HyHTM can better attend to parent-child relationships among topics. HyHTM produces coherent topic hierarchies that specialise in granularity from generic higher-level topics to specific lowerlevel topics. Further, our model is significantly faster and leaves a much smaller memory footprint than our best-performing baseline.We have made the source code for our algorithm publicly accessible.	['Nikaash Puri', 'Balaji Krishnamurthy', 'Sumit Bhatia', 'Nikitha Srikanth', 'Tanay Anand', 'Simra Shahid']	2023-05-16	null	null	null	null	['topic-models']	['natural-language-processing']	[-6.07534289e-01 6.50655746e-01 -4.01968509e-01 -3.17952454e-01 -1.29987931e+00 -4.86154228e-01 6.84113026e-01 5.81189573e-01 3.18691671e-01 5.20560265e-01 7.68820167e-01 -2.69226253e-01 -1.66996300e-01 -1.16545439e+00 -3.58928084e-01 -4.70640272e-01 -5.90934336e-01 1.13651907e+00 9.15992379e-01 1.79957077e-02 5.00336230e-01 -8.73501152e-02 -1.38684630e+00 5.05929947e-01 8.53828073e-01 4.73996788e-01 2.62829989e-01 5.55090725e-01 -3.41000587e-01 1.96226954e-01 -7.65524507e-01 -1.15923263e-01 -1.45609826e-01 -1.58713892e-01 -1.08367491e+00 2.33122200e-01 7.12305546e-01 -3.36007804e-01 -6.56979978e-02 4.36882168e-01 -5.30201755e-03 2.06996739e-01 9.08072114e-01 -1.39395249e+00 -3.00667822e-01 8.21067929e-01 -7.76645839e-01 2.51133651e-01 1.90700665e-01 -5.61432838e-01 1.51822233e+00 -9.83025253e-01 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186e13ca-e9fb-4a92-8cba-4302beb0b8d0	person-recognition-in-personal-photo	1710.03224	null	http://arxiv.org/abs/1710.03224v2	http://arxiv.org/pdf/1710.03224v2.pdf	Person Recognition in Personal Photo Collections	People nowadays share large parts of their personal lives through social media. Being able to automatically recognise people in personal photos may greatly enhance user convenience by easing photo album organisation. For human identification task, however, traditional focus of computer vision has been face recognition and pedestrian re-identification. Person recognition in social media photos sets new challenges for computer vision, including non-cooperative subjects (e.g. backward viewpoints, unusual poses) and great changes in appearance. To tackle this problem, we build a simple person recognition framework that leverages convnet features from multiple image regions (head, body, etc.). We propose new recognition scenarios that focus on the time and appearance gap between training and testing samples. We present an in-depth analysis of the importance of different features according to time and viewpoint generalisability. In the process, we verify that our simple approach achieves the state of the art result on the PIPA benchmark, arguably the largest social media based benchmark for person recognition to date with diverse poses, viewpoints, social groups, and events. Compared the conference version of the paper, this paper additionally presents (1) analysis of a face recogniser (DeepID2+), (2) new method naeil2 that combines the conference version method naeil and DeepID2+ to achieve state of the art results even compared to post-conference works, (3) discussion of related work since the conference version, (4) additional analysis including the head viewpoint-wise breakdown of performance, and (5) results on the open-world setup.	['Seong Joon Oh', 'Rodrigo Benenson', 'Mario Fritz', 'Bernt Schiele']	2017-10-09	null	null	null	null	['person-recognition']	['computer-vision']	[ 2.05730107e-02 -1.23185389e-01 3.92987251e-01 -4.84349519e-01 -4.12159830e-01 -4.55836236e-01 1.00977409e+00 -2.29052141e-01 -5.70279300e-01 5.13226748e-01 2.23615140e-01 3.67115647e-01 -4.79008593e-02 -4.09234434e-01 -4.07490313e-01 -6.79950118e-01 -1.92835867e-01 5.64478636e-01 -2.42739394e-02 -2.06607550e-01 -4.12815958e-02 8.42902482e-01 -2.05643129e+00 3.32495332e-01 1.60284460e-01 8.43032479e-01 -4.51819032e-01 7.05905378e-01 4.17370617e-01 1.40389279e-01 -7.14823782e-01 -9.54811454e-01 5.23124337e-01 -2.80596334e-02 -6.72921002e-01 5.15780628e-01 1.34601796e+00 -3.54038477e-01 -4.49241638e-01 8.67819846e-01 1.31282914e+00 2.20313177e-01 6.22341394e-01 -1.36311364e+00 -4.44790512e-01 -1.27475545e-01 -5.91715097e-01 3.29523206e-01 9.02107596e-01 2.83271462e-01 3.41378033e-01 -1.09718847e+00 5.48655450e-01 1.56987000e+00 1.04625034e+00 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-1.07135642e+00 -4.98885721e-01 7.43839025e-01 2.15433985e-01 -8.39061677e-01 -3.17336231e-01 7.92630017e-02]	[14.322311401367188, 0.9557995200157166]
8f453e86-cd38-49b2-9707-d8b65fefdb83	application-of-three-graph-laplacian-based	1211.4289	null	http://arxiv.org/abs/1211.4289v3	http://arxiv.org/pdf/1211.4289v3.pdf	Application of three graph Laplacian based semi-supervised learning methods to protein function prediction problem	Protein function prediction is the important problem in modern biology. In this paper, the un-normalized, symmetric normalized, and random walk graph Laplacian based semi-supervised learning methods will be applied to the integrated network combined from multiple networks to predict the functions of all yeast proteins in these multiple networks. These multiple networks are network created from Pfam domain structure, co-participation in a protein complex, protein-protein interaction network, genetic interaction network, and network created from cell cycle gene expression measurements. Multiple networks are combined with fixed weights instead of using convex optimization to determine the combination weights due to high time complexity of convex optimization method. This simple combination method will not affect the accuracy performance measures of the three semi-supervised learning methods. Experiment results show that the un-normalized and symmetric normalized graph Laplacian based methods perform slightly better than random walk graph Laplacian based method for integrated network. Moreover, the accuracy performance measures of these three semi-supervised learning methods for integrated network are much better than the best accuracy performance measures of these three methods for the individual network.	['Loc Tran']	2012-11-19	null	null	null	null	['protein-function-prediction']	['medical']	[ 1.74402639e-01 1.56788975e-01 -3.66838217e-01 -1.59743562e-01 -2.00110778e-01 -4.55258071e-01 6.96432665e-02 2.31138602e-01 -4.40363020e-01 1.31414878e+00 -2.33224764e-01 -1.14564717e-01 -5.83772540e-01 -7.43516386e-01 -5.20160615e-01 -1.00096762e+00 -4.85738575e-01 6.10533059e-01 5.51588297e-01 3.45042460e-02 -4.67349915e-03 3.42117876e-01 -9.54279602e-01 -3.92447636e-02 8.63674045e-01 4.78730381e-01 1.18562363e-01 7.08037317e-01 -1.15489110e-01 4.72666830e-01 -4.86053169e-01 -1.00983813e-01 -2.30288859e-02 -7.10604727e-01 -6.29957378e-01 -1.24545060e-01 -3.35796565e-01 5.71253479e-01 -6.70215338e-02 1.22344112e+00 6.16851926e-01 1.15114212e-01 8.58075619e-01 -1.71843004e+00 -3.61903548e-01 7.05864787e-01 -8.25626254e-01 4.17829044e-02 3.83975565e-01 -2.37230957e-01 1.15756500e+00 -7.36597002e-01 8.71642053e-01 1.43830776e+00 7.43578136e-01 6.43266216e-02 -1.44142008e+00 -5.65342426e-01 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2.71028340e-01 -1.37225002e-01 -6.29584312e-01 -5.91209292e-01 8.07860568e-02]	[6.696123123168945, 5.502554893493652]
024f0efe-3701-4311-abbd-eed8e64b6588	mutual-information-estimation-for-graph	2203.16887	null	https://arxiv.org/abs/2203.16887v1	https://arxiv.org/pdf/2203.16887v1.pdf	Mutual information estimation for graph convolutional neural networks	Measuring model performance is a key issue for deep learning practitioners. However, we often lack the ability to explain why a specific architecture attains superior predictive accuracy for a given data set. Often, validation accuracy is used as a performance heuristic quantifying how well a network generalizes to unseen data, but it does not capture anything about the information flow in the model. Mutual information can be used as a measure of the quality of internal representations in deep learning models, and the information plane may provide insights into whether the model exploits the available information in the data. The information plane has previously been explored for fully connected neural networks and convolutional architectures. We present an architecture-agnostic method for tracking a network's internal representations during training, which are then used to create the mutual information plane. The method is exemplified for graph-based neural networks fitted on citation data. We compare how the inductive bias introduced in graph-based architectures changes the mutual information plane relative to a fully connected neural network.	['Signe Riemer-Sørensen', 'Marius C. Landverk']	2022-03-31	null	null	null	null	['mutual-information-estimation', 'information-plane']	['methodology', 'methodology']	[ 8.74724761e-02 3.80693913e-01 -2.63842911e-01 -5.11345088e-01 6.24197461e-02 -6.28636718e-01 6.75773978e-01 4.71824497e-01 -3.95893395e-01 4.27034885e-01 1.61776170e-01 -5.30196071e-01 -5.24146736e-01 -9.85089779e-01 -7.45671272e-01 -4.29429442e-01 -5.75589947e-02 4.24755484e-01 -3.81848440e-02 -1.41042774e-03 2.83174098e-01 6.14767015e-01 -1.19423878e+00 1.16052419e-01 6.81695163e-01 1.04219735e+00 -3.75051312e-02 5.26745141e-01 -4.45868969e-01 8.39526713e-01 -5.52916467e-01 -3.65563989e-01 -6.46958202e-02 -3.42681497e-01 -1.16041231e+00 -4.59044665e-01 5.05011916e-01 3.27094018e-01 -4.41068143e-01 1.03210020e+00 7.02117383e-03 -2.23535731e-01 1.08230901e+00 -1.18458140e+00 -4.79529530e-01 8.71474266e-01 -1.28842434e-02 5.72344124e-01 -1.68250650e-01 1.27330989e-01 1.35964441e+00 -2.03707129e-01 6.73088372e-01 1.05130160e+00 7.52350569e-01 2.10588828e-01 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a8581114-2300-48e7-a4f7-737cf99b1abc	hyperparameter-optimization-in-deep-multi	2211.04362	null	https://arxiv.org/abs/2211.04362v1	https://arxiv.org/pdf/2211.04362v1.pdf	Hyperparameter optimization in deep multi-target prediction	As a result of the ever increasing complexity of configuring and fine-tuning machine learning models, the field of automated machine learning (AutoML) has emerged over the past decade. However, software implementations like Auto-WEKA and Auto-sklearn typically focus on classical machine learning (ML) tasks such as classification and regression. Our work can be seen as the first attempt at offering a single AutoML framework for most problem settings that fall under the umbrella of multi-target prediction, which includes popular ML settings such as multi-label classification, multivariate regression, multi-task learning, dyadic prediction, matrix completion, and zero-shot learning. Automated problem selection and model configuration are achieved by extending DeepMTP, a general deep learning framework for MTP problem settings, with popular hyperparameter optimization (HPO) methods. Our extensive benchmarking across different datasets and MTP problem settings identifies cases where specific HPO methods outperform others.	['Willem Waegeman', 'Bernard De Baets', 'Marcel Wever', 'Dimitrios Iliadis']	2022-11-08	null	null	null	null	['matrix-completion']	['methodology']	[ 3.54704946e-01 -4.54555191e-02 -3.77024233e-01 -4.50221330e-01 -1.27262557e+00 -4.63728189e-01 4.96156603e-01 3.67252558e-01 -4.22402173e-01 5.88942707e-01 -7.27418885e-02 -2.05264583e-01 -4.44426626e-01 -3.76542807e-01 -6.05161309e-01 -7.98305154e-01 8.07719231e-02 1.09954762e+00 -8.05816278e-02 -8.03679749e-02 1.32494852e-01 1.73987359e-01 -1.49692118e+00 3.41830403e-01 4.37368602e-01 9.71644104e-01 -1.73161104e-01 9.00257170e-01 -5.58986664e-02 5.18630922e-01 -2.84628689e-01 -4.69801486e-01 3.06944728e-01 1.99192062e-01 -9.39166307e-01 -3.28233168e-02 5.08694768e-01 2.70083845e-01 7.61411637e-02 6.27498090e-01 9.34346497e-01 1.95605457e-01 6.80131257e-01 -1.76685739e+00 -2.71414310e-01 6.38318300e-01 -5.48099339e-01 3.18749994e-02 1.74945846e-01 5.05252898e-01 1.32949972e+00 -1.00037193e+00 3.16571146e-01 1.56217551e+00 9.88953590e-01 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-1.06359577e+00 -1.32634318e+00 -4.45626378e-01 3.41543972e-01 2.12352261e-01 -6.95219278e-01 -2.58822322e-01 -2.00601846e-01]	[9.190485000610352, 4.197889804840088]
26fe897d-7051-48f3-8c95-b47c0a447f9e	two-step-domain-adaptation-for-mitosis-cell	2109.00109	null	https://arxiv.org/abs/2109.00109v2	https://arxiv.org/pdf/2109.00109v2.pdf	Two-step Domain Adaptation for Mitosis Cell Detection in Histopathology Images	We propose a two-step domain shift-invariant mitosis cell detection method based on Faster RCNN and a convolutional neural network (CNN). We generate various domain-shifted versions of existing histopathology images using a stain augmentation technique, enabling our method to effectively learn various stain domains and achieve better generalization. The performance of our method is evaluated on the preliminary test data set of the MIDOG-2021 challenge. The experimental results demonstrate that the proposed mitosis detection method can achieve promising performance for domain-shifted histopathology images.	['Fattaneh Pourakpour', 'Ramin Nateghi']	2021-08-31	null	null	null	null	['cell-detection', 'mitosis-detection']	['computer-vision', 'medical']	[ 5.41715741e-01 -9.50362347e-03 -1.69623524e-01 -3.10506880e-01 -1.12652278e+00 -4.71814662e-01 5.23440719e-01 1.17463395e-01 -7.21052885e-01 1.12118864e+00 -8.38727131e-02 -2.27253377e-01 4.11380768e-01 -6.26709402e-01 -2.37785041e-01 -1.31618631e+00 -1.22432798e-01 4.27508146e-01 5.99935949e-01 -1.38338000e-01 9.84782055e-02 8.73474002e-01 -9.77500618e-01 5.55200160e-01 4.71667230e-01 8.28220665e-01 -9.11545008e-02 1.18227506e+00 3.69101107e-01 7.55269885e-01 -6.64307833e-01 -4.67477217e-02 1.73253983e-01 -1.45022616e-01 -1.13500214e+00 1.22239642e-01 3.12596023e-01 -3.66998941e-01 -5.36504269e-01 1.06382334e+00 7.78013766e-01 -2.84590155e-01 9.19287741e-01 -8.37527633e-01 -4.80490386e-01 4.41951811e-01 -6.93211913e-01 9.66971338e-01 -2.32503593e-01 3.96175265e-01 3.96318495e-01 -4.67846811e-01 1.14667964e+00 8.50908339e-01 8.04991484e-01 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1a5e88c6-2d31-4b6f-9655-de0d0f49169e	cross-lingual-data-augmentation-for-document	2305.14949	null	https://arxiv.org/abs/2305.14949v1	https://arxiv.org/pdf/2305.14949v1.pdf	Cross-lingual Data Augmentation for Document-grounded Dialog Systems in Low Resource Languages	This paper proposes a framework to address the issue of data scarcity in Document-Grounded Dialogue Systems(DGDS). Our model leverages high-resource languages to enhance the capability of dialogue generation in low-resource languages. Specifically, We present a novel pipeline CLEM (Cross-Lingual Enhanced Model) including adversarial training retrieval (Retriever and Re-ranker), and Fid (fusion-in-decoder) generator. To further leverage high-resource language, we also propose an innovative architecture to conduct alignment across different languages with translated training. Extensive experiment results demonstrate the effectiveness of our model and we achieved 4th place in the DialDoc 2023 Competition. Therefore, CLEM can serve as a solution to resource scarcity in DGDS and provide useful guidance for multi-lingual alignment tasks.	['Wenzhe Du', 'Zehua Xia', 'Qi Gou']	2023-05-24	null	null	null	null	['dialogue-generation', 'dialogue-generation']	['natural-language-processing', 'speech']	[-1.90966174e-01 4.47638720e-01 -1.49681583e-01 -3.89820129e-01 -1.68528771e+00 -8.65391076e-01 9.90168452e-01 -4.01963592e-01 -5.07967234e-01 1.15296650e+00 7.98121810e-01 -5.91059685e-01 5.57922900e-01 -4.62448388e-01 -5.55372179e-01 3.54619622e-02 3.78032029e-01 1.07714343e+00 -3.69283468e-01 -9.20142591e-01 -5.20842895e-02 1.16418824e-01 -3.77913684e-01 5.12714207e-01 1.37434137e+00 1.72266141e-01 5.19044101e-02 5.29765666e-01 -1.31399617e-01 6.96523726e-01 -9.03204978e-01 -1.07683563e+00 3.50156963e-01 -6.17410362e-01 -1.31877649e+00 -5.29420197e-01 5.15408635e-01 -6.59287512e-01 -2.68469185e-01 8.15301239e-01 1.02819431e+00 -5.36086261e-02 5.34602821e-01 -1.18718731e+00 -6.64118648e-01 1.07382500e+00 -2.27676913e-01 -7.11622387e-02 5.89902818e-01 4.01769727e-01 1.09036052e+00 -1.02741170e+00 8.18307519e-01 1.37265754e+00 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-2.38938600e-01 -9.23064351e-01 -1.34376240e+00 4.69613494e-03 3.82045537e-01 -8.98622125e-02 1.72576122e-02 7.25468621e-04 -5.14293388e-02]	[12.36857795715332, 8.55305290222168]
4832a5ae-2256-4d68-9580-1633ac756e91	transfer-learning-for-video-classification	2210.09969	null	https://arxiv.org/abs/2210.09969v1	https://arxiv.org/pdf/2210.09969v1.pdf	Transfer-learning for video classification: Video Swin Transformer on multiple domains	The computer vision community has seen a shift from convolutional-based to pure transformer architectures for both image and video tasks. Training a transformer from zero for these tasks usually requires a lot of data and computational resources. Video Swin Transformer (VST) is a pure-transformer model developed for video classification which achieves state-of-the-art results in accuracy and efficiency on several datasets. In this paper, we aim to understand if VST generalizes well enough to be used in an out-of-domain setting. We study the performance of VST on two large-scale datasets, namely FCVID and Something-Something using a transfer learning approach from Kinetics-400, which requires around 4x less memory than training from scratch. We then break down the results to understand where VST fails the most and in which scenarios the transfer-learning approach is viable. Our experiments show an 85\% top-1 accuracy on FCVID without retraining the whole model which is equal to the state-of-the-art for the dataset and a 21\% accuracy on Something-Something. The experiments also suggest that the performance of the VST decreases on average when the video duration increases which seems to be a consequence of a design choice of the model. From the results, we conclude that VST generalizes well enough to classify out-of-domain videos without retraining when the target classes are from the same type as the classes used to train the model. We observed this effect when we performed transfer-learning from Kinetics-400 to FCVID, where most datasets target mostly objects. On the other hand, if the classes are not from the same type, then the accuracy after the transfer-learning approach is expected to be poor. We observed this effect when we performed transfer-learning from Kinetics-400, where the classes represent mostly objects, to Something-Something, where the classes represent mostly actions.	['David Martins de Matos', 'Daniel Oliveira']	2022-10-18	null	null	null	null	['video-classification']	['computer-vision']	[-4.94779721e-02 -1.85925528e-01 -9.72246751e-02 -2.01821744e-01 -4.92717385e-01 -4.44252253e-01 5.46689987e-01 -2.83637404e-01 -5.32894731e-01 5.79387367e-01 -1.97245181e-01 -4.34909075e-01 2.66579892e-02 -8.48058403e-01 -1.31531656e+00 -7.72795379e-01 -1.51734203e-01 5.99514067e-01 7.36018717e-01 -2.21056551e-01 -9.87593085e-02 3.10456723e-01 -1.80049729e+00 8.44730437e-01 3.17046642e-01 1.22648239e+00 2.38203123e-01 6.33653879e-01 -5.43298833e-02 1.07973623e+00 -6.24730408e-01 -4.66613054e-01 5.20486951e-01 -4.01192427e-01 -8.37799847e-01 2.44539738e-01 7.56803513e-01 -2.73962289e-01 -4.33340788e-01 7.19180703e-01 2.25057855e-01 -1.72905684e-01 6.51229203e-01 -1.32645655e+00 -3.58925104e-01 3.88575792e-01 -3.58099997e-01 3.23565960e-01 1.03171319e-01 3.61593962e-01 6.55297399e-01 -8.85201812e-01 6.71664238e-01 1.12970531e+00 9.64696109e-01 5.13310492e-01 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6365fd4a-fa1b-4e8f-ae64-bf2b9f23a787	cross-lingual-speech-emotion-recognition-urdu	1812.10411	null	https://arxiv.org/abs/1812.10411v2	https://arxiv.org/pdf/1812.10411v2.pdf	Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages	Cross-lingual speech emotion recognition is an important task for practical applications. The performance of automatic speech emotion recognition systems degrades in cross-corpus scenarios, particularly in scenarios involving multiple languages or a previously unseen language such as Urdu for which limited or no data is available. In this study, we investigate the problem of cross-lingual emotion recognition for Urdu language and contribute URDU---the first ever spontaneous Urdu-language speech emotion database. Evaluations are performed using three different Western languages against Urdu and experimental results on different possible scenarios suggest various interesting aspects for designing more adaptive emotion recognition system for such limited languages. In results, selecting training instances of multiple languages can deliver comparable results to baseline and augmentation a fraction of testing language data while training can help to boost accuracy for speech emotion recognition. URDU data is publicly available for further research.	['Muhammad Usman', 'Adnan Qayyum', 'Junaid Qadir', 'Siddique Latif']	2018-12-15	null	null	null	null	['cross-corpus']	['computer-vision']	[-2.56341904e-01 -1.45618066e-01 -3.49230394e-02 -5.92751145e-01 -1.07686710e+00 -7.40811229e-01 5.04424274e-01 -2.27561265e-01 -6.20922029e-01 7.48928905e-01 3.86295617e-01 -3.60761166e-01 7.82173574e-01 -1.18974246e-01 -2.11127400e-01 -5.18208146e-01 3.29051875e-02 2.07447648e-01 -2.57062942e-01 -3.18352103e-01 -2.89347321e-01 7.03535318e-01 -1.44793975e+00 3.47751796e-01 6.49764776e-01 9.62959290e-01 2.13213727e-01 8.46200883e-01 -1.43106684e-01 7.28357434e-01 -9.18748498e-01 -5.00842214e-01 1.16077840e-01 -3.00065398e-01 -7.64402092e-01 3.32451075e-01 -3.05778794e-02 -1.02058917e-01 -8.05590581e-03 8.22433114e-01 9.07493412e-01 3.80168378e-01 5.00728786e-01 -9.58783448e-01 -7.00468540e-01 5.77103913e-01 -1.49007484e-01 3.78027111e-01 4.59686279e-01 -3.83481354e-01 6.95381403e-01 -8.94069076e-01 6.88106954e-01 1.35922492e+00 3.06934059e-01 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-1.41801313e-01 -1.16599369e+00 -6.62445486e-01 2.32726723e-01 3.29249233e-01 -3.13617915e-01 -4.93154407e-01 7.67957121e-02]	[13.612419128417969, 5.870246887207031]
55092a2c-f697-4efa-9007-56a5f98b6eaa	global-minimum-for-a-finsler-elastica-minimal	1612.00343	null	http://arxiv.org/abs/1612.00343v3	http://arxiv.org/pdf/1612.00343v3.pdf	Global Minimum for a Finsler Elastica Minimal Path Approach	In this paper, we propose a novel curvature-penalized minimal path model via an orientation-lifted Finsler metric and the Euler elastica curve. The original minimal path model computes the globally minimal geodesic by solving an Eikonal partial differential equation (PDE). Essentially, this first-order model is unable to penalize curvature which is related to the path rigidity property in the classical active contour models. To solve this problem, we present an Eikonal PDE-based Finsler elastica minimal path approach to address the curvature-penalized geodesic energy minimization problem. We were successful at adding the curvature penalization to the classical geodesic energy. The basic idea of this work is to interpret the Euler elastica bending energy via a novel Finsler elastica metric that embeds a curvature penalty. This metric is non-Riemannian, anisotropic and asymmetric, and is defined over an orientation-lifted space by adding to the image domain the orientation as an extra space dimension. Based on this orientation lifting, the proposed minimal path model can benefit from both the curvature and orientation of the paths. Thanks to the fast marching method, the global minimum of the curvature-penalized geodesic energy can be computed efficiently. We introduce two anisotropic image data-driven speed functions that are computed by steerable filters. Based on these orientation-dependent speed functions, we can apply the proposed Finsler elastica minimal path model to the applications of closed contour detection, perceptual grouping and tubular structure extraction. Numerical experiments on both synthetic and real images show that these applications of the proposed model indeed obtain promising results.	['Jean-Marie Mirebeau', 'Laurent D. Cohen', 'Da Chen']	2016-12-01	null	null	null	null	['contour-detection']	['computer-vision']	[-1.25564002e-02 3.21060151e-01 1.70288414e-01 -2.78866708e-01 -2.05841839e-01 -5.72741807e-01 5.03634989e-01 -4.82114730e-03 -6.81541085e-01 2.90381432e-01 -2.43223444e-01 -1.96270630e-01 -6.05146587e-01 -9.31945801e-01 -4.12770212e-01 -7.50521362e-01 -1.71835169e-01 1.35383919e-01 4.27622378e-01 -4.94051278e-01 5.49948215e-01 7.71296263e-01 -1.13850915e+00 -4.23801929e-01 1.02020812e+00 7.21438229e-01 -1.43128503e-02 7.09310889e-01 1.40690893e-01 -1.59130633e-01 3.00084144e-01 -2.23358199e-01 2.77680218e-01 -4.81219262e-01 -9.89108264e-01 2.55852818e-01 9.90028530e-02 -1.81418478e-01 1.67278990e-01 1.08105361e+00 6.58097044e-02 3.22469413e-01 6.19560122e-01 -7.82241464e-01 -5.41514993e-01 -5.68617545e-02 -4.25091505e-01 -1.01283625e-01 4.93364632e-02 -2.68340319e-01 9.55257356e-01 -1.11077404e+00 1.03272200e+00 9.53259230e-01 7.07204461e-01 3.76156121e-01 -1.28999031e+00 3.55440825e-01 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d74c098d-a635-4ac2-a190-7e5af72e7784	few-shot-video-classification-via-temporal	1906.11415	null	https://arxiv.org/abs/1906.11415v1	https://arxiv.org/pdf/1906.11415v1.pdf	Few-Shot Video Classification via Temporal Alignment	There is a growing interest in learning a model which could recognize novel classes with only a few labeled examples. In this paper, we propose Temporal Alignment Module (TAM), a novel few-shot learning framework that can learn to classify a previous unseen video. While most previous works neglect long-term temporal ordering information, our proposed model explicitly leverages the temporal ordering information in video data through temporal alignment. This leads to strong data-efficiency for few-shot learning. In concrete, TAM calculates the distance value of query video with respect to novel class proxies by averaging the per frame distances along its alignment path. We introduce continuous relaxation to TAM so the model can be learned in an end-to-end fashion to directly optimize the few-shot learning objective. We evaluate TAM on two challenging real-world datasets, Kinetics and Something-Something-V2, and show that our model leads to significant improvement of few-shot video classification over a wide range of competitive baselines.	['Chien-Yi Chang', 'Zhangjie Cao', 'Kaidi Cao', 'Juan Carlos Niebles', 'Jingwei Ji']	2019-06-27	few-shot-video-classification-via-temporal-1	http://openaccess.thecvf.com/content_CVPR_2020/html/Cao_Few-Shot_Video_Classification_via_Temporal_Alignment_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Cao_Few-Shot_Video_Classification_via_Temporal_Alignment_CVPR_2020_paper.pdf	cvpr-2020-6	['few-shot-action-recognition']	['computer-vision']	[ 9.28792953e-02 -4.65699166e-01 -7.34258711e-01 -6.11182332e-01 -1.06926608e+00 -4.09415424e-01 7.48858392e-01 1.00839622e-01 -6.02272332e-01 4.25343841e-01 2.95927733e-01 8.37686434e-02 -5.65623380e-02 -3.61416250e-01 -8.81764472e-01 -4.89510745e-01 -3.54230553e-01 6.85773790e-02 6.30495310e-01 1.25795558e-01 1.65020034e-01 1.78959951e-01 -1.48018730e+00 5.92226803e-01 2.97273397e-01 1.11704254e+00 8.30432121e-03 7.98601925e-01 1.76178068e-01 1.29734504e+00 -1.86622202e-01 -2.88690090e-01 4.02081937e-01 -6.39796317e-01 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bef442bd-23f7-4328-a379-b1927c3b9b0f	reinforce-attack-adversarial-attack-against	null	null	https://openreview.net/forum?id=TUsLgD-Ohfg	https://openreview.net/pdf?id=TUsLgD-Ohfg	Reinforce Attack: Adversarial Attack against BERT with Reinforcement Learning	Adversarial attacks against textual data has been drawing increasing attention in both the NLP and security domains. Current successful attack methods for text typically consist of two stages: word importance ranking and word replacement. The first stage is usually achieved by masking each word in the sentence one at a time and obtaining the resulting output probability of the target model. The second stage involves finding synonyms to replace “vulnerable” words by the order of ranking. In this paper, we first explore the effects of employing the model explanation tool LIME to generate word importance ranking, which has the advantage of taking the local information around the word into account to obtain word importance scores. We then propose Reinforce Attack, a reinforcement learning (RL) based framework to generate adversarial text. Notably, the attack process is controlled by a reward function rather than heuristics as in previous methods to encourage higher semantic similarity and lower query costs. Through automatic and human evaluations, we show that our LIME + Reinforce Attack method achieves better or comparable attack success rate against other state-of-the-art attack frameworks, while the generated samples preserve significantly higher semantic similarity.	['Anonymous']	2021-08-17	null	null	null	acl-arr-august-2021-8	['adversarial-text']	['adversarial']	[ 5.08234143e-01 1.07348278e-01 -3.41122746e-02 -3.35301533e-02 -1.03461134e+00 -7.96737134e-01 9.77238238e-01 4.50668275e-01 -5.77916682e-01 5.96306324e-01 4.28650528e-01 -4.25751626e-01 1.41366050e-01 -8.91601861e-01 -4.65823591e-01 -4.60288525e-01 9.69952196e-02 3.72407377e-01 3.72461051e-01 -6.11925960e-01 5.54363132e-01 4.91656214e-01 -9.60354388e-01 3.51100862e-01 8.84552956e-01 3.50956440e-01 -9.68897492e-02 7.89287686e-01 -1.54249325e-01 7.10707664e-01 -1.04263794e+00 -9.45678890e-01 3.65417957e-01 -4.86933678e-01 -8.20283473e-01 -4.15279627e-01 1.58289149e-01 -2.46249273e-01 -2.94312954e-01 1.26848507e+00 7.33488381e-01 2.01334342e-01 5.31094372e-01 -1.16320741e+00 -4.80707794e-01 9.80192542e-01 -5.37204683e-01 2.82161236e-01 5.69194078e-01 2.97676504e-01 1.33133447e+00 -8.22040141e-01 3.34101260e-01 1.56634605e+00 3.39260966e-01 8.13820004e-01 -1.19664085e+00 -8.04624021e-01 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3ae5a597-e2c3-426e-90ca-078238eb339e	ladra-net-locally-aware-dynamic-re-read	2108.02915	null	https://arxiv.org/abs/2108.02915v1	https://arxiv.org/pdf/2108.02915v1.pdf	LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matching	Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks, such as Natural Language Inference (NLI), Paraphrase Identification (PI), and so on. Much recent progress has been made in this area, especially attention-based methods and pre-trained language model based methods. However, most of these methods focus on all the important parts in sentences in a static way and only emphasize how important the words are to the query, inhibiting the ability of attention mechanism. In order to overcome this problem and boost the performance of attention mechanism, we propose a novel dynamic re-read attention, which can pay close attention to one small region of sentences at each step and re-read the important parts for better sentence representations. Based on this attention variation, we develop a novel Dynamic Re-read Network (DRr-Net) for sentence semantic matching. Moreover, selecting one small region in dynamic re-read attention seems insufficient for sentence semantics, and employing pre-trained language models as input encoders will introduce incomplete and fragile representation problems. To this end, we extend DRrNet to Locally-Aware Dynamic Re-read Attention Net (LadRa-Net), in which local structure of sentences is employed to alleviate the shortcoming of Byte-Pair Encoding (BPE) in pre-trained language models and boost the performance of dynamic reread attention. Extensive experiments on two popular sentence semantic matching tasks demonstrate that DRr-Net can significantly improve the performance of sentence semantic matching. Meanwhile, LadRa-Net is able to achieve better performance by considering the local structures of sentences. In addition, it is exceedingly interesting that some discoveries in our experiments are consistent with some findings of psychological research.	['Meng Wang', 'Qi Liu', 'Enhong Chen', 'Le Wu', 'Guangyi Lv', 'Kun Zhang']	2021-08-06	null	null	null	null	['paraphrase-identification']	['natural-language-processing']	[ 2.20246658e-01 -8.68040323e-02 -1.99575111e-01 -5.40949166e-01 -3.43683600e-01 -2.24930048e-01 4.36129928e-01 3.64708602e-01 -7.11047173e-01 2.82651693e-01 6.44298613e-01 -2.92749316e-01 4.01552813e-03 -1.08377707e+00 -5.79608917e-01 -2.26241812e-01 6.96898460e-01 3.43048662e-01 3.30725998e-01 -7.73107052e-01 4.55702454e-01 1.82748795e-01 -1.28743231e+00 4.17529643e-01 9.87621307e-01 5.57012439e-01 6.90493405e-01 2.87458360e-01 -5.65565348e-01 7.35456765e-01 -5.47350287e-01 -4.18230176e-01 -2.39666641e-01 -6.57755613e-01 -1.22892070e+00 -4.94747758e-01 1.23775668e-01 -2.49541804e-01 -5.66162705e-01 1.24946606e+00 6.48251653e-01 4.68662739e-01 2.65434057e-01 -7.86350667e-01 -1.17724681e+00 8.66608858e-01 -5.45363367e-01 6.85843349e-01 6.80014193e-01 6.30104095e-02 1.24781668e+00 -7.44253695e-01 1.89716995e-01 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1780f1c0-68db-423f-8e6a-96c62de2c4a6	performance-of-ris-aided-nearfield	2303.15176	null	https://arxiv.org/abs/2303.15176v1	https://arxiv.org/pdf/2303.15176v1.pdf	Performance of RIS-Aided Nearfield Localization under Beams Approximation from Real Hardware Characterization	The technology of reconfigurable intelligent surfaces (RIS) has been showing promising potential in a variety of applications relying on Beyond-5G networks. Reconfigurable intelligent surface (RIS) can indeed provide fine channel flexibility to improve communication quality of service (QoS) or restore localization capabilities in challenging operating conditions, while conventional approaches fail (e.g., due to insufficient infrastructure, severe radio obstructions). In this paper, we tackle a general low-complexity approach for optimizing the precoders that control such reflective surfaces under hardware constraints. More specifically, it allows the approximation of any desired beam pattern using a pre-characterized look-up table of feasible complex reflection coefficients for each RIS element. The proposed method is first evaluated in terms of beam fidelity for several examples of RIS hardware prototypes. Then, by means of a theoretical bounds analysis, we examine the impact of RIS beams approximation on the performance of near-field downlink positioning in non-line-of-sight conditions, while considering several RIS phase profiles (incl. directional, random and localization-optimal designs). Simulation results in a canonical scenario illustrate how the introduced RIS profile optimization scheme can reliably produce the desired RIS beams under realistic hardware limitations. They also highlight its sensitivity to both the underlying hardware characteristics and the required beam kinds in relation to the specificity of RIS-aided localization applications.	['Henk Wymeersch', 'George C. Alexandropoulos', 'Bernard Uguen', 'Musa Furkan Keskin', 'Kamran Keykhosravi', 'Benoit Denis', 'Moustafa Rahal']	2023-03-27	null	null	null	null	['specificity']	['natural-language-processing']	[ 3.93112302e-01 5.13922691e-01 2.76663005e-01 -5.97652048e-02 -5.47377646e-01 -5.88328183e-01 4.85197991e-01 -5.49061857e-02 -1.36987427e-02 6.43899560e-01 -2.76674796e-02 -5.49193978e-01 -9.44196343e-01 -8.14613879e-01 -6.23837352e-01 -1.04698813e+00 -5.03304839e-01 3.26261193e-01 -4.46841307e-02 -5.70066035e-01 3.58284265e-02 9.38816369e-01 -1.63354278e+00 -2.63802826e-01 6.10033691e-01 1.13451254e+00 1.75354674e-01 6.03198409e-01 4.36348528e-01 6.27318993e-02 -7.72019029e-01 -1.25778779e-01 3.05841953e-01 1.29659064e-02 -4.19556126e-02 -3.07661146e-02 1.77327260e-01 -3.98796052e-02 -1.51750922e-01 7.64782965e-01 7.83045948e-01 -2.37037018e-01 4.66361195e-01 -7.12136865e-01 2.18195111e-01 4.64867622e-01 -1.34381756e-01 4.60359380e-02 4.10033792e-01 -2.12375447e-01 6.34777963e-01 -5.08015752e-01 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2be17506-ac4c-4128-9233-081862d8a1c3	cross-modal-multi-task-learning-for-graphic	2003.05787	null	https://arxiv.org/abs/2003.05787v1	https://arxiv.org/pdf/2003.05787v1.pdf	Cross-modal Multi-task Learning for Graphic Recognition of Caricature Face	Face recognition of realistic visual images has been well studied and made a significant progress in the recent decade. Unlike the realistic visual images, the face recognition of the caricatures is far from the performance of the visual images. This is largely due to the extreme non-rigid distortions of the caricatures introduced by exaggerating the facial features to strengthen the characters. The heterogeneous modalities of the caricatures and the visual images result the caricature-visual face recognition is a cross-modal problem. In this paper, we propose a method to conduct caricature-visual face recognition via multi-task learning. Rather than the conventional multi-task learning with fixed weights of tasks, this work proposes an approach to learn the weights of tasks according to the importance of tasks. The proposed multi-task learning with dynamic tasks weights enables to appropriately train the hard task and easy task instead of being stuck in the over-training easy task as conventional methods. The experimental results demonstrate the effectiveness of the proposed dynamic multi-task learning for cross-modal caricature-visual face recognition. The performances on the datasets CaVI and WebCaricature show the superiority over the state-of-art methods.	['Jean-Christophe Burie', 'Zuheng Ming', 'Muhammad Muzzamil Luqman']	2020-03-10	null	null	null	null	['caricature']	['computer-vision']	[-1.30615113e-02 -3.52970660e-01 2.26181239e-01 -4.33861166e-01 -6.26668215e-01 -3.74027938e-01 7.95418262e-01 -7.13667750e-01 -3.06814224e-01 4.43897814e-01 -6.73382580e-02 1.39302894e-01 -3.14259648e-01 -1.23034522e-01 -9.03522432e-01 -1.01090407e+00 2.37336472e-01 5.48814774e-01 8.58751759e-02 -7.16886073e-02 1.76762134e-01 4.90213215e-01 -1.90588582e+00 7.32583284e-01 6.36000097e-01 9.49698627e-01 3.82079720e-01 5.54811716e-01 -2.23354280e-01 4.48047578e-01 -5.76024711e-01 -5.70357502e-01 1.49038881e-01 -3.46229877e-03 -4.47394431e-01 4.56182837e-01 9.63491797e-01 -6.45256713e-02 7.72179142e-02 1.11845005e+00 5.85541368e-01 3.40469815e-02 8.97443295e-01 -1.50583994e+00 -1.01735842e+00 1.46081606e-02 -1.13041437e+00 4.46017645e-02 1.44986868e-01 -1.04568496e-01 3.62011492e-01 -1.32736909e+00 9.62215215e-02 1.90559542e+00 5.97558022e-01 7.46519625e-01 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c4f458f0-2dc8-4d34-ab12-5febb6b11f86	invariant-descriptors-for-intrinsic	2204.04076	null	https://arxiv.org/abs/2204.04076v1	https://arxiv.org/pdf/2204.04076v1.pdf	Invariant Descriptors for Intrinsic Reflectance Optimization	Intrinsic image decomposition aims to factorize an image into albedo (reflectance) and shading (illumination) sub-components. Being ill-posed and under-constrained, it is a very challenging computer vision problem. There are infinite pairs of reflectance and shading images that can reconstruct the same input. To address the problem, Intrinsic Images in the Wild provides an optimization framework based on a dense conditional random field (CRF) formulation that considers long-range material relations. We improve upon their model by introducing illumination invariant image descriptors: color ratios. The color ratios and the reflectance intrinsic are both invariant to illumination and thus are highly correlated. Through detailed experiments, we provide ways to inject the color ratios into the dense CRF optimization. Our approach is physics-based, learning-free and leads to more accurate and robust reflectance decompositions.	['Theo Gevers', 'Anil S. Baslamisli']	2022-04-08	null	null	null	null	['intrinsic-image-decomposition']	['computer-vision']	[ 5.36309123e-01 -3.68942201e-01 2.15000987e-01 -5.90318501e-01 -5.22007108e-01 -4.45562243e-01 6.10618472e-01 -4.47410136e-01 -1.39592692e-01 6.32107377e-01 5.74615300e-02 5.35593219e-02 -3.55950855e-02 -9.03691590e-01 -5.28600097e-01 -1.04501438e+00 5.31737804e-01 5.56462467e-01 -8.88019651e-02 1.09363813e-02 5.64282313e-02 7.46212423e-01 -1.65876162e+00 -5.96243031e-02 8.59431446e-01 7.59955525e-01 3.26751202e-01 8.17457259e-01 -2.14108616e-01 7.28194833e-01 -4.23905514e-02 -2.11718580e-05 4.97262746e-01 -3.00557703e-01 -8.30975056e-01 4.57562089e-01 7.88750172e-01 -3.36115241e-01 -1.51658043e-01 1.10333753e+00 3.39573137e-02 3.31377953e-01 7.36464322e-01 -9.24901485e-01 -7.51171052e-01 -2.33292267e-01 -6.53335452e-01 -4.63156909e-01 4.40806389e-01 3.96921486e-02 1.01360941e+00 -8.74315441e-01 4.71594483e-01 1.19711602e+00 5.27164161e-01 3.46099079e-01 -1.58478224e+00 -7.93167055e-02 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4.92858827e-01 6.28623605e-01 -7.67073452e-01 -7.41881788e-01 3.43008429e-01]	[9.959148406982422, -2.9838624000549316]
840b6124-240d-4975-8ed8-b253cbb961dc	it-s-a-long-way-layer-wise-relevance	2210.09958	null	https://arxiv.org/abs/2210.09958v2	https://arxiv.org/pdf/2210.09958v2.pdf	Layer-wise Relevance Propagation for Echo State Networks applied to Earth System Variability	Artificial neural networks (ANNs) are known to be powerful methods for many hard problems (e.g. image classification, speech recognition or time series prediction). However, these models tend to produce black-box results and are often difficult to interpret. Layer-wise relevance propagation (LRP) is a widely used technique to understand how ANN models come to their conclusion and to understand what a model has learned. Here, we focus on Echo State Networks (ESNs) as a certain type of recurrent neural networks, also known as reservoir computing. ESNs are easy to train and only require a small number of trainable parameters, but are still black-box models. We show how LRP can be applied to ESNs in order to open the black-box. We also show how ESNs can be used not only for time series prediction but also for image classification: Our ESN model serves as a detector for El Nino Southern Oscillation (ENSO) from sea surface temperature anomalies. ENSO is actually a well-known problem and has been extensively discussed before. But here we use this simple problem to demonstrate how LRP can significantly enhance the explainablility of ESNs.	['Willi Rath', 'Martin Claus', 'Peer Kröger', 'Marco Landt-Hayen']	2022-10-18	null	null	null	null	['time-series-prediction']	['time-series']	[ 2.11676702e-01 -1.36419177e-01 3.08965817e-02 -2.06893325e-01 1.63018882e-01 -3.10558915e-01 8.90946448e-01 -4.97803017e-02 -2.45203540e-01 7.02903450e-01 -2.07191721e-01 -7.55012214e-01 4.54953946e-02 -8.17216575e-01 -8.07102621e-01 -9.30642664e-01 -5.49730599e-01 1.89059630e-01 2.84067184e-01 -5.86078227e-01 1.49926051e-01 7.58702695e-01 -1.64731836e+00 -7.76138306e-02 7.57675886e-01 9.48559821e-01 2.13314429e-01 7.70203590e-01 -2.39363909e-01 1.01473486e+00 -6.05279386e-01 1.67454630e-01 -1.06912717e-01 -5.53498209e-01 -5.79890847e-01 -5.27098775e-01 -2.80205637e-01 -2.72126850e-02 -3.06594670e-01 1.08534181e+00 2.26774797e-01 9.51709598e-02 6.52474344e-01 -1.26256704e+00 -3.19075048e-01 6.96054757e-01 -7.48256519e-02 3.75408947e-01 -3.86859238e-01 9.02410299e-02 5.53501010e-01 -7.55178988e-01 3.40916961e-01 1.14722824e+00 7.42284417e-01 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7b3d34cc-26e8-47b8-b59e-2e65936153e8	self-consistency-improves-chain-of-thought	2203.11171	null	https://arxiv.org/abs/2203.11171v4	https://arxiv.org/pdf/2203.11171v4.pdf	Self-Consistency Improves Chain of Thought Reasoning in Language Models	Chain-of-thought prompting combined with pre-trained large language models has achieved encouraging results on complex reasoning tasks. In this paper, we propose a new decoding strategy, self-consistency, to replace the naive greedy decoding used in chain-of-thought prompting. It first samples a diverse set of reasoning paths instead of only taking the greedy one, and then selects the most consistent answer by marginalizing out the sampled reasoning paths. Self-consistency leverages the intuition that a complex reasoning problem typically admits multiple different ways of thinking leading to its unique correct answer. Our extensive empirical evaluation shows that self-consistency boosts the performance of chain-of-thought prompting with a striking margin on a range of popular arithmetic and commonsense reasoning benchmarks, including GSM8K (+17.9%), SVAMP (+11.0%), AQuA (+12.2%), StrategyQA (+6.4%) and ARC-challenge (+3.9%).	['Denny Zhou', 'Aakanksha Chowdhery', 'Sharan Narang', 'Ed Chi', 'Quoc Le', 'Dale Schuurmans', 'Jason Wei', 'Xuezhi Wang']	2022-03-21	null	null	null	null	['gsm8k', 'arithmetic-reasoning', 'strategyqa']	['natural-language-processing', 'reasoning', 'reasoning']	[-2.62755509e-02 2.88196832e-01 -3.13267782e-02 -5.96984029e-01 -1.20866597e+00 -6.99444294e-01 7.60087371e-01 3.11792195e-01 -3.54495227e-01 5.28605282e-01 6.91112399e-01 -7.57952809e-01 -5.43999858e-02 -7.66502440e-01 -5.98947942e-01 -2.54655600e-01 1.37541905e-01 8.30405235e-01 5.75021990e-02 -4.47683871e-01 7.57206917e-01 -1.96848214e-01 -1.00523698e+00 7.64191508e-01 1.02549911e+00 4.41358984e-01 -7.11144730e-02 9.34732974e-01 -5.16871631e-01 1.76750481e+00 -4.45653290e-01 -9.06553268e-01 1.76657736e-01 -4.41765338e-01 -1.36758852e+00 -4.61604416e-01 6.03952050e-01 -5.22758842e-01 -1.94253609e-01 1.09608579e+00 2.96610296e-01 2.70754218e-01 5.32213807e-01 -9.64254379e-01 -7.15044618e-01 1.30839527e+00 -3.21685821e-01 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1.71918646e-01 1.67002633e-01 1.34865558e+00 3.74729365e-01 6.11933589e-01 -2.35002816e-01 -9.40359086e-02 -1.24060142e+00 -6.94106400e-01 -8.11863467e-02 1.47194266e-01 8.29862505e-02 -1.60299659e-01 -4.13179934e-01 -2.00137183e-01]	[9.716938972473145, 7.458249092102051]
c8509112-ea60-4759-95fd-28e867269e9d	contragen-effective-contrastive-learning-for	2210.01185	null	https://arxiv.org/abs/2210.01185v2	https://arxiv.org/pdf/2210.01185v2.pdf	ContraCLM: Contrastive Learning For Causal Language Model	Despite exciting progress in causal language models, the expressiveness of the representations is largely limited due to poor discrimination ability. To remedy this issue, we present ContraCLM, a novel contrastive learning framework at both token-level and sequence-level. We assess ContraCLM on a variety of downstream tasks. We show that ContraCLM enhances discrimination of the representations and bridges the gap with the encoder-only models, which makes causal language models better suited for tasks beyond language generation. Specifically, we attain $44\%$ relative improvement on the Semantic Textual Similarity tasks and $34\%$ on Code-to-Code Search tasks. Furthermore, by improving the expressiveness of the representations, ContraCLM also boosts the source code generation capability with $9\%$ relative improvement on execution accuracy on the HumanEval benchmark.	['Bing Xiang', 'Xiaofei Ma', 'Parminder Bhatia', 'Baishakhi Ray', 'Ramesh Nallapati', 'Ming Tan', 'Xiaopeng Li', 'Feng Nan', 'Zijian Wang', 'Wasi Uddin Ahmad', 'Dejiao Zhang', 'Nihal Jain']	2022-10-03	null	null	null	null	['code-search', 'code-search']	['computer-code', 'computer-vision']	[ 3.27956915e-01 2.94652253e-01 -6.38834536e-01 -2.37193212e-01 -1.07594919e+00 -4.98538584e-01 8.64754617e-01 3.93694013e-01 -1.47591516e-01 6.70843959e-01 6.04638636e-01 -5.79303205e-01 -8.97724032e-02 -9.01283324e-01 -7.61778772e-01 -1.22801818e-01 -7.40670785e-02 2.03560069e-02 -1.48339840e-02 -1.89281583e-01 4.65697110e-01 -5.30494638e-02 -1.55109084e+00 6.73609853e-01 9.66490984e-01 7.80150473e-01 1.00272492e-01 4.02154982e-01 -4.41283941e-01 1.33795536e+00 -3.58333260e-01 -5.16789377e-01 -1.13876596e-01 -6.52425289e-01 -9.45112765e-01 -6.18909895e-01 3.18047255e-01 -1.32224768e-01 -3.22735399e-01 1.00217283e+00 2.04184204e-01 -8.16094428e-02 7.79304802e-01 -1.13523412e+00 -9.26330090e-01 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db325f8c-632d-4fb7-8eab-69beeae17945	adversarial-self-attack-defense-and-spatial	2307.03903	null	https://arxiv.org/abs/2307.03903v1	https://arxiv.org/pdf/2307.03903v1.pdf	Adversarial Self-Attack Defense and Spatial-Temporal Relation Mining for Visible-Infrared Video Person Re-Identification	In visible-infrared video person re-identification (re-ID), extracting features not affected by complex scenes (such as modality, camera views, pedestrian pose, background, etc.) changes, and mining and utilizing motion information are the keys to solving cross-modal pedestrian identity matching. To this end, the paper proposes a new visible-infrared video person re-ID method from a novel perspective, i.e., adversarial self-attack defense and spatial-temporal relation mining. In this work, the changes of views, posture, background and modal discrepancy are considered as the main factors that cause the perturbations of person identity features. Such interference information contained in the training samples is used as an adversarial perturbation. It performs adversarial attacks on the re-ID model during the training to make the model more robust to these unfavorable factors. The attack from the adversarial perturbation is introduced by activating the interference information contained in the input samples without generating adversarial samples, and it can be thus called adversarial self-attack. This design allows adversarial attack and defense to be integrated into one framework. This paper further proposes a spatial-temporal information-guided feature representation network to use the information in video sequences. The network cannot only extract the information contained in the video-frame sequences but also use the relation of the local information in space to guide the network to extract more robust features. The proposed method exhibits compelling performance on large-scale cross-modality video datasets. The source code of the proposed method will be released at https://github.com/lhf12278/xxx.	['Zhengtao Yu', 'Dapeng Tao', 'Yafei Zhang', 'Le Xu', 'Huafeng Li']	2023-07-08	null	null	null	null	['adversarial-attack', 'person-re-identification', 'video-based-person-re-identification']	['adversarial', 'computer-vision', 'computer-vision']	[ 1.53106660e-01 -5.68946183e-01 -1.04450628e-01 -2.24538475e-01 -2.75430143e-01 -7.40535557e-01 4.90213990e-01 -5.57536721e-01 -2.99689054e-01 3.78600568e-01 5.29413998e-01 1.74768135e-01 -7.46258274e-02 -6.21711075e-01 -5.55393457e-01 -8.01242471e-01 9.08714160e-02 -2.70522565e-01 -4.78725135e-02 -4.69521344e-01 1.52477622e-01 5.79128146e-01 -1.68305480e+00 4.68748122e-01 5.69281936e-01 6.76171541e-01 -2.18432620e-01 5.46230912e-01 1.11892886e-01 5.96279800e-01 -5.89217603e-01 -2.42523238e-01 5.50577641e-01 -4.52453703e-01 -3.59591782e-01 -9.94550884e-02 3.44114810e-01 -5.21580756e-01 -8.23120654e-01 1.12670088e+00 7.92329550e-01 3.46027285e-01 4.57268715e-01 -1.45638168e+00 -6.07374668e-01 3.91622297e-02 -5.20937502e-01 5.28702557e-01 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c7a5883f-1950-4779-83a3-41f7cc4ac15c	btech-thesis-report-on-adversarial-attack	2205.07859	null	https://arxiv.org/abs/2205.07859v1	https://arxiv.org/pdf/2205.07859v1.pdf	Btech thesis report on adversarial attack detection and purification of adverserially attacked images	This is Btech thesis report on detection and purification of adverserially attacked images. A deep learning model is trained on certain training examples for various tasks such as classification, regression etc. By training, weights are adjusted such that the model performs the task well not only on training examples judged by a certain metric but has an excellent ability to generalize on other unseen examples as well which are typically called the test data. Despite the huge success of machine learning models on a wide range of tasks, security has received a lot less attention along the years. Robustness along various potential cyber attacks also should be a metric for the accuracy of the machine learning models. These cyber attacks can potentially lead to a variety of negative impacts in the real world sensitive applications for which machine learning is used such as medical and transportation systems. Hence, it is a necessity to secure the system from such attacks. Int this report, I focus on a class of these cyber attacks called the adversarial attacks in which the original input sample is modified by small perturbations such that they still look visually the same to human beings but the machine learning models are fooled by such inputs. In this report I discuss 2 novel ways to counter the adversarial attack using AutoEncoders, 1) by detecting the presence of adversaries and 2) purifying these adversaries to make target classification models robust against such attacks.	['Dvij Kalaria']	2022-05-09	null	null	null	null	['adversarial-attack-detection', 'adversarial-attack-detection']	['computer-vision', 'knowledge-base']	[ 4.56934094e-01 1.40153423e-01 2.33417988e-01 -1.29973993e-01 -1.14266284e-01 -8.85675490e-01 8.85072231e-01 2.65689231e-02 -3.02087754e-01 7.24242687e-01 -2.49887690e-01 -3.77186537e-01 2.43608832e-01 -1.02036142e+00 -8.50720167e-01 -9.41754878e-01 -3.81218866e-02 1.10531626e-02 3.42961699e-01 -2.58667380e-01 2.91454643e-01 1.07027912e+00 -1.34493423e+00 3.13732445e-01 3.24063390e-01 8.75901461e-01 -5.12601495e-01 1.03086698e+00 3.84392351e-01 6.59256220e-01 -1.04165328e+00 -6.00041270e-01 6.29910886e-01 -3.07540059e-01 -6.48333907e-01 -3.67633626e-02 1.25252798e-01 -1.29647717e-01 -5.18879294e-01 1.52554524e+00 3.69970202e-01 -7.92778563e-03 7.10392475e-01 -1.48126888e+00 -4.99207437e-01 1.80835694e-01 -2.97940969e-01 1.96747407e-01 2.38175020e-01 4.72378939e-01 2.87979573e-01 -5.09457469e-01 2.11862206e-01 1.14192808e+00 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-8.02374899e-01 -7.85538495e-01 -4.36603725e-01 7.10507274e-01 5.02269030e-01 -5.87974966e-01 -6.75996423e-01 9.44503322e-02]	[5.554721355438232, 7.763762474060059]
be700b38-698d-4723-b2eb-33bcf46c8d88	problematic-cases-in-the-annotation-of	null	null	https://aclanthology.org/W16-5006	https://aclanthology.org/W16-5006.pdf	Problematic Cases in the Annotation of Negation in Spanish	This paper presents the main sources of disagreement found during the annotation of the Spanish SFU Review Corpus with negation (SFU ReviewSP -NEG). Negation detection is a challenge in most of the task related to NLP, so the availability of corpora annotated with this phenomenon is essential in order to advance in tasks related to this area. A thorough analysis of the problems found during the annotation could help in the study of this phenomenon.	["Mariona Taul{\\'e}", "Toni Mart{\\'\\i}", "L. Alfonso Ure{\\~n}a-L{\\'o}pez", "Salud Mar{\\'\\i}a Jim{\\'e}nez-Zafra", 'Maite Martin']	2016-12-01	null	null	null	ws-2016-12	['negation-detection']	['natural-language-processing']	[ 2.37009719e-01 5.49060822e-01 -2.50168324e-01 -4.69842225e-01 -5.60630262e-01 -7.97503412e-01 6.08135641e-01 8.80890071e-01 -5.42582452e-01 1.36654794e+00 3.47084105e-01 -3.79335880e-01 1.51945725e-01 -4.02948111e-01 -5.61751306e-01 -4.86334532e-01 4.49280083e-01 5.22605538e-01 4.45068955e-01 -7.56267011e-01 6.91329062e-01 1.76208854e-01 -1.19246781e+00 6.43841147e-01 8.90410781e-01 4.60354716e-01 3.67408633e-01 1.48069680e-01 -3.45666468e-01 7.43341982e-01 -8.43247831e-01 -7.15596139e-01 -1.01261981e-01 -5.79477668e-01 -1.04932702e+00 -2.78790385e-01 4.88470048e-02 4.16489005e-01 3.89204890e-01 1.48621833e+00 2.86243320e-01 7.79330879e-02 6.99069798e-01 -8.45779836e-01 2.56054014e-01 7.23104239e-01 -3.02123100e-01 6.83539033e-01 7.38552094e-01 -4.42057788e-01 1.17524278e+00 -9.11092222e-01 1.27785122e+00 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bba86fb8-954a-4e6e-b3bd-5e859518cc3f	less-is-more-simplifying-feature-extractors	2210.09537	null	https://arxiv.org/abs/2210.09537v1	https://arxiv.org/pdf/2210.09537v1.pdf	Less is More: Simplifying Feature Extractors Prevents Overfitting for Neural Discourse Parsing Models	Complex feature extractors are widely employed for text representation building. However, these complex feature extractors can lead to severe overfitting problems especially when the training datasets are small, which is especially the case for several discourse parsing tasks. Thus, we propose to remove additional feature extractors and only utilize self-attention mechanism to exploit pretrained neural language models in order to mitigate the overfitting problem. Experiments on three common discourse parsing tasks (News Discourse Profiling, Rhetorical Structure Theory based Discourse Parsing and Penn Discourse Treebank based Discourse Parsing) show that powered by recent pretrained language models, our simplied feature extractors obtain better generalizabilities and meanwhile achieve comparable or even better system performance. The simplified feature extractors have fewer learnable parameters and less processing time. Codes will be released and this simple yet effective model can serve as a better baseline for future research.	['Ruihong Huang', 'Sijing Yu', 'Ming Li']	2022-10-18	null	null	null	null	['discourse-parsing']	['natural-language-processing']	[ 3.33621711e-01 6.36682391e-01 -3.31674784e-01 -5.15024662e-01 -9.10960257e-01 -5.17323554e-01 8.68518710e-01 1.23139411e-01 -6.17864668e-01 7.91171014e-01 7.44993448e-01 -5.27857184e-01 1.93137527e-01 -6.44263506e-01 -4.04220670e-01 -4.51614559e-01 2.09339097e-01 1.37582690e-01 3.93615663e-02 -4.30610031e-01 4.42445427e-01 -3.49417366e-02 -1.25660729e+00 5.04418433e-01 1.05396652e+00 6.50460005e-01 4.35240597e-01 6.82428718e-01 -4.79300916e-01 1.31107461e+00 -1.02255094e+00 -4.97936606e-01 -1.95863038e-01 -3.28170180e-01 -1.01458967e+00 1.08425308e-03 1.34407908e-01 -5.53331017e-01 -3.73012066e-01 8.93249691e-01 4.48828876e-01 2.91179866e-01 5.17845690e-01 -5.16073644e-01 -8.02715242e-01 1.21395957e+00 -3.44778955e-01 3.54356468e-01 2.93488920e-01 -6.44913912e-02 1.21800244e+00 -7.35985219e-01 6.67301595e-01 1.56226063e+00 4.47066545e-01 5.76834559e-01 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-1.52659452e+00 1.10195160e-01 3.81875277e-01 3.48084211e-01 -7.17594981e-01 6.47242516e-02 4.92480919e-02]	[10.836615562438965, 9.3319673538208]
db7df910-5023-4bdc-b93e-0890c2a5ac3c	pit30m-a-benchmark-for-global-localization-in	2012.12437	null	https://arxiv.org/abs/2012.12437v1	https://arxiv.org/pdf/2012.12437v1.pdf	Pit30M: A Benchmark for Global Localization in the Age of Self-Driving Cars	We are interested in understanding whether retrieval-based localization approaches are good enough in the context of self-driving vehicles. Towards this goal, we introduce Pit30M, a new image and LiDAR dataset with over 30 million frames, which is 10 to 100 times larger than those used in previous work. Pit30M is captured under diverse conditions (i.e., season, weather, time of the day, traffic), and provides accurate localization ground truth. We also automatically annotate our dataset with historical weather and astronomical data, as well as with image and LiDAR semantic segmentation as a proxy measure for occlusion. We benchmark multiple existing methods for image and LiDAR retrieval and, in the process, introduce a simple, yet effective convolutional network-based LiDAR retrieval method that is competitive with the state of the art. Our work provides, for the first time, a benchmark for sub-metre retrieval-based localization at city scale. The dataset, additional experimental results, as well as more information about the sensors, calibration, and metadata, are available on the project website: https://uber.com/atg/datasets/pit30m	['Raquel Urtasun', 'Gellért Máttyus', 'Shenlong Wang', 'Ioan Andrei Bârsan', 'Jack Fan', 'Sasha Doubov', 'Julieta Martinez']	2020-12-23	null	null	null	null	['lidar-semantic-segmentation']	['computer-vision']	[-2.47496173e-01 -6.80212200e-01 -5.00246406e-01 -6.33687377e-01 -1.19666195e+00 -8.04247797e-01 6.90119267e-01 2.33930185e-01 -5.04064262e-01 6.20812356e-01 -7.46246949e-02 -2.09245086e-01 -7.24841878e-02 -1.09901595e+00 -8.32587004e-01 -3.45838696e-01 4.39137816e-02 8.17829430e-01 4.68976498e-01 -2.00496003e-01 1.84438631e-01 7.59862542e-01 -1.83654213e+00 -2.52224356e-01 7.11203516e-01 1.22789729e+00 2.49917552e-01 4.36407864e-01 -1.30602524e-01 3.64324093e-01 -3.00772220e-01 -3.46888274e-01 2.73148805e-01 2.96727240e-01 -6.09372497e-01 -1.01429246e-01 1.16999972e+00 -3.27603906e-01 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edcdd8b8-8cdc-42c3-9eb6-a3d099427d7f	syntax-aware-opinion-role-labeling-with	null	null	https://aclanthology.org/2020.acl-main.297	https://aclanthology.org/2020.acl-main.297.pdf	Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks	Opinion role labeling (ORL) is a fine-grained opinion analysis task and aims to answer {``}who expressed what kind of sentiment towards what?{''}. Due to the scarcity of labeled data, ORL remains challenging for data-driven methods. In this work, we try to enhance neural ORL models with syntactic knowledge by comparing and integrating different representations. We also propose dependency graph convolutional networks (DEPGCN) to encode parser information at different processing levels. In order to compensate for parser inaccuracy and reduce error propagation, we introduce multi-task learning (MTL) to train the parser and the ORL model simultaneously. We verify our methods on the benchmark MPQA corpus. The experimental results show that syntactic information is highly valuable for ORL, and our final MTL model effectively boosts the F1 score by 9.29 over the syntax-agnostic baseline. In addition, we find that the contributions from syntactic knowledge do not fully overlap with contextualized word representations (BERT). Our best model achieves 4.34 higher F1 score than the current state-ofthe-art.	['Yue Zhang', 'Rui Wang', 'Zhenghua Li', 'Min Zhang', 'Bo Zhang']	2020-07-01	null	null	null	acl-2020-6	['fine-grained-opinion-analysis']	['natural-language-processing']	[ 7.05914795e-02 3.04031998e-01 -8.39341432e-02 -7.60173023e-01 -1.04332459e+00 -8.44471335e-01 2.17095256e-01 3.82283539e-01 -4.38383341e-01 6.02661848e-01 6.89316690e-01 -5.23050725e-01 3.72600496e-01 -7.46695399e-01 -6.67166352e-01 -4.05816823e-01 5.03713727e-01 2.83863187e-01 1.59998804e-01 -5.68398774e-01 1.82845205e-01 9.78558064e-02 -1.18581700e+00 9.69006896e-01 1.03256929e+00 1.01679838e+00 -1.28348753e-01 4.89995986e-01 -6.37664557e-01 1.30590451e+00 -7.45885849e-01 -8.34382355e-01 -1.88881069e-01 -4.00816053e-01 -9.85475957e-01 -3.08221608e-01 3.84385288e-01 -1.66882440e-01 1.10101394e-01 1.01340675e+00 5.30948341e-01 -2.38958150e-02 4.12108749e-01 -9.29597199e-01 -1.11965764e+00 8.53712201e-01 -4.87928510e-01 1.80108488e-01 2.61032224e-01 -2.54713297e-01 1.50179231e+00 -9.64183807e-01 5.82117200e-01 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-3.12578738e-01 -1.98950633e-01 -8.88064027e-01 -2.15945244e-01 4.18387771e-01 1.62552923e-01 -3.35053951e-01 -3.24316323e-01 -1.64694920e-01]	[11.408956527709961, 6.8172607421875]
b293f7b9-c7ee-43f5-af95-53a8b5a1d4a2	full-gradient-deep-reinforcement-learning-for	2304.03729	null	https://arxiv.org/abs/2304.03729v1	https://arxiv.org/pdf/2304.03729v1.pdf	Full Gradient Deep Reinforcement Learning for Average-Reward Criterion	We extend the provably convergent Full Gradient DQN algorithm for discounted reward Markov decision processes from Avrachenkov et al. (2021) to average reward problems. We experimentally compare widely used RVI Q-Learning with recently proposed Differential Q-Learning in the neural function approximation setting with Full Gradient DQN and DQN. We also extend this to learn Whittle indices for Markovian restless multi-armed bandits. We observe a better convergence rate of the proposed Full Gradient variant across different tasks.	['Konstantin Avrachenkov', 'Vivek Borkar', 'Tejas Pagare']	2023-04-07	null	null	null	null	['q-learning', 'multi-armed-bandits']	['methodology', 'miscellaneous']	[-2.93610275e-01 5.33195473e-02 -7.40846753e-01 -3.01154554e-01 -1.15966547e+00 -6.74226224e-01 4.13571358e-01 -3.06158990e-01 -9.44672227e-01 1.57880008e+00 -1.46556059e-02 -8.17608178e-01 -4.84802395e-01 -4.61787373e-01 -8.09160233e-01 -7.72817910e-01 -1.03169136e-01 8.20972443e-01 2.70013697e-02 -8.73801038e-02 3.10548782e-01 4.84131873e-01 -1.04128790e+00 -1.77038372e-01 8.83489251e-01 1.23222053e+00 -9.35052335e-02 1.13628578e+00 8.67300183e-02 1.23754203e+00 -3.80300671e-01 -6.31977975e-01 5.58791816e-01 -4.17992145e-01 -9.30562079e-01 -3.15437943e-01 3.63201499e-01 -8.77048612e-01 -5.10046363e-01 1.04963100e+00 6.41831100e-01 5.81171334e-01 9.17125702e-01 -1.23167777e+00 -7.97363877e-01 7.38423109e-01 -6.13840163e-01 6.98939860e-01 -9.32528079e-02 2.58022249e-01 1.49746919e+00 -4.47361499e-01 3.85128140e-01 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c50f4c9f-68f9-4938-8a56-206c69b9d272	flow-fields-dense-correspondence-fields-for	1703.02563	null	http://arxiv.org/abs/1703.02563v2	http://arxiv.org/pdf/1703.02563v2.pdf	Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation	Modern large displacement optical flow algorithms usually use an initialization by either sparse descriptor matching techniques or dense approximate nearest neighbor fields. While the latter have the advantage of being dense, they have the major disadvantage of being very outlier-prone as they are not designed to find the optical flow, but the visually most similar correspondence. In this article we present a dense correspondence field approach that is much less outlier-prone and thus much better suited for optical flow estimation than approximate nearest neighbor fields. Our approach does not require explicit regularization, smoothing (like median filtering) or a new data term. Instead we solely rely on patch matching techniques and a novel multi-scale matching strategy. We also present enhancements for outlier filtering. We show that our approach is better suited for large displacement optical flow estimation than modern descriptor matching techniques. We do so by initializing EpicFlow with our approach instead of their originally used state-of-the-art descriptor matching technique. We significantly outperform the original EpicFlow on MPI-Sintel, KITTI 2012, KITTI 2015 and Middlebury. In this extended article of our former conference publication we further improve our approach in matching accuracy as well as runtime and present more experiments and insights.	['Didier Stricker', 'Christian Bailer', 'Bertram Taetz']	2017-03-07	null	null	null	iccv-2015	['patch-matching']	['computer-vision']	[-3.64518672e-01 -6.98390424e-01 2.08447799e-01 1.10740632e-01 -3.42866331e-01 -7.32824028e-01 5.85597038e-01 3.58568072e-01 -4.86927927e-01 6.93792224e-01 2.02262178e-01 -7.48560503e-02 -2.21616477e-01 -7.83813119e-01 -4.61956114e-01 -3.30587685e-01 -4.61732686e-01 4.79885846e-01 7.16198802e-01 -3.05935979e-01 8.35560620e-01 8.66984963e-01 -1.63615096e+00 -7.06492290e-02 7.41788566e-01 8.85300398e-01 -1.89328372e-01 9.95980680e-01 -5.50309420e-02 8.80898476e-01 -2.70735532e-01 -2.03107283e-01 6.93908393e-01 -5.06474137e-01 -1.00451434e+00 -1.27133697e-01 1.44561613e+00 -3.94371659e-01 -4.95962441e-01 7.79741108e-01 7.23750174e-01 6.32838547e-01 3.33608210e-01 -1.30808568e+00 -2.19937474e-01 -7.08173364e-02 -5.47094047e-01 5.22776842e-01 6.21910870e-01 1.60775527e-01 7.57413208e-01 -9.11360860e-01 1.13069606e+00 1.19079971e+00 1.17507708e+00 1.58248514e-01 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a359e91c-0866-4de9-ad3f-b51df0eb847d	fuzzy-conditioned-diffusion-and-diffusion	2306.14891	null	https://arxiv.org/abs/2306.14891v2	https://arxiv.org/pdf/2306.14891v2.pdf	Fuzzy-Conditioned Diffusion and Diffusion Projection Attention Applied to Facial Image Correction	Image diffusion has recently shown remarkable performance in image synthesis and implicitly as an image prior. Such a prior has been used with conditioning to solve the inpainting problem, but only supporting binary user-based conditioning. We derive a fuzzy-conditioned diffusion, where implicit diffusion priors can be exploited with controllable strength. Our fuzzy conditioning can be applied pixel-wise, enabling the modification of different image components to varying degrees. Additionally, we propose an application to facial image correction, where we combine our fuzzy-conditioned diffusion with diffusion-derived attention maps. Our map estimates the degree of anomaly, and we obtain it by projecting on the diffusion space. We show how our approach also leads to interpretable and autonomous facial image correction.	['Majed El Helou']	2023-06-26	null	null	null	null	['image-generation']	['computer-vision']	[ 7.89493322e-01 5.16969979e-01 -1.04825944e-01 -4.25860167e-01 -5.02328694e-01 -3.88981879e-01 6.56708598e-01 -3.99608552e-01 -3.69557649e-01 5.52691519e-01 1.21379092e-01 2.38581508e-01 -8.59066248e-02 -6.63353205e-01 -8.01652491e-01 -7.67982244e-01 3.65890622e-01 3.48735005e-01 7.38826096e-02 -4.19275433e-01 3.25486183e-01 6.80702388e-01 -1.36494100e+00 4.89170372e-01 1.21571958e+00 8.48131359e-01 2.97688246e-01 7.31700063e-01 1.55831069e-01 8.68204594e-01 -5.29236138e-01 -6.26441360e-01 2.73872077e-01 -5.35137177e-01 -5.69056213e-01 5.58445871e-01 7.54628062e-01 -3.86604875e-01 -3.23547691e-01 1.47890055e+00 3.16523939e-01 -9.27803759e-03 9.65574265e-01 -1.16889811e+00 -1.29910052e+00 3.47554862e-01 -7.21374691e-01 7.32978582e-02 1.85239762e-01 1.02757022e-01 6.48618519e-01 -1.01573491e+00 8.39918256e-01 1.35228360e+00 4.31070954e-01 1.11991274e+00 -1.61168766e+00 -4.87565875e-01 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5.84442556e-01 5.30868828e-01 -6.48121059e-01 -4.25759584e-01 4.33503866e-01]	[11.504144668579102, -0.4895149767398834]
14c9a3c0-c7ca-44b9-8f5a-314a7b2b507d	adaptive-sampling-for-fast-constrained	2102.06486	null	https://arxiv.org/abs/2102.06486v1	https://arxiv.org/pdf/2102.06486v1.pdf	Adaptive Sampling for Fast Constrained Maximization of Submodular Function	Several large-scale machine learning tasks, such as data summarization, can be approached by maximizing functions that satisfy submodularity. These optimization problems often involve complex side constraints, imposed by the underlying application. In this paper, we develop an algorithm with poly-logarithmic adaptivity for non-monotone submodular maximization under general side constraints. The adaptive complexity of a problem is the minimal number of sequential rounds required to achieve the objective. Our algorithm is suitable to maximize a non-monotone submodular function under a $p$-system side constraint, and it achieves a $(p + O(\sqrt{p}))$-approximation for this problem, after only poly-logarithmic adaptive rounds and polynomial queries to the valuation oracle function. Furthermore, our algorithm achieves a $(p + O(1))$-approximation when the given side constraint is a $p$-extendible system. This algorithm yields an exponential speed-up, with respect to the adaptivity, over any other known constant-factor approximation algorithm for this problem. It also competes with previous known results in terms of the query complexity. We perform various experiments on various real-world applications. We find that, in comparison with commonly used heuristics, our algorithm performs better on these instances.	['Tobias Friedrich', 'Andreas Göbel', 'Vanja Doskoč', 'Francesco Quinzan']	2021-02-12	null	null	null	null	['data-summarization']	['miscellaneous']	[ 2.45144203e-01 3.78304720e-01 -6.53521717e-01 -3.27651918e-01 -1.25656152e+00 -9.56995308e-01 -2.25025520e-01 4.03699219e-01 -6.47718191e-01 8.91401529e-01 -8.49203169e-02 -3.31117123e-01 -3.91742021e-01 -1.03631592e+00 -1.12910068e+00 -8.28608632e-01 -3.78178239e-01 1.07387185e+00 1.38835415e-01 -2.89230198e-01 4.63698238e-01 3.58172774e-01 -1.18734014e+00 1.33169025e-01 7.55425513e-01 1.27540874e+00 -8.20845645e-03 8.31505835e-01 4.92947400e-02 5.07123053e-01 -3.35740447e-01 -2.97773182e-01 7.95599103e-01 -2.55527705e-01 -1.18822920e+00 3.96747977e-01 5.92198014e-01 -1.42453358e-01 -3.45499665e-01 1.12414372e+00 2.87075073e-01 2.26822346e-02 2.36258358e-01 -1.34303534e+00 -1.05515905e-01 9.09165382e-01 -8.89448702e-01 1.83144927e-01 2.71436781e-01 -1.33033723e-01 1.61900902e+00 -5.84588230e-01 6.67439401e-01 1.15812707e+00 2.46562526e-01 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293d6fcc-00fc-4cb0-a032-6f20be969ffe	background-subtraction-via-generalized-fused	1504.03707	null	http://arxiv.org/abs/1504.03707v1	http://arxiv.org/pdf/1504.03707v1.pdf	Background Subtraction via Generalized Fused Lasso Foreground Modeling	Background Subtraction (BS) is one of the key steps in video analysis. Many background models have been proposed and achieved promising performance on public data sets. However, due to challenges such as illumination change, dynamic background etc. the resulted foreground segmentation often consists of holes as well as background noise. In this regard, we consider generalized fused lasso regularization to quest for intact structured foregrounds. Together with certain assumptions about the background, such as the low-rank assumption or the sparse-composition assumption (depending on whether pure background frames are provided), we formulate BS as a matrix decomposition problem using regularization terms for both the foreground and background matrices. Moreover, under the proposed formulation, the two generally distinctive background assumptions can be solved in a unified manner. The optimization was carried out via applying the augmented Lagrange multiplier (ALM) method in such a way that a fast parametric-flow algorithm is used for updating the foreground matrix. Experimental results on several popular BS data sets demonstrate the advantage of the proposed model compared to state-of-the-arts.	['Yuan Tian', 'Wen Gao', 'Bo Xin', 'Yizhou Wang']	2015-04-14	background-subtraction-via-generalized-fused-1	http://openaccess.thecvf.com/content_cvpr_2015/html/Xin_Background_Subtraction_via_2015_CVPR_paper.html	http://openaccess.thecvf.com/content_cvpr_2015/papers/Xin_Background_Subtraction_via_2015_CVPR_paper.pdf	cvpr-2015-6	['foreground-segmentation']	['computer-vision']	[ 5.48733771e-01 -4.11971092e-01 1.65048033e-01 -6.35658130e-02 -5.30204535e-01 -3.48032653e-01 3.97409886e-01 -2.01438755e-01 -3.53403091e-01 8.44948232e-01 -1.20784521e-01 3.35081369e-02 1.44122720e-01 -3.71051818e-01 -7.54923284e-01 -1.27130127e+00 3.25091183e-01 1.78765386e-01 2.25034416e-01 1.23947941e-01 7.50354752e-02 4.56146091e-01 -1.41665602e+00 -5.41512631e-02 1.04130268e+00 9.48631108e-01 2.67918378e-01 3.96897942e-01 -3.24248821e-01 8.52449536e-01 -4.41657960e-01 -3.71545225e-01 5.86739540e-01 -2.33606070e-01 -3.43551964e-01 8.27839375e-01 5.75637996e-01 -4.14321899e-01 -5.24791598e-01 1.41065061e+00 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c109f4eb-8a7e-4fe1-9820-65bbf855dc0c	omni3d-a-large-benchmark-and-model-for-3d	2207.10660	null	https://arxiv.org/abs/2207.10660v2	https://arxiv.org/pdf/2207.10660v2.pdf	Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild	Recognizing scenes and objects in 3D from a single image is a longstanding goal of computer vision with applications in robotics and AR/VR. For 2D recognition, large datasets and scalable solutions have led to unprecedented advances. In 3D, existing benchmarks are small in size and approaches specialize in few object categories and specific domains, e.g. urban driving scenes. Motivated by the success of 2D recognition, we revisit the task of 3D object detection by introducing a large benchmark, called Omni3D. Omni3D re-purposes and combines existing datasets resulting in 234k images annotated with more than 3 million instances and 98 categories. 3D detection at such scale is challenging due to variations in camera intrinsics and the rich diversity of scene and object types. We propose a model, called Cube R-CNN, designed to generalize across camera and scene types with a unified approach. We show that Cube R-CNN outperforms prior works on the larger Omni3D and existing benchmarks. Finally, we prove that Omni3D is a powerful dataset for 3D object recognition and show that it improves single-dataset performance and can accelerate learning on new smaller datasets via pre-training.	['Nikhila Ravi', 'Abhinav Kumar', 'Georgia Gkioxari', 'Justin Johnson', 'Julian Straub', 'Garrick Brazil']	2022-07-21	null	http://openaccess.thecvf.com//content/CVPR2023/html/Brazil_Omni3D_A_Large_Benchmark_and_Model_for_3D_Object_Detection_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Brazil_Omni3D_A_Large_Benchmark_and_Model_for_3D_Object_Detection_CVPR_2023_paper.pdf	cvpr-2023-1	['3d-object-recognition']	['computer-vision']	[-6.16031652e-03 -3.82096648e-01 -3.12825739e-01 -4.01045382e-01 -5.13346970e-01 -8.19613576e-01 6.67756498e-01 -3.16136599e-01 -2.47160241e-01 7.75260404e-02 6.82634115e-02 -3.55381012e-01 3.11300140e-02 -5.39674461e-01 -1.05436301e+00 -3.22096229e-01 -2.34323964e-01 4.73761708e-01 3.61552417e-01 -4.23094667e-02 1.66287720e-01 1.09102488e+00 -2.00434589e+00 1.01887077e-01 1.45087346e-01 1.24889541e+00 2.40263864e-01 7.44932950e-01 -5.29064015e-02 5.14350474e-01 -5.67508638e-01 -1.08127659e-02 9.46957588e-01 2.37133652e-01 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cfd34a2c-3800-418d-b0d9-6d3075770f31	arabglossbert-fine-tuning-bert-on-context-1	2205.09685	null	https://arxiv.org/abs/2205.09685v1	https://arxiv.org/pdf/2205.09685v1.pdf	ArabGlossBERT: Fine-Tuning BERT on Context-Gloss Pairs for WSD	Using pre-trained transformer models such as BERT has proven to be effective in many NLP tasks. This paper presents our work to fine-tune BERT models for Arabic Word Sense Disambiguation (WSD). We treated the WSD task as a sentence-pair binary classification task. First, we constructed a dataset of labeled Arabic context-gloss pairs (~167k pairs) we extracted from the Arabic Ontology and the large lexicographic database available at Birzeit University. Each pair was labeled as True or False and target words in each context were identified and annotated. Second, we used this dataset for fine-tuning three pre-trained Arabic BERT models. Third, we experimented the use of different supervised signals used to emphasize target words in context. Our experiments achieved promising results (accuracy of 84%) although we used a large set of senses in the experiment.	['Mustafa Jarrar', 'Moustafa Al-Hajj']	2022-05-19	arabglossbert-fine-tuning-bert-on-context	https://aclanthology.org/2021.ranlp-1.5	https://aclanthology.org/2021.ranlp-1.5.pdf	ranlp-2021-9	['word-sense-disambiguation']	['natural-language-processing']	[ 1.42043959e-02 -2.43683867e-02 8.41367096e-02 -5.26053846e-01 -9.48678315e-01 -1.01746809e+00 8.20501506e-01 6.40392780e-01 -7.17995703e-01 8.61349165e-01 2.34810829e-01 -3.53258878e-01 -2.99897008e-02 -7.58087099e-01 -3.06170881e-01 -5.26653290e-01 -3.81672591e-01 1.00800776e+00 3.32844555e-01 -1.00809884e+00 4.40031558e-01 3.00804347e-01 -1.19410539e+00 7.39521325e-01 9.80246782e-01 9.37053323e-01 5.04784763e-01 3.41665864e-01 -3.75606090e-01 4.99723762e-01 -1.04552412e+00 -6.24652028e-01 1.33640438e-01 -2.25589946e-01 -1.20805144e+00 -2.18087554e-01 1.07362956e-01 4.04073358e-01 3.63142818e-01 8.89134169e-01 4.54772949e-01 1.45353675e-01 6.54764593e-01 -1.11554372e+00 -5.60291469e-01 1.26586914e+00 -3.88054192e-01 3.71767491e-01 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d2e51f38-360a-423c-b10e-4d133d04e73d	pointar-efficient-lighting-estimation-for	2004.00006	null	https://arxiv.org/abs/2004.00006v3	https://arxiv.org/pdf/2004.00006v3.pdf	PointAR: Efficient Lighting Estimation for Mobile Augmented Reality	We propose an efficient lighting estimation pipeline that is suitable to run on modern mobile devices, with comparable resource complexities to state-of-the-art mobile deep learning models. Our pipeline, PointAR, takes a single RGB-D image captured from the mobile camera and a 2D location in that image, and estimates 2nd order spherical harmonics coefficients. This estimated spherical harmonics coefficients can be directly utilized by rendering engines for supporting spatially variant indoor lighting, in the context of augmented reality. Our key insight is to formulate the lighting estimation as a point cloud-based learning problem directly from point clouds, which is in part inspired by the Monte Carlo integration leveraged by real-time spherical harmonics lighting. While existing approaches estimate lighting information with complex deep learning pipelines, our method focuses on reducing the computational complexity. Through both quantitative and qualitative experiments, we demonstrate that PointAR achieves lower lighting estimation errors compared to state-of-the-art methods. Further, our method requires an order of magnitude lower resource, comparable to that of mobile-specific DNNs.	['Tian Guo', 'Yiqin Zhao']	2020-03-30	null	https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4405_ECCV_2020_paper.php	https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123680664.pdf	eccv-2020-8	['lighting-estimation']	['computer-vision']	[-1.64754361e-01 -1.80375844e-01 2.32939959e-01 -2.54514188e-01 -1.06543398e+00 -6.69112563e-01 6.25300705e-01 -3.23300213e-01 -3.37606370e-01 3.25050741e-01 1.30807282e-02 -4.75615084e-01 4.25551444e-01 -9.24213171e-01 -1.22270608e+00 -4.30353492e-01 2.27102593e-01 5.35824597e-01 -9.10812467e-02 9.41186026e-02 -3.54377180e-02 6.43555343e-01 -1.74489379e+00 3.65102030e-02 5.66290796e-01 1.15379190e+00 1.25190392e-01 1.10267889e+00 -3.90915841e-01 4.60938573e-01 -3.65544796e-01 -3.76030982e-01 2.49180973e-01 5.31960987e-02 -1.41335547e-01 7.98438787e-02 9.32006538e-01 -6.54179275e-01 -1.18823253e-01 7.75808811e-01 6.63439929e-01 1.18425332e-01 3.33084762e-01 -1.17820251e+00 -4.38131392e-01 -3.29041243e-01 -5.10358870e-01 -2.41077021e-01 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