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78bef8fe-ebec-45c9-b3ba-089d763df2fb | detclipv2-scalable-open-vocabulary-object | 2304.04514 | null | https://arxiv.org/abs/2304.04514v1 | https://arxiv.org/pdf/2304.04514v1.pdf | DetCLIPv2: Scalable Open-Vocabulary Object Detection Pre-training via Word-Region Alignment | This paper presents DetCLIPv2, an efficient and scalable training framework that incorporates large-scale image-text pairs to achieve open-vocabulary object detection (OVD). Unlike previous OVD frameworks that typically rely on a pre-trained vision-language model (e.g., CLIP) or exploit image-text pairs via a pseudo la... | ['Hang Xu', 'Zhenguo Li', 'Wei zhang', 'Dan Xu', 'Xiaodan Liang', 'Jianhua Han', 'Lewei Yao'] | 2023-04-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yao_DetCLIPv2_Scalable_Open-Vocabulary_Object_Detection_Pre-Training_via_Word-Region_Alignment_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yao_DetCLIPv2_Scalable_Open-Vocabulary_Object_Detection_Pre-Training_via_Word-Region_Alignment_CVPR_2023_paper.pdf | cvpr-2023-1 | ['open-vocabulary-object-detection'] | ['computer-vision'] | [ 1.87526103e-02 3.73820215e-02 -4.19725269e-01 -1.67935118e-01
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3.71546179e-01 5.50474942e-01 5.63314378e-01 -3.01540550... | [9.664897918701172, 1.4925458431243896] |
0b1ab59d-ee55-4cd3-85da-d0fb8b2fb7ea | class-attention-network-for-semantic | null | null | https://ieeexplore.ieee.org/abstract/document/9306448 | https://ieeexplore.ieee.org/abstract/document/9306448 | Class Attention Network for Semantic Segmentation of Remote Sensing Images | Semantic segmentation in remote sensing images is beneficial to detect objects and understand the scene in earth observation. However, classical networks always failed to obtain an accuracy segmentation map in remote sensing images due to the imbalanced labels. In this paper, we proposed a novel class attention module ... | ['Yuchao Dai', 'Mingyi He', 'Zhibo Rao'] | 2020-12-31 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [ 5.24042606e-01 2.77578443e-01 -9.10037458e-02 -7.44684100e-01
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1.53358087e-01 3.71238887e-01 4.74825323e-01 -1.33636177... | [9.48173999786377, -1.0229955911636353] |
f8afa651-1208-4f84-a9cc-ca145c88573a | similarity-aware-multimodal-prompt-learning | 2304.04187 | null | https://arxiv.org/abs/2304.04187v3 | https://arxiv.org/pdf/2304.04187v3.pdf | Similarity-Aware Multimodal Prompt Learning for Fake News Detection | The standard paradigm for fake news detection mainly utilizes text information to model the truthfulness of news. However, the discourse of online fake news is typically subtle and it requires expert knowledge to use textual information to debunk fake news. Recently, studies focusing on multimodal fake news detection h... | ['Diana Maynard', 'Xingyi Song', 'Xiaoman Xu', 'Yimin Wang', 'Xiaomin Yu', 'Ye Jiang'] | 2023-04-09 | null | null | null | null | ['fake-news-detection'] | ['natural-language-processing'] | [ 1.50385275e-01 -2.02956244e-01 -4.62058216e-01 -2.00035885e-01
-1.15388799e+00 -6.81087792e-01 9.12532270e-01 8.31345990e-02
-3.40268314e-01 5.20918727e-01 3.69337678e-01 -1.14029199e-01
3.58039707e-01 -5.00615478e-01 -7.21361220e-01 -6.59644485e-01
5.45828402e-01 2.56566912e-01 1.42312482e-01 -5.99058390... | [8.1884126663208, 10.315967559814453] |
7cde01f3-30fc-4bf4-96ef-4e0f0dd9a031 | sim2sg-sim-to-real-scene-graph-generation-for-1 | 2011.14488 | null | https://arxiv.org/abs/2011.14488v2 | https://arxiv.org/pdf/2011.14488v2.pdf | Self-Supervised Real-to-Sim Scene Generation | Synthetic data is emerging as a promising solution to the scalability issue of supervised deep learning, especially when real data are difficult to acquire or hard to annotate. Synthetic data generation, however, can itself be prohibitively expensive when domain experts have to manually and painstakingly oversee the pr... | ['Stan Birchfield', 'Marc T. Law', 'Gavriel State', 'Eric Cameracci', 'Jean-Francois Lafleche', 'Shoubhik Debnath', 'Aayush Prakash'] | 2020-11-30 | sim2sg-sim-to-real-scene-graph-generation-for | http://openaccess.thecvf.com//content/ICCV2021/html/Prakash_Self-Supervised_Real-to-Sim_Scene_Generation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Prakash_Self-Supervised_Real-to-Sim_Scene_Generation_ICCV_2021_paper.pdf | iccv-2021-1 | ['scene-generation'] | ['computer-vision'] | [ 2.92071700e-01 1.33230776e-01 4.02583927e-02 -2.89017528e-01
-1.00470579e+00 -6.87515318e-01 7.33438492e-01 9.35347006e-02
-3.16054493e-01 9.08989012e-01 1.36712402e-01 -8.70307311e-02
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4.45207208e-01 7.49300778e-01 1.23580068e-01 -1.82654291... | [9.944472312927246, 1.1957917213439941] |
fa8d7ff2-24a5-48b6-a2a1-748980fdb86c | ad-corre-adaptive-correlation-based-loss-for | null | null | https://ieeexplore.ieee.org/document/9727163 | https://ieeexplore.ieee.org/document/9727163 | Ad-Corre: Adaptive Correlation-Based Loss for Facial Expression Recognition in the Wild | Automated Facial Expression Recognition (FER) in the wild using deep neural networks is still challenging due to intra-class variations and inter-class similarities in facial images. Deep Metric Learning (DML) is among the widely used methods to deal with these issues by improving the discriminative power of the learne... | ['Mohammad H. Mahoor', 'Ali Pourramezan Fard'] | 2022-03-03 | null | null | null | ieee-access-2022-3 | ['facial-expression-recognition'] | ['computer-vision'] | [ 1.11542888e-01 -3.94529067e-02 1.13876320e-01 -7.77910829e-01
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-2.70795256e-01 -1.35892546e-02 -2.05578119e-01 -2.55379438... | [13.446634292602539, 1.5421992540359497] |
7158ecc2-1461-437b-a121-d6bf81a4ada9 | robot-cooking-with-stir-fry-bimanual-non | 2205.05960 | null | https://arxiv.org/abs/2205.05960v1 | https://arxiv.org/pdf/2205.05960v1.pdf | Robot Cooking with Stir-fry: Bimanual Non-prehensile Manipulation of Semi-fluid Objects | This letter describes an approach to achieve well-known Chinese cooking art stir-fry on a bimanual robot system. Stir-fry requires a sequence of highly dynamic coordinated movements, which is usually difficult to learn for a chef, let alone transfer to robots. In this letter, we define a canonical stir-fry movement, an... | ['Fei Chen', 'Miao Li', 'Sylvain Calinon', 'Shixiong Wang', 'Zhipeng Dong', 'Yiting Chen', 'Junjia Liu'] | 2022-05-12 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [-2.18981996e-01 1.40198112e-01 1.51629850e-01 5.41040488e-02
-2.18255296e-01 -7.13652790e-01 5.11530459e-01 -5.22603154e-01
-1.15812957e-01 5.97879291e-01 -2.37304106e-01 2.21815109e-01
-4.19537395e-01 -7.20039904e-01 -1.32175314e+00 -8.34256530e-01
-3.52537662e-01 7.97352374e-01 3.29234660e-01 -8.49530935... | [4.847192764282227, 0.5923830270767212] |
a958c64c-0258-431f-869f-7709eecc85f3 | multi-agent-deep-reinforcement-learning-for-8 | 2111.02258 | null | https://arxiv.org/abs/2111.02258v1 | https://arxiv.org/pdf/2111.02258v1.pdf | Multi-Agent Deep Reinforcement Learning For Optimising Energy Efficiency of Fixed-Wing UAV Cellular Access Points | Unmanned Aerial Vehicles (UAVs) promise to become an intrinsic part of next generation communications, as they can be deployed to provide wireless connectivity to ground users to supplement existing terrestrial networks. The majority of the existing research into the use of UAV access points for cellular coverage consi... | ['Ivana Dusparic', 'Babatunji Omoniwa', 'Boris Galkin'] | 2021-11-03 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-3.42374176e-01 2.35961154e-01 -2.43248671e-01 4.64949518e-01
6.22445829e-02 -1.06938255e+00 1.86896041e-01 -3.26625966e-02
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-6.20044470e-01 -1.38014793e+00 -4.50488538e-01 -9.29122329e-01
-8.23766470e-01 3.53192210e-01 -8.00971463e-02 -7.52639890... | [5.8354573249816895, 1.5901068449020386] |
f13fe7c5-1b21-4652-bc1d-d2d3b7c34418 | a-new-approach-for-automatic-segmentation-and | 2101.07195 | null | https://arxiv.org/abs/2101.07195v1 | https://arxiv.org/pdf/2101.07195v1.pdf | A New Approach for Automatic Segmentation and Evaluation of Pigmentation Lesion by using Active Contour Model and Speeded Up Robust Features | Digital image processing techniques have wide applications in different scientific fields including the medicine. By use of image processing algorithms, physicians have been more successful in diagnosis of different diseases and have achieved much better treatment results. In this paper, we propose an automatic method ... | ['Amirmehdi Farshad', 'Melika Farshad', 'Mehran Yazdi', 'Akram Jamshidzadeh', 'Zahra Karimi', 'Sara Mardanisamani'] | 2021-01-18 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 4.8484787e-01 -3.6266869e-01 -4.9018513e-02 -6.2931977e-02
-4.1519034e-01 -4.4051576e-01 1.8689154e-01 5.6709677e-01
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7.0934564e-01... | [15.191524505615234, -2.927056074142456] |
2449d828-f790-4ea7-87b0-80d8573fba26 | fast-video-object-segmentation-with-spatio | 1903.12161 | null | http://arxiv.org/abs/1903.12161v1 | http://arxiv.org/pdf/1903.12161v1.pdf | Fast video object segmentation with Spatio-Temporal GANs | Learning descriptive spatio-temporal object models from data is paramount for
the task of semi-supervised video object segmentation. Most existing approaches
mainly rely on models that estimate the segmentation mask based on a reference
mask at the first frame (aided sometimes by optical flow or the previous mask).
The... | ['Luc van Gool', 'Francesc Moreno-Noguer', 'Albert Pumarola', 'Sergi Caelles', 'Alberto Sanfeliu'] | 2019-03-28 | null | null | null | null | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 7.22597912e-02 -9.17933583e-02 -2.07795948e-01 -2.34316424e-01
-5.86125195e-01 -5.86704552e-01 5.59635341e-01 -1.67901963e-01
-5.52464008e-01 6.79419279e-01 -4.57750618e-01 1.77911520e-02
1.08224064e-01 -5.20633399e-01 -9.59603965e-01 -6.10287607e-01
-1.41554549e-02 3.55874151e-01 8.21429729e-01 9.96016487... | [9.062980651855469, -0.17356529831886292] |
e4c9d6c5-33e1-42f4-8ab6-924d7efa4d1b | efficient-and-robust-training-of-dense-object | 2206.12145 | null | https://arxiv.org/abs/2206.12145v1 | https://arxiv.org/pdf/2206.12145v1.pdf | Efficient and Robust Training of Dense Object Nets for Multi-Object Robot Manipulation | We propose a framework for robust and efficient training of Dense Object Nets (DON) with a focus on multi-object robot manipulation scenarios. DON is a popular approach to obtain dense, view-invariant object descriptors, which can be used for a multitude of downstream tasks in robot manipulation, such as, pose estimati... | ['Heiko Neumann', 'Markus Spies', 'Andras Gabor Kupcsik', 'David B. Adrian'] | 2022-06-24 | null | null | null | null | ['robotic-grasping', 'robot-manipulation'] | ['robots', 'robots'] | [ 9.15969908e-02 -3.16381186e-01 -1.54821739e-01 -1.82847291e-01
-5.53372145e-01 -4.70067382e-01 4.66750443e-01 2.64746189e-01
-5.25194466e-01 5.63140690e-01 -3.16615641e-01 2.23050207e-01
-5.83241224e-01 -5.12889504e-01 -1.00024486e+00 -8.29851270e-01
5.51351830e-02 5.92267334e-01 3.57048571e-01 -1.66877076... | [5.899233341217041, -0.9601184725761414] |
800c6013-9e80-4c59-bd43-12af5d542248 | the-chamber-ensemble-generator-limitless-high | 2209.14458 | null | https://arxiv.org/abs/2209.14458v1 | https://arxiv.org/pdf/2209.14458v1.pdf | The Chamber Ensemble Generator: Limitless High-Quality MIR Data via Generative Modeling | Data is the lifeblood of modern machine learning systems, including for those in Music Information Retrieval (MIR). However, MIR has long been mired by small datasets and unreliable labels. In this work, we propose to break this bottleneck using generative modeling. By pipelining a generative model of notes (Coconet tr... | ['Jesse Engel', 'Curtis Hawthorne', 'Ian Simon', 'Ethan Manilow', 'Josh Gardner', 'Yusong Wu'] | 2022-09-28 | null | null | null | null | ['music-transcription', 'music-information-retrieval'] | ['music', 'music'] | [ 2.88111329e-01 -1.36014909e-01 3.23769331e-01 -3.37840766e-02
-1.31449437e+00 -1.20160389e+00 7.82639742e-01 -2.21897766e-01
1.83114246e-01 4.84197587e-01 5.24856389e-01 1.04106270e-01
-1.44409969e-01 -4.05868262e-01 -4.34965372e-01 -7.34255910e-01
-4.94993888e-02 7.39286244e-01 -1.55829832e-01 -3.90497804... | [15.877348899841309, 5.450754165649414] |
1d1031d2-1883-4f55-9003-a0563c474041 | backretrieval-an-image-pivoted-evaluation | 2105.04971 | null | https://arxiv.org/abs/2105.04971v1 | https://arxiv.org/pdf/2105.04971v1.pdf | Backretrieval: An Image-Pivoted Evaluation Metric for Cross-Lingual Text Representations Without Parallel Corpora | Cross-lingual text representations have gained popularity lately and act as the backbone of many tasks such as unsupervised machine translation and cross-lingual information retrieval, to name a few. However, evaluation of such representations is difficult in the domains beyond standard benchmarks due to the necessity ... | ['Danushka Bollegala', 'Niall Twomey', 'Mikhail Fain'] | 2021-05-11 | null | null | null | null | ['unsupervised-machine-translation', 'cross-lingual-information-retrieval'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.43076763e-01 -3.75183105e-01 -3.12038451e-01 -5.77868521e-01
-1.49836719e+00 -9.56774354e-01 1.19410908e+00 4.67885643e-01
-7.43889272e-01 4.32764500e-01 5.41414857e-01 -1.73709646e-03
2.92648017e-01 -3.62416625e-01 -9.27478313e-01 -2.60891348e-01
1.74542725e-01 5.85764110e-01 -2.23572895e-01 -1.30407631... | [11.17158031463623, 1.6574817895889282] |
1e98f1e0-eec5-493f-960e-86ddc3567246 | two-phase-dual-copod-method-for-anomaly | 2305.00982 | null | https://arxiv.org/abs/2305.00982v1 | https://arxiv.org/pdf/2305.00982v1.pdf | Two-phase Dual COPOD Method for Anomaly Detection in Industrial Control System | Critical infrastructures like water treatment facilities and power plants depend on industrial control systems (ICS) for monitoring and control, making them vulnerable to cyber attacks and system malfunctions. Traditional ICS anomaly detection methods lack transparency and interpretability, which make it difficult for ... | ['Jerry Bruce', 'Emmanuel Aboah Boateng'] | 2023-04-30 | null | null | null | null | ['outlier-detection'] | ['methodology'] | [ 1.54050458e-02 -2.69381523e-01 2.35648006e-01 -1.66838229e-01
-4.83939201e-01 -7.77548432e-01 2.94626296e-01 8.31932247e-01
3.62428166e-02 6.13728821e-01 -5.64353347e-01 -3.96704257e-01
-5.50194204e-01 -7.32812047e-01 -3.85708809e-01 -7.95296967e-01
-2.06662297e-01 5.62898099e-01 1.89604804e-01 3.58610362... | [7.239721298217773, 2.6841156482696533] |
f62b79cb-065c-4319-a501-d05133613c25 | weakly-supervised-domain-adaption-for-aspect | 2006.09235 | null | https://arxiv.org/abs/2006.09235v1 | https://arxiv.org/pdf/2006.09235v1.pdf | Weakly-supervised Domain Adaption for Aspect Extraction via Multi-level Interaction Transfer | Fine-grained aspect extraction is an essential sub-task in aspect based opinion analysis. It aims to identify the aspect terms (a.k.a. opinion targets) of a product or service in each sentence. However, expensive annotation process is usually involved to acquire sufficient token-level labels for each domain. To address... | ['Tao Liang', 'Wenya Wang', 'Fengmao Lv'] | 2020-06-16 | null | null | null | null | ['aspect-extraction'] | ['natural-language-processing'] | [ 2.34606639e-01 -2.19472535e-02 -6.76449776e-01 -7.31702089e-01
-1.30656946e+00 -8.93715739e-01 5.56901753e-01 2.42566228e-01
2.44645141e-02 6.12831950e-01 1.31476492e-01 -2.23978221e-01
2.46245876e-01 -9.25848722e-01 -6.60005987e-01 -5.21997392e-01
5.25971234e-01 5.61717093e-01 1.22119159e-01 -4.03889835... | [11.38651180267334, 6.676271915435791] |
8e1ed9dd-1bc4-43e7-b2e4-dfa60f85a742 | octis-comparing-and-optimizing-topic-models | null | null | https://aclanthology.org/2021.eacl-demos.31 | https://aclanthology.org/2021.eacl-demos.31.pdf | OCTIS: Comparing and Optimizing Topic models is Simple! | In this paper, we present OCTIS, a framework for training, analyzing, and comparing Topic Models, whose optimal hyper-parameters are estimated using a Bayesian Optimization approach. The proposed solution integrates several state-of-the-art topic models and evaluation metrics. These metrics can be targeted as objective... | ['Antonio Candelieri', 'Pietro Tropeano', 'Bruno Giovanni Galuzzi', 'Elisabetta Fersini', 'Silvia Terragni'] | 2021-04-19 | null | null | null | eacl-2021-2 | ['bayesian-optimisation'] | ['methodology'] | [-4.26773161e-01 2.91652419e-02 -5.21999359e-01 -3.97400558e-01
-8.32601964e-01 -4.75428462e-01 7.55430520e-01 5.39743900e-01
-1.73605040e-01 4.51581031e-01 -1.27395550e-02 -2.00559333e-01
-3.80994946e-01 -6.07922375e-01 -3.84436071e-01 -5.86054444e-01
-1.88972518e-01 7.84456074e-01 5.04869699e-01 1.78853124... | [10.415432929992676, 6.952080726623535] |
87a13781-0699-41f9-9b16-0ba4b9b29946 | vgstore-a-multimodal-extension-to-sparql-for | 2209.02981 | null | https://arxiv.org/abs/2209.02981v1 | https://arxiv.org/pdf/2209.02981v1.pdf | VGStore: A Multimodal Extension to SPARQL for Querying RDF Scene Graph | Semantic Web technology has successfully facilitated many RDF models with rich data representation methods. It also has the potential ability to represent and store multimodal knowledge bases such as multimodal scene graphs. However, most existing query languages, especially SPARQL, barely explore the implicit multimod... | ['Lei Zou', 'Wenjuan Han', 'Zilong Zheng', 'Yanzeng Li'] | 2022-09-07 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [-4.97223496e-01 2.77181953e-01 -1.44402936e-01 -5.03376722e-01
-2.94266403e-01 -7.06435740e-01 6.34170890e-01 7.46431649e-01
-2.01284781e-01 7.03241229e-01 5.77163756e-01 -8.28624815e-02
-5.29194117e-01 -1.36595404e+00 -5.56849301e-01 -1.98936880e-01
-7.26349943e-04 3.93608302e-01 7.17613399e-01 -6.98849380... | [9.27785587310791, 7.914941787719727] |
4408db60-24ec-4b15-928d-d107ac06ef4b | deep-learning-for-android-malware-defenses-a | 2103.05292 | null | https://arxiv.org/abs/2103.05292v3 | https://arxiv.org/pdf/2103.05292v3.pdf | Deep Learning for Android Malware Defenses: a Systematic Literature Review | Malicious applications (particularly those targeting the Android platform) pose a serious threat to developers and end-users. Numerous research efforts have been devoted to developing effective approaches to defend against Android malware. However, given the explosive growth of Android malware and the continuous advanc... | ['Yepang Liu', 'Li Li', 'Chakkrit Tantithamthavorn', 'Yue Liu'] | 2021-03-09 | null | null | null | null | ['android-malware-detection', 'mobile-security'] | ['miscellaneous', 'miscellaneous'] | [ 2.27238704e-02 -3.35679799e-01 -9.64432299e-01 1.65362731e-01
-2.82068819e-01 -6.42599642e-01 6.68199360e-01 -1.92827269e-01
-7.04855844e-02 2.25618169e-01 -2.70749927e-01 -1.08907425e+00
4.47824737e-03 -5.74363172e-01 -5.47157168e-01 -4.62257773e-01
-4.13957499e-02 -3.07989448e-01 -1.37065038e-01 -2.38450795... | [14.423236846923828, 9.68045425415039] |
a9a9bb56-f292-49ac-9a70-b0a08ff37beb | clac-sentipipe-semeval2015-subtasks-10-be-and | null | null | https://aclanthology.org/S15-2081 | https://aclanthology.org/S15-2081.pdf | CLaC-SentiPipe: SemEval2015 Subtasks 10 B,E, and Task 11 | null | ['Canberk {\\"O}zdemir', 'Sabine Bergler'] | 2015-06-01 | null | null | null | semeval-2015-6 | ['twitter-sentiment-analysis'] | ['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 -6.93831444... | [-7.354000568389893, 3.7918314933776855] |
f3b91149-6030-465f-abdd-476f5efd3935 | transformer-based-generative-adversarial | 2205.10663 | null | https://arxiv.org/abs/2205.10663v2 | https://arxiv.org/pdf/2205.10663v2.pdf | Transformer based Generative Adversarial Network for Liver Segmentation | Automated liver segmentation from radiology scans (CT, MRI) can improve surgery and therapy planning and follow-up assessment in addition to conventional use for diagnosis and prognosis. Although convolutional neural networks (CNNs) have become the standard image segmentation tasks, more recently this has started to ch... | ['Ulas Bagci', 'Daniela Ladner', 'Amir Borhani', 'Debesh Jha', 'Elif Keles', 'Matthew Antalek', 'Bin Wang', 'Zheyuan Zhang', 'Ugur Demir'] | 2022-05-21 | null | null | null | null | ['liver-segmentation'] | ['medical'] | [ 5.49602248e-02 4.79177564e-01 1.40341073e-01 -3.20856929e-01
-6.80808723e-01 -4.78959322e-01 4.50090140e-01 -3.39879207e-02
-2.45854244e-01 7.03805149e-01 1.98927671e-01 -1.12642452e-01
3.13228294e-02 -1.02860034e+00 -4.57539946e-01 -1.04025686e+00
-5.26800677e-02 6.59868062e-01 2.57596433e-01 3.17229256... | [14.215920448303223, -2.2155873775482178] |
2180e2b7-d462-4b06-ae8c-afd78a1dd97a | variable-selection-for-kernel-two-sample | 2302.07415 | null | https://arxiv.org/abs/2302.07415v2 | https://arxiv.org/pdf/2302.07415v2.pdf | Variable Selection for Kernel Two-Sample Tests | We consider the variable selection problem for two-sample tests, aiming to select the most informative variables to distinguish samples from two groups. To solve this problem, we propose a framework based on the kernel maximum mean discrepancy (MMD). Our approach seeks a group of variables with a pre-specified size tha... | ['Yao Xie', 'Santanu S. Dey', 'Jie Wang'] | 2023-02-15 | null | null | null | null | ['variable-selection'] | ['methodology'] | [ 1.49541885e-01 1.82563022e-01 -6.34621322e-01 -5.54613590e-01
-1.26118207e+00 -2.47619346e-01 -1.00146227e-01 2.16935232e-01
-3.48461688e-01 1.15279973e+00 -5.15320420e-01 -3.53830427e-01
-8.42138827e-01 -7.61998296e-01 -4.60335910e-01 -1.05593538e+00
-3.14036608e-01 6.15511179e-01 -2.42022097e-01 4.35345769... | [7.417232036590576, 4.351412773132324] |
e2053b6a-bd7b-4596-9257-f183416acf46 | logg3d-net-locally-guided-global-descriptor | 2109.08336 | null | https://arxiv.org/abs/2109.08336v3 | https://arxiv.org/pdf/2109.08336v3.pdf | LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition | Retrieval-based place recognition is an efficient and effective solution for re-localization within a pre-built map, or global data association for Simultaneous Localization and Mapping (SLAM). The accuracy of such an approach is heavily dependent on the quality of the extracted scene-level representation. While end-to... | ['Clinton Fookes', 'Sridha Sridharan', 'Peyman Moghadam', 'Milad Ramezani', 'Kavisha Vidanapathirana'] | 2021-09-17 | null | null | null | null | ['3d-place-recognition'] | ['computer-vision'] | [-3.17001820e-01 -4.99787867e-01 -1.38498962e-01 -6.24287605e-01
-1.32560229e+00 -6.13162398e-01 5.98168969e-01 3.56590658e-01
-6.49575055e-01 4.93347853e-01 -1.06157094e-01 9.35304072e-03
-3.46917331e-01 -7.00258791e-01 -1.10854411e+00 -2.91641504e-01
-4.74553436e-01 4.96908754e-01 2.18655720e-01 -1.92815512... | [7.616470813751221, -2.047046422958374] |
9c2472a3-7cf4-4677-ae64-5c418ec77b17 | exploration-on-grounded-word-embedding | 1809.02765 | null | http://arxiv.org/abs/1809.02765v1 | http://arxiv.org/pdf/1809.02765v1.pdf | Exploration on Grounded Word Embedding: Matching Words and Images with Image-Enhanced Skip-Gram Model | Word embedding is designed to represent the semantic meaning of a word with
low dimensional vectors. The state-of-the-art methods of learning word
embeddings (word2vec and GloVe) only use the word co-occurrence information.
The learned embeddings are real number vectors, which are obscure to human. In
this paper, we pr... | ['Ruixuan Luo'] | 2018-09-08 | null | null | null | null | ['learning-word-embeddings'] | ['methodology'] | [-4.71886247e-01 1.18476331e-01 -3.51974458e-01 -2.42427886e-01
-7.17163607e-02 -2.42624134e-01 7.07098424e-01 4.38048169e-02
-3.50701690e-01 1.90941215e-01 7.19358742e-01 -3.01950336e-01
3.49335104e-01 -8.12911630e-01 -5.34183204e-01 -5.82293570e-01
1.11262007e-02 -5.41272275e-02 -8.71994346e-02 -4.04852450... | [10.479748725891113, 2.0036351680755615] |
f7a384d1-35f4-486c-a4fb-74c6a8049a6f | effective-data-augmentation-with-diffusion | 2302.07944 | null | https://arxiv.org/abs/2302.07944v2 | https://arxiv.org/pdf/2302.07944v2.pdf | Effective Data Augmentation With Diffusion Models | Data augmentation is one of the most prevalent tools in deep learning, underpinning many recent advances, including those from classification, generative models, and representation learning. The standard approach to data augmentation combines simple transformations like rotations and flips to generate new images from e... | ['Ruslan Salakhutdinov', 'Max Gurinas', 'Kyle Doherty', 'Brandon Trabucco'] | 2023-02-07 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 9.27042484e-01 8.35326910e-02 -2.78977633e-01 -4.44272846e-01
-2.71155927e-02 -7.08750546e-01 1.15320754e+00 3.74889582e-01
-5.88588536e-01 7.28629649e-01 2.27258846e-01 -1.26462206e-01
2.42730621e-02 -9.76723969e-01 -8.67149055e-01 -6.46062970e-01
1.24488972e-01 4.65578943e-01 1.74343511e-01 -4.91037697... | [9.830095291137695, 1.9112056493759155] |
940ee4eb-e55a-4fee-84c5-2f34502a75f8 | intra-inter-camera-similarity-for | 2103.11658 | null | https://arxiv.org/abs/2103.11658v1 | https://arxiv.org/pdf/2103.11658v1.pdf | Intra-Inter Camera Similarity for Unsupervised Person Re-Identification | Most of unsupervised person Re-Identification (Re-ID) works produce pseudo-labels by measuring the feature similarity without considering the distribution discrepancy among cameras, leading to degraded accuracy in label computation across cameras. This paper targets to address this challenge by studying a novel intra-i... | ['Shiliang Zhang', 'Shiyu Xuan'] | 2021-03-22 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Xuan_Intra-Inter_Camera_Similarity_for_Unsupervised_Person_Re-Identification_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Xuan_Intra-Inter_Camera_Similarity_for_Unsupervised_Person_Re-Identification_CVPR_2021_paper.pdf | cvpr-2021-1 | ['unsupervised-person-re-identification'] | ['computer-vision'] | [ 3.56621861e-01 -3.76911402e-01 -2.31141672e-02 -7.41019011e-01
-9.48436677e-01 -8.66338968e-01 8.28390777e-01 1.72794014e-01
-7.37060428e-01 5.22037566e-01 2.73734808e-01 3.04574430e-01
2.56749451e-01 -4.01567787e-01 -5.90125740e-01 -6.27277732e-01
5.64288914e-01 6.19057417e-01 1.19209588e-02 4.03293073... | [14.77997875213623, 1.0400997400283813] |
c5947037-397d-4a3a-90b2-8f49bd801c9f | unsupervised-polychromatic-neural | 2306.15203 | null | https://arxiv.org/abs/2306.15203v1 | https://arxiv.org/pdf/2306.15203v1.pdf | Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction | Emerging neural reconstruction techniques based on tomography (e.g., NeRF, NeAT, and NeRP) have started showing unique capabilities in medical imaging. In this work, we present a novel Polychromatic neural representation (Polyner) to tackle the challenging problem of CT imaging when metallic implants exist within the h... | ['Yuyao Zhang', 'Jingyi Yu', 'S. Kevin Zhou', 'Hongjiang Wei', 'Ce Wang', 'Lixuan Chen', 'Qing Wu'] | 2023-06-27 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 5.62432170e-01 -3.73699963e-02 1.44912034e-01 -2.86539406e-01
-7.11410403e-01 1.35429323e-01 2.31289953e-01 -3.80359083e-01
-3.56753111e-01 5.42387426e-01 3.44085842e-01 -8.11939240e-02
-4.72288668e-01 -7.28074312e-01 -9.33713019e-01 -8.83527040e-01
1.58017948e-01 4.48003531e-01 1.43891111e-01 -3.46339829... | [13.461910247802734, -2.515967845916748] |
40c31516-9547-4fa6-a67a-ffa2b60e5e9b | sparse-3d-convolutional-neural-networks | 1505.02890 | null | http://arxiv.org/abs/1505.02890v2 | http://arxiv.org/pdf/1505.02890v2.pdf | Sparse 3D convolutional neural networks | We have implemented a convolutional neural network designed for processing
sparse three-dimensional input data. The world we live in is three dimensional
so there are a large number of potential applications including 3D object
recognition and analysis of space-time objects. In the quest for efficiency, we
experiment w... | ['Ben Graham'] | 2015-05-12 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [-2.96243399e-01 -2.74071991e-01 2.09109426e-01 -1.37423471e-01
5.09955105e-04 -4.26912270e-02 5.88299274e-01 -3.76288816e-02
-5.72653532e-01 3.19609880e-01 1.62035987e-01 -4.59726065e-01
-2.52268046e-01 -1.08147013e+00 -4.61697847e-01 -4.37189728e-01
-9.70627546e-01 7.90464461e-01 2.21338406e-01 -1.06281608... | [8.195103645324707, -3.7111027240753174] |
e4c3466d-b3a7-48e1-bb30-81d2577c3969 | mareo-memory-and-attention-based-visual | 2206.04928 | null | https://arxiv.org/abs/2206.04928v5 | https://arxiv.org/pdf/2206.04928v5.pdf | GAMR: A Guided Attention Model for (visual) Reasoning | Humans continue to outperform modern AI systems in their ability to flexibly parse and understand complex visual scenes. Here, we present a novel module for visual reasoning, the Guided Attention Model for (visual) Reasoning (GAMR), which instantiates an active vision theory -- positing that the brain solves complex vi... | ['Thomas Serre', 'Mohit Vaishnav'] | 2022-06-10 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 3.17647845e-01 1.16457626e-01 3.80376250e-01 -8.12340900e-02
6.57675043e-02 -8.21486473e-01 7.94253170e-01 2.00427428e-01
-2.08646894e-01 1.83789045e-01 1.63931072e-01 -7.60164857e-01
-4.73258972e-01 -6.14082277e-01 -4.85425144e-01 -3.12257856e-01
1.06355911e-02 4.74925399e-01 1.83307543e-01 -4.05499876... | [10.612617492675781, 2.261005163192749] |
1ad23e5c-d343-4319-89d4-2569f754e3a4 | a-semantically-enhanced-dual-encoder-for | 2306.08373 | null | https://arxiv.org/abs/2306.08373v1 | https://arxiv.org/pdf/2306.08373v1.pdf | A semantically enhanced dual encoder for aspect sentiment triplet extraction | Aspect sentiment triplet extraction (ASTE) is a crucial subtask of aspect-based sentiment analysis (ABSA) that aims to comprehensively identify sentiment triplets. Previous research has focused on enhancing ASTE through innovative table-filling strategies. However, these approaches often overlook the multi-perspective ... | ['Hongye Li', 'Kaifang Dong', 'Peiyu Liu', 'Shehui Liang', 'Baoxing Jiang'] | 2023-06-14 | null | null | null | null | ['sentiment-analysis', 'aspect-based-sentiment-analysis', 'aspect-sentiment-triplet-extraction'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 1.27403975e-01 2.83149838e-01 -3.27407688e-01 -7.09377468e-01
-5.94226539e-01 -5.81861854e-01 6.77200317e-01 5.72881103e-01
-1.45338029e-01 3.06719691e-01 8.67008507e-01 -3.90212178e-01
1.24924943e-01 -1.00000525e+00 -4.72816020e-01 -3.81532311e-01
2.43470713e-01 3.79301943e-02 -1.32918179e-01 -6.84112310... | [11.489026069641113, 6.653900146484375] |
2a9a20b4-2c34-4b00-83c7-d1d9b277eb02 | jrdb-act-a-large-scale-multi-modal-dataset | 2106.08827 | null | https://arxiv.org/abs/2106.08827v2 | https://arxiv.org/pdf/2106.08827v2.pdf | JRDB-Act: A Large-scale Dataset for Spatio-temporal Action, Social Group and Activity Detection | The availability of large-scale video action understanding datasets has facilitated advances in the interpretation of visual scenes containing people. However, learning to recognise human actions and their social interactions in an unconstrained real-world environment comprising numerous people, with potentially highly... | ['Hamid Rezatofighi', 'Ian Reid', 'Silvio Savarese', 'Fatemeh Saleh', 'Mahsa Ehsanpour'] | 2021-06-16 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Ehsanpour_JRDB-Act_A_Large-Scale_Dataset_for_Spatio-Temporal_Action_Social_Group_and_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Ehsanpour_JRDB-Act_A_Large-Scale_Dataset_for_Spatio-Temporal_Action_Social_Group_and_CVPR_2022_paper.pdf | cvpr-2022-1 | ['action-understanding'] | ['computer-vision'] | [ 4.07682747e-01 1.82698175e-01 2.40312353e-01 -4.59094167e-01
-5.96674085e-01 -4.72698867e-01 6.53235197e-01 -1.24182373e-01
-5.60971141e-01 6.97071970e-01 6.19141459e-01 3.44963551e-01
-1.75238580e-01 -2.09283903e-01 -6.40645981e-01 -5.65465033e-01
-5.83751321e-01 9.46193516e-01 4.32246208e-01 -1.66485786... | [8.01789379119873, 0.40511301159858704] |
06666952-2fdc-421b-b7f7-cc5882f0fb5e | secure-multiparty-computations-in-floating | 2001.03192 | null | https://arxiv.org/abs/2001.03192v1 | https://arxiv.org/pdf/2001.03192v1.pdf | Secure multiparty computations in floating-point arithmetic | Secure multiparty computations enable the distribution of so-called shares of sensitive data to multiple parties such that the multiple parties can effectively process the data while being unable to glean much information about the data (at least not without collusion among all parties to put back together all the shar... | ['Mark Tygert', 'Chuan Guo', 'Awni Hannun', 'Brian Knott', 'Ruiyu Zhu', 'Laurens van der Maaten'] | 2020-01-09 | null | null | null | null | ['mathematical-proofs'] | ['miscellaneous'] | [ 2.05886945e-01 1.75596491e-01 5.36690801e-02 -6.03364706e-01
-1.49913168e+00 -1.26966238e+00 1.44419178e-01 7.84014881e-01
-8.59787107e-01 7.47982442e-01 -2.18442932e-01 -5.58093846e-01
6.41048774e-02 -1.11653113e+00 -7.41455197e-01 -1.22336924e+00
-3.49588901e-01 4.80581045e-01 -2.22658291e-01 7.11751804... | [5.889193534851074, 6.680034637451172] |
cc35a25e-c03e-4629-bb7f-bcdf10753e74 | multilingual-multimodal-pre-training-for-zero | 2103.08849 | null | https://arxiv.org/abs/2103.08849v3 | https://arxiv.org/pdf/2103.08849v3.pdf | Multilingual Multimodal Pre-training for Zero-Shot Cross-Lingual Transfer of Vision-Language Models | This paper studies zero-shot cross-lingual transfer of vision-language models. Specifically, we focus on multilingual text-to-video search and propose a Transformer-based model that learns contextualized multilingual multimodal embeddings. Under a zero-shot setting, we empirically demonstrate that performance degrades ... | ['Alexander Hauptmann', 'Florian Metze', 'Graham Neubig', 'Junjie Hu', 'Mandela Patrick', 'Po-Yao Huang'] | 2021-03-16 | null | https://aclanthology.org/2021.naacl-main.195 | https://aclanthology.org/2021.naacl-main.195.pdf | naacl-2021-4 | ['text-to-video-search'] | ['natural-language-processing'] | [-3.30624044e-01 -5.57303667e-01 -6.16482019e-01 -1.58127487e-01
-1.82632542e+00 -9.23590541e-01 7.32822061e-01 -6.16188757e-02
-9.84005272e-01 4.97268170e-01 3.65033537e-01 -5.69712698e-01
2.39861190e-01 -1.00939006e-01 -1.15807998e+00 -2.74335861e-01
3.62205744e-01 5.61947227e-01 2.24902958e-01 -1.35118231... | [11.143969535827637, 1.5204808712005615] |
08432383-a8cb-4501-a37e-95618c3c3c4a | a-retrofitting-model-for-incorporating | null | null | https://aclanthology.org/2020.coling-main.111 | https://aclanthology.org/2020.coling-main.111.pdf | A Retrofitting Model for Incorporating Semantic Relations into Word Embeddings | We present a novel retrofitting model that can leverage relational knowledge available in a knowledge resource to improve word embeddings. The knowledge is captured in terms of relation inequality constraints that compare similarity of related and unrelated entities in the context of an anchor entity. These constraints... | ['Pushpak Bhattacharyya', 'Sreedhar Reddy', 'Sapan Shah'] | 2020-12-01 | null | null | null | coling-2020-8 | ['word-similarity'] | ['natural-language-processing'] | [ 2.27215827e-01 2.83265412e-01 -6.27041459e-01 -6.68256879e-01
-3.85073662e-01 -6.14908755e-01 6.98647976e-01 6.71505272e-01
-9.98681307e-01 4.45537537e-01 7.55201697e-01 -3.37448597e-01
-4.12325501e-01 -1.04652131e+00 -4.93390441e-01 4.14654724e-02
8.15765113e-02 6.21441364e-01 2.38187332e-02 -5.87312222... | [10.067839622497559, 8.722655296325684] |
bc81de84-e7c4-4461-9bc7-7459479e79c2 | category-guided-attention-network-for-brain | 2203.15383 | null | https://arxiv.org/abs/2203.15383v1 | https://arxiv.org/pdf/2203.15383v1.pdf | Category Guided Attention Network for Brain Tumor Segmentation in MRI | Objective: Magnetic resonance imaging (MRI) has been widely used for the analysis and diagnosis of brain diseases. Accurate and automatic brain tumor segmentation is of paramount importance for radiation treatment. However, low tissue contrast in tumor regions makes it a challenging task.Approach: We propose a novel se... | ['Sen Zha', 'Meng Ding', 'Chen Chen', 'Hong Yu', 'Jiangyun Li'] | 2022-03-29 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 2.36719713e-01 1.01806954e-01 -1.68667182e-01 -3.89782310e-01
-9.36471224e-01 3.51217277e-02 2.91792035e-01 1.82824478e-01
-5.33780754e-01 5.92471182e-01 1.81431979e-01 -5.47792278e-02
-2.10229725e-01 -7.27690876e-01 -5.70828557e-01 -9.68689740e-01
1.65628552e-01 5.13972759e-01 5.19342959e-01 6.39889836... | [14.501426696777344, -2.367771625518799] |
79b8c669-5fc9-413b-9b32-2010f34eebc1 | blendmask-top-down-meets-bottom-up-for | 2001.00309 | null | https://arxiv.org/abs/2001.00309v3 | https://arxiv.org/pdf/2001.00309v3.pdf | BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation | Instance segmentation is one of the fundamental vision tasks. Recently, fully convolutional instance segmentation methods have drawn much attention as they are often simpler and more efficient than two-stage approaches like Mask R-CNN. To date, almost all such approaches fall behind the two-stage Mask R-CNN method in m... | ['Yongming Huang', 'Youliang Yan', 'Chunhua Shen', 'Zhi Tian', 'Hao Chen', 'Kunyang Sun'] | 2020-01-02 | blendmask-top-down-meets-bottom-up-for-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Chen_BlendMask_Top-Down_Meets_Bottom-Up_for_Instance_Segmentation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_BlendMask_Top-Down_Meets_Bottom-Up_for_Instance_Segmentation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['real-time-instance-segmentation'] | ['computer-vision'] | [ 4.14727867e-01 2.69743502e-01 -4.13197011e-01 -4.44048077e-01
-8.85369599e-01 -3.02073777e-01 4.14912194e-01 -4.87398393e-02
-6.64048493e-01 2.90716290e-01 -3.09426576e-01 -3.98003668e-01
4.57588404e-01 -8.42645943e-01 -9.66948509e-01 -3.85446668e-01
2.39605397e-01 4.68673825e-01 7.59370983e-01 4.41872105... | [9.464942932128906, 0.09876348823308945] |
5a311d01-ac57-4bab-92bf-99dd52e4b04f | residual-network-based-aggregation-model-for | 1807.09150 | null | http://arxiv.org/abs/1807.09150v1 | http://arxiv.org/pdf/1807.09150v1.pdf | Residual Network based Aggregation Model for Skin Lesion Classification | We recognize that the skin lesion diagnosis is an essential and challenging
sub-task in Image classification, in which the Fisher vector (FV) encoding
algorithm and deep convolutional neural network (DCNN) are two of the most
successful techniques. Since the joint use of FV and DCNN has demonstrated
proven success, the... | ['Yongsheng Pan', 'Yong Xia'] | 2018-07-24 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 5.77313483e-01 -1.87446713e-01 -3.02720726e-01 -2.21147329e-01
-9.79187906e-01 -4.66306359e-01 6.97048903e-01 -2.20420323e-02
-2.11851373e-01 4.41637963e-01 2.01863632e-01 -3.02412927e-01
-2.87989885e-01 -6.04580283e-01 -1.53382376e-01 -9.80079114e-01
6.50662854e-02 -1.90443501e-01 -5.13797253e-02 2.82468796... | [15.63988971710205, -2.9378061294555664] |
2118ef8f-4e2f-4dc9-8a96-fd15a81565d3 | event-extraction-in-video-transcripts | null | null | https://aclanthology.org/2022.coling-1.625 | https://aclanthology.org/2022.coling-1.625.pdf | Event Extraction in Video Transcripts | Event extraction (EE) is one of the fundamental tasks for information extraction whose goal is to identify mentions of events and their participants in text. Due to its importance, different methods and datasets have been introduced for EE. However, existing EE datasets are limited to formally written documents such as... | ['Thien Huu Nguyen', 'Franck Dernoncourt', 'Viet Dac Lai', 'Amir Pouran Ben Veyseh'] | null | null | null | null | coling-2022-10 | ['event-extraction'] | ['natural-language-processing'] | [ 2.68596619e-01 -1.42807141e-01 -1.28916547e-01 -2.58253783e-01
-9.84651446e-01 -6.74152732e-01 7.28329480e-01 2.91607887e-01
-4.37346995e-01 7.77540267e-01 5.20613313e-01 -1.53973162e-01
9.48054194e-02 -5.55270076e-01 -6.29106104e-01 -3.96800131e-01
-1.51837006e-01 -1.87288150e-01 3.85214835e-01 6.54014051... | [8.973428726196289, 9.067146301269531] |
5dbc2289-5f51-48c6-b042-fcd8bddab8b7 | order-flow-and-price-formation | 2105.00521 | null | https://arxiv.org/abs/2105.00521v1 | https://arxiv.org/pdf/2105.00521v1.pdf | Order flow and price formation | I present an overview of some recent advancements on the empirical analysis and theoretical modeling of the process of price formation in financial markets as the result of the arrival of orders in a limit order book exchange. After discussing critically the possible modeling approaches and the observed stylized facts ... | ['Fabrizio Lillo'] | 2021-05-02 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-4.33211654e-01 -1.77161172e-01 -2.69025743e-01 -2.64558971e-01
-1.60268545e-01 -1.14678884e+00 8.09229374e-01 3.98627788e-01
-3.12175155e-01 5.42563975e-01 7.95169175e-02 -7.55247235e-01
-2.47085586e-01 -7.86719918e-01 -6.49191558e-01 -1.91527400e-02
-5.16320467e-01 9.86902535e-01 2.79610425e-01 -1.81988969... | [4.760397911071777, 4.053459167480469] |
d244cae0-a4ed-403b-a3c8-af1c10e2fb0c | a-cnn-rnn-framework-with-a-novel-patch-based | 1902.11274 | null | https://arxiv.org/abs/1902.11274v3 | https://arxiv.org/pdf/1902.11274v3.pdf | A Novel Multi-Attention Driven System For Multi-Label Remote Sensing Image Classification | This paper presents a novel multi-attention driven system that jointly exploits Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) in the context of multi-label remote sensing (RS) image classification. The proposed system consists of four main modules. The first module aims to extract preliminary lo... | ['Begüm Demir', 'Gencer Sumbul'] | 2019-02-28 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 2.71088213e-01 -2.65197337e-01 -1.60780087e-01 -4.62217957e-01
-1.11519253e+00 -6.38931766e-02 5.29353797e-01 3.86005729e-01
-5.87860942e-01 4.03850645e-01 3.20234776e-01 1.07935406e-01
-5.69926679e-01 -1.14310229e+00 -6.14055037e-01 -9.83024776e-01
-1.92270756e-01 3.85671258e-02 1.67495549e-01 -3.69971067... | [9.742246627807617, -1.3571908473968506] |
d2c22761-dc0f-430f-a2dc-ba6b959caa3c | robust-stability-of-gaussian-process-based | 2304.06530 | null | https://arxiv.org/abs/2304.06530v2 | https://arxiv.org/pdf/2304.06530v2.pdf | Robust Stability of Gaussian Process Based Moving Horizon Estimation | In this paper, we introduce a Gaussian process based moving horizon estimation (MHE) framework. The scheme is based on offline collected data and offline hyperparameter optimization. In particular, compared to standard MHE schemes, we replace the mathematical model of the system by the posterior mean of the Gaussian pr... | ['Matthias A. Müller', 'Victor G. Lopez', 'Tobias M. Wolff'] | 2023-04-13 | null | null | null | null | ['hyperparameter-optimization'] | ['methodology'] | [ 1.91471260e-02 4.10446882e-01 3.11702006e-02 3.97990733e-01
-6.77754998e-01 -4.06231046e-01 4.59856540e-01 9.84853432e-02
-4.21087265e-01 8.72793496e-01 -2.07345292e-01 -3.17744434e-01
-6.12621427e-01 -4.16554481e-01 -7.47594357e-01 -1.10740376e+00
-9.80874225e-02 9.35012996e-02 4.19011042e-02 1.87191263... | [5.128200054168701, 2.534946918487549] |
eb23324b-e5a2-40d2-b997-4d41061d600f | shuffle-divide-contrastive-learning-for-long | 2304.09374 | null | https://arxiv.org/abs/2304.09374v1 | https://arxiv.org/pdf/2304.09374v1.pdf | Shuffle & Divide: Contrastive Learning for Long Text | We propose a self-supervised learning method for long text documents based on contrastive learning. A key to our method is Shuffle and Divide (SaD), a simple text augmentation algorithm that sets up a pretext task required for contrastive updates to BERT-based document embedding. SaD splits a document into two sub-docu... | ['Youngjune Gwon', 'Jaeseon Park', 'Hoyoung Kang', 'Bogun Kim', 'Kyoungwon Park', 'Seongho Joe', 'Joonseok Lee'] | 2023-04-19 | null | null | null | null | ['document-embedding', 'text-augmentation', 'unsupervised-text-classification'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 1.30530864e-01 -9.17102024e-02 -2.53583640e-01 -4.45172459e-01
-7.08340883e-01 -5.29571891e-01 1.00999570e+00 5.05992949e-01
-9.39178228e-01 7.64194250e-01 2.22735628e-01 -3.76331598e-01
1.11469693e-01 -5.13435245e-01 -2.58295149e-01 -7.47589588e-01
-2.05752477e-01 9.13251221e-01 1.37622848e-01 -4.10029113... | [10.538511276245117, 7.606536865234375] |
512ef07b-aa70-4f0c-b3d9-5cabdea41d6e | adaptive-behavior-cloning-regularization-for-1 | 2210.13846 | null | https://arxiv.org/abs/2210.13846v1 | https://arxiv.org/pdf/2210.13846v1.pdf | Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning | Offline reinforcement learning, by learning from a fixed dataset, makes it possible to learn agent behaviors without interacting with the environment. However, depending on the quality of the offline dataset, such pre-trained agents may have limited performance and would further need to be fine-tuned online by interact... | ['Joni Pajarinen', 'Juho Kannala', 'Alexander Ilin', 'Rinu Boney', 'Yi Zhao'] | 2022-10-25 | adaptive-behavior-cloning-regularization-for | https://openreview.net/forum?id=JVsvIuMDE0Z | https://openreview.net/pdf?id=JVsvIuMDE0Z | null | ['d4rl'] | ['robots'] | [-4.71821785e-01 5.27430028e-02 -3.00879449e-01 -2.69908488e-01
-7.69180715e-01 -7.45457292e-01 4.48586106e-01 2.57192343e-01
-7.49939919e-01 1.08008504e+00 -2.74041802e-01 -9.43154693e-02
-2.16924533e-01 -7.97930956e-01 -9.28731203e-01 -9.87259746e-01
-2.12824583e-01 7.54780412e-01 2.68675655e-01 -2.41742402... | [3.94535756111145, 2.2279961109161377] |
f307f080-0a63-4ba4-8b0d-e6a6f0672404 | dynamic-sensor-placement-based-on-graph | 2211.04019 | null | https://arxiv.org/abs/2211.04019v1 | https://arxiv.org/pdf/2211.04019v1.pdf | Dynamic Sensor Placement Based on Graph Sampling Theory | In this paper, we consider a dynamic sensor placement problem where sensors can move within a network over time. Sensor placement problem aims to select M sensor positions from N candidates where M < N. Most existing methods assume that sensors are static, i.e., they do not move, however, many mobile sensors like drone... | ['Yuichi Tanaka', 'Junya Hara', 'Saki Nomura'] | 2022-11-08 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 4.26351011e-01 2.16065094e-01 -3.06734681e-01 -1.46006057e-02
-4.19024944e-01 -8.18082750e-01 9.97858495e-03 4.70736474e-01
-4.46245015e-01 6.27014041e-01 -1.05788313e-01 3.28920968e-02
-5.30390859e-01 -1.01628351e+00 -9.20862436e-01 -1.00807822e+00
-3.44587296e-01 3.97733539e-01 3.56893927e-01 -2.40393400... | [6.028942108154297, 1.4445114135742188] |
541ee5de-907c-418f-b9fc-7156e6231383 | deep-mining-external-imperfect-data-for-chest | 2006.03796 | null | https://arxiv.org/abs/2006.03796v1 | https://arxiv.org/pdf/2006.03796v1.pdf | Deep Mining External Imperfect Data for Chest X-ray Disease Screening | Deep learning approaches have demonstrated remarkable progress in automatic Chest X-ray analysis. The data-driven feature of deep models requires training data to cover a large distribution. Therefore, it is substantial to integrate knowledge from multiple datasets, especially for medical images. However, learning a di... | ['Pheng-Ann Heng', 'Xi Wang', 'Quande Liu', 'Luyang Luo', 'Lequan Yu', 'Hao Chen', 'Jiaqi Xu'] | 2020-06-06 | null | null | null | null | ['thoracic-disease-classification'] | ['computer-vision'] | [ 2.47477844e-01 -6.39561787e-02 -4.47751433e-01 -6.67380989e-01
-1.36334503e+00 -5.52414060e-01 8.25853944e-02 -7.94453099e-02
-1.84537694e-01 9.43067372e-01 8.78580380e-03 -4.58520889e-01
-2.86037296e-01 -5.85715652e-01 -6.64752364e-01 -7.26643205e-01
3.80716324e-01 7.50170112e-01 -7.24591911e-02 2.09227830... | [14.887750625610352, -2.093658685684204] |
6a648605-c7f9-442a-a192-1fb68bbdf960 | conversations-are-not-flat-modeling-the | 2106.02227 | null | https://arxiv.org/abs/2106.02227v1 | https://arxiv.org/pdf/2106.02227v1.pdf | Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances | Nowadays, open-domain dialogue models can generate acceptable responses according to the historical context based on the large-scale pre-trained language models. However, they generally concatenate the dialogue history directly as the model input to predict the response, which we named as the flat pattern and ignores t... | ['Jie zhou', 'Yang Feng', 'Zhengcong Fei', 'Jinchao Zhang', 'Zekang Li'] | 2021-06-04 | null | https://aclanthology.org/2021.acl-long.11 | https://aclanthology.org/2021.acl-long.11.pdf | acl-2021-5 | ['dialogue-evaluation'] | ['natural-language-processing'] | [-2.95338541e-01 3.08463961e-01 1.38930812e-01 -5.81664205e-01
-7.18354642e-01 -7.01319396e-01 7.34823704e-01 -3.49390268e-01
-1.75751507e-01 9.02276814e-01 8.12431693e-01 -2.74223864e-01
2.81512529e-01 -5.66729426e-01 -6.11274503e-02 -2.36970827e-01
4.12890881e-01 6.32461011e-01 2.39835590e-01 -8.07467401... | [12.800207138061523, 8.103632926940918] |
d533df23-9393-4789-9075-68a80a66b007 | partial-occlusion-handling-for-visual | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Zhang_Partial_Occlusion_Handling_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Zhang_Partial_Occlusion_Handling_2014_CVPR_paper.pdf | Partial Occlusion Handling for Visual Tracking via Robust Part Matching | Part-based visual tracking is advantageous due to its robustness against partial occlusion. However, how to effectively exploit the confidence scores of individual parts to construct a robust tracker is still a challenging problem. In this paper, we address this problem by simultaneously matching parts in each of multi... | ['Narendra Ahuja', 'Kui Jia', 'Yi Ma', 'Tianzhu Zhang', 'Changsheng Xu'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['occlusion-handling'] | ['computer-vision'] | [ 5.08860983e-02 -6.14752769e-01 -3.13993812e-01 3.10296621e-02
-7.59207904e-01 -4.95339245e-01 5.84813893e-01 -2.90434480e-01
-6.75615966e-02 5.52112639e-01 3.05587709e-01 3.94123703e-01
-1.96590602e-01 -1.50266528e-01 -7.89297462e-01 -7.96981931e-01
-6.33141473e-02 2.57604837e-01 7.09816337e-01 4.37978879... | [6.393035888671875, -2.1303329467773438] |
727143d8-e293-49a9-8693-146e0603265e | real-time-monocular-object-slam | 1504.02398 | null | http://arxiv.org/abs/1504.02398v1 | http://arxiv.org/pdf/1504.02398v1.pdf | Real-time Monocular Object SLAM | We present a real-time object-based SLAM system that leverages the largest
object database to date. Our approach comprises two main components: 1) a
monocular SLAM algorithm that exploits object rigidity constraints to improve
the map and find its real scale, and 2) a novel object recognition algorithm
based on bags of... | ['Juan D. Tardós', 'Dorian Gálvez-López', 'Marta Salas', 'J. M. M. Montiel'] | 2015-04-09 | null | null | null | null | ['object-slam'] | ['computer-vision'] | [-6.87715262e-02 -1.21137522e-01 -4.82985042e-02 -4.83403087e-01
-5.48069060e-01 -6.41634285e-01 6.35188222e-01 4.89441216e-01
-7.00398862e-01 5.96914709e-01 -1.54292077e-01 1.43743947e-01
-2.19113991e-01 -7.32697785e-01 -7.91744173e-01 -4.26376730e-01
-2.57798940e-01 1.32787728e+00 8.35223973e-01 -1.13209300... | [7.34589147567749, -2.2577102184295654] |
a3f9a4c7-5629-4d06-a95f-5e53b42f04ad | cinematic-mindscapes-high-quality-video | 2305.11675 | null | https://arxiv.org/abs/2305.11675v1 | https://arxiv.org/pdf/2305.11675v1.pdf | Cinematic Mindscapes: High-quality Video Reconstruction from Brain Activity | Reconstructing human vision from brain activities has been an appealing task that helps to understand our cognitive process. Even though recent research has seen great success in reconstructing static images from non-invasive brain recordings, work on recovering continuous visual experiences in the form of videos is li... | ['Juan Helen Zhou', 'Jiaxin Qing', 'Zijiao Chen'] | 2023-05-19 | null | null | null | null | ['video-reconstruction'] | ['computer-vision'] | [ 4.95845139e-01 2.33758718e-01 2.73168355e-01 -1.98422983e-01
-4.75151658e-01 -4.13320750e-01 6.37743831e-01 -4.89010870e-01
-5.43875992e-01 8.45572293e-01 3.81683886e-01 1.67676598e-01
2.25468315e-02 -2.54203141e-01 -1.02430522e+00 -6.79137588e-01
-2.00153291e-01 2.01823562e-02 1.28222197e-01 1.43089056... | [10.74774169921875, 2.502939462661743] |
b27df45d-c5de-49cd-912c-ad20c749ba63 | graph-based-label-enhancement-for-multi | 2304.10705 | null | https://arxiv.org/abs/2304.10705v1 | https://arxiv.org/pdf/2304.10705v1.pdf | Graph based Label Enhancement for Multi-instance Multi-label learning | Multi-instance multi-label (MIML) learning is widely applicated in numerous domains, such as the image classification where one image contains multiple instances correlated with multiple logic labels simultaneously. The related labels in existing MIML are all assumed as logical labels with equal significance. However, ... | ['Chi-Man Vong', 'Jiao Li', 'Rui Yan', 'Daixian Liu', 'Jintao Huang', 'Houcheng Su'] | 2023-04-21 | null | null | null | null | ['multi-label-learning'] | ['methodology'] | [ 5.55892706e-01 1.06128938e-01 -5.55293620e-01 -5.97643375e-01
-6.36842012e-01 -1.11053422e-01 1.08386807e-01 5.52831471e-01
-1.98807448e-01 6.83223367e-01 -2.41709843e-01 1.86635792e-01
-4.43443537e-01 -8.51816595e-01 -5.52200854e-01 -8.80788147e-01
1.60402119e-01 3.02830309e-01 2.49817207e-01 2.86708653... | [9.712873458862305, 4.032660484313965] |
9408ec90-799d-40d4-b53b-f2743a364188 | deep-cascaded-bi-network-for-face | 1607.05046 | null | http://arxiv.org/abs/1607.05046v1 | http://arxiv.org/pdf/1607.05046v1.pdf | Deep Cascaded Bi-Network for Face Hallucination | We present a novel framework for hallucinating faces of unconstrained poses
and with very low resolution (face size as small as 5pxIOD). In contrast to
existing studies that mostly ignore or assume pre-aligned face spatial
configuration (e.g. facial landmarks localization or dense correspondence
field), we alternatingl... | ['Sifei Liu', 'Chen Change Loy', 'Xiaoou Tang', 'Shizhan Zhu'] | 2016-07-18 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 2.58835107e-02 2.42492124e-01 3.01342994e-01 -6.11483097e-01
-6.64875090e-01 -1.75149888e-01 4.58537996e-01 -8.97040129e-01
-5.24481479e-03 7.72021770e-01 2.38175765e-01 4.89751399e-01
1.26451358e-01 -7.01742291e-01 -8.05998802e-01 -4.57181007e-01
2.09542945e-01 4.94206250e-01 -2.87910283e-01 -1.32167965... | [12.865338325500488, -0.05446699634194374] |
bb9d9b61-5e97-438e-9580-bfc76df01a80 | 190503246 | 1905.03246 | null | https://arxiv.org/abs/1905.03246v3 | https://arxiv.org/pdf/1905.03246v3.pdf | End-to-End Wireframe Parsing | We present a conceptually simple yet effective algorithm to detect wireframes in a given image. Compared to the previous methods which first predict an intermediate heat map and then extract straight lines with heuristic algorithms, our method is end-to-end trainable and can directly output a vectorized wireframe that ... | ['Haozhi Qi', 'Yi Ma', 'Yichao Zhou'] | 2019-05-08 | end-to-end-wireframe-parsing | http://openaccess.thecvf.com/content_ICCV_2019/html/Zhou_End-to-End_Wireframe_Parsing_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhou_End-to-End_Wireframe_Parsing_ICCV_2019_paper.pdf | iccv-2019-10 | ['line-segment-detection', 'wireframe-parsing'] | ['computer-vision', 'computer-vision'] | [-4.26959880e-02 1.93644121e-01 -3.23182136e-01 -4.87832874e-01
-1.26073754e+00 -9.66794014e-01 4.19767976e-01 1.88156441e-01
-8.94587114e-02 4.71211553e-01 -4.97775106e-03 -6.14769816e-01
1.93859577e-01 -8.79016399e-01 -9.24625397e-01 -1.49434498e-02
9.41656902e-03 2.23372176e-01 5.84628522e-01 -1.54227123... | [8.314665794372559, -1.695186734199524] |
02af9bf4-4f23-478c-8224-bb06ffb0e88a | attention-is-all-we-need-nailing-down-object | 1807.11794 | null | http://arxiv.org/abs/1807.11794v1 | http://arxiv.org/pdf/1807.11794v1.pdf | Attention is All We Need: Nailing Down Object-centric Attention for Egocentric Activity Recognition | In this paper we propose an end-to-end trainable deep neural network model
for egocentric activity recognition. Our model is built on the observation that
egocentric activities are highly characterized by the objects and their
locations in the video. Based on this, we develop a spatial attention mechanism
that enables ... | ['Swathikiran Sudhakaran', 'Oswald Lanz'] | 2018-07-31 | null | null | null | null | ['egocentric-activity-recognition', 'hand-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.53712082e-01 3.83746736e-02 -3.27486932e-01 -2.95811266e-01
-6.66584373e-01 -5.63710690e-01 7.01032162e-01 -2.65155941e-01
-6.29061222e-01 4.13266689e-01 6.62087917e-01 3.60187382e-01
-1.87017415e-02 -3.67759943e-01 -1.12254548e+00 -6.57319665e-01
-2.09417269e-01 3.14052165e-01 6.24073343e-03 2.53738016... | [8.316555976867676, 0.614240825176239] |
4b8209b1-0bc5-42bc-83dd-4e293b22d8ad | active-passive-simstereo-benchmarking-the | 2209.08305 | null | https://arxiv.org/abs/2209.08305v1 | https://arxiv.org/pdf/2209.08305v1.pdf | Active-Passive SimStereo -- Benchmarking the Cross-Generalization Capabilities of Deep Learning-based Stereo Methods | In stereo vision, self-similar or bland regions can make it difficult to match patches between two images. Active stereo-based methods mitigate this problem by projecting a pseudo-random pattern on the scene so that each patch of an image pair can be identified without ambiguity. However, the projected pattern signific... | ['Mohammed Bennamoun', 'Farid Boussaid', 'Hamid Laga', 'Lian Xu', 'Allen Antony', 'Laurent Jospin'] | 2022-09-17 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 4.98986095e-01 2.72710621e-01 2.66502649e-01 -3.92359048e-01
-5.09069085e-01 -7.49202847e-01 8.46567273e-01 -1.90467939e-01
-6.14740968e-01 5.49997568e-01 1.14008449e-01 9.69552249e-02
1.86731234e-01 -8.79583418e-01 -9.50571537e-01 -8.17273080e-01
5.34091711e-01 2.88195699e-01 6.86046958e-01 -3.53620738... | [8.702064514160156, -2.303119421005249] |
a7521747-8b98-444a-af29-0f2e7ce68f72 | capsule-forensics-using-capsule-networks-to | 1810.11215 | null | http://arxiv.org/abs/1810.11215v1 | http://arxiv.org/pdf/1810.11215v1.pdf | Capsule-Forensics: Using Capsule Networks to Detect Forged Images and Videos | Recent advances in media generation techniques have made it easier for
attackers to create forged images and videos. State-of-the-art methods enable
the real-time creation of a forged version of a single video obtained from a
social network. Although numerous methods have been developed for detecting
forged images and ... | ['Junichi Yamagishi', 'Isao Echizen', 'Huy H. Nguyen'] | 2018-10-26 | null | null | null | null | ['detect-forged-images-and-videos'] | ['computer-vision'] | [ 5.12201726e-01 -1.91950679e-01 2.57355332e-01 1.79706231e-01
-3.33106726e-01 -1.00412679e+00 6.53923035e-01 -2.93012589e-01
-2.32728466e-01 5.99588215e-01 -1.36363611e-01 -4.68056291e-01
1.40930280e-01 -1.01904726e+00 -9.80783165e-01 -4.58408326e-01
-4.96269375e-01 -3.54160070e-01 2.75828302e-01 -2.82181889... | [12.511260032653809, 1.1223548650741577] |
57dabad8-bbc3-483e-ada3-92aa82623fb9 | point-cloud-registration-of-non-rigid-objects | 2212.03856 | null | https://arxiv.org/abs/2212.03856v2 | https://arxiv.org/pdf/2212.03856v2.pdf | Point Cloud Registration of non-rigid objects in sparse 3D Scans with applications in Mixed Reality | Point Cloud Registration is the problem of aligning the corresponding points of two 3D point clouds referring to the same object. The challenges include dealing with noise and partial match of real-world 3D scans. For non-rigid objects, there is an additional challenge of accounting for deformations in the object shape... | ['Manorama Jha'] | 2022-12-07 | null | null | null | null | ['point-cloud-registration', 'mixed-reality'] | ['computer-vision', 'computer-vision'] | [ 1.19667441e-01 4.70631942e-02 4.87232089e-01 -2.14437291e-01
-5.55280983e-01 -6.67003274e-01 6.08071566e-01 -5.56908026e-02
-1.99052140e-01 -2.96880417e-02 -2.80285209e-01 1.47019014e-01
-3.47054631e-01 -4.93515611e-01 -9.63937044e-01 -2.59716600e-01
8.63759890e-02 1.45926929e+00 4.98977274e-01 -4.42130178... | [7.779484272003174, -2.8101093769073486] |
4b473ff7-9c35-44b7-85a2-d858a28528b4 | regret-analysis-of-the-stochastic-direct | 2210.05222 | null | https://arxiv.org/abs/2210.05222v1 | https://arxiv.org/pdf/2210.05222v1.pdf | Regret Analysis of the Stochastic Direct Search Method for Blind Resource Allocation | Motivated by programmatic advertising optimization, we consider the task of sequentially allocating budget across a set of resources. At every time step, a feasible allocation is chosen and only a corresponding random return is observed. The goal is to maximize the cumulative expected sum of returns. This is a realisti... | ['Aurélien Garivier', 'Olivier Cappe', 'Juliette Achddou'] | 2022-10-11 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 3.29779655e-01 1.85484603e-01 -6.39249504e-01 -4.21099007e-01
-8.01350117e-01 -8.12060714e-01 2.01524884e-01 1.70985371e-01
-7.38510132e-01 8.09388340e-01 -3.70991193e-02 -5.50410867e-01
-6.75611556e-01 -7.86843359e-01 -8.35319817e-01 -6.08099341e-01
-1.26009822e-01 7.81389236e-01 -2.72100240e-01 -9.10809636... | [4.541106224060059, 3.3141894340515137] |
fd0d7ce0-0d23-4264-8d07-9eb5b25eecfe | boosting-multiple-sclerosis-lesion | 2304.10790 | null | https://arxiv.org/abs/2304.10790v1 | https://arxiv.org/pdf/2304.10790v1.pdf | Boosting multiple sclerosis lesion segmentation through attention mechanism | Magnetic resonance imaging is a fundamental tool to reach a diagnosis of multiple sclerosis and monitoring its progression. Although several attempts have been made to segment multiple sclerosis lesions using artificial intelligence, fully automated analysis is not yet available. State-of-the-art methods rely on slight... | ['Sebastiano Battiato', 'Francesco Pappalardo', 'Davide Maimone', 'Clara Di Lorenzo', 'Giulia Russo', 'Alessandro Ortis', 'Oliver Giudice', 'Francesco Guarnera', 'Elena Crispino', 'Alessia Rondinella'] | 2023-04-21 | null | null | null | null | ['lesion-segmentation'] | ['medical'] | [ 4.43452060e-01 2.58780401e-02 -1.16009563e-01 -4.30761844e-01
-9.21674848e-01 -1.49514705e-01 4.93807107e-01 1.86548874e-01
-7.24278867e-01 6.64349496e-01 -1.53373331e-01 -3.89456861e-02
-4.88045841e-01 -4.99778032e-01 -4.32533026e-01 -5.17188489e-01
-6.70994699e-01 9.03222620e-01 6.15595579e-01 -1.70100741... | [14.217767715454102, -2.104799747467041] |
bfffee7e-47ed-423d-bc60-aa5ea2bebb90 | learning-to-adapt-to-unseen-abnormal | 2203.13610 | null | https://arxiv.org/abs/2203.13610v1 | https://arxiv.org/pdf/2203.13610v1.pdf | Learning to Adapt to Unseen Abnormal Activities under Weak Supervision | We present a meta-learning framework for weakly supervised anomaly detection in videos, where the detector learns to adapt to unseen types of abnormal activities effectively when only video-level annotations of binary labels are available. Our work is motivated by the fact that existing methods suffer from poor general... | ['Bohyung Han', 'Junha Kim', 'Jaeyoo Park'] | 2022-03-25 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 7.64078051e-02 -1.52712762e-01 -2.47235194e-01 -4.74734485e-01
-8.30052376e-01 -4.17888463e-01 4.42039967e-01 2.68967841e-02
-6.14504397e-01 3.48962963e-01 1.02911420e-01 2.56683510e-02
1.96200371e-01 -2.78599799e-01 -8.97148371e-01 -4.72009301e-01
-5.42555571e-01 2.60958642e-01 3.09640318e-01 -1.86374187... | [7.854234218597412, 1.5832692384719849] |
8832bc91-2be0-47a1-bbb3-0b80f6688835 | feature-embedding-in-click-through-rate | 2209.09481 | null | https://arxiv.org/abs/2209.09481v1 | https://arxiv.org/pdf/2209.09481v1.pdf | Feature embedding in click-through rate prediction | We tackle the challenge of feature embedding for the purposes of improving the click-through rate prediction process. We select three models: logistic regression, factorization machines and deep factorization machines, as our baselines and propose five different feature embedding modules: embedding scaling, FM embeddin... | ['Jure Demšar', 'Davorin Kopič', 'Samo Pahor'] | 2022-09-20 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-2.20455118e-02 -1.19451163e-02 -5.39995790e-01 -3.99326622e-01
-7.47471631e-01 -2.59882331e-01 8.33392203e-01 2.66146451e-01
-6.36898816e-01 4.39104319e-01 5.77119052e-01 -5.41028976e-01
-3.35716195e-02 -8.18124592e-01 -5.00848949e-01 -1.93036020e-01
-8.96378756e-02 2.81581938e-01 1.68817595e-01 -2.67733663... | [10.151095390319824, 5.614904880523682] |
c07ec659-5e4a-4242-9334-60f44b66a63d | transfer-meets-hybrid-a-synthetic-approach | 1901.07199 | null | http://arxiv.org/abs/1901.07199v1 | http://arxiv.org/pdf/1901.07199v1.pdf | Transfer Meets Hybrid: A Synthetic Approach for Cross-Domain Collaborative Filtering with Text | Collaborative filtering (CF) is the key technique for recommender systems
(RSs). CF exploits user-item behavior interactions (e.g., clicks) only and
hence suffers from the data sparsity issue. One research thread is to integrate
auxiliary information such as product reviews and news titles, leading to
hybrid filtering ... | ['Guang-Neng Hu', 'Qiang Yang', 'Yu Zhang'] | 2019-01-22 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [ 1.87467456e-01 -2.54637897e-01 -5.02424121e-01 -4.96018350e-01
-5.47494471e-01 -4.44651991e-01 3.13727587e-01 -1.03622779e-01
-2.72342741e-01 6.58414423e-01 5.48985481e-01 -2.78599054e-01
-3.81096780e-01 -8.78844738e-01 -6.99782670e-01 -3.42855722e-01
9.51761305e-02 2.18526855e-01 1.34348646e-01 -6.35763586... | [10.164776802062988, 5.610990524291992] |
9658362e-8212-4de5-a018-e0f2d09d5ca9 | mutual-gaze-and-linguistic-repetition-in-a | null | null | https://aclanthology.org/2022.lrec-1.296 | https://aclanthology.org/2022.lrec-1.296.pdf | Mutual Gaze and Linguistic Repetition in a Multimodal Corpus | This paper investigates the correlation between mutual gaze and linguistic repetition, a form of alignment, which we take as evidence of mutual understanding. We focus on a multimodal corpus made of three-party conversations and explore the question of whether mutual gaze events correspond to moments of repetition or n... | ['Carl Vogel', 'Maria Koutsombogera', 'Anais Murat'] | null | null | null | null | lrec-2022-6 | ['mutual-gaze'] | ['computer-vision'] | [ 1.14271127e-01 1.20085344e-01 -3.56708288e-01 -2.18589872e-01
-3.97558540e-01 -8.59865427e-01 1.26245534e+00 5.21872520e-01
-5.20432353e-01 4.56632406e-01 9.58057046e-01 -3.83454949e-01
3.58810154e-04 -3.53835195e-01 -3.04834753e-01 -5.86104214e-01
-1.52022541e-01 5.73572926e-02 -1.70894712e-01 -5.73874056... | [10.317985534667969, 9.33016586303711] |
e23048e7-d32c-4812-8f30-3ae6be8be847 | triple-structural-information-modelling-for | 2304.11528 | null | https://arxiv.org/abs/2304.11528v1 | https://arxiv.org/pdf/2304.11528v1.pdf | Triple Structural Information Modelling for Accurate, Explainable and Interactive Recommendation | In dynamic interaction graphs, user-item interactions usually follow heterogeneous patterns, represented by different structural information, such as user-item co-occurrence, sequential information of user interactions and the transition probabilities of item pairs. However, the existing methods cannot simultaneously l... | ['Ning Gu', 'Li Shang', 'Peng Zhang', 'Tun Lu', 'Hansu Gu', 'Dongsheng Li', 'Jiahao Liu'] | 2023-04-23 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [-1.69292971e-01 -8.52094442e-02 -3.73985976e-01 -1.54152915e-01
1.99396595e-01 -5.08631110e-01 1.62999868e-01 2.26705328e-01
3.11577260e-01 2.11805999e-01 7.58806348e-01 -3.75060916e-01
-5.84662676e-01 -8.36839676e-01 -5.06429195e-01 -2.47859821e-01
-2.68578053e-01 2.23060846e-01 1.58930514e-02 -5.13679266... | [10.175649642944336, 5.617172718048096] |
8876c320-926a-4f71-9f55-bfa630522196 | convolution-based-channel-frequency-attention | 2210.17310 | null | https://arxiv.org/abs/2210.17310v1 | https://arxiv.org/pdf/2210.17310v1.pdf | Convolution-Based Channel-Frequency Attention for Text-Independent Speaker Verification | Deep convolutional neural networks (CNNs) have been applied to extracting speaker embeddings with significant success in speaker verification. Incorporating the attention mechanism has shown to be effective in improving the model performance. This paper presents an efficient two-dimensional convolution-based attention ... | ['Tan Lee', 'Yusheng Tian', 'Jingyu Li'] | 2022-10-31 | null | null | null | null | ['text-independent-speaker-verification', 'speaker-verification'] | ['speech', 'speech'] | [-2.58958519e-01 -2.24311337e-01 2.17872620e-01 -4.91540223e-01
-8.13074112e-01 -3.64189856e-02 3.92369002e-01 -1.86511889e-01
-5.19753277e-01 2.05014586e-01 6.61833286e-01 -1.80164546e-01
3.05961788e-01 -4.94422644e-01 -3.94994974e-01 -6.65356755e-01
-1.38502687e-01 -4.06513698e-02 -8.10271502e-03 -1.25657171... | [14.367362022399902, 6.127752780914307] |
781a62fe-2fcd-4ae0-8090-ebab7e97adba | vilbert-pretraining-task-agnostic | 1908.02265 | null | https://arxiv.org/abs/1908.02265v1 | https://arxiv.org/pdf/1908.02265v1.pdf | ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks | We present ViLBERT (short for Vision-and-Language BERT), a model for learning task-agnostic joint representations of image content and natural language. We extend the popular BERT architecture to a multi-modal two-stream model, pro-cessing both visual and textual inputs in separate streams that interact through co-atte... | ['Stefan Lee', 'Jiasen Lu', 'Dhruv Batra', 'Devi Parikh'] | 2019-08-06 | vilbert-pretraining-task-agnostic-1 | http://papers.nips.cc/paper/8297-vilbert-pretraining-task-agnostic-visiolinguistic-representations-for-vision-and-language-tasks | http://papers.nips.cc/paper/8297-vilbert-pretraining-task-agnostic-visiolinguistic-representations-for-vision-and-language-tasks.pdf | neurips-2019-12 | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 2.68312663e-01 4.51439053e-01 -6.77765906e-02 -5.45134962e-01
-9.54010725e-01 -7.42681861e-01 1.17082608e+00 1.14613906e-01
-5.10966361e-01 3.61266106e-01 5.16449690e-01 -5.39690197e-01
3.46417397e-01 -4.50956523e-01 -1.15695143e+00 -1.18755028e-01
4.31850672e-01 7.71462321e-01 2.67167181e-01 -3.25750083... | [10.835336685180664, 1.7728861570358276] |
4e6f512e-6536-4d12-a335-1871887ee2e2 | text-sampling-strategies-for-predicting | 2301.01673 | null | https://arxiv.org/abs/2301.01673v1 | https://arxiv.org/pdf/2301.01673v1.pdf | Text sampling strategies for predicting missing bibliographic links | The paper proposes various strategies for sampling text data when performing automatic sentence classification for the purpose of detecting missing bibliographic links. We construct samples based on sentences as semantic units of the text and add their immediate context which consists of several neighboring sentences. ... | ['E. N. Baskakova', 'I. S. Smaznevicha', 'F. V. Krasnova'] | 2023-01-04 | null | null | null | null | ['sentence-classification'] | ['natural-language-processing'] | [ 3.46102297e-01 1.66690901e-01 -3.45680267e-01 -2.28366777e-01
-5.45006514e-01 -5.64407170e-01 8.56324434e-01 7.51805663e-01
-5.66888213e-01 9.86996949e-01 6.00223482e-01 -6.20063066e-01
-3.68019551e-01 -1.14090979e+00 -2.73095846e-01 -3.57350111e-01
4.29495007e-01 4.30378914e-01 5.48798144e-01 -9.31233391... | [12.071832656860352, 9.562891006469727] |
785e2e21-34bd-48c6-8110-410f8d4e5012 | mild-multi-index-hashing-for-loop-closure | 1702.08780 | null | http://arxiv.org/abs/1702.08780v1 | http://arxiv.org/pdf/1702.08780v1.pdf | MILD: Multi-Index hashing for Loop closure Detection | Loop Closure Detection (LCD) has been proved to be extremely useful in global
consistent visual Simultaneously Localization and Mapping (SLAM) and
appearance-based robot relocalization. Methods exploiting binary features in
bag of words representation have recently gained a lot of popularity for their
efficiency, but s... | ['Lu Fang', 'Lei Han'] | 2017-02-28 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [-1.02317110e-02 -5.14495730e-01 -4.12454456e-01 -2.40184397e-01
-8.64445090e-01 -4.64022189e-01 9.06613469e-01 7.87954569e-01
-7.38390863e-01 3.82678181e-01 -1.44598410e-02 1.02129295e-01
-3.03664893e-01 -6.35923982e-01 -5.61641514e-01 -6.44629776e-01
-3.01814467e-01 3.88697386e-01 4.11248386e-01 -7.56219774... | [7.435938358306885, -2.1201446056365967] |
919e91eb-8f6e-4b52-9484-53b24d08bb23 | traffic-analytics-development-kits-tadk | 2208.07558 | null | https://arxiv.org/abs/2208.07558v1 | https://arxiv.org/pdf/2208.07558v1.pdf | Traffic Analytics Development Kits (TADK): Enable Real-Time AI Inference in Networking Apps | Sophisticated traffic analytics, such as the encrypted traffic analytics and unknown malware detection, emphasizes the need for advanced methods to analyze the network traffic. Traditional methods of using fixed patterns, signature matching, and rules to detect known patterns in network traffic are being replaced with ... | ['Shuo Dai', 'Xiaobo Liu', 'Weigang Li', 'Jianwei Ma', 'Yingqi Liu', 'Wenjun Zhu', 'Xiahui Yu', 'Ying Wang', 'Harry Chang', 'Kun Qiu'] | 2022-08-16 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [-2.80810058e-01 -7.50729799e-01 -1.62451357e-01 -2.26352677e-01
2.42411017e-01 -4.91794288e-01 1.85751781e-01 -2.47136548e-01
-1.35336921e-01 2.80721664e-01 -5.54035902e-01 -1.26662397e+00
-1.12999722e-01 -1.08975255e+00 -7.46737048e-02 -3.79624218e-01
-1.71393633e-01 6.54105783e-01 8.01676035e-01 -4.84821945... | [5.127316951751709, 7.212810516357422] |
7be94abe-9daf-4208-9d4c-303bc6edaa2b | interpretable-and-generalizable-deep-image | 1904.10424 | null | https://arxiv.org/abs/1904.10424v4 | https://arxiv.org/pdf/1904.10424v4.pdf | Interpretable and Generalizable Person Re-Identification with Query-Adaptive Convolution and Temporal Lifting | For person re-identification, existing deep networks often focus on representation learning. However, without transfer learning, the learned model is fixed as is, which is not adaptable for handling various unseen scenarios. In this paper, beyond representation learning, we consider how to formulate person image matchi... | ['Shengcai Liao', 'Ling Shao'] | 2019-04-23 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1369_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123560443.pdf | eccv-2020-8 | ['generalizable-person-re-identification'] | ['computer-vision'] | [-1.69804052e-01 -5.66445887e-01 -1.54141616e-02 -7.96659708e-01
-7.21506119e-01 -3.80683631e-01 5.49931645e-01 7.75427148e-02
-6.61484897e-01 4.97221619e-01 4.07583028e-01 3.29456270e-01
-1.33112431e-01 -9.21179354e-01 -6.11353040e-01 -3.10451418e-01
2.99903676e-02 5.94991803e-01 -9.79706198e-02 -1.09246135... | [14.705004692077637, 0.9523134827613831] |
b8de4dd8-117d-40dc-8d17-44375bb9661b | lightesd-fully-automated-and-lightweight | 2305.12266 | null | https://arxiv.org/abs/2305.12266v1 | https://arxiv.org/pdf/2305.12266v1.pdf | LightESD: Fully-Automated and Lightweight Anomaly Detection Framework for Edge Computing | Anomaly detection is widely used in a broad range of domains from cybersecurity to manufacturing, finance, and so on. Deep learning based anomaly detection has recently drawn much attention because of its superior capability of recognizing complex data patterns and identifying outliers accurately. However, deep learnin... | ['Tie Luo', 'Ronit Das'] | 2023-05-20 | null | null | null | null | ['edge-computing'] | ['time-series'] | [-3.61785650e-01 -6.30112410e-01 -2.35716984e-01 -6.46556690e-02
-1.92545533e-01 -3.50379825e-01 1.50155693e-01 5.12438655e-01
-3.08455735e-01 1.19301878e-01 -4.57931459e-01 -5.80141306e-01
-1.00583002e-01 -7.70920336e-01 -4.60878462e-01 -5.65261841e-01
-1.42304122e-01 4.07828599e-01 2.79226989e-01 1.82912126... | [7.397425174713135, 2.7031192779541016] |
4b72f4a2-c4b9-48b5-aad2-d14392f756f6 | ada-nets-face-clustering-via-adaptive-1 | 2202.03800 | null | https://arxiv.org/abs/2202.03800v3 | https://arxiv.org/pdf/2202.03800v3.pdf | Ada-NETS: Face Clustering via Adaptive Neighbour Discovery in the Structure Space | Face clustering has attracted rising research interest recently to take advantage of massive amounts of face images on the web. State-of-the-art performance has been achieved by Graph Convolutional Networks (GCN) due to their powerful representation capacity. However, existing GCN-based methods build face graphs mainly... | ['Yuqi Zhang', 'Xiuyu Sun', 'Ming Lin', 'Senzhang Wang', 'Fangyi Zhang', 'Yaobin Zhang', 'Yaohua Wang'] | 2022-02-08 | ada-nets-face-clustering-via-adaptive | https://openreview.net/forum?id=QJWVP4CTmW4 | https://openreview.net/pdf?id=QJWVP4CTmW4 | iclr-2022-4 | ['face-clustering'] | ['computer-vision'] | [-2.02693731e-01 -1.12694353e-01 2.89853632e-01 -4.77109313e-01
-9.96064618e-02 -2.62913138e-01 4.89047289e-01 -1.98424220e-01
-8.94976258e-02 1.96399868e-01 -6.63970187e-02 2.59745598e-01
-3.46183479e-01 -1.24568582e+00 -4.98329222e-01 -9.82182860e-01
-2.11929381e-01 4.00549173e-01 -3.37220691e-02 -1.21359952... | [13.443795204162598, 1.070569634437561] |
459bec5d-dd04-4712-ba8e-9bbc99ce924f | hiding-data-in-colors-secure-and-lossless | 2201.07444 | null | https://arxiv.org/abs/2201.07444v1 | https://arxiv.org/pdf/2201.07444v1.pdf | Hiding Data in Colors: Secure and Lossless Deep Image Steganography via Conditional Invertible Neural Networks | Deep image steganography is a data hiding technology that conceal data in digital images via deep neural networks. However, existing deep image steganography methods only consider the visual similarity of container images to host images, and neglect the statistical security (stealthiness) of container images. Besides, ... | ['Lina Wang', 'Liming Zhai', 'Ting Liu', 'Yanzhen Ren'] | 2022-01-19 | null | null | null | null | ['steganalysis', 'image-steganography'] | ['computer-vision', 'computer-vision'] | [ 5.82447171e-01 -1.60476461e-01 1.51522115e-01 1.15026675e-01
-1.37217566e-01 -4.25261468e-01 9.14468616e-02 -6.38913691e-01
-5.42518139e-01 3.16925943e-01 -1.61270559e-01 -6.95078433e-01
5.34540892e-01 -1.12885296e+00 -6.50153279e-01 -1.17512047e+00
-2.98385292e-01 -3.43920499e-01 1.33213654e-01 -2.45777145... | [4.322116851806641, 8.04431438446045] |
029a3b51-807e-4457-9106-21151235e33b | multi-dimensional-edge-based-audio-event | 2210.15366 | null | https://arxiv.org/abs/2210.15366v2 | https://arxiv.org/pdf/2210.15366v2.pdf | Multi-dimensional Edge-based Audio Event Relational Graph Representation Learning for Acoustic Scene Classification | Most existing deep learning-based acoustic scene classification (ASC) approaches directly utilize representations extracted from spectrograms to identify target scenes. However, these approaches pay little attention to the audio events occurring in the scene despite they provide crucial semantic information. This paper... | ['Dick Botteldooren', 'Wenwu Wang', 'Yuxin Song', 'Chuang Yu', 'Siyang Song', 'Yuanbo Hou'] | 2022-10-27 | null | null | null | null | ['scene-classification'] | ['computer-vision'] | [ 2.53396273e-01 -2.35683665e-01 2.68533498e-01 -5.39185405e-01
-7.59496570e-01 -4.43434030e-01 3.58919919e-01 4.58768994e-01
-1.95039421e-01 -5.43655008e-02 3.79515231e-01 8.31726417e-02
-1.80781335e-01 -7.93681324e-01 -6.54341042e-01 -5.98027766e-01
-2.28475899e-01 -1.01496078e-01 2.65547425e-01 8.43912885... | [14.988616943359375, 4.990789413452148] |
f8b92e07-ada1-421c-ae35-e0abf631118e | qigen-generating-efficient-kernels-for | 2307.03738 | null | https://arxiv.org/abs/2307.03738v1 | https://arxiv.org/pdf/2307.03738v1.pdf | QIGen: Generating Efficient Kernels for Quantized Inference on Large Language Models | We present ongoing work on a new automatic code generation approach for supporting quantized generative inference on LLMs such as LLaMA or OPT on off-the-shelf CPUs. Our approach is informed by the target architecture and a performance model, including both hardware characteristics and method-specific accuracy constrai... | ['Markus Püschel', 'Dan Alistarh', 'Elias Frantar', 'Tommaso Pegolotti'] | 2023-07-07 | null | null | null | null | ['code-generation'] | ['computer-code'] | [-1.77958012e-01 -4.08407524e-02 -4.14718062e-01 -5.37652910e-01
-1.33911407e+00 -5.91024339e-01 7.62285233e-01 -2.93770999e-01
1.91854998e-01 7.69604921e-01 6.79659918e-02 -9.39799666e-01
4.56902504e-01 -7.23866642e-01 -5.60409307e-01 -4.23373938e-01
-1.94811627e-01 8.46157253e-01 1.02313079e-01 5.25838621... | [8.663736343383789, 3.5102200508117676] |
17f75cf4-a0f7-4daf-b019-386b233e728b | deeper-and-wider-siamese-networks-for-real | 1901.01660 | null | http://arxiv.org/abs/1901.01660v3 | http://arxiv.org/pdf/1901.01660v3.pdf | Deeper and Wider Siamese Networks for Real-Time Visual Tracking | Siamese networks have drawn great attention in visual tracking because of
their balanced accuracy and speed. However, the backbone networks used in
Siamese trackers are relatively shallow, such as AlexNet [18], which does not
fully take advantage of the capability of modern deep neural networks. In this
paper, we inves... | ['Houwen Peng', 'Zhipeng Zhang'] | 2019-01-07 | deeper-and-wider-siamese-networks-for-real-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_Deeper_and_Wider_Siamese_Networks_for_Real-Time_Visual_Tracking_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Deeper_and_Wider_Siamese_Networks_for_Real-Time_Visual_Tracking_CVPR_2019_paper.pdf | cvpr-2019-6 | ['real-time-visual-tracking'] | ['computer-vision'] | [-3.14487785e-01 -2.35293224e-01 -4.00850177e-01 1.93347633e-02
6.49286434e-02 -5.72247744e-01 5.18755138e-01 -3.32648784e-01
-8.40809166e-01 5.66907287e-01 -7.98157007e-02 -9.09747258e-02
9.17471126e-02 -5.50785601e-01 -7.55712628e-01 -4.23456818e-01
-3.47986430e-01 -2.91692108e-01 7.62571752e-01 -3.64740044... | [6.259329795837402, -2.1181745529174805] |
e85b701d-62c2-43f6-91e8-3d8d8e87bbb8 | multimodal-semi-supervised-learning-for3d | 2110.11601 | null | https://arxiv.org/abs/2110.11601v2 | https://arxiv.org/pdf/2110.11601v2.pdf | Multimodal Semi-Supervised Learning for 3D Objects | In recent years, semi-supervised learning has been widely explored and shows excellent data efficiency for 2D data. There is an emerging need to improve data efficiency for 3D tasks due to the scarcity of labeled 3D data. This paper explores how the coherence of different modelities of 3D data (e.g. point cloud, image,... | ['Bing Li', 'YingLi Tian', 'Yang Liang', 'Longlong Jing', 'Zhimin Chen'] | 2021-10-22 | null | null | null | null | ['3d-classification'] | ['computer-vision'] | [ 1.34349670e-02 3.30294445e-02 -4.19045568e-01 -6.88586712e-01
-1.14056933e+00 -4.85061347e-01 7.90287733e-01 3.67283612e-01
-2.70059913e-01 3.78370821e-01 -9.31231305e-02 1.91742226e-01
-2.97823876e-01 -6.46397114e-01 -8.21078420e-01 -8.07724178e-01
-3.40389013e-02 7.47769713e-01 1.17536470e-01 6.45473823... | [8.148521423339844, -3.579679250717163] |
dfdc85b5-dd05-4d02-a6c9-aa5c2cbe236f | multimodal-image-to-image-translation-via | 2008.03529 | null | https://arxiv.org/abs/2008.03529v7 | https://arxiv.org/pdf/2008.03529v7.pdf | Multimodal Image-to-Image Translation via Mutual Information Estimation and Maximization | Multimodal image-to-image translation (I2IT) aims to learn a conditional distribution that explores multiple possible images in the target domain given an input image in the source domain. Conditional generative adversarial networks (cGANs) are often adopted for modeling such a conditional distribution. However, cGANs ... | ['Qijiang Xu', 'Ailin Li', 'Zhizhong Wang', 'Lei Zhao', 'Haibo Chen', 'Zhiwen Zuo', 'Wei Xing', 'Dongming Lu'] | 2020-08-08 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 4.19554979e-01 3.64251584e-01 -1.29762128e-01 -1.45768762e-01
-8.96705627e-01 -7.79718876e-01 6.65362656e-01 -8.05842280e-01
1.61279038e-01 8.42997968e-01 3.16929311e-01 -3.51544544e-02
2.71234423e-01 -7.72646606e-01 -9.76663232e-01 -1.15548754e+00
6.03559434e-01 2.39680126e-01 -4.45026666e-01 -8.93329829... | [11.629826545715332, -0.29737934470176697] |
21eff4f7-23cf-4b24-aba0-c2f69badba3f | adam-few-shot-image-generation-via-adaptation | 2307.01465 | null | https://arxiv.org/abs/2307.01465v2 | https://arxiv.org/pdf/2307.01465v2.pdf | AdAM: Few-Shot Image Generation via Adaptation-Aware Kernel Modulation | Few-shot image generation (FSIG) aims to learn to generate new and diverse images given few (e.g., 10) training samples. Recent work has addressed FSIG by leveraging a GAN pre-trained on a large-scale source domain and adapting it to the target domain with few target samples. Central to recent FSIG methods are knowledg... | ['Ngai-Man Cheung', 'Henghui Ding', 'Ruoteng Li', 'Tianyu Pang', 'Chao Du', 'Abdollahzadeh Milad', 'Keshigeyan Chandrasegaran', 'Yunqing Zhao'] | 2023-07-04 | null | null | null | null | ['image-generation'] | ['computer-vision'] | [ 5.83728254e-01 3.46069261e-02 -4.09274340e-01 -1.93269029e-01
-9.54933405e-01 -6.18717194e-01 8.60823452e-01 -5.13193905e-01
-1.39304623e-01 1.14062619e+00 1.00226127e-01 3.13495845e-01
-1.21514007e-01 -7.34154522e-01 -8.84311676e-01 -7.70704865e-01
3.38430375e-01 4.73158509e-01 4.60152030e-01 -2.48264208... | [10.24509334564209, 2.7154366970062256] |
4b994a5d-12d5-4174-af23-e7f212bf04ef | the-manipulation-problem-conversational-ai-as | 2306.11748 | null | https://arxiv.org/abs/2306.11748v1 | https://arxiv.org/pdf/2306.11748v1.pdf | The Manipulation Problem: Conversational AI as a Threat to Epistemic Agency | The technology of Conversational AI has made significant advancements over the last eighteen months. As a consequence, conversational agents are likely to be deployed in the near future that are designed to pursue targeted influence objectives. Sometimes referred to as the "AI Manipulation Problem," the emerging risk i... | ['Louis Rosenberg'] | 2023-06-19 | null | null | null | null | ['misinformation'] | ['miscellaneous'] | [ 4.72515494e-01 8.93958867e-01 -6.49849400e-02 -1.81685343e-01
-3.43276143e-01 -9.84668911e-01 1.29781950e+00 -1.02726541e-01
-3.12254459e-01 5.18581510e-01 6.37007236e-01 -4.55075920e-01
2.17641905e-01 -6.27868712e-01 -2.78235096e-02 -3.92800331e-01
4.03120369e-01 4.95426238e-01 -2.10097544e-02 -7.09037781... | [9.235128402709961, 6.405200481414795] |
b32da83a-8241-4b32-8fa7-e2cec9e4c8c0 | deepphysics-a-physics-aware-deep-learning | 2109.09491 | null | https://arxiv.org/abs/2109.09491v1 | https://arxiv.org/pdf/2109.09491v1.pdf | DeepPhysics: a physics aware deep learning framework for real-time simulation | Real-time simulation of elastic structures is essential in many applications, from computer-guided surgical interventions to interactive design in mechanical engineering. The Finite Element Method is often used as the numerical method of reference for solving the partial differential equations associated with these pro... | ['Stéphane Cotin', 'Ryadh Haferssas', 'Alban Odot'] | 2021-09-17 | null | null | null | null | ['cantilever-beam'] | ['miscellaneous'] | [ 1.30425125e-01 3.64830464e-01 1.81876585e-01 -1.52986020e-01
-5.99027574e-01 -1.48636401e-01 1.19026765e-01 1.00482695e-01
-4.63166296e-01 8.87308896e-01 -3.83461803e-01 -8.28431547e-02
-3.95753890e-01 -1.01589978e+00 -1.01492882e+00 -8.86103332e-01
-1.43611282e-01 8.11724961e-01 2.23400861e-01 -5.12117445... | [6.366211891174316, 3.379960775375366] |
7b5950f4-37ba-4546-a93e-66b0652caaf4 | speeding-up-one-vs-all-training-for-extreme | 2109.13122 | null | https://arxiv.org/abs/2109.13122v1 | https://arxiv.org/pdf/2109.13122v1.pdf | Speeding-up One-vs-All Training for Extreme Classification via Smart Initialization | In this paper we show that a simple, data dependent way of setting the initial vector can be used to substantially speed up the training of linear one-versus-all (OVA) classifiers in extreme multi-label classification (XMC). We discuss the problem of choosing the initial weights from the perspective of three goals. We ... | ['Rohit Babbar', 'Erik Schultheis'] | 2021-09-27 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 3.48370701e-01 1.66947648e-01 -1.46658465e-01 -7.27221131e-01
-9.99881148e-01 -7.00605214e-02 8.83709788e-02 7.60076284e-01
-8.90713573e-01 7.85841525e-01 -2.65066773e-01 -3.63988549e-01
-4.20677304e-01 -4.97676402e-01 -3.65258723e-01 -1.01512468e+00
-2.48020515e-01 6.96111858e-01 -3.99054550e-02 -2.20577180... | [8.694286346435547, 4.226199626922607] |
98794c11-d5f7-4e3e-8baf-da041a8df630 | plasma-making-small-language-models-better | 2305.19472 | null | https://arxiv.org/abs/2305.19472v1 | https://arxiv.org/pdf/2305.19472v1.pdf | PlaSma: Making Small Language Models Better Procedural Knowledge Models for (Counterfactual) Planning | Procedural planning, which entails decomposing a high-level goal into a sequence of temporally ordered steps, is an important yet intricate task for machines. It involves integrating common-sense knowledge to reason about complex contextualized situations that are often counterfactual, e.g. "scheduling a doctor's appoi... | ['Yejin Choi', 'Xiang Ren', 'Keisuke Sakaguchi', 'Soumya Sanyal', 'Hirona J. Arai', 'Xiang Lorraine Li', 'Jena D. Hwang', 'Valentina Pyatkin', 'Chandra Bhagavatula', 'Faeze Brahman'] | 2023-05-31 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [ 2.83906341e-01 9.70472932e-01 -2.29162976e-01 -3.23533952e-01
-8.66309345e-01 -5.33097506e-01 1.02793729e+00 1.16265498e-01
-3.45226735e-01 1.35213172e+00 6.06595039e-01 -8.78878236e-01
-4.00698364e-01 -7.96988726e-01 -8.19356501e-01 -2.72214293e-01
-2.37746775e-01 7.84480870e-01 8.97780061e-02 -2.14061931... | [4.202368259429932, 1.2268503904342651] |
df7feee3-3a78-4623-8672-cb37530432af | adversarial-learning-based-stance-classifier | 2209.04631 | null | https://arxiv.org/abs/2209.04631v3 | https://arxiv.org/pdf/2209.04631v3.pdf | Adversarial Learning-based Stance Classifier for COVID-19-related Health Policies | The ongoing COVID-19 pandemic has caused immeasurable losses for people worldwide. To contain the spread of the virus and further alleviate the crisis, various health policies (e.g., stay-at-home orders) have been issued which spark heated discussions as users turn to share their attitudes on social media. In this pape... | ['Yusong Tan', 'Lei Tian', 'Jiaying Zou', 'Haiyang Wang', 'Feng Xie', 'Bin Zhou', 'Xuechen Zhao', 'Zhong Zhang'] | 2022-09-10 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [-1.74036957e-02 3.47351640e-01 -5.05001664e-01 -3.54480147e-01
-7.11902142e-01 -3.74718249e-01 7.10485578e-01 4.08031583e-01
-4.23732579e-01 6.27181172e-01 9.60210383e-01 -5.02533495e-01
5.69745243e-01 -9.36603069e-01 -6.56378448e-01 -5.96583366e-01
1.13062061e-01 8.08738708e-01 -4.54252958e-02 -5.62341928... | [8.506062507629395, 9.501327514648438] |
b198824c-35c4-4c58-90c2-e103e9abc245 | stock-market-prediction-using-natural | 2208.13564 | null | https://arxiv.org/abs/2208.13564v1 | https://arxiv.org/pdf/2208.13564v1.pdf | Stock Market Prediction using Natural Language Processing -- A Survey | The stock market is a network which provides a platform for almost all major economic transactions. While investing in the stock market is a good idea, investing in individual stocks may not be, especially for the casual investor. Smart stock-picking requires in-depth research and plenty of dedication. Predicting this ... | ['Saravanakumar kandasamy', 'Om Mane'] | 2022-08-26 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [-6.71528876e-01 -2.79929429e-01 -7.22520888e-01 -2.17859477e-01
1.11809716e-01 -7.05710649e-01 4.82433796e-01 4.99402024e-02
-4.51862276e-01 9.06694770e-01 1.53999543e-02 -6.78564131e-01
-7.82772526e-02 -1.16625679e+00 -4.09165546e-02 -4.17016655e-01
-3.68814737e-01 3.61758828e-01 3.27196956e-01 -6.28633142... | [4.546178817749023, 4.199467182159424] |
111be701-0125-4436-a085-8d832c362674 | estimating-post-ocr-denoising-complexity-on | 2307.01020 | null | https://arxiv.org/abs/2307.01020v1 | https://arxiv.org/pdf/2307.01020v1.pdf | Estimating Post-OCR Denoising Complexity on Numerical Texts | Post-OCR processing has significantly improved over the past few years. However, these have been primarily beneficial for texts consisting of natural, alphabetical words, as opposed to documents of numerical nature such as invoices, payslips, medical certificates, etc. To evaluate the OCR post-processing difficulty of ... | ['Jean-Marc Ogier', 'Mickaël Coustaty', 'Jérôme Brachat', 'Arthur Hemmer'] | 2023-07-03 | null | null | null | null | ['optical-character-recognition'] | ['computer-vision'] | [ 3.16767931e-01 -3.85092795e-01 4.09275979e-01 -2.58558929e-01
-1.01982248e+00 -7.30611205e-01 6.92145109e-01 5.81154585e-01
-9.32192564e-01 5.91319680e-01 2.43591577e-01 -3.98441166e-01
-3.60471189e-01 -6.65272593e-01 -4.79529589e-01 -5.74289441e-01
1.28698677e-01 1.09327123e-01 5.45886829e-02 -3.01181436... | [11.903037071228027, 2.7341768741607666] |
04c0b1c9-607d-4018-af91-8ff15d79afa4 | random-sampling-for-fast-face-sketch | 1701.01911 | null | http://arxiv.org/abs/1701.01911v2 | http://arxiv.org/pdf/1701.01911v2.pdf | Random Sampling for Fast Face Sketch Synthesis | Exemplar-based face sketch synthesis plays an important role in both digital
entertainment and law enforcement. It generally consists of two parts: neighbor
selection and reconstruction weight representation. The most time-consuming or
main computation complexity for exemplar-based face sketch synthesis methods
lies in... | ['Nannan Wang', 'Jie Li', 'Xinbo Gao'] | 2017-01-08 | null | null | null | null | ['face-sketch-synthesis', 'face-hallucination'] | ['computer-vision', 'computer-vision'] | [ 5.46756573e-02 -1.93236396e-01 -2.31329769e-01 -4.27953005e-01
-6.14819705e-01 -2.06420392e-01 4.63194579e-01 -4.51969266e-01
-7.57918954e-02 5.73265791e-01 -1.52222009e-03 -3.47011983e-02
-2.54155874e-01 -9.69117284e-01 -4.79212612e-01 -5.70606887e-01
3.18619937e-01 3.98493052e-01 2.35347878e-02 -2.51480967... | [12.67884349822998, 0.027391087263822556] |
1b1bee1a-0022-4bb5-9b5d-3a8d002d7a52 | ai-generated-incentive-mechanism-and-full | 2303.01896 | null | https://arxiv.org/abs/2303.01896v2 | https://arxiv.org/pdf/2303.01896v2.pdf | AI-Generated Incentive Mechanism and Full-Duplex Semantic Communications for Information Sharing | The next generation of Internet services, such as Metaverse, rely on mixed reality (MR) technology to provide immersive user experiences. However, the limited computation power of MR headset-mounted devices (HMDs) hinders the deployment of such services. Therefore, we propose an efficient information sharing scheme bas... | ['Dong In Kim', 'Zehui Xiong', 'Jiawen Kang', 'Dusit Niyato', 'Jiacheng Wang', 'Hongyang Du'] | 2023-03-03 | null | null | null | null | ['mixed-reality'] | ['computer-vision'] | [-4.47864920e-01 2.92332917e-01 -3.72138172e-01 -5.29353954e-02
-6.60131156e-01 -4.20108348e-01 1.90029472e-01 -4.88213241e-01
-3.70794654e-01 8.92842948e-01 2.73413777e-01 -4.51574892e-01
-1.43595949e-01 -8.17187965e-01 -5.36967158e-01 -7.96669245e-01
-2.70056069e-01 7.78989643e-02 -2.54220545e-01 -1.81602836... | [5.989175796508789, 1.599082589149475] |
02145ca8-a57d-46ce-9634-f0dd225580d0 | open-challenges-for-monocular-single-shot-6d | 2302.11827 | null | https://arxiv.org/abs/2302.11827v1 | https://arxiv.org/pdf/2302.11827v1.pdf | Open Challenges for Monocular Single-shot 6D Object Pose Estimation | Object pose estimation is a non-trivial task that enables robotic manipulation, bin picking, augmented reality, and scene understanding, to name a few use cases. Monocular object pose estimation gained considerable momentum with the rise of high-performing deep learning-based solutions and is particularly interesting f... | ['Markus Vincze', 'Jean-Baptiste Weibel', 'Peter Hönig', 'Stefan Thalhammer'] | 2023-02-23 | null | null | null | null | ['occlusion-handling', '6d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 9.72731262e-02 -2.16971576e-01 -6.42241418e-01 -8.83272663e-02
-2.43298516e-01 -5.13042629e-01 4.05127138e-01 -1.48175552e-01
-3.22220355e-01 5.14803410e-01 -2.19437689e-01 2.09369510e-01
-4.90767002e-01 -3.52191269e-01 -9.16762114e-01 -5.82241893e-01
-2.19363477e-02 6.86539829e-01 2.04004511e-01 -6.04598178... | [7.216326713562012, -2.3118128776550293] |
951077f3-2981-46d6-8dfc-cf72ca94497e | adaptive-real-time-exploration-and | 2211.05495 | null | https://arxiv.org/abs/2211.05495v2 | https://arxiv.org/pdf/2211.05495v2.pdf | Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO Algorithm | We consider the problem of decision-making under uncertainty in an environment with safety constraints. Many business and industrial applications rely on real-time optimization to improve key performance indicators. In the case of unknown characteristics, real-time optimization becomes challenging, particularly because... | ['Mehmet Mercangöz', 'Marta Zagórowska', 'Buse Sibel Korkmaz'] | 2022-11-10 | null | null | null | null | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 4.10400741e-02 3.21053684e-01 -2.37914637e-01 -1.97495237e-01
-1.07565093e+00 -7.73687184e-01 2.31830180e-01 3.53066236e-01
-5.66550016e-01 1.13187444e+00 -1.84012592e-01 -5.34770906e-01
-9.69007015e-01 -7.95027137e-01 -1.02284026e+00 -9.32704628e-01
-2.46561110e-01 7.99264371e-01 -2.99328089e-01 7.88305048... | [4.567654609680176, 3.1244962215423584] |
74c8282e-d975-4411-bde5-04cc954058f2 | simco-similarity-based-object-counting | 1904.07092 | null | https://arxiv.org/abs/1904.07092v2 | https://arxiv.org/pdf/1904.07092v2.pdf | SIMCO: SIMilarity-based object COunting | We present SIMCO, the first agnostic multi-class object counting approach. SIMCO starts by detecting foreground objects through a novel Mask RCNN-based architecture trained beforehand (just once) on a brand-new synthetic 2D shape dataset, InShape; the idea is to highlight every object resembling a primitive 2D shape (c... | ['Andrea Giachetti', 'Marco Godi', 'Marco Cristani', 'Christian Joppi'] | 2019-04-15 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 3.16692173e-01 -7.25800470e-02 1.04583167e-01 4.07638997e-02
-3.23224813e-01 -6.89986885e-01 1.06732869e+00 3.73565018e-01
-7.74189174e-01 2.47854561e-01 -1.28435582e-01 -1.62132122e-02
3.99421781e-01 -7.65034914e-01 -8.06843340e-01 -7.43947089e-01
-1.25683621e-01 1.12107742e+00 9.14046466e-01 1.23184569... | [9.0451078414917, 0.49930113554000854] |
df214a42-30bd-4b34-bf49-eed26d9e99a2 | df-net-unsupervised-joint-learning-of-depth | 1809.01649 | null | http://arxiv.org/abs/1809.01649v1 | http://arxiv.org/pdf/1809.01649v1.pdf | DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency | We present an unsupervised learning framework for simultaneously training
single-view depth prediction and optical flow estimation models using unlabeled
video sequences. Existing unsupervised methods often exploit brightness
constancy and spatial smoothness priors to train depth or flow models. In this
paper, we propo... | ['Jia-Bin Huang', 'Yuliang Zou', 'Zelun Luo'] | 2018-09-05 | df-net-unsupervised-joint-learning-of-depth-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Yuliang_Zou_DF-Net_Unsupervised_Joint_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Yuliang_Zou_DF-Net_Unsupervised_Joint_ECCV_2018_paper.pdf | eccv-2018-9 | ['depth-and-camera-motion'] | ['computer-vision'] | [ 1.98977083e-01 1.55640483e-01 -5.00695229e-01 -5.85267007e-01
-3.17201316e-01 -6.58063948e-01 6.45243406e-01 -6.28511488e-01
-3.24928313e-01 7.14000285e-01 4.15597111e-01 -4.58620638e-02
4.25978035e-01 -4.20436740e-01 -7.55017400e-01 -5.64492226e-01
8.48240182e-02 2.00929493e-01 1.99930206e-01 4.30838913... | [8.645195960998535, -2.0286357402801514] |
18e846fd-9eed-4c71-a2d5-16692cd173cd | abode-net-an-attention-based-deep-learning | 2212.11396 | null | https://arxiv.org/abs/2212.11396v1 | https://arxiv.org/pdf/2212.11396v1.pdf | ABODE-Net: An Attention-based Deep Learning Model for Non-intrusive Building Occupancy Detection Using Smart Meter Data | Occupancy information is useful for efficient energy management in the building sector. The massive high-resolution electrical power consumption data collected by smart meters in the advanced metering infrastructure (AMI) network make it possible to infer buildings' occupancy status in a non-intrusive way. In this pape... | ['Sihua Shao', 'Jun Zheng', 'Qingqing Li', 'Ruobin Qi', 'Zhirui Luo'] | 2022-12-21 | null | null | null | null | ['energy-management'] | ['time-series'] | [-1.30227000e-01 -1.17625564e-01 1.32765114e-01 -3.00447941e-01
-7.66326845e-01 1.55674517e-01 6.67314112e-01 2.20289409e-01
-4.17149633e-01 7.40039051e-01 5.42135239e-01 -2.89200306e-01
-1.42686680e-01 -1.15028000e+00 -2.32908487e-01 -1.25166237e+00
-2.44300634e-01 3.56783599e-01 -2.14849725e-01 -1.30094262... | [16.062183380126953, 7.577486038208008] |
6ed1e62a-8857-4b98-a3c4-85ae93d18425 | unsupervised-shadow-removal-using-target | 2010.01291 | null | https://arxiv.org/abs/2010.01291v2 | https://arxiv.org/pdf/2010.01291v2.pdf | Unsupervised Shadow Removal Using Target Consistency Generative Adversarial Network | Unsupervised shadow removal aims to learn a non-linear function to map the original image from shadow domain to non-shadow domain in the absence of paired shadow and non-shadow data. In this paper, we develop a simple yet efficient target-consistency generative adversarial network (TC-GAN) for the shadow removal task i... | ['Xin Feng', 'Chao Tan'] | 2020-10-03 | null | null | null | null | ['shadow-removal'] | ['computer-vision'] | [ 8.41682374e-01 5.06823242e-01 2.87030607e-01 -3.83909434e-01
-5.44943988e-01 -3.21352541e-01 6.26321912e-01 -1.07778871e+00
1.10988855e-01 1.16636336e+00 -4.76905219e-02 -3.17472160e-01
4.45125222e-01 -8.47883999e-01 -8.09727907e-01 -1.26793098e+00
3.98680747e-01 5.37565649e-01 3.43410254e-01 -2.07655638... | [10.845298767089844, -4.103167533874512] |
67fcec12-b54e-46eb-b928-581328b0b69f | language-models-are-weak-learners | 2306.14101 | null | https://arxiv.org/abs/2306.14101v1 | https://arxiv.org/pdf/2306.14101v1.pdf | Language models are weak learners | A central notion in practical and theoretical machine learning is that of a $\textit{weak learner}$, classifiers that achieve better-than-random performance (on any given distribution over data), even by a small margin. Such weak learners form the practical basis for canonical machine learning methods such as boosting.... | ['J Zico Kolter', 'Yiding Jiang', 'Hariharan Manikandan'] | 2023-06-25 | null | null | null | null | ['few-shot-learning'] | ['methodology'] | [-6.19160496e-02 2.55602300e-01 -6.05401278e-01 -5.66380501e-01
-1.52649355e+00 -4.98974741e-01 9.90680814e-01 5.88858187e-01
-3.95197451e-01 6.99414790e-01 2.53722787e-01 -8.16707730e-01
1.01083949e-01 -9.07779455e-01 -8.58088493e-01 -6.96449161e-01
1.27488211e-01 6.37440860e-01 2.28338927e-01 -3.89611572... | [10.8049898147583, 8.103851318359375] |
1d1a47bc-388e-44e5-b82a-1821d18d6e16 | image-to-gps-verification-through-a-bottom-up | 1811.07288 | null | http://arxiv.org/abs/1811.07288v1 | http://arxiv.org/pdf/1811.07288v1.pdf | Image-to-GPS Verification Through A Bottom-Up Pattern Matching Network | The image-to-GPS verification problem asks whether a given image is taken at
a claimed GPS location. In this paper, we treat it as an image verification
problem -- whether a query image is taken at the same place as a reference
image retrieved at the claimed GPS location. We make three major contributions:
1) we propos... | ['Prem Natarajan', 'Wael Abd-Almageed', 'Jiaxin Cheng', 'Yue Wu'] | 2018-11-18 | null | null | null | null | ['image-to-gps-verification'] | ['computer-vision'] | [ 3.10724974e-01 -2.26996750e-01 -3.21809202e-01 -4.93296415e-01
-1.35147214e+00 -6.73472345e-01 6.52796447e-01 -1.12026319e-01
-3.55741829e-01 2.01064751e-01 -1.84603691e-01 -4.50770587e-01
-5.23582436e-02 -6.84165120e-01 -1.45649040e+00 -5.35364330e-01
-1.64673060e-01 1.01439901e-01 3.78754079e-01 1.95580259... | [7.6977033615112305, -1.9689515829086304] |
2ca705e8-0c35-439e-9606-1d514826050d | meta-learning-with-maml-on-trees | 2103.04691 | null | https://arxiv.org/abs/2103.04691v1 | https://arxiv.org/pdf/2103.04691v1.pdf | Meta-Learning with MAML on Trees | In meta-learning, the knowledge learned from previous tasks is transferred to new ones, but this transfer only works if tasks are related. Sharing information between unrelated tasks might hurt performance, and it is unclear how to transfer knowledge across tasks with a hierarchical structure. Our research extends a mo... | ['Alberto Bernacchia', 'Ye Tian', 'Da-Shan Shiu', 'Tim Nieradzik', 'Jamie McGowan', 'Feng-Ting Liao', 'Federica Freddi', 'Jezabel R. Garcia'] | 2021-03-08 | null | null | null | null | ['cross-lingual-natural-language-inference'] | ['natural-language-processing'] | [ 1.85887203e-01 4.40091379e-02 -2.62678444e-01 -4.44590449e-01
-5.86921811e-01 -7.79963732e-01 7.15221524e-01 4.13620695e-02
-7.29949951e-01 9.03251231e-01 2.14384064e-01 -2.87514240e-01
-1.67031527e-01 -5.04513979e-01 -9.44525540e-01 -5.99630415e-01
-1.14262544e-01 8.12568486e-01 4.59458798e-01 -7.15246201... | [10.95283031463623, 9.30573844909668] |
f9857153-d1a0-415c-9f10-67418925f331 | sscbench-a-large-scale-3d-semantic-scene | 2306.09001 | null | https://arxiv.org/abs/2306.09001v1 | https://arxiv.org/pdf/2306.09001v1.pdf | SSCBench: A Large-Scale 3D Semantic Scene Completion Benchmark for Autonomous Driving | Semantic scene completion (SSC) is crucial for holistic 3D scene understanding by jointly estimating semantics and geometry from sparse observations. However, progress in SSC, particularly in autonomous driving scenarios, is hindered by the scarcity of high-quality datasets. To overcome this challenge, we introduce SSC... | ['Chen Feng', 'Zhiding Yu', 'Hang Zhao', 'Yue Wang', 'Fisher Yu', 'Tao Jiang', 'Zhiheng Li', 'Zijun Wang', 'Nuo Chen', 'Kenan Li', 'Moonjun Gong', 'Xinhao Liu', 'Sihang Li', 'Yiming Li'] | 2023-06-15 | null | null | null | null | ['3d-semantic-scene-completion', 'scene-understanding'] | ['computer-vision', 'computer-vision'] | [ 4.12664041e-02 -1.80013612e-01 1.64620895e-02 -6.91644192e-01
-9.02173460e-01 -7.51605451e-01 6.85018361e-01 -1.44646361e-01
-1.82164222e-01 3.60298783e-01 3.02053690e-01 -1.97055623e-01
1.95586577e-01 -6.63313091e-01 -7.82293499e-01 -5.87933138e-02
1.68015853e-01 5.90440869e-01 6.47403479e-01 -5.57993591... | [7.984550952911377, -2.129031181335449] |
720c4dad-e7a6-446d-991f-14a74e324b8e | deep-learning-approaches-to-osteosarcoma | null | null | https://doi.org/10.3390/cancers15082290 | https://doi.org/10.3390/cancers15082290 | Deep Learning Approaches to Osteosarcoma Diagnosis and Classification: A Comparative Methodological Approach | Background: Osteosarcoma is the most common primary malignancy of the bone, being most prevalent in childhood and adolescence. Despite recent progress in diagnostic methods, histopathology remains the gold standard for disease staging and therapy decisions. Machine learning and deep learning methods have shown potentia... | ['George K. Matsopoulos', 'George I. Lambrou', 'Ioannis A. Vezakis'] | 2023-04-13 | null | null | null | cancers-2023-4 | ['tumour-classification'] | ['medical'] | [ 2.25293059e-02 -2.17179313e-01 -3.53599668e-01 5.57999089e-02
-8.96525264e-01 5.41524887e-02 -3.48462234e-03 4.85676169e-01
-1.00863218e+00 6.99438095e-01 -3.50893326e-02 -4.56379414e-01
-1.94315597e-01 -8.43707561e-01 -2.36695912e-02 -9.83859897e-01
-2.00965386e-02 7.70863831e-01 3.59009773e-01 -7.13588223... | [15.098892211914062, -2.880466938018799] |
cb1aa9c9-a695-4912-bcb9-8f655c3707ed | a-serial-dual-channel-library-occupancy | 2306.16080 | null | https://arxiv.org/abs/2306.16080v1 | https://arxiv.org/pdf/2306.16080v1.pdf | A serial dual-channel library occupancy detection system based on Faster RCNN | The phenomenon of seat occupancy in university libraries is a prevalent issue. However, existing solutions, such as software-based seat reservations and sensors-based occupancy detection, have proven to be inadequate in effectively addressing this problem. In this study, we propose a novel approach: a serial dual-chann... | ['Xin Chen', 'Min Yang', 'Zitong Wang', 'XiaoWen Chang', 'Guoqiang Yang'] | 2023-06-28 | null | null | null | null | ['transfer-learning'] | ['miscellaneous'] | [-1.69806808e-01 -4.39334571e-01 -4.41596322e-02 -4.67668116e-01
-6.72687411e-01 -5.24353385e-01 2.21336395e-01 1.51885469e-02
-7.16842353e-01 7.22405910e-01 -9.89321433e-03 -8.50934148e-01
-4.93518747e-02 -1.23562157e+00 -6.21358812e-01 -5.56406319e-01
4.30239767e-01 1.79518253e-01 2.96635740e-02 -1.21356465... | [8.185853958129883, -0.8962308168411255] |
c2634d30-bd6f-43ee-b89b-c742bffa426a | cross-modal-learning-for-image-guided-point | 2209.09552 | null | https://arxiv.org/abs/2209.09552v1 | https://arxiv.org/pdf/2209.09552v1.pdf | Cross-modal Learning for Image-Guided Point Cloud Shape Completion | In this paper we explore the recent topic of point cloud completion, guided by an auxiliary image. We show how it is possible to effectively combine the information from the two modalities in a localized latent space, thus avoiding the need for complex point cloud reconstruction methods from single views used by the st... | ['Enrico Magli', 'Diego Valsesia', 'Emanuele Aiello'] | 2022-09-20 | null | null | null | null | ['point-cloud-completion', 'point-cloud-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 2.78982699e-01 2.35472515e-01 -1.04801036e-01 -1.91066191e-01
-1.32314777e+00 -6.40011728e-01 9.74760473e-01 7.14453682e-03
-1.83674246e-01 4.97870982e-01 1.73682854e-01 6.98939860e-02
-8.14746618e-02 -5.02712250e-01 -1.14330268e+00 -7.46726692e-01
3.21336418e-01 8.14419925e-01 8.22172612e-02 -1.12169394... | [8.60417366027832, -3.0723557472229004] |
f46b520f-e0e6-436a-a0ae-9d0669959d11 | leveraging-real-conversational-data-for-multi | 2204.03232 | null | https://arxiv.org/abs/2204.03232v1 | https://arxiv.org/pdf/2204.03232v1.pdf | Leveraging Real Conversational Data for Multi-Channel Continuous Speech Separation | Existing multi-channel continuous speech separation (CSS) models are heavily dependent on supervised data - either simulated data which causes data mismatch between the training and real-data testing, or the real transcribed overlapping data, which is difficult to be acquired, hindering further improvements in the conv... | ['Takuya Yoshioka', 'Sefik Emre Eskimez', 'Naoyuki Kanda', 'Dongmei Wang', 'Xiaofei Wang'] | 2022-04-07 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 5.98644197e-01 6.20856322e-02 4.38620716e-01 -5.01593411e-01
-1.65856278e+00 -3.93199742e-01 3.81836444e-01 -1.93206519e-01
-2.47512937e-01 4.43883479e-01 4.65375215e-01 -4.26071346e-01
9.54848342e-03 -1.31537259e-01 -5.43720901e-01 -8.92840624e-01
1.49286119e-02 5.23350716e-01 5.51843680e-02 -3.27121586... | [14.666594505310059, 6.3971686363220215] |
abb3a7d6-88dc-4d7c-9a31-8d08b71527a0 | an-ensemble-of-machine-learning-and-anti | 1607.06190 | null | http://arxiv.org/abs/1607.06190v1 | http://arxiv.org/pdf/1607.06190v1.pdf | An ensemble of machine learning and anti-learning methods for predicting tumour patient survival rates | This paper primarily addresses a dataset relating to cellular, chemical and
physical conditions of patients gathered at the time they are operated upon to
remove colorectal tumours. This data provides a unique insight into the
biochemical and immunological status of patients at the point of tumour removal
along with in... | ['John Scholefield', 'Durga Suryanarayanan', 'Christopher Roadknight', 'Uwe Aickelin', 'Lindy Durrant'] | 2016-07-21 | null | null | null | null | ['tumour-classification'] | ['medical'] | [ 5.05629361e-01 -6.84451908e-02 -4.28728908e-01 -2.22050756e-01
-8.90845180e-01 -3.41900438e-01 5.04736841e-01 1.00873387e+00
-7.04680145e-01 7.81910658e-01 3.53043616e-01 -5.28302729e-01
-4.77192461e-01 -7.04420090e-01 1.18403696e-01 -9.45460021e-01
-5.21487772e-01 7.35511959e-01 -9.33385491e-02 -3.49790484... | [15.165712356567383, -3.065053701400757] |
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