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157ca05b-42e5-40fb-ace2-e6b8f1fd8ec5	constrained-deep-one-class-feature-learning	2111.10610	null	https://arxiv.org/abs/2111.10610v2	https://arxiv.org/pdf/2111.10610v2.pdf	Constrained Deep One-Class Feature Learning For Classifying Imbalanced Medical Images	Medical image data are usually imbalanced across different classes. One-class classification has attracted increasing attention to address the data imbalance problem by distinguishing the samples of the minority class from the majority class. Previous methods generally aim to either learn a new feature space to map training samples together or to fit training samples by autoencoder-like models. These methods mainly focus on capturing either compact or descriptive features, where the information of the samples of a given one class is not sufficiently utilized. In this paper, we propose a novel deep learning-based method to learn compact features by adding constraints on the bottleneck features, and to preserve descriptive features by training an autoencoder at the same time. Through jointly optimizing the constraining loss and the autoencoder's reconstruction loss, our method can learn more relevant features associated with the given class, making the majority and minority samples more distinguishable. Experimental results on three clinical datasets (including the MRI breast images, FFDM breast images and chest X-ray images) obtains state-of-art performance compared to previous methods.	['Shandong Wu', 'Ashok Panigrahy', 'Dooman Arefan', 'Chang Liu', 'Long Gao']	2021-11-20	null	null	null	null	['one-class-classification']	['miscellaneous']	[ 2.50951618e-01 2.36763015e-01 -3.90026122e-01 -7.06985176e-01 -5.06165862e-01 4.11441416e-01 1.44769698e-01 5.04335701e-01 -3.76257271e-01 5.55957675e-01 2.00041667e-01 2.21775532e-01 -3.48680705e-01 -8.27655315e-01 -4.79275972e-01 -1.05680728e+00 -3.07108928e-02 5.70549726e-01 -1.34040624e-01 6.53924122e-02 5.39061055e-02 4.78572994e-01 -1.51693058e+00 7.45113015e-01 8.73846054e-01 1.33898556e+00 1.10659920e-01 2.91333020e-01 -5.90775907e-02 1.12323010e+00 -5.79848051e-01 -1.58889949e-01 1.20704032e-01 -5.63508570e-01 -6.89058959e-01 4.07902032e-01 3.51701677e-01 -4.64757711e-01 -6.48237765e-01 1.01589000e+00 7.02531099e-01 1.29463775e-02 6.55188203e-01 -1.13259840e+00 -5.33569813e-01 3.36099863e-01 -5.41169107e-01 2.63690501e-01 -3.10195208e-01 -2.06331760e-01 7.34821320e-01 -6.09894395e-01 4.49879736e-01 9.21178639e-01 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24faf5f5-9e99-46a0-8d4a-d9c6a22dfc97	grasmos-graph-signage-model-selection-for	2211.09642	null	https://arxiv.org/abs/2211.09642v1	https://arxiv.org/pdf/2211.09642v1.pdf	GRASMOS: Graph Signage Model Selection for Gene Regulatory Networks	Signed networks, i.e., networks with positive and negative edges, commonly arise in various domains from social media to epidemiology. Modeling signed networks has many practical applications, including the creation of synthetic data sets for experiments where obtaining real data is difficult. Influential prior works proposed and studied various graph topology models, as well as the problem of selecting the most fitting model for different application domains. However, these topology models are typically unsigned. In this work, we pose a novel Maximum-Likelihood-based optimization problem for modeling signed networks given their topology and showcase it in the context of gene regulation. Regulatory interactions of genes play a key role in organism development, and when broken can lead to serious organism abnormalities and diseases. Our contributions are threefold: First, we design a new class of signage models for a given topology. Based on the parameter setting, we discuss its biological interpretations for gene regulatory networks (GRNs). Second, we design algorithms computing the Maximum Likelihood -- depending on the parameter setting, our algorithms range from closed-form expressions to MCMC sampling. Third, we evaluated the results of our algorithms on synthetic datasets and real-world large GRNs. Our work can lead to the prediction of unknown gene regulations, the generation of biological hypotheses, and realistic GRN benchmark datasets.	['Ivona Bezáková', 'Hannah Miller', 'Angelina Brilliantova']	2022-11-17	null	null	null	null	['epidemiology']	['medical']	[ 5.69446683e-01 2.22915441e-01 -3.02180469e-01 -4.01346087e-01 1.64577112e-01 -4.98143554e-01 3.26542050e-01 2.15533078e-01 1.12401612e-01 1.16493547e+00 -1.58645421e-01 -4.71969008e-01 -7.29171872e-01 -1.06983984e+00 -7.87761748e-01 -5.40350437e-01 -6.93654478e-01 5.41421473e-01 2.18911543e-01 -2.57957995e-01 5.46019822e-02 3.52907896e-01 -9.86997664e-01 -2.01845497e-01 9.15880859e-01 3.56978178e-01 -3.08409482e-01 6.11993790e-01 6.98493794e-02 2.31101960e-01 -5.21394074e-01 -3.22163016e-01 1.29101962e-01 -4.90088314e-01 -5.75356424e-01 1.13791898e-01 -2.16393888e-01 4.86512482e-01 -1.57451779e-01 1.04328585e+00 5.53828776e-01 -1.35109842e-01 9.33982551e-01 -1.87092996e+00 -2.71048397e-01 7.59548604e-01 -8.00503790e-01 -3.13614190e-01 2.71646559e-01 3.76569144e-02 8.77549350e-01 -4.43425059e-01 9.52780128e-01 1.66705489e+00 7.06474185e-01 3.73460859e-01 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713cff69-72d9-4912-8aa5-188c420db110	theme-matters-fashion-compatibility-learning	1912.06227	null	https://arxiv.org/abs/1912.06227v3	https://arxiv.org/pdf/1912.06227v3.pdf	Theme-Matters: Fashion Compatibility Learning via Theme Attention	Fashion compatibility learning is important to many fashion markets such as outfit composition and online fashion recommendation. Unlike previous work, we argue that fashion compatibility is not only a visual appearance compatible problem but also a theme-matters problem. An outfit, which consists of a set of fashion items (e.g., shirt, suit, shoes, etc.), is considered to be compatible for a "dating" event, yet maybe not for a "business" occasion. In this paper, we aim at solving the fashion compatibility problem given specific themes. To this end, we built the first real-world theme-aware fashion dataset comprising 14K around outfits labeled with 32 themes. In this dataset, there are more than 40K fashion items labeled with 152 fine-grained categories. We also propose an attention model learning fashion compatibility given a specific theme. It starts with a category-specific subspace learning, which projects compatible outfit items in certain categories to be close in the subspace. Thanks to strong connections between fashion themes and categories, we then build a theme-attention model over the category-specific embedding space. This model associates themes with the pairwise compatibility with attention, and thus compute the outfit-wise compatibility. To the best of our knowledge, this is the first attempt to estimate outfit compatibility conditional on a theme. We conduct extensive qualitative and quantitative experiments on our new dataset. Our method outperforms the state-of-the-art approaches.	['Jingen Liu', 'Jui-Hsin Lai', 'Dan Zeng', 'Bo Wu', 'Xin Wang', 'Tao Mei']	2019-12-12	null	null	null	null	['fashion-compatibility-learning']	['computer-vision']	[-2.50020027e-01 -4.32791442e-01 -2.16537729e-01 -6.92639470e-01 -3.95794541e-01 -6.69770598e-01 4.64653254e-01 4.58132476e-02 2.69370601e-02 1.35315403e-01 5.81273437e-01 6.96594417e-02 -2.45971814e-01 -5.81407845e-01 -9.00051355e-01 -4.04829085e-01 2.30829671e-01 3.38836581e-01 -3.38559628e-01 -3.65036845e-01 1.07185118e-01 -2.19498128e-02 -1.34080815e+00 2.81176716e-01 6.08565629e-01 1.05935407e+00 1.40379459e-01 1.64318636e-01 -1.36463419e-01 2.36426309e-01 -1.37943134e-01 -8.93341780e-01 3.37221622e-01 -2.95508891e-01 -5.06835639e-01 4.30994928e-01 9.58443701e-01 -2.17855498e-02 -3.31279278e-01 1.07107759e+00 2.61074416e-02 2.09059224e-01 7.09852815e-01 -1.35975885e+00 -1.73873997e+00 7.18874574e-01 -9.06522512e-01 -2.18159735e-01 2.40393415e-01 2.30089650e-01 1.21247411e+00 -1.17379463e+00 6.27416432e-01 1.33641875e+00 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901b1889-b385-4493-b7f4-7ca6edd71423	radio-sensing-with-large-intelligent-surface	2111.02783	null	https://arxiv.org/abs/2111.02783v2	https://arxiv.org/pdf/2111.02783v2.pdf	Radio Sensing with Large Intelligent Surface for 6G	This paper leverages the potential of Large Intelligent Surface (LIS) for radio sensing in 6G wireless networks. Major research has been undergone about its communication capabilities but it can be exploited as a formidable tool for radio sensing. By taking advantage of arbitrary communication signals occurring in the scenario, we apply direct processing to the output signal from the LIS to obtain a radio map that describes the physical presence of passive devices (scatterers, humans) which act as virtual sources due to the communication signal reflections. We then assess the usage of machine learning (k-means clustering), image processing and computer vision (template matching and component labeling) to extract meaningful information from these radio maps. As an exemplary use case, we evaluate this method for both active and passive user detection in an indoor setting. The results show that the presented method has high application potential as we are able to detect around 98% of humans passively and 100% active users by just using communication signals of commodity devices even in quite unfavorable Signal-to-Noise Ratio (SNR) conditions.	['Elisabeth de Carvalho', 'Zheng-Hua Tan', 'Kimmo Kansanen', 'Pablo Ramirez-Espinosa', 'Cristian J. Vaca-Rubio']	2021-11-04	null	null	null	null	['template-matching']	['computer-vision']	[ 9.52441454e-01 4.89828706e-01 3.13017935e-01 9.65398327e-02 -6.67619705e-01 -4.78750229e-01 5.09263575e-01 -2.03311294e-01 -7.67374709e-02 3.79707158e-01 3.60577293e-02 -4.86410737e-01 -1.82216272e-01 -8.64170849e-01 -1.38291851e-01 -8.69473338e-01 -4.89339322e-01 4.71179098e-01 4.83281501e-02 -8.22887197e-03 -2.66599864e-01 7.34293640e-01 -1.34978986e+00 -3.24571095e-02 4.03394818e-01 1.38646209e+00 1.55632928e-01 7.89126277e-01 3.02004486e-01 5.79391658e-01 -1.09268832e+00 1.56884730e-01 4.40590084e-01 -2.02699974e-01 -5.97857535e-02 8.91496986e-02 4.78500538e-02 -9.20632556e-02 -1.21676557e-01 6.88148201e-01 9.64332819e-01 -3.80752563e-01 6.20576262e-01 -6.98493659e-01 3.08777630e-01 4.46799636e-01 -4.64937299e-01 2.12656334e-01 8.37925255e-01 -1.69891685e-01 6.34740353e-01 -6.43809438e-01 1.85888216e-01 9.28082526e-01 7.86377966e-01 -1.12706181e-02 -9.93563652e-01 -7.76146948e-01 -6.59294009e-01 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e16cb8c3-b0e9-48b0-8bed-c3085e41a85f	ganseg-learning-to-segment-by-unsupervised	2112.01036	null	https://arxiv.org/abs/2112.01036v3	https://arxiv.org/pdf/2112.01036v3.pdf	GANSeg: Learning to Segment by Unsupervised Hierarchical Image Generation	Segmenting an image into its parts is a frequent preprocess for high-level vision tasks such as image editing. However, annotating masks for supervised training is expensive. Weakly-supervised and unsupervised methods exist, but they depend on the comparison of pairs of images, such as from multi-views, frames of videos, and image augmentation, which limits their applicability. To address this, we propose a GAN-based approach that generates images conditioned on latent masks, thereby alleviating full or weak annotations required in previous approaches. We show that such mask-conditioned image generation can be learned faithfully when conditioning the masks in a hierarchical manner on latent keypoints that define the position of parts explicitly. Without requiring supervision of masks or points, this strategy increases robustness to viewpoint and object positions changes. It also lets us generate image-mask pairs for training a segmentation network, which outperforms the state-of-the-art unsupervised segmentation methods on established benchmarks.	['Helge Rhodin', 'Bastian Wandt', 'Xingzhe He']	2021-12-02	null	http://openaccess.thecvf.com//content/CVPR2022/html/He_GANSeg_Learning_To_Segment_by_Unsupervised_Hierarchical_Image_Generation_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/He_GANSeg_Learning_To_Segment_by_Unsupervised_Hierarchical_Image_Generation_CVPR_2022_paper.pdf	cvpr-2022-1	['unsupervised-facial-landmark-detection', 'image-augmentation']	['computer-vision', 'computer-vision']	[ 8.86980116e-01 3.74089032e-01 -2.50551373e-01 -4.38582808e-01 -6.30519688e-01 -7.98651755e-01 6.99228883e-01 5.11298887e-02 -5.08234620e-01 6.05904877e-01 -1.49940148e-01 1.81782227e-02 3.67405325e-01 -7.45264471e-01 -1.07287419e+00 -7.17350960e-01 4.45012420e-01 5.77007711e-01 6.07895911e-01 -7.33504258e-03 1.60091877e-01 5.86580873e-01 -1.66813552e+00 2.97285944e-01 9.24608290e-01 7.61584878e-01 3.31291884e-01 4.88586813e-01 -2.63636172e-01 4.18947160e-01 -5.77027261e-01 -5.40097237e-01 3.88196945e-01 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9156a52a-9f47-4938-b589-76df48e73596	partial-label-learning-with-mixed-closed-set	2307.00553	null	https://arxiv.org/abs/2307.00553v1	https://arxiv.org/pdf/2307.00553v1.pdf	Partial-label Learning with Mixed Closed-set and Open-set Out-of-candidate Examples	Partial-label learning (PLL) relies on a key assumption that the true label of each training example must be in the candidate label set. This restrictive assumption may be violated in complex real-world scenarios, and thus the true label of some collected examples could be unexpectedly outside the assigned candidate label set. In this paper, we term the examples whose true label is outside the candidate label set OOC (out-of-candidate) examples, and pioneer a new PLL study to learn with OOC examples. We consider two types of OOC examples in reality, i.e., the closed-set/open-set OOC examples whose true label is inside/outside the known label space. To solve this new PLL problem, we first calculate the wooden cross-entropy loss from candidate and non-candidate labels respectively, and dynamically differentiate the two types of OOC examples based on specially designed criteria. Then, for closed-set OOC examples, we conduct reversed label disambiguation in the non-candidate label set; for open-set OOC examples, we leverage them for training by utilizing an effective regularization strategy that dynamically assigns random candidate labels from the candidate label set. In this way, the two types of OOC examples can be differentiated and further leveraged for model training. Extensive experiments demonstrate that our proposed method outperforms state-of-the-art PLL methods.	['Guowu Yang', 'Lei Feng', 'Shuo He']	2023-07-02	null	null	null	null	['partial-label-learning']	['methodology']	[ 4.60458040e-01 4.19413537e-01 -5.27871609e-01 -4.86934662e-01 -9.28900361e-01 -7.59180248e-01 2.29941368e-01 3.33279312e-01 -3.31837177e-01 7.96133041e-01 -4.21108037e-01 -1.62079334e-01 -1.58214495e-01 -5.23388803e-01 -4.83779579e-01 -9.81088042e-01 -1.81424897e-02 5.63751936e-01 2.60660082e-01 3.43016118e-01 1.37260824e-01 2.80022591e-01 -1.64732838e+00 1.63890034e-01 9.56110954e-01 1.12703443e+00 5.01500629e-02 -2.49624941e-02 -3.19110543e-01 5.59786856e-01 -7.04539597e-01 -1.58379197e-01 3.65254104e-01 -4.08787429e-01 -7.03772843e-01 2.76888430e-01 3.78817707e-01 2.80613333e-01 2.68943578e-01 1.31060398e+00 3.63763988e-01 1.60082325e-01 8.64636242e-01 -1.38226557e+00 -4.05868232e-01 3.28057379e-01 -5.62037766e-01 -9.79221463e-02 2.62021959e-01 -6.42659655e-03 1.08412743e+00 -8.99499714e-01 5.96695304e-01 9.93417263e-01 7.75627434e-01 8.27872872e-01 -1.19670320e+00 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2ea4bda0-3d00-4d17-8cfa-4e1e766d0deb	image-models-for-large-scale-object-detection	null	null	https://aclanthology.org/2022.clib-1.22	https://aclanthology.org/2022.clib-1.22.pdf	Image Models for large-scale Object Detection and Classification	Recent developments in computer vision applications that are based on machine learning models allow real-time object detection, segmentation and captioning in image or video streams. The paper presents the development of an extension of the 80 COCO categories into a novel ontology with more than 700 classes covering 130 thematic subdomains related to Sport, Transport, Arts and Security. The development of an image dataset of object segmentation was accelerated by machine learning for automatic generation of objects’ boundaries and classes. The Multilingual image dataset contains over 20,000 images and 200,000 annotations. It was used to pre-train 130 models for object detection and classification. We show the established approach for the development of the new models and their integration into an application and evaluation framework.	['Svetla Koeva', 'Jordan Kralev']	null	null	null	null	clib-2022-9	['real-time-object-detection']	['computer-vision']	[ 2.98222363e-01 2.35087439e-01 2.20672414e-02 -5.72008550e-01 -5.55815697e-01 -7.52061069e-01 8.15152168e-01 5.62100351e-01 -7.43529022e-01 4.19829100e-01 -9.30318087e-02 -1.62436187e-01 8.71347543e-03 -7.49915659e-01 -6.66919231e-01 -1.53274924e-01 -8.86450615e-03 9.65699077e-01 8.32090318e-01 -1.10881738e-01 2.43498787e-01 6.71921968e-01 -2.00677133e+00 8.86735201e-01 4.24640566e-01 1.19248581e+00 3.23133558e-01 9.60192561e-01 -4.71058249e-01 6.35957420e-01 -7.95295179e-01 -4.19697374e-01 2.32184708e-01 -9.58031490e-02 -9.99504924e-01 5.80450952e-01 7.02917576e-01 1.02651611e-01 1.49732679e-01 9.99429941e-01 2.08963737e-01 -2.55377859e-01 7.68411338e-01 -1.47833073e+00 -3.22248876e-01 4.93662119e-01 -4.04339172e-02 3.94446045e-01 4.97716129e-01 -1.65616497e-01 7.70945728e-01 -6.26482308e-01 9.63259876e-01 1.24920261e+00 5.46184063e-01 4.42625731e-01 -8.79481077e-01 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9e93392e-418b-4b04-b441-9cc63f04f59f	a-dual-semantic-aware-recurrent-global	2305.03602	null	https://arxiv.org/abs/2305.03602v2	https://arxiv.org/pdf/2305.03602v2.pdf	A Dual Semantic-Aware Recurrent Global-Adaptive Network For Vision-and-Language Navigation	Vision-and-Language Navigation (VLN) is a realistic but challenging task that requires an agent to locate the target region using verbal and visual cues. While significant advancements have been achieved recently, there are still two broad limitations: (1) The explicit information mining for significant guiding semantics concealed in both vision and language is still under-explored; (2) The previously structured map method provides the average historical appearance of visited nodes, while it ignores distinctive contributions of various images and potent information retention in the reasoning process. This work proposes a dual semantic-aware recurrent global-adaptive network (DSRG) to address the above problems. First, DSRG proposes an instruction-guidance linguistic module (IGL) and an appearance-semantics visual module (ASV) for boosting vision and language semantic learning respectively. For the memory mechanism, a global adaptive aggregation module (GAA) is devised for explicit panoramic observation fusion, and a recurrent memory fusion module (RMF) is introduced to supply implicit temporal hidden states. Extensive experimental results on the R2R and REVERIE datasets demonstrate that our method achieves better performance than existing methods. Code is available at https://github.com/CrystalSixone/DSRG.	['Qijun Chen', 'Chengju Liu', 'Naijia Wang', 'Ronghao Dang', 'Jiagui Tang', 'Zongtao He', 'Liuyi Wang']	2023-05-05	null	null	null	null	['vision-and-language-navigation']	['robots']	[ 1.22159012e-02 -7.56727606e-02 -3.61900359e-01 -4.73990858e-01 -3.56568635e-01 -4.50496078e-02 8.82380664e-01 -2.25127444e-01 -4.57429379e-01 3.96370202e-01 2.67753929e-01 -2.52805114e-01 -8.69577825e-02 -7.48195231e-01 -6.30233347e-01 -8.85607719e-01 8.43762457e-02 1.39401227e-01 7.21557915e-01 -4.32500541e-01 3.57761085e-01 3.15149248e-01 -1.79754305e+00 1.62513584e-01 1.06942487e+00 1.05938554e+00 9.70911682e-01 2.25631848e-01 -5.13723731e-01 1.18341863e+00 -5.99503182e-02 1.25765223e-02 8.72289389e-02 -4.22589779e-01 -5.78857124e-01 1.09184477e-02 2.05090880e-01 -3.11098188e-01 -4.58292931e-01 1.29268920e+00 4.00217086e-01 4.86090690e-01 3.61724228e-01 -1.20781410e+00 -8.94092798e-01 4.09805983e-01 -8.15782905e-01 2.90282339e-01 2.50878185e-01 1.10880002e-01 6.14916801e-01 -1.05791712e+00 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fdb65d7e-66d8-4667-86f2-148147e187c6	arabic-word-level-readability-visualization	2210.10672	null	https://arxiv.org/abs/2210.10672v1	https://arxiv.org/pdf/2210.10672v1.pdf	Arabic Word-level Readability Visualization for Assisted Text Simplification	This demo paper presents a Google Docs add-on for automatic Arabic word-level readability visualization. The add-on includes a lemmatization component that is connected to a five-level readability lexicon and Arabic WordNet-based substitution suggestions. The add-on can be used for assessing the reading difficulty of a text and identifying difficult words as part of the task of manual text simplification. We make our add-on and its code publicly available.	['Nizar Habash', 'Muhamed Al Khalil', 'Bashar Alhafni', 'Hind Saddiki', 'Reem Hazim']	2022-10-19	null	null	null	null	['lemmatization']	['natural-language-processing']	[-1.04625165e-01 5.10575175e-01 1.82618856e-01 -1.46578461e-01 -8.26204419e-01 -6.57621086e-01 3.63349020e-01 9.62339282e-01 -2.53414482e-01 3.28120023e-01 7.22988188e-01 -9.41440403e-01 -3.64950031e-01 -7.33213723e-01 -1.08450532e-01 7.37866983e-02 4.74004447e-01 5.18895149e-01 -1.01979256e-01 -8.93220127e-01 8.84681940e-01 3.51426393e-01 -1.33312845e+00 2.99182892e-01 1.70934558e+00 2.74152219e-01 1.42974898e-01 6.94943428e-01 -3.98234904e-01 5.21705747e-01 -9.19865727e-01 -6.78669453e-01 -1.95667613e-02 -2.98024148e-01 -1.08895791e+00 -4.98100966e-02 4.32712793e-01 -3.32896382e-01 -1.07808039e-01 1.22861660e+00 4.96618241e-01 7.18747079e-02 6.92340255e-01 -6.02821589e-01 -1.00492513e+00 7.69383848e-01 9.58525911e-02 3.51592660e-01 1.04731500e+00 4.60569188e-02 7.36537218e-01 -1.13492954e+00 3.91148031e-01 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385cfe8a-6e81-4d5d-8427-3c46ca8b9c28	3d-lidar-and-stereo-fusion-using-stereo	1904.02917	null	http://arxiv.org/abs/1904.02917v1	http://arxiv.org/pdf/1904.02917v1.pdf	3D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume Normalization	The complementary characteristics of active and passive depth sensing techniques motivate the fusion of the Li-DAR sensor and stereo camera for improved depth perception. Instead of directly fusing estimated depths across LiDAR and stereo modalities, we take advantages of the stereo matching network with two enhanced techniques: Input Fusion and Conditional Cost Volume Normalization (CCVNorm) on the LiDAR information. The proposed framework is generic and closely integrated with the cost volume component that is commonly utilized in stereo matching neural networks. We experimentally verify the efficacy and robustness of our method on the KITTI Stereo and Depth Completion datasets, obtaining favorable performance against various fusion strategies. Moreover, we demonstrate that, with a hierarchical extension of CCVNorm, the proposed method brings only slight overhead to the stereo matching network in terms of computation time and model size. For project page, see https://zswang666.github.io/Stereo-LiDAR-CCVNorm-Project-Page/	['Wei-Chen Chiu', 'Yi-Hsuan Tsai', 'Hou-Ning Hu', 'Tsun-Hsuan Wang', 'Min Sun', 'Chieh Hubert Lin']	2019-04-05	null	null	null	null	['stereo-matching', 'stereo-lidar-fusion']	['computer-vision', 'computer-vision']	[ 4.38009292e-01 -7.09510669e-02 1.63209029e-02 -6.38533533e-01 -9.30783749e-01 -2.91258693e-01 6.91410184e-01 6.88180700e-02 -7.87007809e-01 6.40921474e-01 1.26105189e-01 -1.24536306e-01 -1.01415128e-01 -9.75541353e-01 -6.44331217e-01 -6.44288778e-01 3.89085472e-01 1.79847822e-01 2.35634655e-01 1.21747598e-01 2.39497602e-01 7.77554333e-01 -2.06220031e+00 -4.82926294e-02 9.87302125e-01 1.40433300e+00 4.30654645e-01 5.15299082e-01 -6.63168430e-02 5.47615767e-01 9.05039161e-03 -3.71912777e-01 7.05193818e-01 2.28828803e-01 -3.35337818e-01 6.08248229e-04 8.85894001e-01 -8.14492583e-01 -4.50005949e-01 9.92985427e-01 7.54104674e-01 1.72774702e-01 4.22674537e-01 -1.09907174e+00 1.04489781e-01 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56e0c9a5-d607-4013-b510-5c491fed8930	model-based-single-image-deep-dehazing	2111.10943	null	https://arxiv.org/abs/2111.10943v3	https://arxiv.org/pdf/2111.10943v3.pdf	Model-Based Single Image Deep Dehazing	Model-based single image dehazing algorithms restore images with sharp edges and rich details at the expense of low PSNR values. Data-driven ones restore images with high PSNR values but with low contrast, and even some remaining haze. In this paper, a novel single image dehazing algorithm is introduced by fusing model-based and data-driven approaches. Both transmission map and atmospheric light are initialized by the model-based methods, and refined by deep learning approaches which form a neural augmentation. Haze-free images are restored by using the transmission map and atmospheric light. Experimental results indicate that the proposed algorithm can remove haze well from real-world and synthetic hazy images.	['Shiqian Wu', 'Haiyan Shu', 'Chaobing Zheng', 'Zhengguo Li']	2021-11-22	null	null	null	null	['image-dehazing']	['computer-vision']	[ 4.88881350e-01 -4.61191237e-01 5.17070234e-01 -1.30662695e-01 -2.73616880e-01 5.46737351e-02 6.54776096e-01 -2.46390641e-01 -3.57866138e-01 8.08955371e-01 1.07374154e-01 2.96426024e-02 -1.13014981e-01 -1.09606421e+00 -5.85742235e-01 -1.42126191e+00 -5.72016602e-03 -1.77507490e-01 4.13411915e-01 -6.74918354e-01 3.49263757e-01 2.67878294e-01 -1.74217045e+00 2.59535015e-01 1.35290515e+00 9.39973235e-01 4.14897770e-01 8.63727033e-01 -4.45577949e-02 1.07429492e+00 -6.24653935e-01 -7.75487646e-02 6.66459858e-01 -4.14087683e-01 -8.25501755e-02 4.65697706e-01 7.39240468e-01 -7.46270001e-01 -6.21454537e-01 1.45533502e+00 3.67366582e-01 2.39190206e-01 3.15545231e-01 -9.94988918e-01 -1.11083531e+00 -9.87554565e-02 -6.34294212e-01 2.69402802e-01 -1.38817400e-01 3.67566377e-01 1.95470423e-01 -9.64559495e-01 3.30627859e-01 1.00416279e+00 5.68187058e-01 2.47413203e-01 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-1.08644092e+00 4.20083076e-01 1.29433751e+00 -4.39467281e-01 -7.03643680e-01 5.79943180e-01]	[10.900113105773926, -3.162824869155884]
286c10da-6c49-40f5-ada1-41668478920b	change-aware-sampling-and-contrastive	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Mall_Change-Aware_Sampling_and_Contrastive_Learning_for_Satellite_Images_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Mall_Change-Aware_Sampling_and_Contrastive_Learning_for_Satellite_Images_CVPR_2023_paper.pdf	Change-Aware Sampling and Contrastive Learning for Satellite Images	Automatic remote sensing tools can help inform many large-scale challenges such as disaster management, climate change, etc. While a vast amount of spatio-temporal satellite image data is readily available, most of it remains unlabelled. Without labels, this data is not very useful for supervised learning algorithms. Self-supervised learning instead provides a way to learn effective representations for various downstream tasks without labels. In this work, we leverage characteristics unique to satellite images to learn better self-supervised features. Specifically, we use the temporal signal to contrast images with long-term and short-term differences, and we leverage the fact that satellite images do not change frequently. Using these characteristics, we formulate a new loss contrastive loss called Change-Aware Contrastive (CACo) Loss. Further, we also present a novel method of sampling different geographical regions. We show that leveraging these properties leads to better performance on diverse downstream tasks. For example, we see a 6.5% relative improvement for semantic segmentation and an 8.5% relative improvement for change detection over the best-performing baseline with our method.	['Kavita Bala', 'Bharath Hariharan', 'Utkarsh Mall']	2023-01-01	null	null	null	cvpr-2023-1	['change-detection']	['computer-vision']	[ 3.66169453e-01 -2.78437763e-01 -2.46823564e-01 -8.17883015e-01 -1.08636534e+00 -5.88472068e-01 7.35596657e-01 4.04044807e-01 -6.36562884e-01 9.25641537e-01 1.71235800e-01 -1.66776866e-01 -3.56788076e-02 -9.52131391e-01 -7.11889505e-01 -8.96734595e-01 -3.92077923e-01 4.56872629e-03 2.39878535e-01 -2.50944525e-01 -8.81500095e-02 5.75610280e-01 -1.34572721e+00 -9.06089991e-02 1.12855208e+00 9.99013722e-01 2.61634380e-01 2.85230666e-01 -6.20557815e-02 4.55011338e-01 -1.64974824e-01 2.89692938e-01 4.44996327e-01 -3.72795969e-01 -8.43843699e-01 3.04303825e-01 4.17501301e-01 -2.34240100e-01 -2.04272285e-01 1.00343823e+00 4.47683305e-01 3.00474405e-01 7.35757172e-01 -9.26551938e-01 -5.43852635e-02 3.85035664e-01 -9.27345872e-01 4.71836239e-01 -2.04976201e-01 2.08877042e-01 1.17121148e+00 -4.64682072e-01 5.48273861e-01 1.03886509e+00 7.85295546e-01 3.43644731e-02 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537ed428-2a2c-4441-af26-944f11899753	agentgraph-towards-universal-dialogue	1905.11259	null	https://arxiv.org/abs/1905.11259v1	https://arxiv.org/pdf/1905.11259v1.pdf	AgentGraph: Towards Universal Dialogue Management with Structured Deep Reinforcement Learning	Dialogue policy plays an important role in task-oriented spoken dialogue systems. It determines how to respond to users. The recently proposed deep reinforcement learning (DRL) approaches have been used for policy optimization. However, these deep models are still challenging for two reasons: 1) Many DRL-based policies are not sample-efficient. 2) Most models don't have the capability of policy transfer between different domains. In this paper, we propose a universal framework, AgentGraph, to tackle these two problems. The proposed AgentGraph is the combination of GNN-based architecture and DRL-based algorithm. It can be regarded as one of the multi-agent reinforcement learning approaches. Each agent corresponds to a node in a graph, which is defined according to the dialogue domain ontology. When making a decision, each agent can communicate with its neighbors on the graph. Under AgentGraph framework, we further propose Dual GNN-based dialogue policy, which implicitly decomposes the decision in each turn into a high-level global decision and a low-level local decision. Experiments show that AgentGraph models significantly outperform traditional reinforcement learning approaches on most of the 18 tasks of the PyDial benchmark. Moreover, when transferred from the source task to a target task, these models not only have acceptable initial performance but also converge much faster on the target task.	['Bowen Tan', 'Zhi Chen', 'Kai Yu', 'Sishan Long', 'Milica Gasic', 'Lu Chen']	2019-05-27	null	null	null	null	['dialogue-management']	['natural-language-processing']	[-3.77106875e-01 1.35384306e-01 -1.84246078e-01 -1.20939866e-01 -2.98522890e-01 -4.57511157e-01 9.48647976e-01 1.37716413e-01 -6.99811637e-01 1.09601748e+00 3.77806783e-01 -2.32062548e-01 -8.98090377e-02 -8.15551400e-01 -2.03802481e-01 -7.51735866e-01 1.12395100e-01 1.23077166e+00 4.14016962e-01 -8.47854078e-01 2.35489041e-01 1.66556105e-01 -1.04853284e+00 3.69668379e-02 1.05886590e+00 6.86009526e-01 3.91168714e-01 5.82980573e-01 -5.16637325e-01 1.08023655e+00 -7.58759916e-01 7.38957990e-03 -1.71079621e-01 -7.87244320e-01 -1.26150966e+00 2.68210620e-02 -3.02928150e-01 -4.85048532e-01 -1.95107639e-01 1.00252175e+00 7.94026434e-01 5.45036852e-01 6.61502898e-01 -1.41334224e+00 -2.18448654e-01 8.53498578e-01 -3.71727943e-01 -3.23826335e-02 5.03477752e-01 2.56954342e-01 9.31512058e-01 -2.08074436e-01 5.04815817e-01 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d7fff58a-f3e1-4ece-88c6-4a15a2d01506	solving-qsat-problems-with-neural-mcts	2101.06619	null	https://arxiv.org/abs/2101.06619v1	https://arxiv.org/pdf/2101.06619v1.pdf	Solving QSAT problems with neural MCTS	Recent achievements from AlphaZero using self-play has shown remarkable performance on several board games. It is plausible to think that self-play, starting from zero knowledge, can gradually approximate a winning strategy for certain two-player games after an amount of training. In this paper, we try to leverage the computational power of neural Monte Carlo Tree Search (neural MCTS), the core algorithm from AlphaZero, to solve Quantified Boolean Formula Satisfaction (QSAT) problems, which are PSPACE complete. Knowing that every QSAT problem is equivalent to a QSAT game, the game outcome can be used to derive the solutions of the original QSAT problems. We propose a way to encode Quantified Boolean Formulas (QBFs) as graphs and apply a graph neural network (GNN) to embed the QBFs into the neural MCTS. After training, an off-the-shelf QSAT solver is used to evaluate the performance of the algorithm. Our result shows that, for problems within a limited size, the algorithm learns to solve the problem correctly merely from self-play.	['Karl Lieberherr', 'Ruiyang Xu']	2021-01-17	null	null	null	null	['board-games']	['playing-games']	[ 1.34718150e-01 6.64498568e-01 -9.83790960e-03 2.72763539e-02 -1.04615963e+00 -7.62146533e-01 -1.58502355e-01 3.61167565e-02 -1.26222223e-01 1.13798606e+00 -3.71054590e-01 -7.93193281e-01 -2.63934165e-01 -1.78340566e+00 -1.18944728e+00 -4.40619558e-01 -1.65778771e-01 8.51555526e-01 6.80173695e-01 -4.96254414e-01 -3.51428054e-02 6.63630441e-02 -1.18515635e+00 5.16641617e-01 6.42633677e-01 9.13737118e-01 -1.19084507e-01 1.10187459e+00 -1.52076513e-01 1.45567548e+00 -4.81675774e-01 -9.06927288e-01 3.73293787e-01 -6.50883317e-01 -1.39991617e+00 -5.55940330e-01 1.02453299e-01 -3.71637762e-01 -4.50106412e-01 1.41701400e+00 5.39048016e-02 -9.68468934e-03 9.04192626e-02 -1.52891839e+00 -2.74620485e-02 1.18397200e+00 4.99301217e-02 -1.92464180e-02 5.87899923e-01 6.04472518e-01 1.63026929e+00 3.81892204e-01 5.85264444e-01 1.04779220e+00 8.14549565e-01 7.47363508e-01 -1.15513134e+00 -6.06642485e-01 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beadac75-d6e8-44c0-a6fe-3a7347c11db7	ontology-aware-learning-and-evaluation-for	2211.12195	null	https://arxiv.org/abs/2211.12195v1	https://arxiv.org/pdf/2211.12195v1.pdf	Ontology-aware Learning and Evaluation for Audio Tagging	This study defines a new evaluation metric for audio tagging tasks to overcome the limitation of the conventional mean average precision (mAP) metric, which treats different kinds of sound as independent classes without considering their relations. Also, due to the ambiguities in sound labeling, the labels in the training and evaluation set are not guaranteed to be accurate and exhaustive, which poses challenges for robust evaluation with mAP. The proposed metric, ontology-aware mean average precision (OmAP) addresses the weaknesses of mAP by utilizing the AudioSet ontology information during the evaluation. Specifically, we reweight the false positive events in the model prediction based on the ontology graph distance to the target classes. The OmAP measure also provides more insights into model performance by evaluations with different coarse-grained levels in the ontology graph. We conduct human evaluations and demonstrate that OmAP is more consistent with human perception than mAP. To further verify the importance of utilizing the ontology information, we also propose a novel loss function (OBCE) that reweights binary cross entropy (BCE) loss based on the ontology distance. Our experiment shows that OBCE can improve both mAP and OmAP metrics on the AudioSet tagging task.	['Mark D. Plumbley', 'Wenwu Wang', 'Xinhao Mei', 'Xubo Liu', 'Qiuqiang Kong', 'Haohe Liu']	2022-11-22	null	null	null	null	['audio-tagging']	['audio']	[ 1.59885481e-01 -1.27591668e-02 -3.55875604e-02 -5.01010418e-01 -7.84882307e-01 -5.01698256e-01 8.13527480e-02 6.14668310e-01 -5.60903609e-01 3.36714476e-01 2.15140164e-01 1.94300011e-01 -6.71685815e-01 -8.14521253e-01 -4.97886568e-01 -3.98870081e-01 -3.89251888e-01 1.64902925e-01 6.28038466e-01 3.62812802e-02 4.54716245e-03 6.91360086e-02 -1.96309853e+00 4.69303578e-01 7.86908865e-01 1.42059147e+00 1.31877497e-01 3.22902232e-01 2.01775040e-03 5.14593840e-01 -8.65849197e-01 -6.18127465e-01 -6.59346581e-02 -1.39954835e-01 -8.20205510e-01 -4.94157463e-01 4.35333639e-01 1.42889231e-01 -1.09986588e-01 1.29453635e+00 7.54751801e-01 2.23810032e-01 5.10694325e-01 -1.63500941e+00 -2.76471615e-01 8.12684655e-01 -9.49700996e-02 2.42755294e-01 4.22976792e-01 -4.77409005e-01 1.50307798e+00 -7.41849482e-01 2.10970134e-01 1.24449492e+00 1.03916645e+00 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f54e76dc-9a90-45c1-867b-d7044dbf0bdd	dual-adversarial-neural-transfer-for-low	null	null	https://aclanthology.org/P19-1336	https://aclanthology.org/P19-1336.pdf	Dual Adversarial Neural Transfer for Low-Resource Named Entity Recognition	We propose a new neural transfer method termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER). Specifically, two variants of DATNet, i.e., DATNet-F and DATNet-P, are investigated to explore effective feature fusion between high and low resource. To address the noisy and imbalanced training data, we propose a novel Generalized Resource-Adversarial Discriminator (GRAD). Additionally, adversarial training is adopted to boost model generalization. In experiments, we examine the effects of different components in DATNet across domains and languages and show that significant improvement can be obtained especially for low-resource data, without augmenting any additional hand-crafted features and pre-trained language model.	['Rick Siow Mong Goh', 'Meng Fang', 'Hao Zhang', 'Joey Tianyi Zhou', 'Hongyuan Zhu', 'Kenneth Kwok', 'Di Jin']	2019-07-01	null	null	null	acl-2019-7	['low-resource-named-entity-recognition']	['natural-language-processing']	[-4.78040278e-02 -2.47708112e-01 -2.52763350e-02 -5.00872850e-01 -9.96642351e-01 -6.62852585e-01 6.02348506e-01 -1.13166936e-01 -8.31586242e-01 1.16657400e+00 1.74760029e-01 -2.06010640e-01 2.14112222e-01 -8.70657206e-01 -5.47993243e-01 -3.19297463e-01 2.01556161e-01 1.58362553e-01 -1.25137657e-01 -4.04194415e-01 -3.49988759e-01 6.82636857e-01 -8.11385095e-01 2.04145819e-01 1.12270331e+00 8.92688215e-01 -1.69813782e-01 3.23792756e-01 -3.10275733e-01 5.67606628e-01 -9.69124556e-01 -6.45016551e-01 3.63701463e-01 -3.59100670e-01 -6.74012899e-01 -4.56371665e-01 2.09470913e-02 -2.51496434e-01 -4.63781178e-01 8.99780869e-01 1.08683586e+00 3.63397270e-01 7.41006255e-01 -1.18201911e+00 -9.83327866e-01 5.28246462e-01 -3.41532558e-01 3.63752633e-01 2.09251329e-01 2.18685746e-01 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f5090954-b50f-44a1-8822-840b6ff057e0	improving-covid-19-ct-classification-of-cnns	2208.04718	null	https://arxiv.org/abs/2208.04718v1	https://arxiv.org/pdf/2208.04718v1.pdf	Improving COVID-19 CT Classification of CNNs by Learning Parameter-Efficient Representation	COVID-19 pandemic continues to spread rapidly over the world and causes a tremendous crisis in global human health and the economy. Its early detection and diagnosis are crucial for controlling the further spread. Many deep learning-based methods have been proposed to assist clinicians in automatic COVID-19 diagnosis based on computed tomography imaging. However, challenges still remain, including low data diversity in existing datasets, and unsatisfied detection resulting from insufficient accuracy and sensitivity of deep learning models. To enhance the data diversity, we design augmentation techniques of incremental levels and apply them to the largest open-access benchmark dataset, COVIDx CT-2A. Meanwhile, similarity regularization (SR) derived from contrastive learning is proposed in this study to enable CNNs to learn more parameter-efficient representations, thus improving the accuracy and sensitivity of CNNs. The results on seven commonly used CNNs demonstrate that CNN performance can be improved stably through applying the designed augmentation and SR techniques. In particular, DenseNet121 with SR achieves an average test accuracy of 99.44% in three trials for three-category classification, including normal, non-COVID-19 pneumonia, and COVID-19 pneumonia. And the achieved precision, sensitivity, and specificity for the COVID-19 pneumonia category are 98.40%, 99.59%, and 99.50%, respectively. These statistics suggest that our method has surpassed the existing state-of-the-art methods on the COVIDx CT-2A dataset.	['Xinqi Bao', 'Junkai Liao', 'Jian Jiang', 'Guangyu Jia', 'Hak-Keung Lam', 'Yujia Xu']	2022-08-09	null	null	null	null	['covid-19-detection']	['medical']	[ 7.70127699e-02 -5.21285832e-01 -2.37808838e-01 -1.87189728e-01 -5.38666070e-01 -1.05274186e-01 2.27971539e-01 -5.02627939e-02 -6.31073594e-01 7.75899053e-01 -2.65716668e-02 -3.82006496e-01 -9.88315493e-02 -5.70898592e-01 -2.83041686e-01 -8.62361073e-01 -1.93981484e-01 6.74323380e-01 1.90752015e-01 1.03319176e-01 -2.74368346e-01 5.57375252e-01 -1.12084925e+00 3.39543372e-01 9.10897911e-01 1.12947118e+00 3.44956666e-01 5.26449621e-01 1.60928890e-01 6.94175661e-01 -4.30755824e-01 -4.35394682e-02 9.52889696e-02 -1.70368478e-01 -5.16526341e-01 -4.79852796e-01 1.12853929e-01 -8.23984504e-01 -3.76760304e-01 5.88684976e-01 7.23272562e-01 -1.19124942e-01 1.03133118e+00 -1.02930009e+00 -6.70644820e-01 -3.07871439e-02 -6.69448674e-01 6.84848130e-01 -2.92257190e-01 3.51808757e-01 6.44667566e-01 -8.35157454e-01 3.79378259e-01 9.04155076e-01 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61a09884-35ef-4628-8a3e-a3ea400ce543	nine-challenges-in-modern-algorithmic-trading	2101.08813	null	https://arxiv.org/abs/2101.08813v1	https://arxiv.org/pdf/2101.08813v1.pdf	Nine Challenges in Modern Algorithmic Trading and Controls	This editorial article partially informs the algorithmic trading community about launching of the new journal "Algorithmic Trading and Controls" (ATC). ATC is an online open-access journal that publishes novel works on algorithmic trading and its control methodologies. In this inaugural article, we discuss nine major challenges that contemporary Algo trading faces. There is nothing superstitiously magical about the number "nine," but so is any other one. Several of these challenges are at the strategy level, including for example, trading of illiquid securities or optimal portfolio execution. Others are more at the level of risk management and controls, such as on how to develop automated controls, testing and simulations. The editorial views could be inevitably personal and biased, but have been explored with the most innocent intention of contributing to this important field in modern financial services and technologies.	['Jackie Shen']	2021-01-21	null	null	null	null	['algorithmic-trading']	['time-series']	[-2.45312467e-01 2.74117310e-02 -1.02913208e-01 1.10870056e-01 -1.65544629e-01 -1.09246981e+00 6.00064993e-01 -6.29047081e-02 -3.71253133e-01 7.79546022e-01 -6.62539825e-02 -9.52944160e-01 -3.96416247e-01 -6.66296363e-01 -3.26292962e-01 -6.19607449e-01 -8.74535143e-02 6.42388046e-01 1.38499677e-01 -3.30148339e-01 9.79497135e-01 6.51771963e-01 -1.08380795e+00 -1.18533924e-01 4.70866829e-01 1.58229256e+00 -7.14016676e-01 4.82643962e-01 -4.00791496e-01 1.17776895e+00 -4.69179541e-01 -1.18427384e+00 7.92110384e-01 -3.26091051e-01 -1.69642746e-01 -2.58159012e-01 -3.26917946e-01 -4.00934130e-01 3.64008248e-01 1.18515062e+00 1.12187229e-01 -2.66906351e-01 4.83322054e-01 -1.21296263e+00 -2.90236235e-01 9.06927228e-01 -6.18884742e-01 6.55416131e-01 -2.93314487e-01 3.69306564e-01 1.16149282e+00 -5.46549082e-01 1.37584239e-01 9.57789063e-01 5.22415936e-01 1.00566909e-01 -9.36875522e-01 -8.36094499e-01 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1ead1be1-26f4-41ac-9e6c-4fa4ed8359f0	the-projected-covariance-measure-for	2211.02039	null	https://arxiv.org/abs/2211.02039v1	https://arxiv.org/pdf/2211.02039v1.pdf	The Projected Covariance Measure for assumption-lean variable significance testing	Testing the significance of a variable or group of variables $X$ for predicting a response $Y$, given additional covariates $Z$, is a ubiquitous task in statistics. A simple but common approach is to specify a linear model, and then test whether the regression coefficient for $X$ is non-zero. However, when the model is misspecified, the test may have poor power, for example when $X$ is involved in complex interactions, or lead to many false rejections. In this work we study the problem of testing the model-free null of conditional mean independence, i.e. that the conditional mean of $Y$ given $X$ and $Z$ does not depend on $X$. We propose a simple and general framework that can leverage flexible nonparametric or machine learning methods, such as additive models or random forests, to yield both robust error control and high power. The procedure involves using these methods to perform regressions, first to estimate a form of projection of $Y$ on $X$ and $Z$ using one half of the data, and then to estimate the expected conditional covariance between this projection and $Y$ on the remaining half of the data. While the approach is general, we show that a version of our procedure using spline regression achieves what we show is the minimax optimal rate in this nonparametric testing problem. Numerical experiments demonstrate the effectiveness of our approach both in terms of maintaining Type I error control, and power, compared to several existing approaches.	['Richard J. Samworth', 'Rajen D. Shah', 'Ilmun Kim', 'Anton Rask Lundborg']	2022-11-03	null	null	null	null	['additive-models']	['methodology']	[ 1.42663985e-01 -5.89065179e-02 -4.25655007e-01 -5.08517444e-01 -8.78672004e-01 -3.93330097e-01 2.25722883e-02 3.54108028e-02 -3.13849926e-01 1.13262594e+00 -3.31649542e-01 -6.08556271e-01 -3.38231027e-01 -1.05241430e+00 -1.18016863e+00 -7.58408725e-01 -2.71970749e-01 2.75359660e-01 -2.99686760e-01 4.03894067e-01 3.94108146e-01 2.15398952e-01 -1.54414523e+00 -4.56245333e-01 1.14989233e+00 1.03511763e+00 -2.63745666e-01 3.88375193e-01 2.40855351e-01 2.25484818e-01 -3.66974294e-01 -3.38752478e-01 1.71385586e-01 -5.08896589e-01 -4.00085300e-01 -2.41327778e-01 3.68570864e-01 -2.26390719e-01 2.68481374e-01 1.13875794e+00 3.19360584e-01 2.54826367e-01 9.40315902e-01 -1.28694785e+00 -3.96114498e-01 5.41382968e-01 -9.58343327e-01 -3.96402270e-01 2.33363032e-01 7.81560764e-02 1.02621758e+00 -8.00361156e-01 2.60399848e-01 1.17687321e+00 8.67657661e-01 1.58468992e-01 -1.94249725e+00 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-1.01122415e+00 1.82429001e-01 2.24639520e-01 -8.92708480e-01 -6.98411882e-01 -9.32895690e-02]	[7.6062140464782715, 4.694582462310791]
111559f9-8883-48fd-97ab-d211c49a8a43	federated-tensor-factorization-for	1704.03141	null	http://arxiv.org/abs/1704.03141v1	http://arxiv.org/pdf/1704.03141v1.pdf	Federated Tensor Factorization for Computational Phenotyping	Tensor factorization models offer an effective approach to convert massive electronic health records into meaningful clinical concepts (phenotypes) for data analysis. These models need a large amount of diverse samples to avoid population bias. An open challenge is how to derive phenotypes jointly across multiple hospitals, in which direct patient-level data sharing is not possible (e.g., due to institutional policies). In this paper, we developed a novel solution to enable federated tensor factorization for computational phenotyping without sharing patient-level data. We developed secure data harmonization and federated computation procedures based on alternating direction method of multipliers (ADMM). Using this method, the multiple hospitals iteratively update tensors and transfer secure summarized information to a central server, and the server aggregates the information to generate phenotypes. We demonstrated with real medical datasets that our method resembles the centralized training model (based on combined datasets) in terms of accuracy and phenotypes discovery while respecting privacy.	['Hwanjo Yu', 'Jimeng Sun', 'Yejin Kim', 'Xiaoqian Jiang']	2017-04-11	null	null	null	null	['computational-phenotyping']	['medical']	[-5.27786575e-02 -2.08456844e-01 -4.73661385e-02 -5.19714713e-01 -6.24685049e-01 -7.58204341e-01 -3.48260283e-01 5.14056027e-01 -3.29051197e-01 9.28299189e-01 2.81198770e-01 -4.65518236e-01 -3.52320671e-01 -7.83526182e-01 -4.73324537e-01 -7.16380537e-01 -1.21914968e-01 5.44998050e-01 -8.03478718e-01 7.21694976e-02 -3.85029972e-01 4.09071535e-01 -7.94331253e-01 7.19658434e-01 1.04756808e+00 7.71260977e-01 -3.09972256e-01 5.63854158e-01 8.11454430e-02 6.31131411e-01 -3.47657889e-01 -9.66457129e-01 5.41518092e-01 -1.56077072e-01 -8.37151527e-01 -1.06731169e-01 -6.48011491e-02 -5.11206329e-01 -1.22073986e-01 9.18147445e-01 5.57945430e-01 -3.53991419e-01 -1.38709217e-01 -1.37679124e+00 -7.01229811e-01 7.19542563e-01 -3.22223574e-01 -5.05095482e-01 2.18469501e-01 6.03285655e-02 8.44604492e-01 -5.35293818e-01 8.43093574e-01 6.98683321e-01 9.19275463e-01 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150cd247-a68d-4ed1-ac88-523a0c03b668	overview-of-the-shared-task-on-hope-speech	null	null	https://aclanthology.org/2022.ltedi-1.58	https://aclanthology.org/2022.ltedi-1.58.pdf	Overview of the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion	Hope Speech detection is the task of classifying a sentence as hope speech or non-hope speech given a corpus of sentences. Hope speech is any message or content that is positive, encouraging, reassuring, inclusive and supportive that inspires and engenders optimism in the minds of people. In contrast to identifying and censoring negative speech patterns, hope speech detection is focussed on recognising and promoting positive speech patterns online. In this paper, we report an overview of the findings and results from the shared task on hope speech detection for Tamil, Malayalam, Kannada, English and Spanish languages conducted in the second workshop on Language Technology for Equality, Diversity and Inclusion (LT-EDI-2022) organised as a part of ACL 2022. The participants were provided with annotated training & development datasets and unlabelled test datasets in all the five languages. The goal of the shared task is to classify the given sentences into one of the two hope speech classes. The performances of the systems submitted by the participants were evaluated in terms of micro-F1 score and weighted-F1 score. The datasets for this challenge are openly available	['José García-Díaz', 'Daniel García-Baena', 'Rahul Ponnusamy', 'Prasanna Kumaresan', 'Rafael Valencia-García', 'Salud María Jiménez-Zafra', 'Miguel Ángel García', 'John McCrae', 'Subalalitha Cn', 'Ruba Priyadharshini', 'Vigneshwaran Muralidaran', 'Bharathi Raja Chakravarthi']	null	null	null	null	ltedi-acl-2022-5	['hope-speech-detection-for-tamil', 'hope-speech-detection']	['natural-language-processing', 'natural-language-processing']	[ 1.49107337e-01 6.65417612e-01 -2.87718445e-01 -6.03781939e-01 -1.01255250e+00 -3.66202742e-01 1.18972826e+00 6.01160884e-01 -6.69743493e-02 6.25510931e-01 1.24124694e+00 -2.81441510e-01 3.43724489e-02 -2.53221720e-01 -1.42622083e-01 -2.24566311e-01 1.46688610e-01 3.64397228e-01 -1.90035939e-01 -5.23831487e-01 5.69317222e-01 4.30841967e-02 -1.31960797e+00 7.34166622e-01 4.86225337e-01 4.60011542e-01 -1.46653280e-01 1.09205937e+00 -2.53672421e-01 1.59937763e+00 -6.63788915e-01 -6.67720079e-01 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92cd5c38-1c8d-4d62-8ca6-197a88099c55	radars-for-autonomous-driving-a-review-of	2306.09304	null	https://arxiv.org/abs/2306.09304v2	https://arxiv.org/pdf/2306.09304v2.pdf	Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges	Radar is a key component of the suite of perception sensors used for safe and reliable navigation of autonomous vehicles. Its unique capabilities include high-resolution velocity imaging, detection of agents in occlusion and over long ranges, and robust performance in adverse weather conditions. However, the usage of radar data presents some challenges: it is characterized by low resolution, sparsity, clutter, high uncertainty, and lack of good datasets. These challenges have limited radar deep learning research. As a result, current radar models are often influenced by lidar and vision models, which are focused on optical features that are relatively weak in radar data, thus resulting in under-utilization of radar's capabilities and diminishing its contribution to autonomous perception. This review seeks to encourage further deep learning research on autonomous radar data by 1) identifying key research themes, and 2) offering a comprehensive overview of current opportunities and challenges in the field. Topics covered include early and late fusion, occupancy flow estimation, uncertainty modeling, and multipath detection. The paper also discusses radar fundamentals and data representation, presents a curated list of recent radar datasets, and reviews state-of-the-art lidar and vision models relevant for radar research. For a summary of the paper and more results, visit the website: autonomous-radars.github.io.	['Soumyajit Mandal', 'Arvind Srivastav']	2023-06-15	null	null	null	null	['autonomous-vehicles']	['computer-vision']	[ 9.27806720e-02 -7.09652543e-01 -2.02244461e-01 -4.82860446e-01 -5.39030790e-01 -3.14446002e-01 7.48256207e-01 -1.76779136e-01 -5.02840281e-01 9.68544841e-01 -1.23208225e-01 -1.73185468e-01 -3.42506051e-01 -1.01865232e+00 -2.11654931e-01 -9.21181321e-01 -3.71703893e-01 5.30379474e-01 1.89258754e-01 -2.28819564e-01 2.22036913e-01 8.63992512e-01 -1.91227663e+00 -2.02725962e-01 8.39300752e-01 1.14629614e+00 2.16689423e-01 6.34683490e-01 -1.06957734e-01 5.58993399e-01 -6.16187394e-01 1.06158912e-01 3.09844345e-01 -3.17252316e-02 3.93280625e-01 -3.73478681e-01 8.66173506e-01 -5.47988832e-01 -6.64429307e-01 1.06824136e+00 5.17403126e-01 1.30729616e-01 9.45145309e-01 -1.29023921e+00 -7.13912010e-01 4.04931605e-02 -5.74921787e-01 5.55682003e-01 -6.56935126e-02 1.13197044e-01 8.87764454e-01 -9.88607407e-01 3.26236576e-01 1.12403536e+00 5.81288218e-01 4.97439355e-01 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8045aada-2dfe-4831-a7fa-b50998c11aa4	learning-to-navigate-intersections-with	2109.06783	null	https://arxiv.org/abs/2109.06783v2	https://arxiv.org/pdf/2109.06783v2.pdf	Learning to Navigate Intersections with Unsupervised Driver Trait Inference	Navigation through uncontrolled intersections is one of the key challenges for autonomous vehicles. Identifying the subtle differences in hidden traits of other drivers can bring significant benefits when navigating in such environments. We propose an unsupervised method for inferring driver traits such as driving styles from observed vehicle trajectories. We use a variational autoencoder with recurrent neural networks to learn a latent representation of traits without any ground truth trait labels. Then, we use this trait representation to learn a policy for an autonomous vehicle to navigate through a T-intersection with deep reinforcement learning. Our pipeline enables the autonomous vehicle to adjust its actions when dealing with drivers of different traits to ensure safety and efficiency. Our method demonstrates promising performance and outperforms state-of-the-art baselines in the T-intersection scenario.	['Katherine Driggs-Campbell', 'Neeloy Chakraborty', 'Haonan Chen', 'Peixin Chang', 'Shuijing Liu']	2021-09-14	null	null	null	null	['personality-trait-recognition']	['computer-vision']	[-2.64010668e-01 3.74805540e-01 -4.22037661e-01 -1.01211870e+00 -5.68181157e-01 -5.68373144e-01 4.54448938e-01 -4.19733584e-01 -4.60481226e-01 2.26447880e-01 1.11222588e-01 -5.73102236e-01 -1.27493843e-01 -7.73739278e-01 -8.93313348e-01 -6.35228097e-01 1.77379727e-01 4.43719208e-01 -5.32800592e-02 -4.39046532e-01 1.02627851e-01 4.14841235e-01 -2.04085231e+00 -3.12954873e-01 8.40843856e-01 6.46060526e-01 -8.14007893e-02 7.53471255e-01 2.71332771e-01 7.96624303e-01 -4.77372855e-02 -6.72788441e-01 3.01011622e-01 2.03240037e-01 -2.60744333e-01 5.84251359e-02 6.82865143e-01 -5.77496588e-01 -8.17452133e-01 1.00282538e+00 3.93458232e-02 2.44832799e-01 1.05714977e+00 -1.75220168e+00 -5.48113763e-01 4.05279249e-01 1.39910698e-01 -6.99173138e-02 -1.85196370e-01 6.94316804e-01 8.05416524e-01 -2.24446416e-01 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c1d0c16d-273c-489e-a001-176b9206f735	covid-19-pneumonia-and-influenza-pneumonia	2112.07102	null	https://arxiv.org/abs/2112.07102v1	https://arxiv.org/pdf/2112.07102v1.pdf	COVID-19 Pneumonia and Influenza Pneumonia Detection Using Convolutional Neural Networks	In the research, we developed a computer vision solution to support diagnostic radiology in differentiating between COVID-19 pneumonia, influenza virus pneumonia, and normal biomarkers. The chest radiograph appearance of COVID-19 pneumonia is thought to be nonspecific, having presented a challenge to identify an optimal architecture of a convolutional neural network (CNN) that would classify with a high sensitivity among the pulmonary inflammation features of COVID-19 and non-COVID-19 types of pneumonia. Rahman (2021) states that COVID-19 radiography images observe unavailability and quality issues impacting the diagnostic process and affecting the accuracy of the deep learning detection models. A significant scarcity of COVID-19 radiography images introduced an imbalance in data motivating us to use over-sampling techniques. In the study, we include an extensive set of X-ray imaging of human lungs (CXR) with COVID-19 pneumonia, influenza virus pneumonia, and normal biomarkers to achieve an extensible and accurate CNN model. In the experimentation phase of the research, we evaluated a variety of convolutional network architectures, selecting a sequential convolutional network with two traditional convolutional layers and two pooling layers with maximum function. In its classification performance, the best performing model demonstrated a validation accuracy of 93% and an F1 score of 0.95. We chose the Azure Machine Learning service to perform network experimentation and solution deployment. The auto-scaling compute clusters offered a significant time reduction in network training. We would like to see scientists across fields of artificial intelligence and human biology collaborating and expanding on the proposed solution to provide rapid and comprehensive diagnostics, effectively mitigating the spread of the virus	['Robin Singh', 'Philip Melanchthon', 'Benjamin Prescott', 'Julianna Antonchuk']	2021-12-14	null	null	null	null	['pneumonia-detection']	['medical']	[-6.52793646e-02 -2.34265178e-01 1.85687780e-01 -5.30513525e-02 -6.13244027e-02 -4.63301361e-01 8.22899789e-02 -1.64157748e-02 -5.13119519e-01 3.30553681e-01 -1.59503184e-02 -8.67108524e-01 -6.39744222e-01 -7.49223948e-01 -3.34597677e-01 -7.47381687e-01 -2.32315198e-01 6.23907864e-01 1.70934036e-01 1.74278855e-01 -3.14131342e-02 9.67144012e-01 -1.26613200e+00 4.73350585e-01 2.38833085e-01 8.28171074e-01 5.71879864e-01 1.24620104e+00 2.42772371e-01 9.08240855e-01 -4.88332897e-01 1.40910044e-01 3.57754469e-01 -7.13087618e-02 -8.76941860e-01 -2.63151884e-01 8.94053429e-02 -6.68174863e-01 -1.41641855e-01 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a7095177-f74f-43bd-8407-887fd703d74d	sequence-to-sequence-load-disaggregation	2009.12355	null	https://arxiv.org/abs/2009.12355v1	https://arxiv.org/pdf/2009.12355v1.pdf	Sequence-to-Sequence Load Disaggregation Using Multi-Scale Residual Neural Network	With the increased demand on economy and efficiency of measurement technology, Non-Intrusive Load Monitoring (NILM) has received more and more attention as a cost-effective way to monitor electricity and provide feedback to users. Deep neural networks has been shown a great potential in the field of load disaggregation. In this paper, firstly, a new convolutional model based on residual blocks is proposed to avoid the degradation problem which traditional networks more or less suffer from when network layers are increased in order to learn more complex features. Secondly, we propose dilated convolution to curtail the excessive quantity of model parameters and obtain bigger receptive field, and multi-scale structure to learn mixed data features in a more targeted way. Thirdly, we give details about generating training and test set under certain rules. Finally, the algorithm is tested on real-house public dataset, UK-DALE, with three existing neural networks. The results are compared and analysed, the proposed model shows improvements on F1 score, MAE as well as model complexity across different appliances.	['Yanjun Feng', 'Zhi Li', 'Meng Fu', 'Gan Zhou', 'Chengwei Huang', 'Xingyao Wang']	2020-09-25	null	null	null	null	['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring']	['knowledge-base', 'miscellaneous', 'time-series']	[-1.27154011e-02 -1.92018107e-01 2.12152153e-01 -5.70061088e-01 -1.93092182e-01 -3.33538353e-01 6.18987024e-01 -3.05284768e-01 -2.74821788e-01 8.52790833e-01 1.31909773e-01 -8.36220309e-02 -2.92773068e-01 -1.11965001e+00 -2.11262599e-01 -9.05368924e-01 1.69846397e-02 1.49089232e-01 -1.74857765e-01 -2.37932235e-01 1.26762301e-01 6.16955757e-01 -1.49028993e+00 1.37664348e-01 1.03340542e+00 1.13978922e+00 4.05507237e-01 1.86902210e-01 1.15661852e-01 9.16449487e-01 -7.84318507e-01 -1.09562077e-01 1.56208917e-01 -2.33648986e-01 -8.83180201e-01 6.76193163e-02 -2.85129566e-02 -6.27059698e-01 -3.44516069e-01 9.77379799e-01 1.04828322e+00 1.48955286e-01 5.60848057e-01 -1.15494978e+00 -6.95959628e-01 8.62517297e-01 -5.93281865e-01 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00536379-2478-40b9-9a86-66c6a9f43378	gallery-filter-network-for-person-search	2210.12903	null	https://arxiv.org/abs/2210.12903v2	https://arxiv.org/pdf/2210.12903v2.pdf	Gallery Filter Network for Person Search	In person search, we aim to localize a query person from one scene in other gallery scenes. The cost of this search operation is dependent on the number of gallery scenes, making it beneficial to reduce the pool of likely scenes. We describe and demonstrate the Gallery Filter Network (GFN), a novel module which can efficiently discard gallery scenes from the search process, and benefit scoring for persons detected in remaining scenes. We show that the GFN is robust under a range of different conditions by testing on different retrieval sets, including cross-camera, occluded, and low-resolution scenarios. In addition, we develop the base SeqNeXt person search model, which improves and simplifies the original SeqNet model. We show that the SeqNeXt+GFN combination yields significant performance gains over other state-of-the-art methods on the standard PRW and CUHK-SYSU person search datasets. To aid experimentation for this and other models, we provide standardized tooling for the data processing and evaluation pipeline typically used for person search research.	['Avideh Zakhor', 'Lucas Jaffe']	2022-10-24	null	null	null	null	['person-search']	['computer-vision']	[-4.99648862e-02 -6.06779277e-01 2.12341338e-01 -4.12904769e-01 -8.71151328e-01 -6.02996051e-01 7.12254941e-01 -2.67606050e-01 -7.84843564e-01 4.51765686e-01 5.00697792e-01 4.15237576e-01 -2.30826467e-01 -4.71837759e-01 -3.36124629e-01 -2.50377238e-01 4.60393615e-02 6.69823706e-01 5.28242350e-01 -1.35246694e-01 -1.87185183e-01 3.83869469e-01 -1.81691301e+00 2.35477954e-01 5.12152612e-01 7.12055683e-01 4.34500366e-01 6.54543400e-01 5.18427074e-01 3.99756521e-01 -8.48249316e-01 -6.67051435e-01 5.56745112e-01 -1.26782477e-01 -8.13398182e-01 -5.81098869e-02 1.15245962e+00 -4.48028982e-01 -7.88399458e-01 8.73004675e-01 1.13142991e+00 5.56139827e-01 3.17219436e-01 -1.03906763e+00 -5.28286755e-01 1.01098113e-01 -2.63483077e-01 5.38030207e-01 9.96429026e-01 2.68751949e-01 9.02202368e-01 -1.02431118e+00 7.78832972e-01 1.59425032e+00 7.66132712e-01 6.27383530e-01 -9.81679797e-01 -5.97731411e-01 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c4c3813d-1675-40fe-99b0-0fbe8d87e6b8	an-efficient-deep-learning-model-for	2110.04980	null	https://arxiv.org/abs/2110.04980v1	https://arxiv.org/pdf/2110.04980v1.pdf	An Efficient Deep Learning Model for Automatic Modulation Recognition Based on Parameter Estimation and Transformation	Automatic modulation recognition (AMR) is a promising technology for intelligent communication receivers to detect signal modulation schemes. Recently, the emerging deep learning (DL) research has facilitated high-performance DL-AMR approaches. However, most DL-AMR models only focus on recognition accuracy, leading to huge model sizes and high computational complexity, while some lightweight and low-complexity models struggle to meet the accuracy requirements. This letter proposes an efficient DL-AMR model based on phase parameter estimation and transformation, with convolutional neural network (CNN) and gated recurrent unit (GRU) as the feature extraction layers, which can achieve high recognition accuracy equivalent to the existing state-of-the-art models but reduces more than a third of the volume of their parameters. Meanwhile, our model is more competitive in training time and test time than the benchmark models with similar recognition accuracy. Moreover, we further propose to compress our model by pruning, which maintains the recognition accuracy higher than 90% while has less than 1/8 of the number of parameters comparing with state-of-the-art models.	['Yang Luo', 'Jialang Xu', 'Chunbo Luo', 'Fuxin Zhang']	2021-10-11	null	null	null	null	['automatic-modulation-recognition', 'intelligent-communication']	['time-series', 'time-series']	[ 3.98166120e-01 -4.30030584e-01 -4.15179312e-01 -8.28282759e-02 -8.54145825e-01 1.09491758e-01 3.70851129e-01 -2.00745642e-01 -3.67519498e-01 4.02757674e-01 -8.31311345e-02 -6.36466026e-01 -1.28000394e-01 -5.97400546e-01 -2.07138196e-01 -7.35239267e-01 5.15819862e-02 -2.46729791e-01 1.03355840e-01 -1.35703087e-01 2.08971590e-01 6.39440119e-01 -1.24860847e+00 1.35145217e-01 6.29030108e-01 1.53945541e+00 9.32650492e-02 6.21092916e-01 4.33686413e-02 1.00611246e+00 -7.95348465e-01 -3.32067072e-01 -9.39065684e-03 -5.04293859e-01 -1.30439728e-01 -1.46012932e-01 1.51883541e-02 -2.72111088e-01 -1.29226220e+00 9.20430481e-01 9.29655492e-01 -3.04244995e-01 4.61477488e-01 -8.07944775e-01 -6.61107659e-01 6.38239741e-01 -6.12644434e-01 3.15180928e-01 -5.71798384e-02 -1.87721595e-01 7.96763778e-01 -8.00166309e-01 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0ed040cf-230d-45ef-b1ba-4909ae4afc2c	meta-reinforcement-learning-for-mastering	null	null	https://aclanthology.org/2021.metanlp-1.1	https://aclanthology.org/2021.metanlp-1.1.pdf	Meta-Reinforcement Learning for Mastering Multiple Skills and Generalizing across Environments in Text-based Games	Text-based games can be used to develop task-oriented text agents for accomplishing tasks with high-level language instructions, which has potential applications in domains such as human-robot interaction. Given a text instruction, reinforcement learning is commonly used to train agents to complete the intended task owing to its convenience of learning policies automatically. However, because of the large space of combinatorial text actions, learning a policy network that generates an action word by word with reinforcement learning is challenging. Recent research works show that imitation learning provides an effective way of training a generation-based policy network. However, trained agents with imitation learning are hard to master a wide spectrum of task types or skills, and it is also difficult for them to generalize to new environments. In this paper, we propose a meta reinforcement learning based method to train text agents through learning-to-explore. In particular, the text agent first explores the environment to gather task-specific information and then adapts the execution policy for solving the task with this information. On the publicly available testbed ALFWorld, we conducted a comparison study with imitation learning and show the superiority of our method.	['Xiaojuan Ma', 'Mingfei Sun', 'Zhenjie Zhao']	null	null	null	null	acl-metanlp-2021-8	['text-based-games']	['playing-games']	[ 2.35103026e-01 1.63361598e-02 -2.05932349e-01 -6.29933625e-02 -3.39743406e-01 -4.21441019e-01 7.64168620e-01 -1.02743022e-01 -6.63255394e-01 9.21797097e-01 -3.79060991e-02 -3.78646016e-01 4.08128500e-02 -7.35541403e-01 -7.59205937e-01 -6.29071772e-01 1.11580439e-01 7.08443642e-01 3.70446414e-01 -6.16623819e-01 4.41727757e-01 3.37208480e-01 -1.70199525e+00 -3.20424773e-02 1.16577601e+00 4.59049940e-01 1.09542596e+00 5.60710549e-01 -3.52691650e-01 1.16108656e+00 -8.36574078e-01 2.46379584e-01 1.97415814e-01 -7.28594244e-01 -8.98468971e-01 1.27136514e-01 -3.82856458e-01 -4.82276857e-01 -3.74962837e-01 1.03534091e+00 3.55035245e-01 6.38181865e-01 7.05569744e-01 -1.36565089e+00 -2.99965799e-01 6.91330671e-01 -2.59564996e-01 -1.96435124e-01 3.51586908e-01 5.23286581e-01 6.35256588e-01 -2.72548795e-01 6.57112539e-01 1.34538221e+00 -1.34639367e-01 1.02023208e+00 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cd88549c-bbfe-4634-a4aa-694e5fd0b4ce	ghn-q-parameter-prediction-for-unseen	2208.12489	null	https://arxiv.org/abs/2208.12489v1	https://arxiv.org/pdf/2208.12489v1.pdf	GHN-Q: Parameter Prediction for Unseen Quantized Convolutional Architectures via Graph Hypernetworks	Deep convolutional neural network (CNN) training via iterative optimization has had incredible success in finding optimal parameters. However, modern CNN architectures often contain millions of parameters. Thus, any given model for a single architecture resides in a massive parameter space. Models with similar loss could have drastically different characteristics such as adversarial robustness, generalizability, and quantization robustness. For deep learning on the edge, quantization robustness is often crucial. Finding a model that is quantization-robust can sometimes require significant efforts. Recent works using Graph Hypernetworks (GHN) have shown remarkable performance predicting high-performant parameters of varying CNN architectures. Inspired by these successes, we wonder if the graph representations of GHN-2 can be leveraged to predict quantization-robust parameters as well, which we call GHN-Q. We conduct the first-ever study exploring the use of graph hypernetworks for predicting parameters of unseen quantized CNN architectures. We focus on a reduced CNN search space and find that GHN-Q can in fact predict quantization-robust parameters for various 8-bit quantized CNNs. Decent quantized accuracies are observed even with 4-bit quantization despite GHN-Q not being trained on it. Quantized finetuning of GHN-Q at lower bitwidths may bring further improvements and is currently being explored.	['Alexander Wong', 'Stone Yun']	2022-08-26	null	null	null	null	['parameter-prediction']	['miscellaneous']	[ 6.94858208e-02 3.69340986e-01 -2.92234391e-01 -3.59270722e-01 -8.71160924e-01 -6.90268219e-01 2.09918752e-01 1.02783784e-01 -4.54219699e-01 4.61605906e-01 -1.45997047e-01 -7.47591257e-01 -1.46991253e-01 -6.77541137e-01 -9.51861680e-01 -6.04954898e-01 -3.46726149e-01 1.35637224e-01 2.80937940e-01 -3.39315504e-01 2.52661943e-01 6.51724994e-01 -1.14577901e+00 -2.06008982e-02 3.30715239e-01 1.34483528e+00 -3.19659680e-01 1.02757370e+00 3.42573375e-01 4.37082231e-01 -6.01463318e-01 -6.60275638e-01 7.35876858e-01 -1.64613247e-01 -6.93127096e-01 -4.90976125e-01 1.05114520e+00 -3.26579452e-01 -6.60734177e-01 1.33386612e+00 6.64428949e-01 -2.66448837e-02 4.84553546e-01 -1.53468275e+00 -6.72448277e-01 9.58822548e-01 1.13591611e-01 2.60509551e-01 -4.20838833e-01 5.33094168e-01 1.37490892e+00 -1.44561023e-01 3.04268360e-01 1.45260942e+00 9.12932038e-01 7.05002487e-01 -1.42674553e+00 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33e9d5a9-f925-4798-bf5c-b4d12a737a00	spatial-temporal-residual-aggregation-for	2111.03574	null	https://arxiv.org/abs/2111.03574v1	https://arxiv.org/pdf/2111.03574v1.pdf	Spatial-Temporal Residual Aggregation for High Resolution Video Inpainting	Recent learning-based inpainting algorithms have achieved compelling results for completing missing regions after removing undesired objects in videos. To maintain the temporal consistency among the frames, 3D spatial and temporal operations are often heavily used in the deep networks. However, these methods usually suffer from memory constraints and can only handle low resolution videos. We propose STRA-Net, a novel spatial-temporal residual aggregation framework for high resolution video inpainting. The key idea is to first learn and apply a spatial and temporal inpainting network on the downsampled low resolution videos. Then, we refine the low resolution results by aggregating the learned spatial and temporal image residuals (details) to the upsampled inpainted frames. Both the quantitative and qualitative evaluations show that we can produce more temporal-coherent and visually appealing results than the state-of-the-art methods on inpainting high resolution videos.	['Zhan Xu', 'Zili Yi', 'Qiang Tang', 'Rui Ma', 'Vishnu Sanjay Ramiya Srinivasan']	2021-11-05	null	null	null	null	['video-inpainting']	['computer-vision']	[ 6.43568709e-02 -3.59745115e-01 -1.10453099e-01 -1.35505259e-01 -8.05250823e-01 -7.09538832e-02 2.72522986e-01 -5.16682804e-01 -2.20576301e-01 9.77341115e-01 5.26197076e-01 4.02410209e-01 -2.57180899e-01 -6.36273980e-01 -1.02932620e+00 -5.63822925e-01 -9.99539346e-02 -2.38915533e-01 3.43858600e-01 -3.31584848e-02 2.23495271e-02 6.22129619e-01 -1.52005017e+00 6.92377210e-01 7.83832550e-01 9.82448399e-01 4.15010720e-01 6.03455603e-01 1.21735558e-01 1.23038018e+00 -4.75546837e-01 3.66096981e-02 5.17628133e-01 -6.42618001e-01 -5.28931797e-01 2.37908915e-01 7.40058422e-01 -8.52687657e-01 -8.90912056e-01 9.77304697e-01 2.93005943e-01 4.08315092e-01 4.30908464e-02 -8.62209976e-01 -7.85569668e-01 4.72925991e-01 -1.07672369e+00 2.98021346e-01 6.07037246e-01 8.33699703e-02 5.74479401e-01 -1.09489918e+00 8.28483224e-01 1.38677824e+00 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acb00304-e514-4f27-aca1-9804c03332e0	apricot-submodular-selection-for-data	1906.03543	null	https://arxiv.org/abs/1906.03543v1	https://arxiv.org/pdf/1906.03543v1.pdf	apricot: Submodular selection for data summarization in Python	We present apricot, an open source Python package for selecting representative subsets from large data sets using submodular optimization. The package implements an efficient greedy selection algorithm that offers strong theoretical guarantees on the quality of the selected set. Two submodular set functions are implemented in apricot: facility location, which is broadly applicable but requires memory quadratic in the number of examples in the data set, and a feature-based function that is less broadly applicable but can scale to millions of examples. Apricot is extremely efficient, using both algorithmic speedups such as the lazy greedy algorithm and code optimizers such as numba. We demonstrate the use of subset selection by training machine learning models to comparable accuracy using either the full data set or a representative subset thereof. This paper presents an explanation of submodular selection, an overview of the features in apricot, and an application to several data sets. The code and tutorial Jupyter notebooks are available at https://github.com/jmschrei/apricot	['William Stafford Noble', 'Jeffrey Bilmes', 'Jacob Schreiber']	2019-06-08	null	null	null	null	['data-summarization']	['miscellaneous']	[-7.02152133e-01 -1.18969150e-01 -4.91986215e-01 -5.40851057e-01 -1.03236580e+00 -7.25596070e-01 -1.05337566e-03 3.50400954e-01 -2.63611406e-01 1.08444095e+00 1.58968136e-01 1.00477256e-01 -4.89543021e-01 -9.64832008e-01 -7.40083933e-01 -7.62196779e-01 -3.52424949e-01 1.03841269e+00 -1.90226898e-01 -1.91051513e-01 1.38306856e-01 4.58957702e-01 -1.67766845e+00 3.16450953e-01 7.35429168e-01 1.12277806e+00 2.92438269e-01 4.63834971e-01 1.77309200e-01 3.75083745e-01 -2.94493616e-01 8.21647942e-02 1.00199819e+00 -3.43076363e-02 -5.01105547e-01 1.33131007e-02 6.15663886e-01 -3.61693829e-01 -2.33866394e-01 6.21939778e-01 6.61844492e-01 1.76955983e-01 1.82074413e-01 -1.59746242e+00 -3.88776273e-01 6.49516582e-01 -3.46522868e-01 -3.49100903e-02 4.73981619e-01 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826921cb-8f9a-4764-a9aa-7ba34b781750	computing-steiner-trees-using-graph-neural	2108.08368	null	https://arxiv.org/abs/2108.08368v1	https://arxiv.org/pdf/2108.08368v1.pdf	Computing Steiner Trees using Graph Neural Networks	Graph neural networks have been successful in many learning problems and real-world applications. A recent line of research explores the power of graph neural networks to solve combinatorial and graph algorithmic problems such as subgraph isomorphism, detecting cliques, and the traveling salesman problem. However, many NP-complete problems are as of yet unexplored using this method. In this paper, we tackle the Steiner Tree Problem. We employ four learning frameworks to compute low cost Steiner trees: feed-forward neural networks, graph neural networks, graph convolutional networks, and a graph attention model. We use these frameworks in two fundamentally different ways: 1) to train the models to learn the actual Steiner tree nodes, 2) to train the model to learn good Steiner point candidates to be connected to the constructed tree using a shortest path in a greedy fashion. We illustrate the robustness of our heuristics on several random graph generation models as well as the SteinLib data library. Our finding suggests that the out-of-the-box application of GNN methods does worse than the classic 2-approximation method. However, when combined with a greedy shortest path construction, it even does slightly better than the 2-approximation algorithm. This result sheds light on the fundamental capabilities and limitations of graph learning techniques on classical NP-complete problems.	['Stephen Kobourov', 'Keaton Hamm', 'Mithun Ghosh', 'Faryad Darabi Sahneh', 'Md Asadullah Turja', 'Reyan Ahmed']	2021-08-18	null	null	null	null	['steiner-tree-problem']	['graphs']	[ 7.60288164e-02 7.27973759e-01 -5.05885422e-01 -1.37013167e-01 -4.25844997e-01 -6.30655646e-01 2.45550126e-01 3.41558278e-01 -1.32377490e-01 6.83767080e-01 -2.40470678e-01 -9.35312629e-01 -4.84202087e-01 -1.28666353e+00 -9.27517593e-01 -4.71523255e-01 -7.51798093e-01 1.04554379e+00 -5.48758358e-03 -2.97515213e-01 1.13212384e-01 6.40350401e-01 -1.14811134e+00 1.08591765e-01 7.03597605e-01 6.81991041e-01 -1.63801443e-02 7.84786642e-01 -2.56300122e-01 6.74460530e-01 -2.40863949e-01 -3.68921936e-01 5.53913832e-01 -6.23114817e-02 -1.22213054e+00 1.15958219e-02 4.14275885e-01 -9.10449401e-02 -5.55856228e-01 8.01013589e-01 1.04316428e-01 1.03092194e-03 2.18913525e-01 -1.64771807e+00 -6.65468693e-01 1.03526473e+00 -5.07401466e-01 2.86487520e-01 3.80822837e-01 2.84145117e-01 1.29600632e+00 -1.50972635e-01 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94b0d072-d594-48d1-b77d-9a65ea5068dc	depthwise-separable-convolutions-versus	2007.02683	null	https://arxiv.org/abs/2007.02683v1	https://arxiv.org/pdf/2007.02683v1.pdf	Depthwise Separable Convolutions Versus Recurrent Neural Networks for Monaural Singing Voice Separation	Recent approaches for music source separation are almost exclusively based on deep neural networks, mostly employing recurrent neural networks (RNNs). Although RNNs are in many cases superior than other types of deep neural networks for sequence processing, they are known to have specific difficulties in training and parallelization, especially for the typically long sequences encountered in music source separation. In this paper we present a use-case of replacing RNNs with depth-wise separable (DWS) convolutions, which are a lightweight and faster variant of the typical convolutions. We focus on singing voice separation, employing an RNN architecture, and we replace the RNNs with DWS convolutions (DWS-CNNs). We conduct an ablation study and examine the effect of the number of channels and layers of DWS-CNNs on the source separation performance, by utilizing the standard metrics of signal-to-artifacts, signal-to-interference, and signal-to-distortion ratio. Our results show that by replacing RNNs with DWS-CNNs yields an improvement of 1.20, 0.06, 0.37 dB, respectively, while using only 20.57% of the amount of parameters of the RNN architecture.	['Tuomas Virtanen', 'Pyry Pyykkönen', 'Styliannos I. Mimilakis', 'Konstantinos Drossos']	2020-07-06	null	null	null	null	['music-source-separation']	['music']	[ 2.83958584e-01 -4.95755732e-01 3.44582379e-01 7.42895454e-02 -5.64760149e-01 -6.18915081e-01 1.56186342e-01 -4.16127294e-01 -5.17491758e-01 4.41323578e-01 3.72586310e-01 -3.93601924e-01 -6.61224648e-02 -2.89055556e-01 -4.90683168e-01 -8.29312444e-01 -1.35160834e-01 -4.08011913e-01 -1.21256389e-01 -2.67856687e-01 -1.36149734e-01 5.27912080e-01 -1.59442830e+00 2.41395399e-01 6.26292586e-01 1.05256212e+00 5.62808774e-02 9.95388508e-01 4.98936772e-02 8.44380498e-01 -1.05665171e+00 -1.90700993e-01 3.80502790e-01 -8.26219559e-01 -4.40400690e-01 -3.39956015e-01 3.38641018e-01 -3.83906305e-01 -6.88142598e-01 9.82271433e-01 1.17880654e+00 3.24632227e-01 3.78886431e-01 -9.73836780e-01 -5.07487118e-01 1.07561779e+00 -5.08338630e-01 5.88922322e-01 8.33947435e-02 -8.18767026e-02 9.46369648e-01 -8.88436913e-01 1.39292449e-01 9.96217549e-01 1.18977165e+00 4.15890902e-01 -1.16145551e+00 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2dbc4284-bd69-463b-994c-03afcdcca384	supervised-sentence-fusion-with-single-stage	null	null	https://aclanthology.org/I13-1198	https://aclanthology.org/I13-1198.pdf	Supervised Sentence Fusion with Single-Stage Inference	null	['Kapil Thadani', 'Kathleen McKeown']	2013-10-01	supervised-sentence-fusion-with-single-stage-1	https://aclanthology.org/I13-1198	https://aclanthology.org/I13-1198.pdf	ijcnlp-2013-10	['sentence-compression']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 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913316e7-ea5e-45cb-adbd-fce551a5d8ad	investigating-the-edge-of-stability	2307.04210	null	https://arxiv.org/abs/2307.04210v1	https://arxiv.org/pdf/2307.04210v1.pdf	Investigating the Edge of Stability Phenomenon in Reinforcement Learning	Recent progress has been made in understanding optimisation dynamics in neural networks trained with full-batch gradient descent with momentum with the uncovering of the edge of stability phenomenon in supervised learning. The edge of stability phenomenon occurs as the leading eigenvalue of the Hessian reaches the divergence threshold of the underlying optimisation algorithm for a quadratic loss, after which it starts oscillating around the threshold, and the loss starts to exhibit local instability but decreases over long time frames. In this work, we explore the edge of stability phenomenon in reinforcement learning (RL), specifically off-policy Q-learning algorithms across a variety of data regimes, from offline to online RL. Our experiments reveal that, despite significant differences to supervised learning, such as non-stationarity of the data distribution and the use of bootstrapping, the edge of stability phenomenon can be present in off-policy deep RL. Unlike supervised learning, however, we observe strong differences depending on the underlying loss, with DQN -- using a Huber loss -- showing a strong edge of stability effect that we do not observe with C51 -- using a cross entropy loss. Our results suggest that, while neural network structure can lead to optimisation dynamics that transfer between problem domains, certain aspects of deep RL optimisation can differentiate it from domains such as supervised learning.	['Mihaela Rosca', 'Marc Peter Deisenroth', 'Rares Iordan']	2023-07-09	null	null	null	null	['q-learning', 'reinforcement-learning-1']	['methodology', 'methodology']	[ 3.86267044e-02 2.43067890e-01 -3.81275773e-01 -2.06153736e-01 -4.32014823e-01 -5.13640761e-01 6.96512759e-01 2.93317229e-01 -7.05249786e-01 8.70588779e-01 4.82235625e-02 -4.86640960e-01 -4.52749670e-01 -3.90112519e-01 -9.23407018e-01 -1.11902213e+00 -4.90573406e-01 1.59369797e-01 4.57795942e-03 -5.82155228e-01 1.52900934e-01 3.16712260e-01 -1.17499578e+00 -9.69375968e-02 4.82401669e-01 1.08086681e+00 -2.67714590e-01 6.85122132e-01 5.15527949e-02 9.27230358e-01 -4.78737146e-01 -2.60101736e-01 6.24762833e-01 -6.78383291e-01 -8.68710160e-01 -2.10936010e-01 3.54570121e-01 -6.83244737e-03 -2.38865495e-01 1.17957044e+00 6.90307677e-01 2.72541374e-01 6.69772983e-01 -1.15883958e+00 -3.18653464e-01 3.46441448e-01 -3.38628024e-01 4.31651801e-01 -9.90954638e-02 4.97851580e-01 1.17439401e+00 -1.82271108e-01 6.91260219e-01 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fd20dcd9-daa8-4237-86ad-3b51cf4eccc0	textual-augmentation-techniques-applied-to	2306.07414	null	https://arxiv.org/abs/2306.07414v1	https://arxiv.org/pdf/2306.07414v1.pdf	Textual Augmentation Techniques Applied to Low Resource Machine Translation: Case of Swahili	In this work we investigate the impact of applying textual data augmentation tasks to low resource machine translation. There has been recent interest in investigating approaches for training systems for languages with limited resources and one popular approach is the use of data augmentation techniques. Data augmentation aims to increase the quantity of data that is available to train the system. In machine translation, majority of the language pairs around the world are considered low resource because they have little parallel data available and the quality of neural machine translation (NMT) systems depend a lot on the availability of sizable parallel corpora. We study and apply three simple data augmentation techniques popularly used in text classification tasks; synonym replacement, random insertion and contextual data augmentation and compare their performance with baseline neural machine translation for English-Swahili (En-Sw) datasets. We also present results in BLEU, ChrF and Meteor scores. Overall, the contextual data augmentation technique shows some improvements both in the $EN \rightarrow SW$ and $SW \rightarrow EN$ directions. We see that there is potential to use these methods in neural machine translation when more extensive experiments are done with diverse datasets.	['Vukosi Marivate', 'Catherine Gitau']	2023-06-12	null	null	null	null	['nmt', 'text-classification', 'machine-translation']	['computer-code', 'natural-language-processing', 'natural-language-processing']	[ 3.44273925e-01 -1.02255538e-01 -5.80882370e-01 -3.64366531e-01 -1.04742908e+00 -6.02878809e-01 9.79741454e-01 1.31737024e-01 -9.35203731e-01 1.16405320e+00 4.35311258e-01 -8.66997421e-01 3.58604491e-01 -6.22293353e-01 -7.13132203e-01 -3.47604871e-01 5.91330767e-01 1.18123162e+00 -2.39889458e-01 -1.00604177e+00 1.30534321e-01 3.16520303e-01 -8.51781130e-01 4.91678208e-01 9.15309787e-01 2.81078815e-01 1.43290386e-01 4.48291808e-01 -2.96403170e-01 3.34353328e-01 -5.81763506e-01 -7.58243501e-01 5.62273562e-01 -6.37837946e-01 -1.03802073e+00 -5.15300810e-01 3.77904117e-01 2.10463092e-01 1.30416244e-01 6.82028234e-01 7.31847048e-01 2.10091152e-04 4.98839676e-01 -9.45106030e-01 -6.66083872e-01 9.06966686e-01 -6.14980578e-01 4.67069924e-01 2.41702750e-01 -6.95812777e-02 8.64674330e-01 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4cb488c0-702a-41e2-b841-bf3a62abdb3a	pose-guided-human-image-synthesis-with	2210.03627	null	https://arxiv.org/abs/2210.03627v1	https://arxiv.org/pdf/2210.03627v1.pdf	Pose Guided Human Image Synthesis with Partially Decoupled GAN	Pose Guided Human Image Synthesis (PGHIS) is a challenging task of transforming a human image from the reference pose to a target pose while preserving its style. Most existing methods encode the texture of the whole reference human image into a latent space, and then utilize a decoder to synthesize the image texture of the target pose. However, it is difficult to recover the detailed texture of the whole human image. To alleviate this problem, we propose a method by decoupling the human body into several parts (\eg, hair, face, hands, feet, \etc) and then using each of these parts to guide the synthesis of a realistic image of the person, which preserves the detailed information of the generated images. In addition, we design a multi-head attention-based module for PGHIS. Because most convolutional neural network-based methods have difficulty in modeling long-range dependency due to the convolutional operation, the long-range modeling capability of attention mechanism is more suitable than convolutional neural networks for pose transfer task, especially for sharp pose deformation. Extensive experiments on Market-1501 and DeepFashion datasets reveal that our method almost outperforms other existing state-of-the-art methods in terms of both qualitative and quantitative metrics.	['Jing Xiao', 'Xiaoyang Qu', 'Shijing Si', 'Jianzong Wang', 'Jianhan Wu']	2022-10-07	null	null	null	null	['pose-transfer', 'long-range-modeling']	['computer-vision', 'natural-language-processing']	[ 2.21616596e-01 2.33507544e-01 2.66367823e-01 -3.12824130e-01 -4.08937752e-01 -1.95633829e-01 3.20114464e-01 -8.79323423e-01 -1.44766495e-01 5.41842937e-01 4.69701529e-01 5.87030709e-01 3.98394525e-01 -8.00243258e-01 -1.02998710e+00 -8.36554468e-01 5.95178068e-01 4.73003477e-01 1.84443220e-01 -4.05398995e-01 -1.62992984e-01 2.72309184e-01 -1.29406035e+00 2.66345412e-01 5.32334208e-01 1.11448658e+00 1.51634648e-01 2.60952860e-01 2.78161436e-01 4.93143648e-01 -6.59665585e-01 -7.36072183e-01 2.97713548e-01 -5.56312978e-01 -6.96549714e-01 5.48799813e-01 4.54084903e-01 -7.27512419e-01 -7.78323889e-01 1.07927501e+00 6.98903561e-01 9.91593972e-02 5.98985255e-01 -1.08748591e+00 -7.93449342e-01 3.11285526e-01 -8.55534256e-01 -3.56425792e-01 6.14543617e-01 3.36995423e-01 3.90851855e-01 -7.79942453e-01 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b1788006-38ef-4ec2-9d4d-60dcf9aced3b	graphix-a-pre-trained-graph-edit-model-for	null	null	https://openreview.net/forum?id=uB12zutkXJR	https://openreview.net/pdf?id=uB12zutkXJR	GRAPHIX: A Pre-trained Graph Edit Model for Automated Program Repair	We present GRAPHIX, a pre-trained graph edit model for automatically detecting and fixing bugs and code quality issues in Java programs. Unlike sequence-to-sequence models, GRAPHIX leverages the abstract syntax structure of code and represents the code using a multi-head graph encoder. Along with an autoregressive tree decoder, the model learns to perform graph edit actions for automated program repair. We devise a novel pre-training strategy for GRAPHIX, namely deleted sub-tree reconstruction, to enrich the model with implicit knowledge of program structures from unlabeled source code. The pre-training objective is made consistent with the bug fixing task to facilitate the downstream learning. We evaluate GRAPHIX on the Patches in The Wild Java benchmark, using both abstract and concrete code. Experimental results show that GRAPHIX significantly outperforms a wide range of baselines including CodeBERT and BART and is as competitive as other state-of-the-art pre-trained Transformer models despite using one order of magnitude fewer parameters. Further analysis demonstrates strong inductive biases of GRAPHIX in learning meaningful structural and semantic code patterns, both in abstract and concrete source code.	['Srinivasan H. Sengamedu', 'Thanh V Nguyen']	2021-09-29	null	null	null	null	['program-repair', 'program-repair']	['computer-code', 'reasoning']	[ 2.4992585e-01 6.2886930e-01 -2.6738355e-01 -4.4810143e-01 -9.8335218e-01 -6.2408632e-01 1.4161451e-01 5.4620183e-01 2.5628921e-01 5.7730898e-02 1.4731520e-01 -7.0129722e-01 3.1998467e-01 -6.7126149e-01 -1.2957761e+00 1.7100336e-01 -4.1380095e-01 7.0991493e-03 1.1380436e-01 1.6455792e-02 4.5189494e-01 -5.3460884e-01 -1.2946873e+00 5.6461322e-01 1.0797158e+00 3.6358771e-01 2.1228701e-01 8.4604788e-01 -1.5550862e-01 1.4472250e+00 -5.1652026e-01 -9.0285897e-01 -6.2635213e-02 -3.4630391e-01 -8.0607694e-01 -2.6790294e-01 6.9046801e-01 -2.6210862e-01 -2.1263495e-01 1.3375431e+00 -2.4007285e-02 -3.4152189e-01 1.2744650e-01 -1.0849438e+00 -1.1832783e+00 1.0974251e+00 -5.8102328e-01 6.1264470e-02 6.0952622e-01 2.1112923e-01 1.5295106e+00 -8.0645567e-01 6.9207275e-01 1.1391158e+00 1.2796673e+00 3.5865909e-01 -1.4343162e+00 -3.1494069e-01 2.1571207e-01 -9.9899545e-02 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66172b42-9f9b-41fe-aba5-2d4a52801f73	graph-neural-networks-for-knowledge-enhanced	2105.08190	null	https://arxiv.org/abs/2105.08190v1	https://arxiv.org/pdf/2105.08190v1.pdf	Graph Neural Networks for Knowledge Enhanced Visual Representation of Paintings	We propose ArtSAGENet, a novel multimodal architecture that integrates Graph Neural Networks (GNNs) and Convolutional Neural Networks (CNNs), to jointly learn visual and semantic-based artistic representations. First, we illustrate the significant advantages of multi-task learning for fine art analysis and argue that it is conceptually a much more appropriate setting in the fine art domain than the single-task alternatives. We further demonstrate that several GNN architectures can outperform strong CNN baselines in a range of fine art analysis tasks, such as style classification, artist attribution, creation period estimation, and tag prediction, while training them requires an order of magnitude less computational time and only a small amount of labeled data. Finally, through extensive experimentation we show that our proposed ArtSAGENet captures and encodes valuable relational dependencies between the artists and the artworks, surpassing the performance of traditional methods that rely solely on the analysis of visual content. Our findings underline a great potential of integrating visual content and semantics for fine art analysis and curation.	['Nachoem Wijnberg', 'Marcel Worring', 'Monika Kackovic', 'Stevan Rudinac', 'Athanasios Efthymiou']	2021-05-17	null	null	null	null	['art-analysis']	['computer-vision']	[ 5.58417067e-02 -8.28548819e-02 -4.02132720e-01 -1.61274359e-01 -3.51297915e-01 -9.48633075e-01 9.35779572e-01 2.41188452e-01 -6.52103275e-02 3.06012034e-01 6.08007133e-01 9.32869539e-02 -2.38075256e-01 -7.68608212e-01 -6.32763624e-01 -1.12854637e-01 1.93761766e-01 6.38658583e-01 -1.97495937e-01 -2.39901096e-01 3.68340999e-01 6.58133388e-01 -1.44188309e+00 2.70129621e-01 2.35431194e-01 1.27712393e+00 -2.19237357e-01 5.40300190e-01 -4.67974216e-01 1.22641516e+00 -5.27867615e-01 -8.95045638e-01 4.09726351e-02 3.88842076e-02 -8.77793670e-01 1.79847181e-01 8.75721693e-01 -1.70888573e-01 -5.48160911e-01 6.73646808e-01 2.10455209e-01 1.62936941e-01 8.65530908e-01 -1.33709085e+00 -1.27543640e+00 8.00779760e-01 -6.45702660e-01 2.80093104e-01 7.35033751e-02 2.78288096e-01 1.27402866e+00 -5.61304450e-01 8.01708639e-01 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27614019-9e09-4511-9d47-7e00ee33496f	domain-generalization-in-robust-invariant	2304.03431	null	https://arxiv.org/abs/2304.03431v1	https://arxiv.org/pdf/2304.03431v1.pdf	Domain Generalization In Robust Invariant Representation	Unsupervised approaches for learning representations invariant to common transformations are used quite often for object recognition. Learning invariances makes models more robust and practical to use in real-world scenarios. Since data transformations that do not change the intrinsic properties of the object cause the majority of the complexity in recognition tasks, models that are invariant to these transformations help reduce the amount of training data required. This further increases the model's efficiency and simplifies training. In this paper, we investigate the generalization of invariant representations on out-of-distribution data and try to answer the question: Do model representations invariant to some transformations in a particular seen domain also remain invariant in previously unseen domains? Through extensive experiments, we demonstrate that the invariant model learns unstructured latent representations that are robust to distribution shifts, thus making invariance a desirable property for training in resource-constrained settings.	['Ramesh Raskar', 'Keshav Gupta', 'Ritvik Kapila', 'Gauri Gupta']	2023-04-07	null	null	null	null	['object-recognition']	['computer-vision']	[ 4.85175073e-01 -2.66949743e-01 -3.23807955e-01 -7.39156246e-01 -2.40785941e-01 -7.00663865e-01 7.08611786e-01 -1.02332048e-01 -2.09291190e-01 5.75203240e-01 2.10467786e-01 1.32360190e-01 -3.29471260e-01 -7.04041541e-01 -6.23925507e-01 -8.78919125e-01 1.24421485e-01 5.52960217e-01 2.96083122e-01 -1.25314564e-01 5.78192137e-02 1.17168212e+00 -1.73109841e+00 1.99352518e-01 4.04340476e-01 5.56082964e-01 8.61157030e-02 3.98940772e-01 5.55761307e-02 6.53363585e-01 -4.14787471e-01 1.38052240e-01 5.97580016e-01 -3.43522578e-01 -8.02121401e-01 4.43216592e-01 6.32959723e-01 -8.36827382e-02 -4.96851206e-01 1.16421425e+00 1.71643376e-01 6.85019553e-01 1.30271471e+00 -1.10108507e+00 -9.51644361e-01 9.27425642e-03 -3.27849299e-01 4.14256424e-01 3.75670008e-02 -2.49109238e-01 8.91807020e-01 -7.23073423e-01 6.97387397e-01 1.39750803e+00 4.54129279e-01 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28d26eba-bfd0-4d16-8645-f137241ef513	unsupervised-video-object-segmentation-with	1812.07712	null	http://arxiv.org/abs/1812.07712v1	http://arxiv.org/pdf/1812.07712v1.pdf	Unsupervised Video Object Segmentation with Distractor-Aware Online Adaptation	Unsupervised video object segmentation is a crucial application in video analysis without knowing any prior information about the objects. It becomes tremendously challenging when multiple objects occur and interact in a given video clip. In this paper, a novel unsupervised video object segmentation approach via distractor-aware online adaptation (DOA) is proposed. DOA models spatial-temporal consistency in video sequences by capturing background dependencies from adjacent frames. Instance proposals are generated by the instance segmentation network for each frame and then selected by motion information as hard negatives if they exist and positives. To adopt high-quality hard negatives, the block matching algorithm is then applied to preceding frames to track the associated hard negatives. General negatives are also introduced in case that there are no hard negatives in the sequence and experiments demonstrate both kinds of negatives (distractors) are complementary. Finally, we conduct DOA using the positive, negative, and hard negative masks to update the foreground/background segmentation. The proposed approach achieves state-of-the-art results on two benchmark datasets, DAVIS 2016 and FBMS-59 datasets.	['C. -C. Jay Kuo', 'Ming-Sui Lee', 'Yueru Chen', 'Ye Wang', 'Siyang Li', 'Qin Huang', 'Kaitai Zhang', 'Jongmoo Choi']	2018-12-19	null	null	null	null	['unsupervised-video-object-segmentation']	['computer-vision']	[ 5.41987598e-01 -1.80707574e-01 -2.60981858e-01 -3.02245200e-01 -3.89293432e-01 -4.44467276e-01 4.48677182e-01 -8.80896598e-02 -6.77117765e-01 7.75127470e-01 -2.81795859e-01 4.05050777e-02 2.10057601e-01 -5.14001131e-01 -9.69329834e-01 -8.52536440e-01 -8.42924714e-02 3.98639351e-01 1.15570986e+00 1.97100669e-01 1.97447285e-01 2.16912583e-01 -1.54576564e+00 4.61811095e-01 7.66952753e-01 1.08238244e+00 6.87029839e-01 7.70428181e-01 -3.01442713e-01 1.11494911e+00 -5.68561077e-01 -2.96529949e-01 6.13613784e-01 -5.35133958e-01 -6.30663455e-01 6.51225746e-01 4.98865336e-01 -5.46483934e-01 -2.15371743e-01 1.25265992e+00 1.09673642e-01 4.34673518e-01 4.57993835e-01 -1.37074673e+00 -2.82066405e-01 5.08916318e-01 -1.14947116e+00 9.17640984e-01 1.36181235e-03 4.21002746e-01 6.54441059e-01 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3336d9e3-6d05-4e04-ad53-1ce22db93045	situation-recognition-with-graph-neural	1708.04320	null	http://arxiv.org/abs/1708.04320v1	http://arxiv.org/pdf/1708.04320v1.pdf	Situation Recognition with Graph Neural Networks	We address the problem of recognizing situations in images. Given an image, the task is to predict the most salient verb (action), and fill its semantic roles such as who is performing the action, what is the source and target of the action, etc. Different verbs have different roles (e.g. attacking has weapon), and each role can take on many possible values (nouns). We propose a model based on Graph Neural Networks that allows us to efficiently capture joint dependencies between roles using neural networks defined on a graph. Experiments with different graph connectivities show that our approach that propagates information between roles significantly outperforms existing work, as well as multiple baselines. We obtain roughly 3-5% improvement over previous work in predicting the full situation. We also provide a thorough qualitative analysis of our model and influence of different roles in the verbs.	['Ruiyu Li', 'Sanja Fidler', 'Raquel Urtasun', 'Jiaya Jia', 'Renjie Liao', 'Makarand Tapaswi']	2017-08-14	situation-recognition-with-graph-neural-1	http://openaccess.thecvf.com/content_iccv_2017/html/Li_Situation_Recognition_With_ICCV_2017_paper.html	http://openaccess.thecvf.com/content_ICCV_2017/papers/Li_Situation_Recognition_With_ICCV_2017_paper.pdf	iccv-2017-10	['grounded-situation-recognition', 'situation-recognition']	['computer-vision', 'computer-vision']	[ 6.18420601e-01 3.65333945e-01 -3.46252412e-01 -5.05717576e-01 -1.22679889e-01 -7.86926448e-01 8.13867390e-01 4.12772119e-01 -4.43792552e-01 3.27777624e-01 8.41900289e-01 -2.72762179e-01 -3.62142585e-02 -7.51836181e-01 -7.56249070e-01 -4.81809258e-01 -1.26514331e-01 4.84001040e-01 3.36141229e-01 -2.58042395e-01 6.63087294e-02 4.62120622e-01 -1.16695333e+00 6.17990375e-01 4.51743975e-02 6.87956572e-01 3.12977761e-01 4.75955606e-01 8.80120993e-02 1.58492541e+00 -7.21220970e-01 -7.14309931e-01 4.99373525e-02 -2.79082626e-01 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29c87ddc-ad2a-48f0-8881-dd92c5f4ec89	adaptation-to-criticality-through	1712.05284	null	http://arxiv.org/abs/1712.05284v3	http://arxiv.org/pdf/1712.05284v3.pdf	Adaptation to criticality through organizational invariance in embodied agents	Many biological and cognitive systems do not operate deep within one or other regime of activity. Instead, they are poised at critical points located at phase transitions in their parameter space. The pervasiveness of criticality suggests that there may be general principles inducing this behaviour, yet there is no well-founded theory for understanding how criticality is generated at a wide span of levels and contexts. In order to explore how criticality might emerge from general adaptive mechanisms, we propose a simple learning rule that maintains an internal organizational structure from a specific family of systems at criticality. We implement the mechanism in artificial embodied agents controlled by a neural network maintaining a correlation structure randomly sampled from an Ising model at critical temperature. Agents are evaluated in two classical reinforcement learning scenarios: the Mountain Car and the Acrobot double pendulum. In both cases the neural controller appears to reach a point of criticality, which coincides with a transition point between two regimes of the agent's behaviour. These results suggest that adaptation to criticality could be used as a general adaptive mechanism in some circumstances, providing an alternative explanation for the pervasive presence of criticality in biological and cognitive systems.	['Manuel G. Bedia', 'Miguel Aguilera']	2017-12-13	null	null	null	null	['acrobot']	['playing-games']	[ 2.09410250e-01 2.97408164e-01 -4.02569696e-02 1.18199594e-01 2.64081806e-01 -4.10568118e-01 1.20646942e+00 2.89681613e-01 -4.73514229e-01 9.08508360e-01 -3.50991368e-01 -1.48353606e-01 -5.77291429e-01 -6.49005234e-01 -6.49693191e-01 -1.23460305e+00 -3.02218080e-01 4.14383918e-01 5.71539104e-01 -7.97414601e-01 4.10179138e-01 1.94204792e-01 -1.71945524e+00 -1.19090192e-01 9.46249008e-01 3.78107786e-01 4.42832977e-01 8.11460555e-01 3.52865964e-01 6.45379663e-01 -4.97653604e-01 2.13397771e-01 1.87940598e-01 -9.18824613e-01 -6.81678534e-01 3.11768018e-02 -8.95285904e-02 5.78540266e-01 -3.61193828e-02 1.07849872e+00 1.22169547e-01 2.44831279e-01 1.22458780e+00 -1.18201053e+00 -7.51808047e-01 4.98940468e-01 -2.63184220e-01 4.91037577e-01 1.14710003e-01 5.03434777e-01 9.50234890e-01 -2.56151974e-01 4.07705963e-01 1.17031598e+00 5.01367986e-01 7.82805979e-01 -1.44872010e+00 -1.05165141e-02 -7.91859776e-02 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a272d3aa-5c4b-4cfb-a073-b74de1134cff	smoothed-dilated-convolutions-for-improved	1808.08931	null	http://arxiv.org/abs/1808.08931v2	http://arxiv.org/pdf/1808.08931v2.pdf	Smoothed Dilated Convolutions for Improved Dense Prediction	Dilated convolutions, also known as atrous convolutions, have been widely explored in deep convolutional neural networks (DCNNs) for various dense prediction tasks. However, dilated convolutions suffer from the gridding artifacts, which hampers the performance. In this work, we propose two simple yet effective degridding methods by studying a decomposition of dilated convolutions. Unlike existing models, which explore solutions by focusing on a block of cascaded dilated convolutional layers, our methods address the gridding artifacts by smoothing the dilated convolution itself. In addition, we point out that the two degridding approaches are intrinsically related and define separable and shared (SS) operations, which generalize the proposed methods. We further explore SS operations in view of operations on graphs and propose the SS output layer, which is able to smooth the entire DCNNs by only replacing the output layer. We evaluate our degridding methods and the SS output layer thoroughly, and visualize the smoothing effect through effective receptive field analysis. Results show that our methods degridding yield consistent improvements on the performance of dense prediction tasks, while adding negligible amounts of extra training parameters. And the SS output layer improves the performance significantly and is very efficient in terms of number of training parameters.	['Zhengyang Wang', 'Shuiwang Ji']	2018-08-27	null	null	null	null	['audio-generation']	['audio']	[-6.14687160e-04 4.21219915e-01 3.56657535e-01 -3.94479871e-01 2.47775495e-01 -3.16581249e-01 5.93909681e-01 -2.84054369e-01 -5.62835574e-01 3.69589806e-01 1.75333411e-01 -3.67958844e-01 9.91799384e-02 -9.42249894e-01 -8.07155550e-01 -8.77428114e-01 -1.05588138e-01 -4.44537073e-01 4.01575685e-01 -1.87733278e-01 -1.39176130e-01 7.94902146e-01 -1.32823050e+00 3.70261073e-01 9.65641499e-01 1.10061443e+00 3.54727685e-01 4.47205931e-01 -1.93491876e-01 6.89419031e-01 -4.98862773e-01 -6.50771081e-01 3.99178058e-01 -1.17841668e-01 -6.54780746e-01 -1.33110747e-01 6.05749905e-01 -3.58763695e-01 -5.14561534e-01 1.29733515e+00 3.46095920e-01 2.73972541e-01 3.83653760e-01 -1.00249183e+00 -8.48097980e-01 7.95044541e-01 -4.10061508e-01 8.63509700e-02 -5.71527123e-01 -7.72857964e-02 7.46331036e-01 -1.01429009e+00 3.96923453e-01 1.42248189e+00 1.13136339e+00 6.85564876e-01 -1.37497127e+00 -5.49838543e-01 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3685cfb2-1dc9-40e3-85f1-036ac074e000	escaping-data-scarcity-for-high-resolution	2203.16669	null	https://arxiv.org/abs/2203.16669v1	https://arxiv.org/pdf/2203.16669v1.pdf	Escaping Data Scarcity for High-Resolution Heterogeneous Face Hallucination	In Heterogeneous Face Recognition (HFR), the objective is to match faces across two different domains such as visible and thermal. Large domain discrepancy makes HFR a difficult problem. Recent methods attempting to fill the gap via synthesis have achieved promising results, but their performance is still limited by the scarcity of paired training data. In practice, large-scale heterogeneous face data are often inaccessible due to the high cost of acquisition and annotation process as well as privacy regulations. In this paper, we propose a new face hallucination paradigm for HFR, which not only enables data-efficient synthesis but also allows to scale up model training without breaking any privacy policy. Unlike existing methods that learn face synthesis entirely from scratch, our approach is particularly designed to take advantage of rich and diverse facial priors from visible domain for more faithful hallucination. On the other hand, large-scale training is enabled by introducing a new federated learning scheme to allow institution-wise collaborations while avoiding explicit data sharing. Extensive experiments demonstrate the advantages of our approach in tackling HFR under current data limitations. In a unified framework, our method yields the state-of-the-art hallucination results on multiple HFR datasets.	['Vishal M. Patel', 'Pengfei Guo', 'Yiqun Mei']	2022-03-30	null	http://openaccess.thecvf.com//content/CVPR2022/html/Mei_Escaping_Data_Scarcity_for_High-Resolution_Heterogeneous_Face_Hallucination_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Mei_Escaping_Data_Scarcity_for_High-Resolution_Heterogeneous_Face_Hallucination_CVPR_2022_paper.pdf	cvpr-2022-1	['heterogeneous-face-recognition', 'face-hallucination']	['computer-vision', 'computer-vision']	[ 9.69893187e-02 1.29449695e-01 -1.12697072e-01 -4.25650060e-01 -8.50448310e-01 -3.41528386e-01 5.99765599e-01 -4.51073796e-01 2.20001303e-02 9.07538235e-01 3.40028405e-01 1.37061730e-01 -2.59194244e-02 -7.58181334e-01 -5.98191023e-01 -6.76002264e-01 3.27103436e-01 4.65019673e-01 -3.26082438e-01 -1.96482360e-01 -3.71892512e-01 6.19961202e-01 -1.71075368e+00 2.34154761e-01 8.48925531e-01 1.16283214e+00 1.81124181e-01 -1.29012719e-01 6.02067597e-02 6.99311972e-01 -4.54656690e-01 -8.52009356e-01 6.98717415e-01 -3.26511621e-01 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777335d0-e731-4d32-9e75-22c8c641c74b	integrating-dictionary-and-web-n-grams-for	null	null	https://aclanthology.org/O13-5002	https://aclanthology.org/O13-5002.pdf	Integrating Dictionary and Web N-grams for Chinese Spell Checking	null	['Hsun-wen Chiu', 'Jian-Cheng Wu', 'Jason S. Chang']	2013-12-01	integrating-dictionary-and-web-n-grams-for-1	https://aclanthology.org/O13-5002	https://aclanthology.org/O13-5002.pdf	roclingijclclp-2013-12	['chinese-spell-checking']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 -8.59008506e-02 -2.09758401e-01 9.22211111e-01 5.37373662e-01 4.49454933e-02 1.08379531e+00 -1.42548037e+00 -5.91619551e-01 6.10313773e-01 -4.01565880e-02 1.21627934e-01 1.03678203e+00 -5.39447069e-01 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f381ff9e-7ba4-42cc-afee-72a1e44971e1	towards-inferring-network-properties-from	2302.02470	null	https://arxiv.org/abs/2302.02470v1	https://arxiv.org/pdf/2302.02470v1.pdf	Towards inferring network properties from epidemic data	Epidemic propagation on networks represents an important departure from traditional massaction models. However, the high-dimensionality of the exact models poses a challenge to both mathematical analysis and parameter inference. By using mean-field models, such as the pairwise model (PWM), the complexity becomes tractable. While such models have been used extensively for model analysis, there is limited work in the context of statistical inference. In this paper, we explore the extent to which the PWM with the susceptible-infected-recovered (SIR) epidemic can be used to infer disease- and network-related parameters. The widely-used MLE approach exhibits several issues pertaining to parameter unidentifiability and a lack of robustness to exact knowledge about key quantities such as population size and/or proportion of under reporting. As an alternative, we considered the recently developed dynamical survival analysis (DSA). For scenarios in which there is no model mismatch, such as when data are generated via simulations, both methods perform well despite strong dependence between parameters. However, for real-world data, such as foot-and-mouth, H1N1 and COVID19, the DSA method appears more robust to potential model mismatch and the parameter estimates appear more epidemiologically plausible. Taken together, however, our findings suggest that network-based mean-field models can be used to formulate approximate likelihoods which, coupled with an efficient inference scheme, make it possible to not only learn about the parameters of the disease dynamics but also that of the underlying network.	['Wasiur R. KhudaBukhsh', 'Luc Berthouze', 'István Z. Kiss']	2023-02-05	null	null	null	null	['survival-analysis']	['miscellaneous']	[ 2.30247974e-01 -6.89576790e-02 -2.48976156e-01 1.64539456e-01 -1.56525508e-01 -5.13875902e-01 7.94276357e-01 4.22590762e-01 -4.63678539e-01 9.91469204e-01 -2.11476311e-02 -6.23312294e-01 -7.26657152e-01 -8.47877443e-01 -5.37595749e-01 -9.02019143e-01 -6.15546346e-01 6.37616932e-01 -3.22098918e-02 -1.63165972e-01 -2.54944507e-02 4.48005915e-01 -8.64881158e-01 -6.46372437e-01 7.99130857e-01 4.35656697e-01 1.63645148e-02 6.89097226e-01 2.54121751e-01 2.96811074e-01 -7.18511522e-01 -3.95468593e-01 -1.39786936e-02 -2.38341659e-01 -3.16849917e-01 -1.98056981e-01 -4.06511128e-01 -5.03577232e-01 -4.20971245e-01 8.59685123e-01 3.12378436e-01 -3.24970931e-01 9.80594218e-01 -1.53232980e+00 -2.01490521e-01 4.30354565e-01 -5.76570034e-01 3.84934127e-01 2.66052812e-01 2.27050573e-01 6.14165366e-01 -1.09369084e-01 7.62095511e-01 1.42366517e+00 9.72519159e-01 6.31923825e-02 -1.64083612e+00 -5.90870857e-01 1.48495398e-02 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3fceb611-3319-4240-9417-73de4e3218d8	sata-sparsity-aware-training-accelerator-for	2204.05422	null	https://arxiv.org/abs/2204.05422v3	https://arxiv.org/pdf/2204.05422v3.pdf	SATA: Sparsity-Aware Training Accelerator for Spiking Neural Networks	Spiking Neural Networks (SNNs) have gained huge attention as a potential energy-efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent high-sparsity activation. Recently, SNNs with backpropagation through time (BPTT) have achieved a higher accuracy result on image recognition tasks than other SNN training algorithms. Despite the success from the algorithm perspective, prior works neglect the evaluation of the hardware energy overheads of BPTT due to the lack of a hardware evaluation platform for this SNN training algorithm. Moreover, although SNNs have long been seen as an energy-efficient counterpart of ANNs, a quantitative comparison between the training cost of SNNs and ANNs is missing. To address the aforementioned issues, in this work, we introduce SATA (Sparsity-Aware Training Accelerator), a BPTT-based training accelerator for SNNs. The proposed SATA provides a simple and re-configurable systolic-based accelerator architecture, which makes it easy to analyze the training energy for BPTT-based SNN training algorithms. By utilizing the sparsity, SATA increases its computation energy efficiency by $5.58 \times$ compared to the one without using sparsity. Based on SATA, we show quantitative analyses of the energy efficiency of SNN training and compare the training cost of SNNs and ANNs. The results show that, on Eyeriss-like systolic-based architecture, SNNs consume $1.27\times$ more total energy with sparsities when compared to ANNs. We find that such high training energy cost is from time-repetitive convolution operations and data movements during backpropagation. Moreover, to propel the future SNN training algorithm design, we provide several observations on energy efficiency for different SNN-specific training parameters and propose an energy estimation framework for SNN training. Code for our framework is made publicly available.	['Priyadarshini Panda', 'Youngeun Kim', 'Abhiroop Bhattacharjee', 'Abhishek Moitra', 'Ruokai Yin']	2022-04-11	null	null	null	null	['total-energy']	['miscellaneous']	[ 2.81569898e-01 -3.11434746e-01 -2.15666622e-01 -3.71909738e-01 3.23386014e-01 -1.53095990e-01 3.60067397e-01 -8.01371131e-03 -9.78886724e-01 4.87518221e-01 -3.90525401e-01 -6.37175798e-01 -8.50036442e-02 -9.83173907e-01 -9.11126614e-01 -9.09301817e-01 -4.25205380e-02 4.81542870e-02 1.10344045e-01 -1.79901365e-02 1.69073552e-01 6.50231540e-01 -1.70848906e+00 -2.19144411e-02 7.15532541e-01 1.26315141e+00 4.14332837e-01 2.92392850e-01 -1.24504276e-01 4.75575656e-01 -6.14824951e-01 -3.05567086e-01 4.59264636e-01 -5.10504782e-01 -3.47113401e-01 -6.29877865e-01 1.30045071e-01 -7.32653514e-02 -5.25822818e-01 9.90973115e-01 4.84218091e-01 -2.58782040e-02 2.66365916e-01 -1.35988390e+00 2.95528620e-02 9.01984036e-01 -2.80606866e-01 5.21413922e-01 -5.25349855e-01 3.21066618e-01 3.32769781e-01 -5.72343707e-01 2.71705866e-01 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0ef34964-9961-4163-9c8a-73538af73b0a	led2-net-monocular-360-layout-estimation-via	2104.00568	null	https://arxiv.org/abs/2104.00568v2	https://arxiv.org/pdf/2104.00568v2.pdf	LED2-Net: Monocular 360 Layout Estimation via Differentiable Depth Rendering	Although significant progress has been made in room layout estimation, most methods aim to reduce the loss in the 2D pixel coordinate rather than exploiting the room structure in the 3D space. Towards reconstructing the room layout in 3D, we formulate the task of 360 layout estimation as a problem of predicting depth on the horizon line of a panorama. Specifically, we propose the Differentiable Depth Rendering procedure to make the conversion from layout to depth prediction differentiable, thus making our proposed model end-to-end trainable while leveraging the 3D geometric information, without the need of providing the ground truth depth. Our method achieves state-of-the-art performance on numerous 360 layout benchmark datasets. Moreover, our formulation enables a pre-training step on the depth dataset, which further improves the generalizability of our layout estimation model.	['Yi-Hsuan Tsai', 'Wei-Chen Chiu', 'Min Sun', 'Yu-Hsuan Yeh', 'Fu-En Wang']	2021-04-01	null	null	null	null	['3d-room-layouts-from-a-single-rgb-panorama']	['computer-vision']	[ 2.99474120e-01 2.54393935e-01 5.48622720e-02 -4.45615232e-01 -7.07248688e-01 -7.31633067e-01 5.06585240e-01 2.23482221e-01 -1.32777184e-01 8.16768557e-02 4.93719965e-01 -5.93593180e-01 1.15872324e-01 -1.08287370e+00 -8.61430407e-01 -3.20874065e-01 1.38660835e-03 2.31677711e-01 -1.40235975e-01 1.49153680e-01 4.08481508e-01 6.28097117e-01 -1.31703663e+00 -6.61695749e-02 9.44506168e-01 1.17161047e+00 2.63178378e-01 8.77047062e-01 1.71730854e-02 6.79926753e-01 -3.11742455e-01 -1.68081429e-02 3.66629273e-01 -4.06856723e-02 -6.47681534e-01 4.87706363e-01 9.19582903e-01 -5.48822105e-01 -5.34499466e-01 6.52926683e-01 4.63337034e-01 3.39274764e-01 6.08838022e-01 -7.29054689e-01 -3.95830691e-01 -1.52221071e-02 -5.89723289e-01 -4.00096983e-01 5.51281631e-01 -1.09065212e-01 1.02082860e+00 -7.83695936e-01 3.04566741e-01 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900251af-7c96-4b56-a734-aac83ea803d4	safe-deep-reinforcement-learning-based	null	null	https://www.sciencedirect.com/science/article/pii/S0306261920302841#ab015	https://reader.elsevier.com/reader/sd/pii/S0306261920302841?token=F8F3B5C3A9B94B9464C8804C4DD3B0571D1931074703DF28ECF8A15E49EB9192D322DC47A3959181F2ABE761702CFD2B&originRegion=eu-west-1&originCreation=20211015062006	Safe deep reinforcement learning-based constrained optimal control scheme for active distribution networks	Reinforcement learning-based schemes are being recently applied for model-free voltage control in active distribution networks. However, existing reinforcement learning methods face challenges when it comes to continuous state and action spaces problems or problems with operation constraints. To address these limitations, this paper proposes an optimal voltage control scheme based on the safe deep reinforcement learning. In this scheme, the optimal voltage control problem is formulated as a constrained Markov decision process, in which both state and action spaces are continuous. To solve this problem efficiently, the deep deterministic policy gradient algorithm is utilized to learn the reactive power control policies, which determine the optimal control actions from the states. In contrast to existing reinforcement learning methods, deep deterministic policy gradient is naturally capable of addressing control problems with continuous state and action spaces. This is due to the utilization of deep neural networks to approximate both value function and policy. In addition, in order to handle the operation constraints in active distribution networks, a safe exploration approach is proposed to form a safety layer, which is composed directly on top the deep deterministic policy gradient actor network. This safety layer predicts the change in the constrained states and prevents the violation of active distribution networks operation constraints. Numerical simulations on modified IEEE test systems demonstrate that the proposed scheme successfully maintains all bus voltage within the allowed range, and reduces the system loss by 15% compared to the no control case.	['Lin Gaoa', 'Zihao Wu', 'Chen Wang', 'Deliang Liang', 'Peng Kou']	2020-04-15	null	null	null	elsevier-applied-energy-2020-4	['safe-exploration']	['robots']	[-3.71564388e-01 1.16409771e-01 -5.89952052e-01 -3.54212150e-02 -3.45787734e-01 -4.22302514e-01 1.99479073e-01 1.27929151e-01 -2.16424108e-01 1.29605091e+00 -1.75585136e-01 -4.76784021e-01 -3.81058067e-01 -1.06849694e+00 -3.27053785e-01 -1.05489862e+00 -5.05654216e-01 2.57867366e-01 -2.39436850e-02 -3.53693753e-01 1.96590990e-01 5.89585364e-01 -9.08596575e-01 -3.15855742e-01 1.18638647e+00 1.11575520e+00 -2.54264660e-02 2.35636190e-01 -1.71547607e-02 7.66406536e-01 -8.11769485e-01 4.16617095e-01 9.47770327e-02 -3.97291511e-01 -6.58047259e-01 -3.68166366e-03 -5.32308578e-01 -6.77082777e-01 -5.76840043e-01 1.35098267e+00 6.45444572e-01 4.73113418e-01 4.69793648e-01 -1.58346736e+00 -3.78329962e-01 7.84524620e-01 -5.82558870e-01 3.07811260e-01 3.34654868e-01 4.26392049e-01 8.41991961e-01 -1.66400313e-01 4.71142493e-02 1.06906259e+00 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376f9f0a-4607-4dca-9f0d-872b96149526	tipcb-a-simple-but-effective-part-based	2105.11628	null	https://arxiv.org/abs/2105.11628v1	https://arxiv.org/pdf/2105.11628v1.pdf	TIPCB: A Simple but Effective Part-based Convolutional Baseline for Text-based Person Search	Text-based person search is a sub-task in the field of image retrieval, which aims to retrieve target person images according to a given textual description. The significant feature gap between two modalities makes this task very challenging. Many existing methods attempt to utilize local alignment to address this problem in the fine-grained level. However, most relevant methods introduce additional models or complicated training and evaluation strategies, which are hard to use in realistic scenarios. In order to facilitate the practical application, we propose a simple but effective end-to-end learning framework for text-based person search named TIPCB (i.e., Text-Image Part-based Convolutional Baseline). Firstly, a novel dual-path local alignment network structure is proposed to extract visual and textual local representations, in which images are segmented horizontally and texts are aligned adaptively. Then, we propose a multi-stage cross-modal matching strategy, which eliminates the modality gap from three feature levels, including low level, local level and global level. Extensive experiments are conducted on the widely-used benchmark dataset (CUHK-PEDES) and verify that our method outperforms the state-of-the-art methods by 3.69%, 2.95% and 2.31% in terms of Top-1, Top-5 and Top-10. Our code has been released in https://github.com/OrangeYHChen/TIPCB.	['Ruili Wang', 'yuhui Zheng', 'zhenxing Wang', 'Yujiang Lu', 'Guoqing Zhang', 'Yuhao Chen']	2021-05-25	null	null	null	null	['nlp-based-person-retrival', 'person-search']	['computer-vision', 'computer-vision']	[ 1.26787812e-01 -6.82341754e-01 -2.16688409e-01 -3.07287306e-01 -9.59557891e-01 -3.36297333e-01 7.64110148e-01 -1.22171596e-01 -7.18019605e-01 2.96543986e-01 2.71999955e-01 1.11628525e-01 -1.43814772e-01 -6.01879239e-01 -5.28507292e-01 -6.74028218e-01 5.54430008e-01 4.11523372e-01 2.84323633e-01 -5.55373877e-02 1.65332019e-01 6.87497333e-02 -1.56927872e+00 4.18823719e-01 8.73004377e-01 1.00714719e+00 1.25214741e-01 2.83668309e-01 -8.53435621e-02 3.15321207e-01 -2.84490675e-01 -7.21439540e-01 3.46676826e-01 -4.24811006e-01 -7.78897524e-01 1.95922107e-01 7.70994365e-01 -3.40643525e-01 -6.23289824e-01 1.13603330e+00 9.85181391e-01 2.54819989e-01 5.61098695e-01 -1.07020795e+00 -6.97187245e-01 1.05097599e-01 -8.80376160e-01 1.44772053e-01 3.84287596e-01 3.27327043e-01 7.68252552e-01 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ae84864d-b572-4f14-b8cc-4b97dbbb61d4	point-cloud-video-anomaly-detection-based-on	2306.04466	null	https://arxiv.org/abs/2306.04466v1	https://arxiv.org/pdf/2306.04466v1.pdf	Point Cloud Video Anomaly Detection Based on Point Spatio-Temporal Auto-Encoder	Video anomaly detection has great potential in enhancing safety in the production and monitoring of crucial areas. Currently, most video anomaly detection methods are based on RGB modality, but its redundant semantic information may breach the privacy of residents or patients. The 3D data obtained by depth camera and LiDAR can accurately locate anomalous events in 3D space while preserving human posture and motion information. Identifying individuals through the point cloud is difficult due to its sparsity, which protects personal privacy. In this study, we propose Point Spatio-Temporal Auto-Encoder (PSTAE), an autoencoder framework that uses point cloud videos as input to detect anomalies in point cloud videos. We introduce PSTOp and PSTTransOp to maintain spatial geometric and temporal motion information in point cloud videos. To measure the reconstruction loss of the proposed autoencoder framework, we propose a reconstruction loss measurement strategy based on a shallow feature extractor. Experimental results on the TIMo dataset show that our method outperforms currently representative depth modality-based methods in terms of AUROC and has superior performance in detecting Medical Issue anomalies. These results suggest the potential of point cloud modality in video anomaly detection. Our method sets a new state-of-the-art (SOTA) on the TIMo dataset.	['Wenguang Wang', 'Tengjiao He']	2023-06-04	null	null	null	null	['video-anomaly-detection']	['computer-vision']	[-2.52369076e-01 -3.38148326e-01 4.21690792e-01 -2.11668611e-01 -3.15172493e-01 -2.03887090e-01 2.56247163e-01 3.56628776e-01 -4.79268134e-01 9.86587256e-02 9.79931056e-02 1.02674058e-02 -1.43762574e-01 -8.42378855e-01 -8.08202922e-01 -7.91548669e-01 -4.82189327e-01 1.23770922e-01 2.25087762e-01 -1.01584427e-01 1.11195944e-01 9.61741447e-01 -1.74999535e+00 3.63702446e-01 8.53289187e-01 1.41928101e+00 -3.89686465e-01 4.64208692e-01 -1.90690517e-01 5.80054939e-01 -6.10517919e-01 -6.32151425e-01 4.61568892e-01 -3.17427292e-02 -3.84105712e-01 1.43869907e-01 6.03319943e-01 -7.75319517e-01 -9.27800298e-01 1.11679363e+00 5.72912812e-01 8.54435638e-02 4.60350603e-01 -1.65470147e+00 -4.16870117e-01 -3.44381154e-01 -5.33277154e-01 6.72443032e-01 5.76601148e-01 1.33296758e-01 4.10603404e-01 -7.22906828e-01 8.26626569e-02 9.24415171e-01 1.04613483e+00 5.16957879e-01 -5.93527675e-01 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c0afb2c5-6693-4a3c-b6ec-dd78ceef3715	none-class-ranking-loss-for-document-level	2205.00476	null	https://arxiv.org/abs/2205.00476v2	https://arxiv.org/pdf/2205.00476v2.pdf	None Class Ranking Loss for Document-Level Relation Extraction	Document-level relation extraction (RE) aims at extracting relations among entities expressed across multiple sentences, which can be viewed as a multi-label classification problem. In a typical document, most entity pairs do not express any pre-defined relation and are labeled as "none" or "no relation". For good document-level RE performance, it is crucial to distinguish such none class instances (entity pairs) from those of pre-defined classes (relations). However, most existing methods only estimate the probability of pre-defined relations independently without considering the probability of "no relation". This ignores the context of entity pairs and the label correlations between the none class and pre-defined classes, leading to sub-optimal predictions. To address this problem, we propose a new multi-label loss that encourages large margins of label confidence scores between each pre-defined class and the none class, which enables captured label correlations and context-dependent thresholding for label prediction. To gain further robustness against positive-negative imbalance and mislabeled data that could appear in real-world RE datasets, we propose a margin regularization and a margin shifting technique. Experimental results demonstrate that our method significantly outperforms existing multi-label losses for document-level RE and works well in other multi-label tasks such as emotion classification when none class instances are available for training.	['Wee Sun Lee', 'Yang Zhou']	2022-05-01	null	null	null	null	['video-super-resolution', 'document-level-relation-extraction']	['computer-vision', 'natural-language-processing']	[ 3.97529930e-01 1.42493606e-01 -6.85749590e-01 -7.13790417e-01 -9.10965621e-01 -5.17821670e-01 2.84103304e-01 7.96249866e-01 -3.01616162e-01 9.40798879e-01 -1.68960720e-01 -9.64659303e-02 -2.26353839e-01 -9.23658550e-01 -4.87732232e-01 -8.20386589e-01 1.45376951e-01 5.26276648e-01 -4.33990359e-02 -4.09739502e-02 -1.77280888e-01 2.96920091e-01 -1.46799362e+00 6.77262008e-01 9.70048606e-01 1.29998136e+00 -3.03657830e-01 1.82375029e-01 -1.71880558e-01 9.13304925e-01 -9.15018082e-01 -7.56955862e-01 -7.63542652e-02 -3.78986359e-01 -8.60901713e-01 9.59976241e-02 3.08915585e-01 3.18902075e-01 2.66029835e-01 1.20558989e+00 3.19664687e-01 3.43924426e-02 1.01000404e+00 -1.42842126e+00 -4.17520046e-01 4.85620648e-01 -8.76180053e-01 7.83430114e-02 2.68892229e-01 -7.67896533e-01 1.60515034e+00 -1.00169218e+00 5.73033094e-01 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0f055f64-409e-4692-80ea-7e572abf0d0d	solo-or-ensemble-choosing-a-cnn-architecture	1904.12724	null	http://arxiv.org/abs/1904.12724v1	http://arxiv.org/pdf/1904.12724v1.pdf	Solo or Ensemble? Choosing a CNN Architecture for Melanoma Classification	Convolutional neural networks (CNNs) deliver exceptional results for computer vision, including medical image analysis. With the growing number of available architectures, picking one over another is far from obvious. Existing art suggests that, when performing transfer learning, the performance of CNN architectures on ImageNet correlates strongly with their performance on target tasks. We evaluate that claim for melanoma classification, over 9 CNNs architectures, in 5 sets of splits created on the ISIC Challenge 2017 dataset, and 3 repeated measures, resulting in 135 models. The correlations we found were, to begin with, much smaller than those reported by existing art, and disappeared altogether when we considered only the top-performing networks: uncontrolled nuisances (i.e., splits and randomness) overcome any of the analyzed factors. Whenever possible, the best approach for melanoma classification is still to create ensembles of multiple models. We compared two choices for selecting which models to ensemble: picking them at random (among a pool of high-quality ones) vs. using the validation set to determine which ones to pick first. For small ensembles, we found a slight advantage on the second approach but found that random choice was also competitive. Although our aim in this paper was not to maximize performance, we easily reached AUCs comparable to the first place on the ISIC Challenge 2017.	['Fábio Perez', 'Sandra Avila', 'Eduardo Valle']	2019-04-29	null	null	null	null	['skin-lesion-classification', 'skin-lesion-identification']	['medical', 'medical']	[ 2.12801844e-01 -2.87632775e-02 2.97729727e-02 -1.88159510e-01 -8.42328906e-01 -4.80387002e-01 7.72136748e-01 9.84780639e-02 -1.09917057e+00 8.28779817e-01 2.15766117e-01 -3.74629706e-01 -4.55306619e-01 -6.11266196e-01 -4.60096985e-01 -9.52147841e-01 -1.09650768e-01 4.12315458e-01 2.34390110e-01 -1.57509148e-01 2.08043337e-01 4.12319899e-01 -1.46842492e+00 5.06726563e-01 9.24112022e-01 7.97295928e-01 -1.28566712e-01 7.59775162e-01 3.12139234e-03 7.38944948e-01 -6.95891142e-01 -7.46056795e-01 9.27229822e-02 -3.97207975e-01 -7.58913338e-01 -8.79420191e-02 3.79128903e-01 5.78834629e-03 4.60787714e-02 7.93432057e-01 6.79336369e-01 -4.22160506e-01 8.16464841e-01 -9.11865115e-01 -3.72141778e-01 6.46681786e-01 -3.18393022e-01 1.45805314e-01 -8.77579302e-02 4.65466708e-01 7.87271202e-01 -5.53822756e-01 7.77844012e-01 8.43981624e-01 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1bae1fdc-c0d3-41a5-8f09-90da7339875a	a-performance-consistent-and-computation	2205.01239	null	https://arxiv.org/abs/2205.01239v1	https://arxiv.org/pdf/2205.01239v1.pdf	A Performance-Consistent and Computation-Efficient CNN System for High-Quality Automated Brain Tumor Segmentation	The research on developing CNN-based fully-automated Brain-Tumor-Segmentation systems has been progressed rapidly. For the systems to be applicable in practice, a good The research on developing CNN-based fully-automated Brain-Tumor-Segmentation systems has been progressed rapidly. For the systems to be applicable in practice, a good processing quality and reliability are the must. Moreover, for wide applications of such systems, a minimization of computation complexity is desirable, which can also result in a minimization of randomness in computation and, consequently, a better performance consistency. To this end, the CNN in the proposed system has a unique structure with 2 distinguished characters. Firstly, the three paths of its feature extraction block are designed to extract, from the multi-modality input, comprehensive feature information of mono-modality, paired-modality and cross-modality data, respectively. Also, it has a particular three-branch classification block to identify the pixels of 4 classes. Each branch is trained separately so that the parameters are updated specifically with the corresponding ground truth data of a target tumor areas. The convolution layers of the system are custom-designed with specific purposes, resulting in a very simple config of 61,843 parameters in total. The proposed system is tested extensively with BraTS2018 and BraTS2019 datasets. The mean Dice scores, obtained from the ten experiments on BraTS2018 validation samples, are 0.787+0.003, 0.886+0.002, 0.801+0.007, for enhancing tumor, whole tumor and tumor core, respectively, and 0.751+0.007, 0.885+0.002, 0.776+0.004 on BraTS2019. The test results demonstrate that the proposed system is able to perform high-quality segmentation in a consistent manner. Furthermore, its extremely low computation complexity will facilitate its implementation/application in various environments.	['Chunyan Wang', 'Juncheng Tong']	2022-05-02	null	null	null	null	['brain-tumor-segmentation']	['medical']	[-6.25246689e-02 -1.69892684e-01 4.01249342e-02 -4.88640457e-01 -5.09036303e-01 2.13234201e-02 3.44116837e-01 -3.57751437e-02 -7.58803666e-01 7.87025154e-01 -2.66480327e-01 -1.22765221e-01 -1.70879066e-01 -7.42350459e-01 -2.09162712e-01 -1.15129507e+00 -5.03030159e-02 3.95088553e-01 2.12805226e-01 6.98694885e-02 1.25083640e-01 5.06849825e-01 -1.19780338e+00 3.32101099e-02 9.33022082e-01 1.26961195e+00 5.22444129e-01 4.42921668e-01 -1.97033584e-02 3.88482839e-01 -4.48478341e-01 -1.02085322e-01 5.61038852e-02 -3.00492018e-01 -5.52359343e-01 1.89340428e-01 -6.04870394e-02 -1.91109255e-01 -2.54770190e-01 1.18161941e+00 5.04436255e-01 -8.66847709e-02 7.96516776e-01 -9.86636043e-01 -9.20507982e-02 3.46525252e-01 -7.20342278e-01 1.26732126e-01 -4.65076596e-01 3.49117488e-01 5.95796824e-01 -5.50469160e-01 5.33161521e-01 5.90453029e-01 4.22619730e-01 4.47538942e-01 -8.42820168e-01 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b87e6f28-c7e8-4887-ad1e-d26c1ab65de0	pointersect-neural-rendering-with-cloud-ray	2304.12390	null	https://arxiv.org/abs/2304.12390v1	https://arxiv.org/pdf/2304.12390v1.pdf	Pointersect: Neural Rendering with Cloud-Ray Intersection	We propose a novel method that renders point clouds as if they are surfaces. The proposed method is differentiable and requires no scene-specific optimization. This unique capability enables, out-of-the-box, surface normal estimation, rendering room-scale point clouds, inverse rendering, and ray tracing with global illumination. Unlike existing work that focuses on converting point clouds to other representations--e.g., surfaces or implicit functions--our key idea is to directly infer the intersection of a light ray with the underlying surface represented by the given point cloud. Specifically, we train a set transformer that, given a small number of local neighbor points along a light ray, provides the intersection point, the surface normal, and the material blending weights, which are used to render the outcome of this light ray. Localizing the problem into small neighborhoods enables us to train a model with only 48 meshes and apply it to unseen point clouds. Our model achieves higher estimation accuracy than state-of-the-art surface reconstruction and point-cloud rendering methods on three test sets. When applied to room-scale point clouds, without any scene-specific optimization, the model achieves competitive quality with the state-of-the-art novel-view rendering methods. Moreover, we demonstrate ability to render and manipulate Lidar-scanned point clouds such as lighting control and object insertion.	['Oncel Tuzel', 'Kwang Moo Yi', 'Anurag Ranjan', 'Wei-Yu Chen', 'Jen-Hao Rick Chang']	2023-04-24	null	http://openaccess.thecvf.com//content/CVPR2023/html/Chang_Pointersect_Neural_Rendering_With_Cloud-Ray_Intersection_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Chang_Pointersect_Neural_Rendering_With_Cloud-Ray_Intersection_CVPR_2023_paper.pdf	cvpr-2023-1	['neural-rendering', 'inverse-rendering']	['computer-vision', 'computer-vision']	[ 3.89996588e-01 -1.84548512e-01 5.00169516e-01 -2.68114626e-01 -7.72625566e-01 -4.95520204e-01 6.40282273e-01 1.29734814e-01 -2.23115623e-01 3.35238993e-01 -5.36418557e-01 -3.73244613e-01 2.55601436e-01 -1.43518829e+00 -1.07984757e+00 -5.08697391e-01 2.54956126e-01 1.13188696e+00 4.66547221e-01 -3.82969141e-01 4.56536412e-01 1.04423273e+00 -1.79274201e+00 1.82066068e-01 9.40492511e-01 1.05261695e+00 1.79433078e-01 6.92813933e-01 -5.80238223e-01 -2.51825213e-01 -3.23491216e-01 -2.18251467e-01 3.58843684e-01 1.84091389e-01 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6920bdf5-a659-4306-a4fb-a4f7fa9406b0	hitrans-a-transformer-based-context-and	null	null	https://aclanthology.org/2020.coling-main.370	https://aclanthology.org/2020.coling-main.370.pdf	HiTrans: A Transformer-Based Context- and Speaker-Sensitive Model for Emotion Detection in Conversations	Emotion detection in conversations (EDC) is to detect the emotion for each utterance in conversations that have multiple speakers. Different from the traditional non-conversational emotion detection, the model for EDC should be context-sensitive (e.g., understanding the whole conversation rather than one utterance) and speaker-sensitive (e.g., understanding which utterance belongs to which speaker). In this paper, we propose a transformer-based context- and speaker-sensitive model for EDC, namely HiTrans, which consists of two hierarchical transformers. We utilize BERT as the low-level transformer to generate local utterance representations, and feed them into another high-level transformer so that utterance representations could be sensitive to the global context of the conversation. Moreover, we exploit an auxiliary task to make our model speaker-sensitive, called pairwise utterance speaker verification (PUSV), which aims to classify whether two utterances belong to the same speaker. We evaluate our model on three benchmark datasets, namely EmoryNLP, MELD and IEMOCAP. Results show that our model outperforms previous state-of-the-art models.	['Yijiang Liu', 'Meishan Zhang', 'Fei Li', 'Donghong Ji', 'Jingye Li']	2020-12-01	null	null	null	coling-2020-8	['emotion-recognition-in-conversation']	['natural-language-processing']	[ 1.56270027e-01 8.25499147e-02 2.69536704e-01 -9.97433901e-01 -1.17381716e+00 -4.66176808e-01 4.56472874e-01 -6.69977739e-02 1.88698322e-02 3.34602982e-01 7.43106604e-01 -1.22007497e-01 5.45576632e-01 -3.42049003e-01 -2.59902775e-01 -7.13719308e-01 1.88700318e-01 3.00165385e-01 1.83907524e-02 -4.09726858e-01 -2.80795414e-02 3.29823270e-02 -1.55323112e+00 7.99572766e-01 5.22863090e-01 1.36683404e+00 -9.27213058e-02 6.89443827e-01 -5.14130831e-01 1.17473650e+00 -8.05176854e-01 -3.60791713e-01 -6.95080936e-01 -7.27555394e-01 -1.03058171e+00 3.51193547e-01 -1.29824087e-01 -1.09993769e-02 3.12544972e-01 1.00292313e+00 4.34212476e-01 3.20897520e-01 5.75606942e-01 -1.48563015e+00 -4.59434867e-01 9.40515518e-01 -2.56997466e-01 -5.81152812e-02 6.16896689e-01 -2.99146295e-01 1.00403023e+00 -1.05812192e+00 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473e4467-5117-4f63-b167-8013472f86bf	incorporating-intra-class-variance-to-fine	1703.00196	null	http://arxiv.org/abs/1703.00196v1	http://arxiv.org/pdf/1703.00196v1.pdf	Incorporating Intra-Class Variance to Fine-Grained Visual Recognition	Fine-grained visual recognition aims to capture discriminative characteristics amongst visually similar categories. The state-of-the-art research work has significantly improved the fine-grained recognition performance by deep metric learning using triplet network. However, the impact of intra-category variance on the performance of recognition and robust feature representation has not been well studied. In this paper, we propose to leverage intra-class variance in metric learning of triplet network to improve the performance of fine-grained recognition. Through partitioning training images within each category into a few groups, we form the triplet samples across different categories as well as different groups, which is called Group Sensitive TRiplet Sampling (GS-TRS). Accordingly, the triplet loss function is strengthened by incorporating intra-class variance with GS-TRS, which may contribute to the optimization objective of triplet network. Extensive experiments over benchmark datasets CompCar and VehicleID show that the proposed GS-TRS has significantly outperformed state-of-the-art approaches in both classification and retrieval tasks.	['Ling-Yu Duan', 'Tiejun Huang', 'Yihang Lou', 'Shiqi Wang', 'Feng Gao', 'Yan Bai']	2017-03-01	null	null	null	null	['fine-grained-visual-recognition']	['computer-vision']	[ 1.24763750e-01 -7.34481514e-01 -5.36474325e-02 -8.60827625e-01 -8.51704478e-01 -3.88934016e-01 8.53777468e-01 -1.86392426e-01 -1.47338808e-01 4.96527165e-01 2.05593213e-01 2.37288728e-01 -5.37456512e-01 -6.09630287e-01 -5.76045394e-01 -7.96787381e-01 2.87590951e-01 -3.35158743e-02 9.46104452e-02 9.51321200e-02 5.96947074e-01 6.56522095e-01 -1.69639909e+00 3.17560643e-01 8.32717478e-01 1.68751204e+00 -1.88689321e-01 -6.15908690e-02 -1.68668292e-02 5.87802947e-01 -5.06853461e-01 -2.57182091e-01 5.48809707e-01 -1.33930057e-01 -2.47272253e-01 2.84457415e-01 9.80497062e-01 -2.47460663e-01 -3.17411035e-01 1.07789481e+00 4.41196382e-01 3.18404734e-01 9.53423858e-01 -1.41787410e+00 -8.72295022e-01 1.68345645e-01 -7.86161542e-01 2.19587415e-01 -1.90321609e-01 1.56957597e-01 9.38645780e-01 -1.32881677e+00 7.46363699e-02 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1.02741301e+00 -8.63640845e-01 -9.63065326e-01 -9.87485290e-01 -7.26226091e-01 4.32212025e-01 5.04599273e-01 -1.23041356e+00 -5.66915631e-01 -4.10243660e-01]	[9.652022361755371, 2.0200607776641846]
7ce84a80-078e-4ba6-8500-f905ddc02c95	advanced-deep-learning-methodologies-for-skin	2003.06356	null	https://arxiv.org/abs/2003.06356v1	https://arxiv.org/pdf/2003.06356v1.pdf	Advanced Deep Learning Methodologies for Skin Cancer Classification in Prodromal Stages	Technology-assisted platforms provide reliable solutions in almost every field these days. One such important application in the medical field is the skin cancer classification in preliminary stages that need sensitive and precise data analysis. For the proposed study the Kaggle skin cancer dataset is utilized. The proposed study consists of two main phases. In the first phase, the images are preprocessed to remove the clutters thus producing a refined version of training images. To achieve that, a sharpening filter is applied followed by a hair removal algorithm. Different image quality measurement metrics including Peak Signal to Noise (PSNR), Mean Square Error (MSE), Maximum Absolute Squared Deviation (MXERR) and Energy Ratio/ Ratio of Squared Norms (L2RAT) are used to compare the overall image quality before and after applying preprocessing operations. The results from the aforementioned image quality metrics prove that image quality is not compromised however it is upgraded by applying the preprocessing operations. The second phase of the proposed research work incorporates deep learning methodologies that play an imperative role in accurate, precise and robust classification of the lesion mole. This has been reflected by using two state of the art deep learning models: Inception-v3 and MobileNet. The experimental results demonstrate notable improvement in train and validation accuracy by using the refined version of images of both the networks, however, the Inception-v3 network was able to achieve better validation accuracy thus it was finally selected to evaluate it on test data. The final test accuracy using state of art Inception-v3 network was 86%.	['Asma Khatoon', 'Muhammad Ali Farooq', 'Viktor Varkarakis', 'Peter Corcoran']	2020-03-13	null	null	null	null	['skin-cancer-classification']	['medical']	[ 6.05312586e-01 8.87653511e-03 1.74304456e-01 -3.18622701e-02 -6.64265633e-01 -1.82946876e-01 5.50679862e-01 5.52834451e-01 -8.05516660e-01 8.46665084e-01 -1.83350265e-01 -1.29462704e-01 -5.91423035e-01 -7.91875899e-01 -2.73678273e-01 -9.69884574e-01 -7.16396514e-03 -1.17493741e-01 2.51458049e-01 -3.29272270e-01 3.35701436e-01 5.65325379e-01 -1.37962723e+00 3.67311031e-01 8.81966770e-01 1.01037467e+00 2.34493449e-01 9.39344287e-01 8.80792737e-02 5.18671095e-01 -4.06937510e-01 -2.19443977e-01 3.25120330e-01 -1.08364142e-01 -7.33117640e-01 2.28290722e-01 1.84911102e-01 -1.87376708e-01 8.17882046e-02 1.05944455e+00 8.62546504e-01 1.36964366e-01 5.63218415e-01 -9.27206457e-01 -8.83968472e-02 9.13198441e-02 -7.62436688e-01 5.13637006e-01 2.08335981e-01 1.94657683e-01 3.17625880e-01 -7.71619737e-01 5.48596799e-01 6.73003495e-01 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bc4c1704-bd9f-446f-9a06-312f93bd0b3a	language-models-with-image-descriptors-are	2205.10747	null	https://arxiv.org/abs/2205.10747v4	https://arxiv.org/pdf/2205.10747v4.pdf	Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners	The goal of this work is to build flexible video-language models that can generalize to various video-to-text tasks from few examples, such as domain-specific captioning, question answering, and future event prediction. Existing few-shot video-language learners focus exclusively on the encoder, resulting in the absence of a video-to-text decoder to handle generative tasks. Video captioners have been pretrained on large-scale video-language datasets, but they rely heavily on finetuning and lack the ability to generate text for unseen tasks in a few-shot setting. We propose VidIL, a few-shot Video-language Learner via Image and Language models, which demonstrates strong performance on few-shot video-to-text tasks without the necessity of pretraining or finetuning on any video datasets. We use the image-language models to translate the video content into frame captions, object, attribute, and event phrases, and compose them into a temporal structure template. We then instruct a language model, with a prompt containing a few in-context examples, to generate a target output from the composed content. The flexibility of prompting allows the model to capture any form of text input, such as automatic speech recognition (ASR) transcripts. Our experiments demonstrate the power of language models in understanding videos on a wide variety of video-language tasks, including video captioning, video question answering, video caption retrieval, and video future event prediction. Especially, on video future event prediction, our few-shot model significantly outperforms state-of-the-art supervised models trained on large-scale video datasets. Code and resources are publicly available for research purposes at https://github.com/MikeWangWZHL/VidIL .	['Heng Ji', 'Mohit Bansal', 'Shih-Fu Chang', 'Derek Hoiem', 'Chenguang Zhu', 'ZiYi Yang', 'Shuohang Wang', 'Xudong Lin', 'Jie Lei', 'Luowei Zhou', 'Ruochen Xu', 'Manling Li', 'Zhenhailong Wang']	2022-05-22	null	null	null	null	['video-question-answering']	['computer-vision']	[ 4.08516794e-01 5.07844500e-02 -4.44557369e-01 -5.52272677e-01 -1.13183606e+00 -4.38284874e-01 6.98365033e-01 -4.26342577e-01 -2.81456679e-01 6.22021317e-01 5.39689243e-01 -2.90966094e-01 5.52519321e-01 -4.57123220e-01 -1.28912508e+00 -3.07331264e-01 6.99785277e-02 2.70134956e-01 3.34451795e-01 4.80069928e-02 -1.61887661e-01 -1.32860720e-01 -1.44638395e+00 9.62211311e-01 1.92581415e-01 8.97449017e-01 6.31701648e-01 1.12445939e+00 -6.93679005e-02 1.34682560e+00 -2.87738055e-01 -2.75745779e-01 5.81768900e-02 -7.84118235e-01 -5.96103907e-01 3.97586048e-01 4.48810935e-01 -8.87593269e-01 -1.01484025e+00 4.83470738e-01 3.41254532e-01 2.33251363e-01 5.20725667e-01 -1.52708244e+00 -9.03556406e-01 5.76954603e-01 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4852f8d1-a688-48c7-a6f5-3a1433aef04f	improving-low-resource-named-entity-1	null	null	https://aclanthology.org/2021.ccl-1.101	https://aclanthology.org/2021.ccl-1.101.pdf	Improving Low-Resource Named Entity Recognition via Label-Aware Data Augmentation and Curriculum Denoising	“Deep neural networks have achieved state-of-the-art performances on named entity recognition(NER) with sufficient training data while they perform poorly in low-resource scenarios due to data scarcity. To solve this problem we propose a novel data augmentation method based on pre-trained language model (PLM) and curriculum learning strategy. Concretely we use the PLMto generate diverse training instances through predicting different masked words and design atask-specific curriculum learning strategy to alleviate the influence of noises. We evaluate the effectiveness of our approach on three datasets: CoNLL-2003 OntoNotes5.0 and MaScip of which the first two are simulated low-resource scenarios and the last one is a real low-resource dataset in material science domain. Experimental results show that our method consistently outperform the baseline model. Specifically our method achieves an absolute improvement of3.46% F1 score on the 1% CoNLL-2003 2.58% on the 1% OntoNotes5.0 and 0.99% on the full of MaScip.”	['Zhang Yujie', 'Chen Yufeng', 'Xu Jinan', 'Liu Jian', 'Zhu Wenjing']	null	null	null	null	ccl-2021-8	['low-resource-named-entity-recognition']	['natural-language-processing']	[-1.25856400e-01 9.97173265e-02 5.08721694e-02 -4.11969125e-01 -1.24645853e+00 -7.49503851e-01 7.27837086e-01 -2.48862077e-02 -1.17506218e+00 1.04395020e+00 4.09313947e-01 -4.41583365e-01 1.85262531e-01 -7.94021189e-01 -9.36279058e-01 -4.02220935e-01 6.06775880e-01 5.47815084e-01 -1.93541929e-01 -8.76797438e-02 1.01276850e-02 5.16964018e-01 -1.03042793e+00 1.81180432e-01 9.22975779e-01 5.25592029e-01 2.91852534e-01 5.22922337e-01 -5.45323610e-01 6.29447103e-01 -9.48369086e-01 -4.19887781e-01 3.50789994e-01 -4.58095223e-02 -9.00949001e-01 -3.41424733e-01 4.00234401e-01 1.21021718e-01 -5.89559972e-01 9.31339383e-01 1.00787508e+00 2.84395784e-01 2.98495561e-01 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30592227-5b02-41fd-be44-05d24f2149c6	evaluating-out-of-distribution-performance-on	2210.07448	null	https://arxiv.org/abs/2210.07448v2	https://arxiv.org/pdf/2210.07448v2.pdf	Evaluating Out-of-Distribution Performance on Document Image Classifiers	The ability of a document classifier to handle inputs that are drawn from a distribution different from the training distribution is crucial for robust deployment and generalizability. The RVL-CDIP corpus is the de facto standard benchmark for document classification, yet to our knowledge all studies that use this corpus do not include evaluation on out-of-distribution documents. In this paper, we curate and release a new out-of-distribution benchmark for evaluating out-of-distribution performance for document classifiers. Our new out-of-distribution benchmark consists of two types of documents: those that are not part of any of the 16 in-domain RVL-CDIP categories (RVL-CDIP-O), and those that are one of the 16 in-domain categories yet are drawn from a distribution different from that of the original RVL-CDIP dataset (RVL-CDIP-N). While prior work on document classification for in-domain RVL-CDIP documents reports high accuracy scores, we find that these models exhibit accuracy drops of between roughly 15-30% on our new out-of-domain RVL-CDIP-N benchmark, and further struggle to distinguish between in-domain RVL-CDIP-N and out-of-domain RVL-CDIP-O inputs. Our new benchmark provides researchers with a valuable new resource for analyzing out-of-distribution performance on document classifiers. Our new out-of-distribution data can be found at https://github.com/gxlarson/rvl-cdip-ood.	['Kevin Leach', 'David Kuang', 'Yutong Ai', 'Gordon Lim', 'Stefan Larson']	2022-10-14	null	null	null	null	['document-classification']	['natural-language-processing']	[-2.74847001e-01 -4.47510809e-01 -6.05128050e-01 -3.91120404e-01 -9.55513537e-01 -1.40239394e+00 9.16324556e-01 4.92175937e-01 -1.95100173e-01 6.53014362e-01 -5.78784645e-02 -8.32537770e-01 -3.50056827e-01 -6.89449728e-01 -5.25788009e-01 -5.85997999e-01 2.21028045e-01 9.48871136e-01 2.89277941e-01 -2.12610945e-01 3.30951303e-01 6.23467743e-01 -1.34500408e+00 6.48227632e-01 4.95837629e-01 9.74646747e-01 -1.37958154e-01 9.01602149e-01 -3.04686546e-01 4.06877548e-01 -1.21599960e+00 -2.01894015e-01 1.73382938e-01 -2.60229677e-01 -8.52816164e-01 -3.25387985e-01 7.26435661e-01 -2.30687812e-01 -2.83109844e-01 7.30223596e-01 5.59139669e-01 -1.85129046e-01 1.35106373e+00 -1.47464275e+00 -1.09268892e+00 5.46589851e-01 -4.93830651e-01 6.54339969e-01 4.39351529e-01 -3.63652520e-02 1.16423523e+00 -7.52565265e-01 8.67417812e-01 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ec349837-d1c0-441c-ac4f-65a100bd60b5	computer-aided-diagnosis-and-prediction-in	2206.14683	null	https://arxiv.org/abs/2206.14683v2	https://arxiv.org/pdf/2206.14683v2.pdf	Computer-aided diagnosis and prediction in brain disorders	Computer-aided methods have shown added value for diagnosing and predicting brain disorders and can thus support decision making in clinical care and treatment planning. This chapter will provide insight into the type of methods, their working, their input data - such as cognitive tests, imaging and genetic data - and the types of output they provide. We will focus on specific use cases for diagnosis, i.e. estimating the current 'condition' of the patient, such as early detection and diagnosis of dementia, differential diagnosis of brain tumours, and decision making in stroke. Regarding prediction, i.e. estimation of the future 'condition' of the patient, we will zoom in on use cases such as predicting the disease course in multiple sclerosis and predicting patient outcomes after treatment in brain cancer. Furthermore, based on these use cases, we will assess the current state-of-the-art methodology and highlight current efforts on benchmarking of these methods and the importance of open science therein. Finally, we assess the current clinical impact of computer-aided methods and discuss the required next steps to increase clinical impact.	['Esther E. Bron', 'Stefan Klein', 'Wiro J. Niessen', 'Frederik Barkhof', 'Marion Smits', 'Daniel Bos', 'Sebastian R. van der Voort', 'Vikram Venkatraghavan']	2022-06-29	null	null	null	null	['predicting-patient-outcomes']	['medical']	[ 3.29183936e-01 4.89445686e-01 -1.27334772e-02 -5.12776554e-01 -4.99467283e-01 -1.01693146e-01 2.80292869e-01 5.49935937e-01 -5.98596334e-01 8.27072680e-01 3.84305388e-01 -5.65054178e-01 -4.87071365e-01 -6.60367787e-01 -1.87130690e-01 -5.40857255e-01 -4.97101098e-01 1.11912274e+00 2.43708953e-01 1.46353364e-01 3.18692744e-01 6.11277640e-01 -1.66845632e+00 4.75079715e-01 9.07288611e-01 9.30629134e-01 4.93373007e-01 5.90243578e-01 -2.53391922e-01 6.21508002e-01 -3.84978265e-01 -2.03786567e-01 -4.30999756e-01 -4.20276403e-01 -9.20847535e-01 -1.48332968e-01 -2.76960969e-01 -3.75617176e-01 -2.78188323e-04 6.96167767e-01 9.63992238e-01 -1.38928115e-01 1.03972661e+00 -1.04100955e+00 3.29940766e-02 1.93905205e-01 8.47985297e-02 4.64789957e-01 6.52039707e-01 2.32101735e-02 8.94229710e-02 -6.53248847e-01 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1c401153-240e-4a38-8b09-b50560ba6bf0	smddh-singleton-mention-detection-using-deep	2301.09361	null	https://arxiv.org/abs/2301.09361v1	https://arxiv.org/pdf/2301.09361v1.pdf	SMDDH: Singleton Mention detection using Deep Learning in Hindi Text	Mention detection is an important component of coreference resolution system, where mentions such as name, nominal, and pronominals are identified. These mentions can be purely coreferential mentions or singleton mentions (non-coreferential mentions). Coreferential mentions are those mentions in a text that refer to the same entities in a real world. Whereas, singleton mentions are mentioned only once in the text and do not participate in the coreference as they are not mentioned again in the following text. Filtering of these singleton mentions can substantially improve the performance of a coreference resolution process. This paper proposes a singleton mention detection module based on a fully connected network and a Convolutional neural network for Hindi text. This model utilizes a few hand-crafted features and context information, and word embedding for words. The coreference annotated Hindi dataset comprising of 3.6K sentences, and 78K tokens are used for the task. In terms of Precision, Recall, and F-measure, the experimental findings obtained are excellent.	['Kamlesh Dutta', 'Pardeep Singh', 'Kusum Lata']	2023-01-23	null	null	null	null	['coreference-resolution']	['natural-language-processing']	[-6.93358108e-02 3.48758578e-01 -3.27163160e-01 -5.15016019e-01 -8.10616195e-01 -7.76509047e-01 6.04951441e-01 4.87873763e-01 -8.00539672e-01 9.90330637e-01 8.11110973e-01 -1.53610513e-01 -2.37457931e-01 -7.98056722e-01 -3.44496906e-01 -6.38453245e-01 -5.52139208e-02 7.07919955e-01 2.15155482e-01 -6.05253994e-01 3.03974271e-01 5.32417715e-01 -1.10533023e+00 3.78036201e-01 6.43935919e-01 4.47008938e-01 2.31150419e-01 5.88068701e-02 -4.37681317e-01 5.16887188e-01 -8.80694032e-01 -7.82194972e-01 -3.23243588e-01 1.47813827e-01 -1.31819975e+00 -6.99949026e-01 1.67604119e-01 1.25861540e-01 -4.96909976e-01 1.17565167e+00 5.23250759e-01 2.72573918e-01 3.97788525e-01 -9.16428030e-01 -6.78211629e-01 1.42701030e+00 -4.09464240e-01 5.06789744e-01 4.21528339e-01 -5.97816348e-01 1.54153311e+00 -7.28818655e-01 7.72331715e-01 1.31444585e+00 5.47736824e-01 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6bbd2ca6-1d26-4248-8ac7-09ecd8d8a3f8	skeleon-based-typing-style-learning-for	2012.03212	null	https://arxiv.org/abs/2012.03212v1	https://arxiv.org/pdf/2012.03212v1.pdf	Skeleon-Based Typing Style Learning For Person Identification	We present a novel architecture for person identification based on typing-style, constructed of adaptive non-local spatio-temporal graph convolutional network. Since type style dynamics convey meaningful information that can be useful for person identification, we extract the joints positions and then learn their movements' dynamics. Our non-local approach increases our model's robustness to noisy input data while analyzing joints locations instead of RGB data provides remarkable robustness to alternating environmental conditions, e.g., lighting, noise, etc. We further present two new datasets for typing style based person identification task and extensive evaluation that displays our model's superior discriminative and generalization abilities, when compared with state-of-the-art skeleton-based models.	['Dan Raviv', 'David Mendlovic', 'Lior Gelberg']	2020-12-06	null	null	null	null	['person-identification']	['computer-vision']	[-9.84720811e-02 -6.07551277e-01 2.77627800e-02 -3.38048220e-01 1.10770985e-02 -4.59329784e-01 4.16938961e-01 -1.30766109e-01 -7.29088545e-01 5.48395693e-01 3.07985604e-01 2.86794037e-01 -1.61450468e-02 -6.41463995e-01 -3.14231277e-01 -5.65238714e-01 -2.34064534e-01 4.88955379e-01 1.25106618e-01 -4.98230845e-01 -9.82385725e-02 6.61384344e-01 -1.15707099e+00 8.63680765e-02 4.15298313e-01 8.53429794e-01 -3.92443210e-01 9.21335578e-01 7.28488192e-02 2.66052663e-01 -8.95530641e-01 -6.42232358e-01 1.83379501e-01 -1.36804149e-01 -6.64090157e-01 -2.18247518e-01 7.17791915e-01 -2.68227845e-01 -6.95528805e-01 9.39946771e-01 8.75656188e-01 2.46273413e-01 6.08481407e-01 -1.07274568e+00 -8.87942910e-01 1.89627573e-01 -3.98304403e-01 3.21854562e-01 1.06270051e+00 7.10026383e-01 5.87300599e-01 -3.66285920e-01 6.11270070e-01 1.66280854e+00 1.31026840e+00 1.01034200e+00 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-3.47112417e-01 1.04623377e+00 3.20862353e-01 -8.80452991e-01 1.08135395e-01 1.42159849e-01]	[14.496756553649902, 1.068596363067627]
3f56989c-dad4-4af1-8bff-db08451f405b	sanet-scene-agnostic-network-for-camera	null	null	http://openaccess.thecvf.com/content_ICCV_2019/html/Yang_SANet_Scene_Agnostic_Network_for_Camera_Localization_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Yang_SANet_Scene_Agnostic_Network_for_Camera_Localization_ICCV_2019_paper.pdf	SANet: Scene Agnostic Network for Camera Localization	This paper presents a scene agnostic neural architecture for camera localization, where model parameters and scenes are independent from each other.Despite recent advancement in learning based methods, most approaches require training for each scene one by one, not applicable for online applications such as SLAM and robotic navigation, where a model must be built on-the-fly.Our approach learns to build a hierarchical scene representation and predicts a dense scene coordinate map of a query RGB image on-the-fly given an arbitrary scene. The 6D camera pose of the query image can be estimated with the predicted scene coordinate map. Additionally, the dense prediction can be used for other online robotic and AR applications such as obstacle avoidance. We demonstrate the effectiveness and efficiency of our method on both indoor and outdoor benchmarks, achieving state-of-the-art performance.	[' Ping Tan', ' Yasutaka Furukawa', ' Honghua Li', ' Chengzhou Tang', ' Ziqian Bai', 'Luwei Yang']	2019-10-01	null	null	null	iccv-2019-10	['camera-localization']	['computer-vision']	[ 4.84691113e-02 -2.51906693e-01 -2.22315431e-01 -7.08029568e-01 -7.73305893e-01 -7.06751466e-01 2.32250899e-01 1.34247661e-01 -6.04518116e-01 1.96929514e-01 -8.12624991e-02 -2.30300322e-01 -6.84586987e-02 -7.95069337e-01 -1.12606680e+00 -5.13061523e-01 3.12472284e-02 6.53475702e-01 3.31652969e-01 -2.02594548e-02 1.98775217e-01 6.44729316e-01 -1.35215724e+00 -1.23198196e-01 3.22885841e-01 1.14206827e+00 8.02549064e-01 9.10220027e-01 -7.29479408e-03 9.97255445e-01 -3.05776875e-02 2.95056608e-02 5.24730384e-01 -3.19720283e-02 -6.97178364e-01 3.62542450e-01 6.24976695e-01 -4.93281960e-01 -8.29660237e-01 9.77657616e-01 2.83554941e-01 3.10508728e-01 2.50544369e-01 -9.64055896e-01 -1.92825601e-01 -1.69269279e-01 -4.13020313e-01 -3.29478800e-01 5.21459579e-01 1.39318153e-01 8.83937895e-01 -8.13431263e-01 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7dbb3039-c270-4d26-8d76-f8030200a6f4	multiphase-flow-prediction-with-deep-neural	1910.09657	null	https://arxiv.org/abs/1910.09657v1	https://arxiv.org/pdf/1910.09657v1.pdf	Multiphase flow prediction with deep neural networks	This paper proposes a deep neural network approach for predicting multiphase flow in heterogeneous domains with high computational efficiency. The deep neural network model is able to handle permeability heterogeneity in high dimensional systems, and can learn the interplay of viscous, gravity, and capillary forces from small data sets. Using the example of carbon dioxide (CO2) storage, we demonstrate that the model can generate highly accurate predictions of a CO2 saturation distribution given a permeability field, injection duration, injection rate, and injection location. The trained neural network model has an excellent ability to interpolate and to a limited extent, the ability to extrapolate beyond the training data ranges. To improve the prediction accuracy when the neural network model needs to extrapolate, we propose a transfer learning (fine-tuning) procedure that can quickly teach the neural network model new information without going through massive data collection and retraining. Based on this trained neural network model, a web-based tool is provided that allows users to perform CO2-water multiphase flow calculations online. With the tools provided in this paper, the deep neural network approach can provide a computationally efficient substitute for repetitive forward multiphase flow simulations, which can be adopted to the context of history matching and uncertainty quantification.	['Meng Tang', 'Gege Wen', 'Sally M. Benson']	2019-10-21	null	null	null	null	['small-data']	['computer-vision']	[-1.85129344e-01 -1.14654511e-01 -3.35043669e-02 -1.42405868e-01 -3.04110795e-01 -2.13293791e-01 3.89244854e-01 5.14506876e-01 -2.67142594e-01 1.03633428e+00 -2.12293953e-01 -7.19321132e-01 -1.65251985e-01 -1.27277732e+00 -8.70321214e-01 -6.83861434e-01 -3.96984786e-01 9.00187671e-01 3.61388057e-01 -2.34530866e-01 2.41159648e-01 8.57517898e-01 -1.50195897e+00 3.20631415e-02 9.79407847e-01 1.18956518e+00 5.86125962e-02 7.80842483e-01 -2.26741526e-02 1.08212924e+00 -2.75646269e-01 3.31019342e-01 2.71160901e-01 -3.27103466e-01 -8.62281382e-01 -6.51194870e-01 5.69078792e-03 -5.43525100e-01 -3.18764895e-01 4.63328958e-01 6.49732292e-01 4.59055334e-01 9.33089674e-01 -7.13201404e-01 -1.55990005e-01 4.64715987e-01 1.62777871e-01 4.45264935e-01 -1.05953664e-01 5.34846961e-01 3.34446907e-01 -6.23915613e-01 2.83520907e-01 8.04544568e-01 9.26154137e-01 3.40561599e-01 -9.71466780e-01 -3.16396743e-01 -3.72791231e-01 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a8f9b722-cbd8-42f0-bf9b-6885814ee6f5	a-de-raining-semantic-segmentation-network	2104.07877	null	https://arxiv.org/abs/2104.07877v1	https://arxiv.org/pdf/2104.07877v1.pdf	A De-raining semantic segmentation network for real-time foreground segmentation	Few researches have been proposed specifically for real-time semantic segmentation in rainy environments. However, the demand in this area is huge and it is challenging for lightweight networks. Therefore, this paper proposes a lightweight network which is specially designed for the foreground segmentation in rainy environments, named De-raining Semantic Segmentation Network (DRSNet). By analyzing the characteristics of raindrops, the MultiScaleSE Block is targetedly designed to encode the input image, it uses multi-scale dilated convolutions to increase the receptive field, and SE attention mechanism to learn the weights of each channels. In order to combine semantic information between different encoder and decoder layers, it is proposed to use Asymmetric Skip, that is, the higher semantic layer of encoder employs bilinear interpolation and the output passes through pointwise convolution, then added element-wise to the lower semantic layer of decoder. According to the control experiments, the performances of MultiScaleSE Block and Asymmetric Skip compared with SEResNet18 and Symmetric Skip respectively are improved to a certain degree on the Foreground Accuracy index. The parameters and the floating point of operations (FLOPs) of DRSNet is only 0.54M and 0.20GFLOPs separately. The state-of-the-art results and real-time performances are achieved on both the UESTC all-day Scenery add rain (UAS-add-rain) and the Baidu People Segmentation add rain (BPS-add-rain) benchmarks with the input sizes of 192128, 384256 and 768*512. The speed of DRSNet exceeds all the networks within 1GFLOPs, and Foreground Accuracy index is also the best among the similar magnitude networks on both benchmarks.	['Yihui Zhang', 'Fanyi Wang']	2021-04-16	null	null	null	null	['foreground-segmentation']	['computer-vision']	[ 2.64788792e-02 -3.35874379e-01 3.13089013e-01 -5.41070282e-01 1.43992691e-03 -3.36205289e-02 -1.29838884e-01 -5.21414638e-01 -7.51426339e-01 9.06737030e-01 -4.42382365e-01 -5.77552438e-01 3.16710353e-01 -1.01362550e+00 -6.81547225e-01 -1.12186456e+00 -1.13926210e-01 1.29846632e-01 7.53319740e-01 -1.00590251e-01 -1.24554802e-02 5.41816235e-01 -1.54183161e+00 3.28112304e-01 1.20722210e+00 1.03863990e+00 6.48817360e-01 9.80519295e-01 -3.97028446e-01 8.83316576e-01 -7.72602320e-01 -1.93363428e-01 2.94674873e-01 -4.69597220e-01 -4.68891531e-01 -1.45383045e-01 5.92098057e-01 -7.10505128e-01 -3.72397572e-01 9.14067447e-01 7.38659978e-01 -1.02305412e-01 1.19364813e-01 -9.49522138e-01 -1.68044031e-01 5.34135818e-01 -6.90808654e-01 8.69478285e-01 -5.78413248e-01 2.55722791e-01 5.77438414e-01 -7.45303154e-01 1.75015524e-01 1.45921338e+00 4.92500484e-01 4.77198005e-01 -3.59800637e-01 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-8.64387751e-02 1.65929630e-01 -2.69772023e-01 -4.98003125e-01 6.01596385e-02]	[10.913066864013672, -3.2525134086608887]
94a4344b-44ff-41db-ac24-40d2fcfe1378	an-evaluation-on-large-language-model-outputs	2304.08637	null	https://arxiv.org/abs/2304.08637v1	https://arxiv.org/pdf/2304.08637v1.pdf	An Evaluation on Large Language Model Outputs: Discourse and Memorization	We present an empirical evaluation of various outputs generated by nine of the most widely-available large language models (LLMs). Our analysis is done with off-the-shelf, readily-available tools. We find a correlation between percentage of memorized text, percentage of unique text, and overall output quality, when measured with respect to output pathologies such as counterfactual and logically-flawed statements, and general failures like not staying on topic. Overall, 80.0% of the outputs evaluated contained memorized data, but outputs containing the most memorized content were also more likely to be considered of high quality. We discuss and evaluate mitigation strategies, showing that, in the models evaluated, the rate of memorized text being output is reduced. We conclude with a discussion on potential implications around what it means to learn, to memorize, and to evaluate quality text.	['Si-Qing Chen', 'Qilong Gu', 'Alex Sokolov', 'Xun Wang', 'Adrian de Wynter']	2023-04-17	null	null	null	null	['memorization']	['natural-language-processing']	[ 2.25471277e-02 6.16575897e-01 -1.15900166e-01 -2.17603762e-02 -9.14791822e-01 -8.74335825e-01 9.45569515e-01 7.63206482e-01 -5.06110251e-01 1.24999952e+00 7.46678531e-01 -7.38269925e-01 -2.84639329e-01 -1.00389564e+00 -9.83638108e-01 -1.96999758e-01 4.47975427e-01 1.26389652e-01 -2.55494624e-01 1.60557888e-02 5.50181985e-01 1.52207196e-01 -1.58186579e+00 4.78945822e-01 1.12010622e+00 4.02852386e-01 3.08498796e-02 6.12070560e-01 -2.70488054e-01 1.15776134e+00 -1.41457379e+00 -7.98051298e-01 -1.57298014e-01 -5.00248134e-01 -7.11039066e-01 -2.86319226e-01 4.11827505e-01 -9.10294876e-02 2.15417877e-01 7.54792869e-01 5.22438943e-01 -4.56100479e-02 7.26909518e-01 -1.33936250e+00 -7.99466908e-01 1.30160296e+00 -6.02147728e-02 4.21496063e-01 7.36607432e-01 5.47033787e-01 8.08850408e-01 -8.83019805e-01 1.10476363e+00 1.39651847e+00 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c7ec79c2-eb22-4297-9e79-3ce97b0585a8	video-enhancement-of-people-wearing-polarized	null	null	http://openaccess.thecvf.com/content_cvpr_2013/html/Ye_Video_Enhancement_of_2013_CVPR_paper.html	http://openaccess.thecvf.com/content_cvpr_2013/papers/Ye_Video_Enhancement_of_2013_CVPR_paper.pdf	Video Enhancement of People Wearing Polarized Glasses: Darkening Reversal and Reflection Reduction	With the wide-spread of consumer 3D-TV technology, stereoscopic videoconferencing systems are emerging. However, the special glasses participants wear to see 3D can create distracting images. This paper presents a computational framework to reduce undesirable artifacts in the eye regions caused by these 3D glasses. More specifically, we add polarized filters to the stereo camera so that partial images of reflection can be captured. A novel Bayesian model is then developed to describe the imaging process of the eye regions including darkening and reflection, and infer the eye regions based on Classification ExpectationMaximization (EM). The recovered eye regions under the glasses are brighter and with little reflections, leading to a more nature videoconferencing experience. Qualitative evaluations and user studies are conducted to demonstrate the substantial improvement our approach can achieve.	['Ruigang Yang', 'Cha Zhang', 'Mao Ye']	2013-06-01	null	null	null	cvpr-2013-6	['video-enhancement']	['computer-vision']	[ 1.01339862e-01 1.80201083e-01 2.95055270e-01 -2.25399971e-01 -3.87148187e-02 -2.96317458e-01 3.08587641e-01 -1.06863546e+00 3.09633072e-02 5.08419454e-01 4.93044496e-01 -5.46442270e-02 2.82688230e-01 -1.35785624e-01 -4.86558110e-01 -8.06604922e-01 2.91741282e-01 -3.35227400e-01 2.16483936e-01 4.72570285e-02 4.81906593e-01 3.51859391e-01 -2.17648268e+00 5.93757212e-01 9.02328372e-01 9.23261583e-01 3.67382318e-01 5.05605876e-01 2.82827824e-01 6.53016686e-01 -5.52456498e-01 -2.37041518e-01 5.15444338e-01 -4.35461849e-01 -1.73515618e-01 9.44010615e-01 7.16413558e-01 -9.76548612e-01 -3.08060467e-01 1.21405399e+00 4.84312475e-01 2.42739260e-01 3.63490433e-01 -1.01750362e+00 -4.31112766e-01 -3.89769167e-01 -8.31082046e-01 -1.98676512e-01 1.09375393e+00 3.76308352e-01 2.30533808e-01 -9.84761715e-01 5.65754116e-01 1.27131879e+00 2.62444586e-01 7.05737054e-01 -1.15171301e+00 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-1.21875346e+00 5.44417202e-01 4.55628544e-01 -5.68255365e-01 -8.18007231e-01 1.46318138e-01]	[10.39669418334961, -2.7276058197021484]
2894acc2-19e4-4d89-91de-c39d8e32ac36	auto-card-efficient-and-robust-codec-avatar	2304.11835	null	https://arxiv.org/abs/2304.11835v1	https://arxiv.org/pdf/2304.11835v1.pdf	Auto-CARD: Efficient and Robust Codec Avatar Driving for Real-time Mobile Telepresence	Real-time and robust photorealistic avatars for telepresence in AR/VR have been highly desired for enabling immersive photorealistic telepresence. However, there still exists one key bottleneck: the considerable computational expense needed to accurately infer facial expressions captured from headset-mounted cameras with a quality level that can match the realism of the avatar's human appearance. To this end, we propose a framework called Auto-CARD, which for the first time enables real-time and robust driving of Codec Avatars when exclusively using merely on-device computing resources. This is achieved by minimizing two sources of redundancy. First, we develop a dedicated neural architecture search technique called AVE-NAS for avatar encoding in AR/VR, which explicitly boosts both the searched architectures' robustness in the presence of extreme facial expressions and hardware friendliness on fast evolving AR/VR headsets. Second, we leverage the temporal redundancy in consecutively captured images during continuous rendering and develop a mechanism dubbed LATEX to skip the computation of redundant frames. Specifically, we first identify an opportunity from the linearity of the latent space derived by the avatar decoder and then propose to perform adaptive latent extrapolation for redundant frames. For evaluation, we demonstrate the efficacy of our Auto-CARD framework in real-time Codec Avatar driving settings, where we achieve a 5.05x speed-up on Meta Quest 2 while maintaining a comparable or even better animation quality than state-of-the-art avatar encoder designs.	['Yingyan Lin', 'Xiaoliang Dai', 'Peizhao Zhang', 'Jason Saragih', 'Chenghui Li', 'Yuecheng Li', 'Yonggan Fu']	2023-04-24	null	http://openaccess.thecvf.com//content/CVPR2023/html/Fu_Auto-CARD_Efficient_and_Robust_Codec_Avatar_Driving_for_Real-Time_Mobile_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Fu_Auto-CARD_Efficient_and_Robust_Codec_Avatar_Driving_for_Real-Time_Mobile_CVPR_2023_paper.pdf	cvpr-2023-1	['architecture-search']	['methodology']	[ 2.63665140e-01 1.22144751e-01 2.83405393e-01 -1.59684956e-01 -6.04093313e-01 -4.49565649e-01 4.97718602e-01 -7.64669657e-01 -2.41448343e-01 2.75264651e-01 9.14702639e-02 -7.16250688e-02 2.24333867e-01 -3.27076703e-01 -7.47269988e-01 -4.61626709e-01 -3.98777910e-02 5.68393916e-02 -3.03544104e-01 -4.28263634e-01 -1.49241820e-01 6.00775301e-01 -2.03600621e+00 -1.06576055e-01 6.51623189e-01 1.14423597e+00 -6.04012832e-02 1.00994897e+00 6.41419291e-01 1.02285850e+00 -4.93212700e-01 -6.42674506e-01 5.05687475e-01 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d429edcd-2f79-4060-a11a-34c994a0fe1e	learning-from-missing-data-using-selection	1509.09130	null	http://arxiv.org/abs/1509.09130v1	http://arxiv.org/pdf/1509.09130v1.pdf	Learning From Missing Data Using Selection Bias in Movie Recommendation	Recommending items to users is a challenging task due to the large amount of missing information. In many cases, the data solely consist of ratings or tags voluntarily contributed by each user on a very limited subset of the available items, so that most of the data of potential interest is actually missing. Current approaches to recommendation usually assume that the unobserved data is missing at random. In this contribution, we provide statistical evidence that existing movie recommendation datasets reveal a significant positive association between the rating of items and the propensity to select these items. We propose a computationally efficient variational approach that makes it possible to exploit this selection bias so as to improve the estimation of ratings from small populations of users. Results obtained with this approach applied to neighborhood-based collaborative filtering illustrate its potential for improving the reliability of the recommendation.	['Olivier Cappé', 'Claire Vernade']	2015-09-30	null	null	null	null	['movie-recommendation']	['miscellaneous']	[-4.59592268e-02 -1.16078973e-01 -7.88982213e-01 -5.34833729e-01 -6.65733278e-01 -6.38305247e-01 3.95955086e-01 1.10727087e-01 -2.94333637e-01 7.95200408e-01 8.89201522e-01 -1.39711112e-01 -3.61629426e-01 -1.03622091e+00 -6.37513638e-01 -5.72965980e-01 1.26062036e-01 5.77680409e-01 -1.31458347e-03 -1.41202196e-01 1.38056129e-01 3.63653079e-02 -1.44590724e+00 3.96720946e-01 8.70274186e-01 7.12114096e-01 3.46669883e-01 5.51959500e-02 1.36536509e-01 4.23572361e-01 -7.87165239e-02 -4.33086246e-01 3.89485240e-01 -2.32944369e-01 -1.16935909e-01 2.14228868e-01 4.05251801e-01 -5.16984344e-01 -2.12080210e-01 9.24044311e-01 2.03541085e-01 7.20243037e-01 1.02992952e+00 -6.17761433e-01 -7.23894417e-01 8.94845605e-01 -2.43148893e-01 1.56864733e-01 2.72868723e-01 -4.68073547e-01 1.53556943e+00 -9.46099877e-01 5.84984601e-01 5.85601032e-01 5.93766510e-01 2.27167845e-01 -1.37709367e+00 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7.95995593e-01 1.57145143e-01 -8.29827905e-01 -4.89688158e-01 -2.21072242e-01]	[9.819479942321777, 5.569430351257324]
48a8d9bc-54d0-42e2-bf51-a7ebdfc0d6db	japanese-lexical-simplification-for-non	null	null	https://aclanthology.org/W16-4912	https://aclanthology.org/W16-4912.pdf	Japanese Lexical Simplification for Non-Native Speakers	This paper introduces Japanese lexical simplification. Japanese lexical simplification is the task of replacing difficult words in a given sentence to produce a new sentence with simple words without changing the original meaning of the sentence. We purpose a method of supervised regression learning to estimate difficulty ordering of words with statistical features obtained from two types of Japanese corpora. For the similarity of words, we use a Japanese thesaurus and dependency-based word embeddings. Evaluation of the proposed method is performed by comparing the difficulty ordering of the words.	['Muhaimin Hading', 'Maki Sakamoto', 'Yuji Matsumoto']	2016-12-01	null	null	null	ws-2016-12	['embeddings-evaluation']	['natural-language-processing']	[ 1.04560852e-02 1.97651774e-01 1.94665000e-01 -7.94410706e-01 -5.88556468e-01 -2.50849456e-01 -6.64576888e-02 3.11676294e-01 -1.05373776e+00 1.17731714e+00 7.49627471e-01 -1.64771914e-01 2.19684556e-01 -5.40244877e-01 -3.76066118e-02 -6.98679090e-01 3.70047867e-01 2.22806156e-01 -1.33720875e-01 -6.11415684e-01 3.71181726e-01 3.42376262e-01 -1.03304136e+00 1.30638972e-01 1.03100693e+00 2.71787286e-01 7.59400487e-01 6.83199525e-01 -2.42283374e-01 4.05888915e-01 -1.16081417e+00 -5.82690418e-01 2.00344726e-01 -4.20302898e-01 -7.77065933e-01 -4.73984256e-02 2.77154118e-01 -4.68937270e-02 -2.81343251e-01 1.27961600e+00 4.58415031e-01 6.03635132e-01 7.92719305e-01 -4.52091873e-01 -1.09435534e+00 9.20982540e-01 1.13036074e-01 4.84919935e-01 4.90371257e-01 -5.06101012e-01 1.10464013e+00 -9.52453375e-01 7.08442032e-01 1.39628947e+00 7.71529078e-01 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38ef2f42-fbf5-41a8-a7e1-f69e11119a06	auto-exposure-fusion-for-single-image-shadow	2103.01255	null	https://arxiv.org/abs/2103.01255v2	https://arxiv.org/pdf/2103.01255v2.pdf	Auto-Exposure Fusion for Single-Image Shadow Removal	Shadow removal is still a challenging task due to its inherent background-dependent and spatial-variant properties, leading to unknown and diverse shadow patterns. Even powerful state-of-the-art deep neural networks could hardly recover traceless shadow-removed background. This paper proposes a new solution for this task by formulating it as an exposure fusion problem to address the challenges. Intuitively, we can first estimate multiple over-exposure images w.r.t. the input image to let the shadow regions in these images have the same color with shadow-free areas in the input image. Then, we fuse the original input with the over-exposure images to generate the final shadow-free counterpart. Nevertheless, the spatial-variant property of the shadow requires the fusion to be sufficiently `smart', that is, it should automatically select proper over-exposure pixels from different images to make the final output natural. To address this challenge, we propose the shadow-aware FusionNet that takes the shadow image as input to generate fusion weight maps across all the over-exposure images. Moreover, we propose the boundary-aware RefineNet to eliminate the remaining shadow trace further. We conduct extensive experiments on the ISTD, ISTD+, and SRD datasets to validate our method's effectiveness and show better performance in shadow regions and comparable performance in non-shadow regions over the state-of-the-art methods. We release the model and code in https://github.com/tsingqguo/exposure-fusion-shadow-removal.	['Song Wang', 'Yang Liu', 'Wei Feng', 'Hongkai Yu', 'Felix Juefei-Xu', 'Qing Guo', 'Changqing Zhou', 'Lan Fu']	2021-03-01	null	http://openaccess.thecvf.com//content/CVPR2021/html/Fu_Auto-Exposure_Fusion_for_Single-Image_Shadow_Removal_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Fu_Auto-Exposure_Fusion_for_Single-Image_Shadow_Removal_CVPR_2021_paper.pdf	cvpr-2021-1	['shadow-removal', 'image-shadow-removal']	['computer-vision', 'computer-vision']	[ 5.70371151e-01 -2.46581227e-01 4.26349759e-01 -5.07678032e-01 -3.43120426e-01 -4.06691045e-01 2.58690447e-01 -5.95657587e-01 -2.03253627e-01 7.87309587e-01 -9.28574353e-02 -5.29305816e-01 3.90470654e-01 -8.49733412e-01 -7.63710797e-01 -1.01607549e+00 2.76897162e-01 -6.27139509e-02 8.92042875e-01 -3.66784632e-01 -1.52684465e-01 4.82624829e-01 -1.46667230e+00 1.27408013e-01 1.30666077e+00 1.09641445e+00 6.56281590e-01 6.53313100e-01 -5.88574931e-02 5.45764685e-01 -8.01080227e-01 -1.50423512e-01 6.85296178e-01 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36595914-3c4d-40f5-8928-e1f602f5df27	190910063	1909.10063	null	https://arxiv.org/abs/1909.10063v1	https://arxiv.org/pdf/1909.10063v1.pdf	Algorithms for certain classes of Tamil Spelling correction	Tamil language has an agglutinative, diglossic, alpha-syllabary structure which provides a significant combinatorial explosion of morphological forms all of which are effectively used in Tamil prose, poetry from antiquity to the modern age in an unbroken chain of continuity. However, for the language understanding, spelling correction purposes some of these present challenges as out-of-dictionary words. In this paper the authors propose algorithmic techniques to handle specific problems of conjoined-words (out-of-dictionary) (transliteration)[thendRalkattRu] = [thendRal]+[kattRu] when parts are alone present in word-list in efficient way. Morphological structure of Tamil makes it necessary to depend on synthesis-analysis approach and dictionary lists will never be sufficient to truly capture the language. In this paper we have attempted to make a summary of various known algorithms for specific classes of Tamil spelling errors. We believe this collection of suggestions to improve future spelling checkers. We also note do not cover many important techniques like affix removal and other such techniques of key importance in rule-based spell checkers.	['Muthiah Annamalai', 'T. Shrinivasan']	2019-09-22	null	null	null	null	['spelling-correction']	['natural-language-processing']	[ 2.58305162e-01 -4.66922224e-01 5.51391626e-03 -5.32943159e-02 -3.61268848e-01 -1.07088530e+00 2.83491194e-01 4.58127946e-01 -6.31803572e-01 1.09160125e+00 1.39446393e-01 -9.41657305e-01 -7.68863410e-02 -6.63136005e-01 -3.01750571e-01 -3.76820117e-01 2.79669374e-01 5.79566956e-01 2.10544080e-01 -9.90767479e-01 8.25703204e-01 4.70576316e-01 -1.40964389e+00 3.46784949e-01 1.08554232e+00 5.08172847e-02 3.90405297e-01 7.45399237e-01 -4.61133868e-01 7.01750934e-01 -8.51872206e-01 -8.70522022e-01 2.79210448e-01 -5.05969405e-01 -6.23368382e-01 -2.19443738e-01 4.82813925e-01 1.59248874e-01 2.32534498e-01 1.32230210e+00 2.71533787e-01 -1.42633319e-01 6.11525357e-01 -6.01837397e-01 -6.08055055e-01 1.08377802e+00 -3.68750215e-01 4.18893129e-01 4.12888139e-01 -3.17381054e-01 1.12886119e+00 -1.02576506e+00 4.85093385e-01 1.03917611e+00 7.58107841e-01 4.75977331e-01 -6.63618147e-01 -4.26503301e-01 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8f1f0249-726d-4ae1-bae7-dcd3182d6004	policy-architectures-for-compositional	2203.05960	null	https://arxiv.org/abs/2203.05960v1	https://arxiv.org/pdf/2203.05960v1.pdf	Policy Architectures for Compositional Generalization in Control	Many tasks in control, robotics, and planning can be specified using desired goal configurations for various entities in the environment. Learning goal-conditioned policies is a natural paradigm to solve such tasks. However, current approaches struggle to learn and generalize as task complexity increases, such as variations in number of environment entities or compositions of goals. In this work, we introduce a framework for modeling entity-based compositional structure in tasks, and create suitable policy designs that can leverage this structure. Our policies, which utilize architectures like Deep Sets and Self Attention, are flexible and can be trained end-to-end without requiring any action primitives. When trained using standard reinforcement and imitation learning methods on a suite of simulated robot manipulation tasks, we find that these architectures achieve significantly higher success rates with less data. We also find these architectures enable broader and compositional generalization, producing policies that extrapolate to different numbers of entities than seen in training, and stitch together (i.e. compose) learned skills in novel ways. Videos of the results can be found at https://sites.google.com/view/comp-gen-rl.	['Aravind Rajeswaran', 'Chelsea Finn', 'Vikash Kumar', 'Allan Zhou']	2022-03-10	null	null	null	null	['robot-manipulation']	['robots']	[ 1.84061769e-02 1.53195709e-01 -9.46695432e-02 -3.46363544e-01 -4.79144365e-01 -8.43309224e-01 8.37244570e-01 -1.60730526e-01 -4.40441847e-01 9.78813827e-01 4.28051949e-01 -1.64456472e-01 -2.77699143e-01 -5.77463210e-01 -1.06595707e+00 -4.28662300e-01 -2.21439078e-01 8.46854270e-01 1.15346313e-01 -4.71149594e-01 4.15462881e-01 5.14159143e-01 -1.57814491e+00 1.05878085e-01 8.18224311e-01 4.48814303e-01 6.34684443e-01 6.69446647e-01 2.98261613e-01 8.79903138e-01 -3.30442667e-01 1.78982481e-01 5.11407197e-01 -2.71685243e-01 -8.71934891e-01 9.88865644e-02 4.38538879e-01 -4.77118999e-01 -3.58084738e-01 8.58262658e-01 3.88571739e-01 6.37003779e-01 6.74907506e-01 -1.27078366e+00 -1.07313204e+00 8.63038063e-01 1.14143312e-01 -9.97646824e-02 4.34918374e-01 7.73097396e-01 9.65111136e-01 -6.13443494e-01 7.95331538e-01 1.48803377e+00 3.82141262e-01 1.12120628e+00 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-1.07829535e+00 -1.73408762e-01 5.05685270e-01 3.80531222e-01 -9.71326292e-01 -8.18164498e-02 9.95384715e-03]	[4.358019828796387, 1.0232455730438232]
903f7db0-95ee-4b4f-8d48-5a479575516a	chinese-lexical-analysis-with-deep-bi-gru-crf	1807.01882	null	http://arxiv.org/abs/1807.01882v1	http://arxiv.org/pdf/1807.01882v1.pdf	Chinese Lexical Analysis with Deep Bi-GRU-CRF Network	Lexical analysis is believed to be a crucial step towards natural language understanding and has been widely studied. Recent years, end-to-end lexical analysis models with recurrent neural networks have gained increasing attention. In this report, we introduce a deep Bi-GRU-CRF network that jointly models word segmentation, part-of-speech tagging and named entity recognition tasks. We trained the model using several massive corpus pre-tagged by our best Chinese lexical analysis tool, together with a small, yet high-quality human annotated corpus. We conducted balanced sampling between different corpora to guarantee the influence of human annotations, and fine-tune the CRF decoding layer regularly during the training progress. As evaluated by linguistic experts, the model achieved a 95.5% accuracy on the test set, roughly 13% relative error reduction over our (previously) best Chinese lexical analysis tool. The model is computationally efficient, achieving the speed of 2.3K characters per second with one thread.	['Shuqi Sun', 'Ke Sun', 'Zhenyu Jiao']	2018-07-05	null	null	null	null	['lexical-analysis']	['natural-language-processing']	[ 4.45868634e-02 9.68525335e-02 -2.73233950e-01 -3.31812799e-01 -1.06512821e+00 -6.01027966e-01 1.84927240e-01 2.45563969e-01 -1.14200568e+00 7.77308822e-01 1.24371506e-01 -6.62985384e-01 7.58137047e-01 -6.48805082e-01 -4.37247247e-01 -4.56067264e-01 1.60822675e-01 6.46457672e-01 2.03989252e-01 2.16214880e-01 2.08887130e-01 1.48060605e-01 -9.40568984e-01 2.20887482e-01 1.06616068e+00 7.07707942e-01 4.34186906e-01 7.91587234e-01 -5.72549939e-01 8.40884030e-01 -8.10496926e-01 -5.89364588e-01 -2.01889902e-01 -2.16231674e-01 -1.09820449e+00 -1.33211032e-01 -1.88662335e-01 2.53741723e-02 3.07543635e-01 1.10655177e+00 4.48253334e-01 8.81136358e-02 2.28752971e-01 -3.43240768e-01 -5.94515204e-01 1.32611024e+00 -4.61995482e-01 2.58303553e-01 -1.96359437e-02 -2.66180426e-01 1.08531022e+00 -8.84502649e-01 8.36790264e-01 7.25149572e-01 6.13062799e-01 6.66533470e-01 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5f617432-55f5-4093-9574-e98ba85a1d08	generative-voxelnet-learning-energy-based	2012.13522	null	https://arxiv.org/abs/2012.13522v1	https://arxiv.org/pdf/2012.13522v1.pdf	Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis	3D data that contains rich geometry information of objects and scenes is valuable for understanding 3D physical world. With the recent emergence of large-scale 3D datasets, it becomes increasingly crucial to have a powerful 3D generative model for 3D shape synthesis and analysis. This paper proposes a deep 3D energy-based model to represent volumetric shapes. The maximum likelihood training of the model follows an "analysis by synthesis" scheme. The benefits of the proposed model are six-fold: first, unlike GANs and VAEs, the model training does not rely on any auxiliary models; second, the model can synthesize realistic 3D shapes by Markov chain Monte Carlo (MCMC); third, the conditional model can be applied to 3D object recovery and super resolution; fourth, the model can serve as a building block in a multi-grid modeling and sampling framework for high resolution 3D shape synthesis; fifth, the model can be used to train a 3D generator via MCMC teaching; sixth, the unsupervisedly trained model provides a powerful feature extractor for 3D data, which is useful for 3D object classification. Experiments demonstrate that the proposed model can generate high-quality 3D shape patterns and can be useful for a wide variety of 3D shape analysis.	['Ying Nian Wu', 'Song-Chun Zhu', 'Wenguan Wang', 'Ruiqi Gao', 'Zilong Zheng', 'Jianwen Xie']	2020-12-25	null	null	null	null	['3d-object-classification']	['computer-vision']	[-1.39029965e-01 -6.89157546e-02 2.20796913e-01 -1.72072813e-01 -6.40132904e-01 -3.22599590e-01 7.96182215e-01 -1.25312179e-01 4.10608500e-01 5.57363749e-01 1.65739954e-01 -7.29780942e-02 4.66621928e-02 -1.39568210e+00 -6.40036404e-01 -8.94378006e-01 2.25548476e-01 1.00481796e+00 3.41793358e-01 -9.85103846e-02 9.13141444e-02 1.34720063e+00 -1.47742820e+00 1.32157393e-02 7.79629648e-01 1.17243063e+00 4.28479135e-01 2.65299588e-01 -5.08427441e-01 3.15020919e-01 -1.17154904e-01 2.98561547e-02 2.70956457e-01 -5.65431833e-01 -2.38854706e-01 3.21818590e-01 -1.64254248e-01 -3.91422570e-01 -1.45917594e-01 7.64631748e-01 6.70234382e-01 1.34175375e-01 1.29212701e+00 -7.86677957e-01 -5.65142870e-01 -1.71315998e-01 -5.72905183e-01 -3.49001288e-01 5.25402017e-02 2.09394038e-01 5.00415444e-01 -9.50240970e-01 6.06251299e-01 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c2fa6b89-ba3b-4419-941d-1092083aec63	open-images-v5-text-annotation-and-yet	2106.12326	null	https://arxiv.org/abs/2106.12326v1	https://arxiv.org/pdf/2106.12326v1.pdf	Open Images V5 Text Annotation and Yet Another Mask Text Spotter	A large scale human-labeled dataset plays an important role in creating high quality deep learning models. In this paper we present text annotation for Open Images V5 dataset. To our knowledge it is the largest among publicly available manually created text annotations. Having this annotation we trained a simple Mask-RCNN-based network, referred as Yet Another Mask Text Spotter (YAMTS), which achieves competitive performance or even outperforms current state-of-the-art approaches in some cases on ICDAR2013, ICDAR2015 and Total-Text datasets. Code for text spotting model available online at: https://github.com/openvinotoolkit/training_extensions. The model can be exported to OpenVINO-format and run on Intel CPUs.	['Vladislav Sovrasov', 'Sergei Nosov', 'Ilya Krylov']	2021-06-23	null	null	null	null	['text-spotting', 'text-annotation']	['computer-vision', 'natural-language-processing']	[ 2.17900157e-01 2.26243272e-01 -2.09843352e-01 -4.38383877e-01 -9.88786757e-01 -5.12252450e-01 7.08167434e-01 2.70591732e-02 -6.51977420e-01 5.16751111e-01 2.35665247e-01 -3.31866950e-01 4.41952288e-01 -4.00253922e-01 -6.90238476e-01 -3.71846616e-01 6.41626418e-01 1.00798106e+00 3.37111115e-01 2.84606993e-01 -9.79239494e-03 6.94278255e-02 -1.32906389e+00 7.34872282e-01 4.50926632e-01 9.59940195e-01 4.47013229e-01 6.63898230e-01 -4.86057341e-01 9.34661388e-01 -4.57458109e-01 -6.69336438e-01 4.41093206e-01 -1.88742220e-01 -1.09431112e+00 -7.43504316e-02 7.74940968e-01 -3.48916024e-01 -5.38348675e-01 8.43310058e-01 7.02532351e-01 -3.02020013e-01 3.66010249e-01 -9.37851131e-01 -7.87178457e-01 1.20564008e+00 -5.91078579e-01 1.48794815e-01 -1.25577420e-01 1.11514077e-01 1.04298961e+00 -1.22666824e+00 9.58937585e-01 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880d9901-4d5f-476d-954c-612090bb0660	multilingual-holistic-bias-extending	2305.13198	null	https://arxiv.org/abs/2305.13198v1	https://arxiv.org/pdf/2305.13198v1.pdf	Multilingual Holistic Bias: Extending Descriptors and Patterns to Unveil Demographic Biases in Languages at Scale	We introduce a multilingual extension of the HOLISTICBIAS dataset, the largest English template-based taxonomy of textual people references: MULTILINGUALHOLISTICBIAS. This extension consists of 20,459 sentences in 50 languages distributed across all 13 demographic axes. Source sentences are built from combinations of 118 demographic descriptors and three patterns, excluding nonsensical combinations. Multilingual translations include alternatives for gendered languages that cover gendered translations when there is ambiguity in English. Our benchmark is intended to uncover demographic imbalances and be the tool to quantify mitigations towards them. Our initial findings show that translation quality for EN-to-XX translations is an average of 8 spBLEU better when evaluating with the masculine human reference compared to feminine. In the opposite direction, XX-to-EN, we compare the robustness of the model when the source input only differs in gender (masculine or feminine) and masculine translations are an average of almost 4 spBLEU better than feminine. When embedding sentences to a joint multilingual sentence representations space, we find that for most languages masculine translations are significantly closer to the English neutral sentences when embedded.	['Carleigh Wood', 'Daniel Licht', 'Cynthia Gao', 'Elahe Kalbassi', 'Christophe Ropers', 'Prangthip Hansanti', 'Eric Smith', 'Pierre Andrews', 'Marta R. Costa-jussà']	2023-05-22	null	null	null	null	['joint-multilingual-sentence-representations']	['natural-language-processing']	[-2.99001813e-01 4.39675674e-02 -5.56348324e-01 -4.18614328e-01 -7.56374419e-01 -8.43348384e-01 1.20300031e+00 3.70151073e-01 -8.71112704e-01 1.18154657e+00 1.11557639e+00 -5.07077694e-01 9.98762846e-02 -7.14284062e-01 -4.14434612e-01 -2.71490753e-01 6.11268222e-01 9.48638499e-01 -5.54483593e-01 -8.35879862e-01 2.08371460e-01 -4.50978894e-03 -1.12289762e+00 9.12632421e-02 8.91678452e-01 2.22648516e-01 -3.93102676e-01 4.63697731e-01 -1.09981157e-01 2.68877357e-01 -7.54485905e-01 -1.22982228e+00 1.47730842e-01 -3.04719269e-01 -8.10267448e-01 -6.56593919e-01 9.89646852e-01 1.58127919e-01 -2.63733685e-01 1.02672029e+00 9.31121767e-01 -4.77379531e-01 9.80050087e-01 -8.90733838e-01 -9.43282366e-01 1.30405641e+00 -6.14659846e-01 5.74424982e-01 6.97238982e-01 4.78330776e-02 1.15257418e+00 -1.21793044e+00 1.18336117e+00 1.97218144e+00 7.62510717e-01 4.64825839e-01 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57a3df99-0034-45b3-98d5-873ccf406a0f	progressive-pose-attention-transfer-for	1904.03349	null	https://arxiv.org/abs/1904.03349v3	https://arxiv.org/pdf/1904.03349v3.pdf	Progressive Pose Attention Transfer for Person Image Generation	This paper proposes a new generative adversarial network for pose transfer, i.e., transferring the pose of a given person to a target pose. The generator of the network comprises a sequence of Pose-Attentional Transfer Blocks that each transfers certain regions it attends to, generating the person image progressively. Compared with those in previous works, our generated person images possess better appearance consistency and shape consistency with the input images, thus significantly more realistic-looking. The efficacy and efficiency of the proposed network are validated both qualitatively and quantitatively on Market-1501 and DeepFashion. Furthermore, the proposed architecture can generate training images for person re-identification, alleviating data insufficiency. Codes and models are available at: https://github.com/tengteng95/Pose-Transfer.git.	['Zhen Zhu', 'Miao Yu', 'Bofei Wang', 'Baoguang Shi', 'Xiang Bai', 'Tengteng Huang']	2019-04-06	progressive-pose-attention-transfer-for-1	http://openaccess.thecvf.com/content_CVPR_2019/html/Zhu_Progressive_Pose_Attention_Transfer_for_Person_Image_Generation_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhu_Progressive_Pose_Attention_Transfer_for_Person_Image_Generation_CVPR_2019_paper.pdf	cvpr-2019-6	['pose-transfer']	['computer-vision']	[ 2.53363345e-02 6.72228485e-02 3.43988270e-01 -3.77101064e-01 -4.33228105e-01 -6.13966346e-01 6.01751983e-01 -6.72050595e-01 -3.01335394e-01 8.46540451e-01 2.10689411e-01 2.99486816e-01 3.08258325e-01 -8.79691899e-01 -9.06661630e-01 -5.89801788e-01 3.20650548e-01 6.02636576e-01 -1.65203065e-01 -1.42640755e-01 -1.90265581e-01 4.27090973e-01 -1.31592298e+00 4.73441035e-02 7.29609132e-01 6.68113828e-01 6.93412200e-02 6.06472969e-01 5.86100638e-01 2.76659071e-01 -9.79883194e-01 -9.11131322e-01 4.08802599e-01 -4.80323046e-01 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83c93ae5-fca8-484d-a417-c839ddf08140	fast-3d-line-segment-detection-from	1901.02532	null	http://arxiv.org/abs/1901.02532v1	http://arxiv.org/pdf/1901.02532v1.pdf	Fast 3D Line Segment Detection From Unorganized Point Cloud	This paper presents a very simple but efficient algorithm for 3D line segment detection from large scale unorganized point cloud. Unlike traditional methods which usually extract 3D edge points first and then link them to fit for 3D line segments, we propose a very simple 3D line segment detection algorithm based on point cloud segmentation and 2D line detection. Given the input unorganized point cloud, three steps are performed to detect 3D line segments. Firstly, the point cloud is segmented into 3D planes via region growing and region merging. Secondly, for each 3D plane, all the points belonging to it are projected onto the plane itself to form a 2D image, which is followed by 2D contour extraction and Least Square Fitting to get the 2D line segments. Those 2D line segments are then re-projected onto the 3D plane to get the corresponding 3D line segments. Finally, a post-processing procedure is proposed to eliminate outliers and merge adjacent 3D line segments. Experiments on several public datasets demonstrate the efficiency and robustness of our method. More results and the C++ source code of the proposed algorithm are publicly available at https://github.com/xiaohulugo/3DLineDetection.	['Yahui Liu', 'Xiaohu Lu', 'Kai Li']	2019-01-08	null	null	null	null	['line-segment-detection', 'line-detection']	['computer-vision', 'computer-vision']	[-8.95812958e-02 -3.12126189e-01 -1.03082575e-01 -4.34936286e-04 -5.54710329e-01 -6.09406710e-01 5.87363169e-02 4.90181684e-01 -1.45883232e-01 6.13320321e-02 -5.98866045e-01 -4.08732027e-01 2.70115227e-01 -7.41014361e-01 -5.43789089e-01 -3.97977740e-01 -9.10619274e-02 6.74917698e-01 6.53180003e-01 2.24463195e-01 7.04655051e-01 1.35602641e+00 -9.76082027e-01 -5.63779950e-01 9.51463580e-01 1.01299465e+00 -3.30751151e-01 4.59371746e-01 -5.79991519e-01 -5.12183428e-01 -2.74930775e-01 -5.11220610e-03 7.41637468e-01 -1.30336508e-01 -2.81103849e-01 6.47598207e-01 2.39156142e-01 -4.58335429e-01 1.16672032e-01 1.07812309e+00 3.22721839e-01 -1.23200297e-01 7.46531427e-01 -1.31862199e+00 4.61217463e-02 -3.34155142e-01 -1.40227807e+00 -2.15145379e-01 5.01714230e-01 -6.33014813e-02 4.47081625e-01 -1.43815410e+00 5.29771686e-01 1.07456219e+00 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3b999da2-4c07-43e1-8443-06a769a4a64d	cmx-cross-modal-fusion-for-rgb-x-semantic	2203.04838	null	https://arxiv.org/abs/2203.04838v3	https://arxiv.org/pdf/2203.04838v3.pdf	CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers	Scene understanding based on image segmentation is a crucial component for autonomous vehicles. Pixel-wise semantic segmentation of RGB images can be advanced by exploiting informative features from the supplementary modality (X-modality). In this work, we propose CMX, a transformer-based cross-modal fusion framework for RGB-X semantic segmentation. To generalize to different sensing modalities encompassing various supplements and uncertainties, we consider that comprehensive cross-modal interactions should be provided. CMX is built with two streams to extract features from RGB images and the X-modality. In each feature extraction stage, we design a Cross-Modal Feature Rectification Module (CM-FRM) to calibrate the feature of the current modality by combining the feature from the other modality, in spatial- and channel-wise dimensions. With rectified feature pairs, we deploy a Feature Fusion Module (FFM) to mix them for the final semantic prediction. FFM is constructed with a cross-attention mechanism, which enables exchange of long-range contexts, enhancing bi-modal features globally. Extensive experiments show that CMX generalizes to diverse multi-modal combinations, achieving state-of-the-art performances on five RGB-Depth benchmarks, as well as RGB-Thermal, RGB-Polarization, and RGB-LiDAR datasets. Besides, to investigate the generalizability to dense-sparse data fusion, we establish an RGB-Event semantic segmentation benchmark based on the EventScape dataset, on which CMX sets the new state-of-the-art. The source code of CMX is publicly available at https://github.com/huaaaliu/RGBX_Semantic_Segmentation.	['Ruiping Liu', 'Huayao Liu', 'Jiaming Zhang', 'Rainer Stiefelhagen', 'Xinxin Hu', 'Kailun Yang']	2022-03-09	null	null	null	null	['multispectral-object-detection', 'thermal-image-segmentation']	['computer-vision', 'computer-vision']	[ 3.78144503e-01 -6.33884519e-02 -1.07812181e-01 -8.02900374e-01 -1.21094823e+00 -3.23038071e-01 6.04600251e-01 -2.73001283e-01 -4.55764621e-01 4.01379406e-01 -2.27190480e-02 -1.25286683e-01 -2.35356212e-01 -9.91047263e-01 -8.65955889e-01 -8.14049304e-01 5.18809080e-01 2.08667353e-01 1.81169406e-01 -2.87366420e-01 3.57052381e-03 3.37517232e-01 -1.88433766e+00 2.40282103e-01 9.80906367e-01 1.71451616e+00 3.11493754e-01 2.06493303e-01 -4.28311259e-01 3.13977957e-01 1.43358260e-01 -3.44772607e-01 4.50117558e-01 -1.67143494e-01 -7.46308029e-01 1.30475923e-01 2.98250973e-01 -1.01278722e-01 -2.94871747e-01 9.75762010e-01 3.45655113e-01 1.61816150e-01 3.24620783e-01 -1.63511944e+00 -4.19739783e-01 4.13099974e-01 -6.35686994e-01 -1.29916519e-01 1.01287477e-01 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963f1f1f-b783-4e79-a66f-5a2fafd27e1e	deeplung-3d-deep-convolutional-nets-for	1709.05538	null	http://arxiv.org/abs/1709.05538v1	http://arxiv.org/pdf/1709.05538v1.pdf	DeepLung: 3D Deep Convolutional Nets for Automated Pulmonary Nodule Detection and Classification	In this work, we present a fully automated lung CT cancer diagnosis system, DeepLung. DeepLung contains two parts, nodule detection and classification. Considering the 3D nature of lung CT data, two 3D networks are designed for the nodule detection and classification respectively. Specifically, a 3D Faster R-CNN is designed for nodule detection with a U-net-like encoder-decoder structure to effectively learn nodule features. For nodule classification, gradient boosting machine (GBM) with 3D dual path network (DPN) features is proposed. The nodule classification subnetwork is validated on a public dataset from LIDC-IDRI, on which it achieves better performance than state-of-the-art approaches, and surpasses the average performance of four experienced doctors. For the DeepLung system, candidate nodules are detected first by the nodule detection subnetwork, and nodule diagnosis is conducted by the classification subnetwork. Extensive experimental results demonstrate the DeepLung is comparable to the experienced doctors both for the nodule-level and patient-level diagnosis on the LIDC-IDRI dataset.	['Chaochun Liu', 'Wentao Zhu', 'Wei Fan', 'Xiaohui Xie']	2017-09-16	null	null	null	null	['automated-pulmonary-nodule-detection-and']	['medical']	[-2.01566666e-01 6.03846788e-01 -6.02200091e-01 -2.11415663e-01 -1.03918839e+00 -1.24919206e-01 2.91501820e-01 -3.49706262e-01 -2.47651413e-01 2.35874474e-01 2.80636072e-01 -5.89531481e-01 7.48576820e-02 -7.95233727e-01 -3.91233116e-01 -7.19765604e-01 8.98343846e-02 8.75401974e-01 7.20158577e-01 2.74918169e-01 -6.00518346e-01 4.94763672e-01 -9.97420967e-01 5.51755190e-01 4.10904258e-01 1.25632310e+00 4.02655870e-01 8.57749164e-01 1.84629411e-01 1.42575383e+00 1.35767132e-01 -1.62504956e-01 3.78255457e-01 -5.36517322e-01 -9.97330487e-01 3.06239694e-01 8.41754004e-02 -7.61430323e-01 -6.63291991e-01 7.16010392e-01 6.13725245e-01 -7.41560459e-01 9.02023613e-01 -6.98592901e-01 -4.23823178e-01 6.10169053e-01 -3.15619528e-01 4.85705808e-02 -3.91092211e-01 2.36678421e-01 1.09661686e+00 -1.01016402e+00 4.33775932e-01 6.63213968e-01 8.87903988e-01 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8924f7ae-ccf3-4d7a-8ca6-7a81165025f7	thinking-hallucination-for-video-captioning	2209.13853	null	https://arxiv.org/abs/2209.13853v1	https://arxiv.org/pdf/2209.13853v1.pdf	Thinking Hallucination for Video Captioning	With the advent of rich visual representations and pre-trained language models, video captioning has seen continuous improvement over time. Despite the performance improvement, video captioning models are prone to hallucination. Hallucination refers to the generation of highly pathological descriptions that are detached from the source material. In video captioning, there are two kinds of hallucination: object and action hallucination. Instead of endeavoring to learn better representations of a video, in this work, we investigate the fundamental sources of the hallucination problem. We identify three main factors: (i) inadequate visual features extracted from pre-trained models, (ii) improper influences of source and target contexts during multi-modal fusion, and (iii) exposure bias in the training strategy. To alleviate these problems, we propose two robust solutions: (a) the introduction of auxiliary heads trained in multi-label settings on top of the extracted visual features and (b) the addition of context gates, which dynamically select the features during fusion. The standard evaluation metrics for video captioning measures similarity with ground truth captions and do not adequately capture object and action relevance. To this end, we propose a new metric, COAHA (caption object and action hallucination assessment), which assesses the degree of hallucination. Our method achieves state-of-the-art performance on the MSR-Video to Text (MSR-VTT) and the Microsoft Research Video Description Corpus (MSVD) datasets, especially by a massive margin in CIDEr score.	['Partha Pratim Mohanta', 'Nasib Ullah']	2022-09-28	null	null	null	null	['video-description']	['computer-vision']	[ 5.35967648e-01 -2.70157233e-02 -9.26687866e-02 -1.11355223e-01 -1.05295157e+00 -5.22157133e-01 7.93393552e-01 1.70669332e-01 -2.28259563e-01 7.16631770e-01 8.42416465e-01 7.34021366e-02 3.05980563e-01 -1.42643943e-01 -8.19659472e-01 -3.96429718e-01 3.18267107e-01 2.93080926e-01 9.36665684e-02 -1.65331990e-01 2.06149131e-01 1.41223922e-01 -1.63970315e+00 7.89500833e-01 8.31680536e-01 9.55553472e-01 4.18569744e-01 6.49411023e-01 2.42658518e-02 1.19949198e+00 -6.57151699e-01 -4.77248996e-01 -1.15169145e-01 -8.00982416e-01 -6.43895209e-01 5.46875119e-01 4.39924031e-01 -3.96044105e-01 -5.48125267e-01 1.07079828e+00 6.63195670e-01 3.60089540e-02 6.00146234e-01 -1.67400944e+00 -8.25296402e-01 3.89955580e-01 -4.66967553e-01 3.10764283e-01 8.54316175e-01 4.30104136e-01 7.06396341e-01 -1.16771710e+00 7.69007027e-01 1.31694710e+00 3.49763364e-01 7.28419781e-01 -1.01007211e+00 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-3.47586930e-01 8.25256348e-01 4.39939529e-01 -8.35785210e-01 -6.37793720e-01 2.47062713e-01]	[10.681403160095215, 0.7942745685577393]
42554e91-ef8e-41df-82a2-c2c9aaa8c843	long-tailed-continual-learning-for-visual	2307.00183	null	https://arxiv.org/abs/2307.00183v1	https://arxiv.org/pdf/2307.00183v1.pdf	Long-Tailed Continual Learning For Visual Food Recognition	Deep learning based food recognition has achieved remarkable progress in predicting food types given an eating occasion image. However, there are two major obstacles that hinder deployment in real world scenario. First, as new foods appear sequentially overtime, a trained model needs to learn the new classes continuously without causing catastrophic forgetting for already learned knowledge of existing food types. Second, the distribution of food images in real life is usually long-tailed as a small number of popular food types are consumed more frequently than others, which can vary in different populations. This requires the food recognition method to learn from class-imbalanced data by improving the generalization ability on instance-rare food classes. In this work, we focus on long-tailed continual learning and aim to address both aforementioned challenges. As existing long-tailed food image datasets only consider healthy people population, we introduce two new benchmark food image datasets, VFN-INSULIN and VFN-T2D, which exhibits on the real world food consumption for insulin takers and individuals with type 2 diabetes without taking insulin, respectively. We propose a novel end-to-end framework for long-tailed continual learning, which effectively addresses the catastrophic forgetting by applying an additional predictor for knowledge distillation to avoid misalignment of representation during continual learning. We also introduce a novel data augmentation technique by integrating class-activation-map (CAM) and CutMix, which significantly improves the generalization ability for instance-rare food classes to address the class-imbalance issue. The proposed method show promising performance with large margin improvements compared with existing methods.	['Fengqing Zhu', 'Heather A. Eicher-Miller', 'Jack Ma', 'Luotao Lin', 'Jiangpeng He']	2023-07-01	null	null	null	null	['food-recognition', 'continual-learning']	['computer-vision', 'methodology']	[ 1.95157558e-01 -3.09610844e-01 -4.18531030e-01 -5.27275860e-01 -2.79737949e-01 -3.06861699e-01 -9.21840779e-03 7.50042081e-01 -4.38250929e-01 6.36729777e-01 8.66642371e-02 8.63742828e-02 -7.85179716e-03 -9.72903728e-01 -1.11080289e+00 -5.81756830e-01 -1.59671307e-01 3.51659447e-01 -8.78194720e-02 -7.95514435e-02 -3.74999821e-01 -3.00698429e-01 -1.60718882e+00 4.10251915e-01 1.13742840e+00 1.16080689e+00 1.43121690e-01 3.86953831e-01 -2.33516648e-01 6.16920471e-01 -4.32409525e-01 -2.54766881e-01 4.04102981e-01 -4.06047344e-01 -1.24168016e-01 1.94809765e-01 6.14380121e-01 -3.73251051e-01 -2.18649477e-01 1.26833713e+00 5.49177229e-01 6.68562800e-02 6.95699692e-01 -1.25165403e+00 -1.21786094e+00 7.26501346e-01 -9.19066548e-01 3.89363945e-01 6.15835674e-02 4.80414301e-01 3.04067433e-01 -6.08997822e-01 -7.22850394e-03 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1cde2ef9-e9af-4f0d-9b46-361f178b7ed5	subdomain-adaptation-with-manifolds	2005.03229	null	https://arxiv.org/abs/2005.03229v1	https://arxiv.org/pdf/2005.03229v1.pdf	Subdomain Adaptation with Manifolds Discrepancy Alignment	Reducing domain divergence is a key step in transfer learning problems. Existing works focus on the minimization of global domain divergence. However, two domains may consist of several shared subdomains, and differ from each other in each subdomain. In this paper, we take the local divergence of subdomains into account in transfer. Specifically, we propose to use low-dimensional manifold to represent subdomain, and align the local data distribution discrepancy in each manifold across domains. A Manifold Maximum Mean Discrepancy (M3D) is developed to measure the local distribution discrepancy in each manifold. We then propose a general framework, called Transfer with Manifolds Discrepancy Alignment (TMDA), to couple the discovery of data manifolds with the minimization of M3D. We instantiate TMDA in the subspace learning case considering both the linear and nonlinear mappings. We also instantiate TMDA in the deep learning framework. Extensive experimental studies demonstrate that TMDA is a promising method for various transfer learning tasks.	['Tze-Yun Leong', 'Pengfei Wei', 'Yiping Ke', 'Xinghua Qu']	2020-05-06	null	null	null	null	['subdomain-adaptation']	['methodology']	[-3.50924551e-01 -1.41522765e-01 -4.70078103e-02 -3.42666209e-01 -9.30476665e-01 -5.64554751e-01 5.49400687e-01 -1.76708087e-01 -4.04036269e-02 7.50294089e-01 1.77393794e-01 -1.28918467e-03 -4.23383921e-01 -8.00377131e-01 -7.60867417e-01 -7.90674686e-01 1.30000129e-01 4.71795440e-01 -8.09773058e-02 1.06505211e-02 1.16241053e-01 3.60281289e-01 -1.05751550e+00 2.27458403e-02 1.39228439e+00 7.92959511e-01 5.88450246e-02 -4.13402393e-02 -2.00164139e-01 1.10107444e-01 -2.70390093e-01 2.37323176e-02 4.32234108e-01 -7.87599325e-01 -9.38827515e-01 4.89331782e-01 4.97427046e-01 -3.22418898e-01 -1.19194254e-01 1.30404258e+00 4.72693771e-01 2.96634555e-01 1.29535997e+00 -1.66572928e+00 -6.95134819e-01 6.35253713e-02 -7.59532571e-01 1.88780412e-01 -3.58444303e-02 -1.45801201e-01 8.09658945e-01 -1.21371603e+00 5.14960766e-01 1.28392327e+00 4.93754148e-01 4.42666948e-01 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-9.80883837e-01 6.08692884e-01 5.27991235e-01 -8.04307580e-01 -4.22221333e-01 2.68425405e-01]	[10.358701705932617, 3.120917320251465]
7a61951c-135f-4913-b434-59f876a5f148	topological-relational-learning-on-graphs	2110.15529	null	https://arxiv.org/abs/2110.15529v1	https://arxiv.org/pdf/2110.15529v1.pdf	Topological Relational Learning on Graphs	Graph neural networks (GNNs) have emerged as a powerful tool for graph classification and representation learning. However, GNNs tend to suffer from over-smoothing problems and are vulnerable to graph perturbations. To address these challenges, we propose a novel topological neural framework of topological relational inference (TRI) which allows for integrating higher-order graph information to GNNs and for systematically learning a local graph structure. The key idea is to rewire the original graph by using the persistent homology of the small neighborhoods of nodes and then to incorporate the extracted topological summaries as the side information into the local algorithm. As a result, the new framework enables us to harness both the conventional information on the graph structure and information on the graph higher order topological properties. We derive theoretical stability guarantees for the new local topological representation and discuss their implications on the graph algebraic connectivity. The experimental results on node classification tasks demonstrate that the new TRI-GNN outperforms all 14 state-of-the-art baselines on 6 out 7 graphs and exhibit higher robustness to perturbations, yielding up to 10\% better performance under noisy scenarios.	['Yulia R. Gel', 'Baris Coskunuzer', 'Yuzhou Chen']	2021-10-29	null	http://proceedings.neurips.cc/paper/2021/hash/e334fd9dac68f13fa1a57796148cf812-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/e334fd9dac68f13fa1a57796148cf812-Paper.pdf	neurips-2021-12	['relational-reasoning']	['natural-language-processing']	[ 1.02058973e-03 3.98410857e-01 -2.81908870e-01 -1.91409081e-01 -1.17657065e-01 -4.97780055e-01 6.07928753e-01 4.20480758e-01 9.67037007e-02 4.43272442e-01 1.52557507e-01 -4.49961782e-01 -3.38116318e-01 -1.26645339e+00 -9.32766855e-01 -8.44821751e-01 -7.39400148e-01 3.21274161e-01 3.57070982e-01 -3.83601248e-01 1.35866821e-01 7.27154791e-01 -1.13012815e+00 -8.88057351e-02 9.83635664e-01 8.81133616e-01 -1.21015385e-01 5.27395248e-01 6.11387612e-03 6.87640011e-01 -3.87523383e-01 -2.99496472e-01 2.19420582e-01 -8.29600841e-02 -7.73104727e-01 -2.81661063e-01 8.33901584e-01 -4.68654186e-03 -1.03940403e+00 1.29801941e+00 4.06181365e-01 3.08859348e-01 5.55463314e-01 -1.01324666e+00 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157afc10-2e38-461d-9816-36d7e31da023	topology-free-type-structures-with	2212.07246	null	https://arxiv.org/abs/2212.07246v2	https://arxiv.org/pdf/2212.07246v2.pdf	Topology-Free Type Structures with Conditioning Events	We establish the existence of the universal type structure in presence of conditioning events without any topological assumption, namely, a type structure that is terminal, belief-complete, and non-redundant, by performing a construction \`a la Heifetz & Samet (1998). In doing so, we answer affirmatively to a longstanding conjecture made by Battigalli & Siniscalchi (1999) concerning the possibility of performing such a construction with conditioning events. In particular, we obtain the result by exploiting arguments from category theory and the theory of coalgebras, thus, making explicit the mathematical structure underlining all the constructions of large interactive structures and obtaining the belief-completeness of the structure as an immediate corollary of known results from these fields.	['Pierfrancesco Guarino']	2022-12-14	null	null	null	null	['type']	['speech']	[-9.51648876e-02 9.24105644e-01 1.72707781e-01 2.04319227e-03 1.74768627e-01 -8.91140759e-01 9.32965517e-01 3.09565216e-01 -2.37399697e-01 7.66843438e-01 1.99264303e-01 -7.81566441e-01 -5.26519060e-01 -1.24390852e+00 -8.02316964e-01 -8.12368095e-01 -5.21483779e-01 3.62714946e-01 4.35694188e-01 -3.97934169e-01 4.07472730e-01 3.99956048e-01 -1.63648331e+00 -1.90732852e-02 8.45951080e-01 9.87806380e-01 -4.19361927e-02 5.48484623e-01 -1.43675292e-02 1.09390664e+00 -1.22458218e-02 -6.82098091e-01 -2.79022027e-02 -5.36163986e-01 -1.38054276e+00 4.13421318e-02 -1.87761374e-02 1.86726794e-01 -1.91646144e-01 1.12949133e+00 -3.39491278e-01 1.88057601e-01 7.03963578e-01 -1.02687299e+00 -5.88872552e-01 8.09258223e-01 1.51258912e-02 -6.63093552e-02 4.00913924e-01 -1.12173840e-01 1.10888565e+00 -8.69811594e-01 7.77238846e-01 1.01955652e+00 4.38746750e-01 2.30220795e-01 -1.60496092e+00 7.08643794e-02 -1.37866467e-01 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53d6c816-38b6-4d38-a85c-fa909cb73436	semi-supervised-image-classification-with	2108.13673	null	https://arxiv.org/abs/2108.13673v1	https://arxiv.org/pdf/2108.13673v1.pdf	Semi-supervised Image Classification with Grad-CAM Consistency	Consistency training, which exploits both supervised and unsupervised learning with different augmentations on image, is an effective method of utilizing unlabeled data in semi-supervised learning (SSL) manner. Here, we present another version of the method with Grad-CAM consistency loss, so it can be utilized in training model with better generalization and adjustability. We show that our method improved the baseline ResNet model with at most 1.44 % and 0.31 $\pm$ 0.59 %p accuracy improvement on average with CIFAR-10 dataset. We conducted ablation study comparing to using only psuedo-label for consistency training. Also, we argue that our method can adjust in different environments when targeted to different units in the model. The code is available: https://github.com/gimme1dollar/gradcam-consistency-semi-sup.	['Seunghyuk Cho', 'Juyong Lee']	2021-08-31	null	null	null	null	['semi-supervised-image-classification']	['computer-vision']	[-1.73592776e-01 4.15185153e-01 -4.03357774e-01 -7.94921458e-01 -9.49430585e-01 -5.90581238e-01 2.32402995e-01 -8.75410885e-02 -7.62367189e-01 1.11777949e+00 -5.34059517e-02 -2.87430793e-01 8.17974284e-02 -4.59746212e-01 -1.09161794e+00 -6.68427587e-01 9.87100303e-02 3.30900788e-01 9.28023010e-02 6.08438626e-02 -5.21429740e-02 1.13941781e-01 -9.65339124e-01 2.97921095e-02 8.23498070e-01 9.50455904e-01 2.26987705e-01 4.07635927e-01 3.20702106e-01 1.12849736e+00 -3.84400398e-01 -4.07160044e-01 5.01433611e-01 -3.20421785e-01 -7.97206283e-01 -1.46639505e-02 1.02005863e+00 -2.59108484e-01 -3.18891495e-01 1.36237764e+00 4.67772752e-01 1.76642150e-01 4.68457729e-01 -1.25260401e+00 -1.06213474e+00 9.32064176e-01 -6.76747501e-01 5.48464179e-01 -4.52140838e-01 1.93040166e-02 8.59559298e-01 -1.02522206e+00 4.41679895e-01 8.11282992e-01 8.48363578e-01 8.10118675e-01 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86dca52f-faa1-474d-b91b-b50b27a7a1d9	an-interpretable-model-for-scene-graph	1811.09543	null	http://arxiv.org/abs/1811.09543v1	http://arxiv.org/pdf/1811.09543v1.pdf	An Interpretable Model for Scene Graph Generation	We propose an efficient and interpretable scene graph generator. We consider three types of features: visual, spatial and semantic, and we use a late fusion strategy such that each feature's contribution can be explicitly investigated. We study the key factors about these features that have the most impact on the performance, and also visualize the learned visual features for relationships and investigate the efficacy of our model. We won the champion of the OpenImages Visual Relationship Detection Challenge on Kaggle, where we outperform the 2nd place by 5\% (20\% relatively). We believe an accurate scene graph generator is a fundamental stepping stone for higher-level vision-language tasks such as image captioning and visual QA, since it provides a semantic, structured comprehension of an image that is beyond pixels and objects.	['Ahmed Elgammal', 'Andrew Tao', 'Kevin Shih', 'Ji Zhang', 'Bryan Catanzaro']	2018-11-21	null	null	null	null	['visual-relationship-detection']	['computer-vision']	[ 4.33293194e-01 4.72553670e-01 5.59215471e-02 -3.23766857e-01 -3.94416332e-01 -6.17119014e-01 7.90994585e-01 4.27357048e-01 -2.65303820e-01 4.55829442e-01 3.61572623e-01 -5.36481977e-01 -2.35715937e-02 -8.71430159e-01 -1.05028999e+00 -3.42247277e-01 -6.33362383e-02 3.19425732e-01 4.97661948e-01 -5.06837666e-01 2.15059400e-01 5.13252437e-01 -1.86007428e+00 5.16000092e-01 7.91456819e-01 9.22556341e-01 4.61841017e-01 8.99332941e-01 -7.59084374e-02 1.16737247e+00 -3.48464817e-01 -4.44514304e-01 4.26653102e-02 -3.99444401e-01 -1.03642678e+00 1.35381043e-01 8.26923072e-01 -1.42279431e-01 -3.31686258e-01 1.01085329e+00 2.14591637e-01 5.96091002e-02 7.03998804e-01 -1.55608559e+00 -7.09940553e-01 6.12063348e-01 -7.46278703e-01 2.60091126e-01 6.37417138e-01 4.21232790e-01 1.22112942e+00 -6.97550714e-01 8.61218870e-01 1.50251460e+00 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99da35d3-e6d8-414a-96f6-78862fa3168d	calling-out-bluff-attacking-the-robustness-of	2007.06796	null	https://arxiv.org/abs/2007.06796v5	https://arxiv.org/pdf/2007.06796v5.pdf	Evaluation Toolkit For Robustness Testing Of Automatic Essay Scoring Systems	Automatic scoring engines have been used for scoring approximately fifteen million test-takers in just the last three years. This number is increasing further due to COVID-19 and the associated automation of education and testing. Despite such wide usage, the AI-based testing literature of these "intelligent" models is highly lacking. Most of the papers proposing new models rely only on quadratic weighted kappa (QWK) based agreement with human raters for showing model efficacy. However, this effectively ignores the highly multi-feature nature of essay scoring. Essay scoring depends on features like coherence, grammar, relevance, sufficiency and, vocabulary. To date, there has been no study testing Automated Essay Scoring: AES systems holistically on all these features. With this motivation, we propose a model agnostic adversarial evaluation scheme and associated metrics for AES systems to test their natural language understanding capabilities and overall robustness. We evaluate the current state-of-the-art AES models using the proposed scheme and report the results on five recent models. These models range from feature-engineering-based approaches to the latest deep learning algorithms. We find that AES models are highly overstable. Even heavy modifications(as much as 25%) with content unrelated to the topic of the questions do not decrease the score produced by the models. On the other hand, irrelevant content, on average, increases the scores, thus showing that the model evaluation strategy and rubrics should be reconsidered. We also ask 200 human raters to score both an original and adversarial response to seeing if humans can detect differences between the two and whether they agree with the scores assigned by auto scores.	['Junyi Jessy Li', 'Anubha Kabra', 'Rajiv Ratn Shah', 'Yaman Kumar', 'Mehar Bhatia']	2020-07-14	null	null	null	null	['automated-essay-scoring']	['natural-language-processing']	[-1.24139123e-01 -1.43382717e-02 2.53220111e-01 -4.28341836e-01 -5.65841675e-01 -1.02044773e+00 3.70039046e-01 2.11610749e-01 -6.33041680e-01 8.27730417e-01 -1.94780037e-01 -4.54906642e-01 -4.78255361e-01 -8.68237257e-01 -4.29959655e-01 -1.79928839e-01 3.55857491e-01 3.48919243e-01 3.33425194e-01 -5.66327691e-01 6.35053396e-01 2.59129465e-01 -1.51807690e+00 2.21670106e-01 1.30340040e+00 7.57621586e-01 -4.14010674e-01 8.76503766e-01 6.28427491e-02 7.22717941e-01 -1.22783160e+00 -1.07302439e+00 3.19137067e-01 -4.36340421e-01 -6.61715209e-01 -6.57131493e-01 6.37714148e-01 -4.17376280e-01 -1.60557702e-01 1.21048844e+00 5.83872378e-01 -2.73011327e-01 5.60775995e-01 -1.27078128e+00 -7.61912763e-01 5.88525534e-01 -8.25076699e-02 6.17030784e-02 7.68292367e-01 3.90716910e-01 1.06055188e+00 -3.65587831e-01 2.49835581e-01 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7.16501772e-02 -1.15385818e+00 -1.05263424e+00 -3.01703334e-01 3.16761315e-01 -1.94840148e-01 -4.04581666e-01 -4.58073229e-01 -3.24120045e-01]	[11.310174942016602, 9.324749946594238]
eecc2fc1-6196-4855-99df-4fcf4f87d57e	unsupervised-and-semi-supervised-learning	1511.06390	null	http://arxiv.org/abs/1511.06390v2	http://arxiv.org/pdf/1511.06390v2.pdf	Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks	In this paper we present a method for learning a discriminative classifier from unlabeled or partially labeled data. Our approach is based on an objective function that trades-off mutual information between observed examples and their predicted categorical class distribution, against robustness of the classifier to an adversarial generative model. The resulting algorithm can either be interpreted as a natural generalization of the generative adversarial networks (GAN) framework or as an extension of the regularized information maximization (RIM) framework to robust classification against an optimal adversary. We empirically evaluate our method - which we dub categorical generative adversarial networks (or CatGAN) - on synthetic data as well as on challenging image classification tasks, demonstrating the robustness of the learned classifiers. We further qualitatively assess the fidelity of samples generated by the adversarial generator that is learned alongside the discriminative classifier, and identify links between the CatGAN objective and discriminative clustering algorithms (such as RIM).	['Jost Tobias Springenberg']	2015-11-19	null	null	null	null	['unsupervised-image-classification', 'unsupervised-mnist']	['computer-vision', 'methodology']	[ 7.34784126e-01 5.32943487e-01 2.24398822e-01 -4.00628954e-01 -1.14343965e+00 -1.17485261e+00 1.05925977e+00 -4.71698374e-01 -6.07525632e-02 6.40453637e-01 9.80694145e-02 -9.44331437e-02 -7.60306269e-02 -7.74811745e-01 -1.06121898e+00 -1.16617906e+00 9.43934172e-02 7.29626060e-01 -2.92804927e-01 2.28748843e-01 -1.88097963e-03 6.74804270e-01 -1.10579443e+00 1.57275140e-01 7.34502137e-01 7.10322082e-01 -4.50257540e-01 7.97111571e-01 5.19545972e-01 8.53154123e-01 -8.22703898e-01 -5.91437519e-01 4.73591506e-01 -8.24708045e-01 -9.13243234e-01 3.00566882e-01 3.29145968e-01 1.59063458e-01 -3.86437178e-01 1.16377282e+00 1.63751587e-01 7.82484487e-02 1.26165807e+00 -1.65584159e+00 -8.89208376e-01 4.65749234e-01 -1.05672523e-01 -1.70158014e-01 1.26969412e-01 2.99379975e-01 6.92045093e-01 -5.03725231e-01 7.86760271e-01 1.20018649e+00 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a88fa5a7-0f8f-4298-935f-233f28975925	cot-mae-v2-contextual-masked-auto-encoder	2304.03158	null	https://arxiv.org/abs/2304.03158v1	https://arxiv.org/pdf/2304.03158v1.pdf	CoT-MAE v2: Contextual Masked Auto-Encoder with Multi-view Modeling for Passage Retrieval	Growing techniques have been emerging to improve the performance of passage retrieval. As an effective representation bottleneck pretraining technique, the contextual masked auto-encoder utilizes contextual embedding to assist in the reconstruction of passages. However, it only uses a single auto-encoding pre-task for dense representation pre-training. This study brings multi-view modeling to the contextual masked auto-encoder. Firstly, multi-view representation utilizes both dense and sparse vectors as multi-view representations, aiming to capture sentence semantics from different aspects. Moreover, multiview decoding paradigm utilizes both autoencoding and auto-regressive decoders in representation bottleneck pre-training, aiming to provide both reconstructive and generative signals for better contextual representation pretraining. We refer to this multi-view pretraining method as CoT-MAE v2. Through extensive experiments, we show that CoT-MAE v2 is effective and robust on large-scale passage retrieval benchmarks and out-of-domain zero-shot benchmarks.	['Songlin Hu', 'Fuzheng Zhang', 'Zijia Lin', 'Meng Lin', 'Peng Wang', 'Guangyuan Ma', 'Xing Wu']	2023-04-05	null	null	null	null	['passage-retrieval']	['natural-language-processing']	[-5.52617013e-02 -3.40872854e-01 -3.23181778e-01 -2.21921042e-01 -1.27791226e+00 -3.92501801e-01 8.86392117e-01 2.35152543e-01 -2.06597462e-01 5.26069641e-01 1.05167675e+00 2.15191126e-01 2.25466654e-01 -8.13430727e-01 -8.52100432e-01 -4.53385651e-01 4.03695166e-01 3.96738023e-01 -1.58610329e-01 -5.72962224e-01 9.11924392e-02 -3.75614882e-01 -1.41163695e+00 1.02210474e+00 5.18114567e-01 6.27013028e-01 4.22376662e-01 7.51504242e-01 -3.65165859e-01 9.01487648e-01 -5.15014470e-01 -4.02849674e-01 -4.64551114e-02 -6.70962751e-01 -7.21365392e-01 -1.15309201e-01 8.21394697e-02 -5.83287537e-01 -7.96071112e-01 7.18870282e-01 8.64964724e-01 5.19532263e-01 1.02265942e+00 -4.48177189e-01 -1.21147776e+00 7.70782650e-01 -4.33217347e-01 5.31724155e-01 7.94710577e-01 -1.26036495e-01 1.34936059e+00 -1.36445141e+00 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7.92895973e-01 8.33286226e-01 -7.87625074e-01 -6.66049421e-01 -1.25232768e+00 -4.23191309e-01 3.87122899e-01 2.68865943e-01 -4.97782111e-01 -2.38058195e-01 -1.70957580e-01]	[11.40931224822998, 7.7848076820373535]
9551a2de-5f17-4f84-bd63-88ee2e306f9f	pragmatic-information-in-translation-a-corpus	2007.05234	null	https://arxiv.org/abs/2007.05234v1	https://arxiv.org/pdf/2007.05234v1.pdf	Pragmatic information in translation: a corpus-based study of tense and mood in English and German	Grammatical tense and mood are important linguistic phenomena to consider in natural language processing (NLP) research. We consider the correspondence between English and German tense and mood in translation. Human translators do not find this correspondence easy, and as we will show through careful analysis, there are no simplistic ways to map tense and mood from one language to another. Our observations about the challenges of human translation of tense and mood have important implications for multilingual NLP. Of particular importance is the challenge of modeling tense and mood in rule-based, phrase-based statistical and neural machine translation.	['Ekaterina Lapshinova-Koltunski', 'Alexander Fraser', 'Anita Ramm']	2020-07-10	null	null	null	null	['multilingual-nlp']	['natural-language-processing']	[-1.71559289e-01 -1.38288230e-01 -5.83644092e-01 -5.81997991e-01 -7.07813919e-01 -1.01597607e+00 7.20803142e-01 3.91472697e-01 -5.28045774e-01 9.74616528e-01 5.33280015e-01 -8.21197748e-01 1.03606611e-01 -6.66869819e-01 -6.00784957e-01 -3.75931375e-02 7.46844634e-02 5.85555911e-01 -5.43089569e-01 -9.84033108e-01 4.59817462e-02 1.60174116e-01 -8.54667068e-01 3.40247601e-01 9.44516540e-01 2.83538073e-01 2.92926520e-01 2.54796088e-01 -1.72642678e-01 5.88900924e-01 -2.53620356e-01 -8.19501698e-01 2.47550622e-01 -4.39157128e-01 -1.02087045e+00 -5.27405918e-01 3.59436780e-01 1.63747177e-01 1.19768545e-01 1.23682833e+00 4.17355031e-01 -1.63633674e-02 4.55277056e-01 -7.22206652e-01 -8.89446855e-01 9.16658700e-01 -2.75270283e-01 8.24512303e-01 6.48512602e-01 9.58581343e-02 1.48800135e+00 -7.39586353e-01 8.46304178e-01 1.62801945e+00 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eb674dc1-47b6-4a14-a98f-620167124e5a	ultra-sensitive-flexible-sponge-sensor-array	2205.03238	null	https://arxiv.org/abs/2205.03238v2	https://arxiv.org/pdf/2205.03238v2.pdf	Ultra-sensitive Flexible Sponge-Sensor Array for Muscle Activities Detection and Human Limb Motion Recognition	Human limb motion tracking and recognition plays an important role in medical rehabilitation training, lower limb assistance, prosthetics design for amputees, feedback control for assistive robots, etc. Lightweight wearable sensors, including inertial sensors, surface electromyography sensors, and flexible strain/pressure, are promising to become the next-generation human motion capture devices. Herein, we present a wireless wearable device consisting of a sixteen-channel flexible sponge-based pressure sensor array to recognize various human lower limb motions by detecting contours on the human skin caused by calf gastrocnemius muscle actions. Each sensing element is a round porous structure of thin carbon nanotube/polydimethylsiloxane nanocomposites with a diameter of 4 mm and thickness of about 400 {\mu}m. Ten human subjects were recruited to perform ten different lower limb motions while wearing the developed device. The motion classification result with the support vector machine method shows a macro-recall of about 97.3% for all ten motions tested. This work demonstrates a portable wearable muscle activity detection device with a lower limb motion recognition application, which can be potentially used in assistive robot control, healthcare, sports monitoring, etc.	['Vivian W. Q. Lou', 'Wen Jung Li', 'Ning Xi', 'Roy Vellaisamy', 'Ho-Yin Chan', 'Meng Chen', 'Keer Wang', 'Clio Cheng', 'Yifan Liu', 'Jiao Suo']	2022-04-30	null	null	null	null	['activity-detection']	['computer-vision']	[ 4.77669358e-01 -2.83984188e-02 -6.07769847e-01 5.00242174e-01 1.72646400e-02 -2.38552943e-01 -2.48351887e-01 -6.79662883e-01 -7.38246500e-01 7.99092650e-01 6.17719173e-01 2.17135921e-01 1.28743902e-01 -3.56101662e-01 -2.95361131e-01 -7.27785230e-01 -3.56893629e-01 -1.18778639e-01 6.07689977e-01 -1.17190100e-01 2.54583567e-01 1.70257509e-01 -1.22849011e+00 1.78777888e-01 6.56687498e-01 1.06918824e+00 5.43168008e-01 4.32083040e-01 7.59655237e-01 3.14353853e-01 -3.26872766e-01 4.34316732e-02 -1.81566719e-02 -1.88501626e-01 -2.40875438e-01 7.20838681e-02 -3.05051088e-01 -2.96185791e-01 -3.88353437e-01 8.07560384e-01 8.58539760e-01 -1.00222446e-01 6.53734803e-01 -3.01288098e-01 -3.37477416e-01 4.06528115e-01 -5.69171369e-01 6.79164827e-02 7.48487890e-01 3.07056963e-01 1.04671881e-01 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dcba0208-e859-473e-a508-8242fae840ab	revisiting-the-transferability-of-supervised	2112.00496	null	https://arxiv.org/abs/2112.00496v3	https://arxiv.org/pdf/2112.00496v3.pdf	Revisiting the Transferability of Supervised Pretraining: an MLP Perspective	The pretrain-finetune paradigm is a classical pipeline in visual learning. Recent progress on unsupervised pretraining methods shows superior transfer performance to their supervised counterparts. This paper revisits this phenomenon and sheds new light on understanding the transferability gap between unsupervised and supervised pretraining from a multilayer perceptron (MLP) perspective. While previous works focus on the effectiveness of MLP on unsupervised image classification where pretraining and evaluation are conducted on the same dataset, we reveal that the MLP projector is also the key factor to better transferability of unsupervised pretraining methods than supervised pretraining methods. Based on this observation, we attempt to close the transferability gap between supervised and unsupervised pretraining by adding an MLP projector before the classifier in supervised pretraining. Our analysis indicates that the MLP projector can help retain intra-class variation of visual features, decrease the feature distribution distance between pretraining and evaluation datasets, and reduce feature redundancy. Extensive experiments on public benchmarks demonstrate that the added MLP projector significantly boosts the transferability of supervised pretraining, e.g. +7.2% top-1 accuracy on the concept generalization task, +5.8% top-1 accuracy for linear evaluation on 12-domain classification tasks, and +0.8% AP on COCO object detection task, making supervised pretraining comparable or even better than unsupervised pretraining.	['Wanli Ouyang', 'Donglian Qi', 'Rui Zhao', 'Lei Bai', 'Feng Zhu', 'Shixiang Tang', 'Yizhou Wang']	2021-12-01	null	http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Revisiting_the_Transferability_of_Supervised_Pretraining_An_MLP_Perspective_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Revisiting_the_Transferability_of_Supervised_Pretraining_An_MLP_Perspective_CVPR_2022_paper.pdf	cvpr-2022-1	['unsupervised-image-classification']	['computer-vision']	[ 4.92065996e-01 1.35336757e-01 -4.82159048e-01 -5.98130405e-01 -1.05971172e-01 -4.69286501e-01 4.66121852e-01 3.57815742e-01 -8.26823056e-01 3.82967621e-01 -1.44965097e-01 -2.53969938e-01 6.26914427e-02 -5.55767238e-01 -9.45920408e-01 -7.09518969e-01 -3.00798137e-02 3.26940119e-01 2.59096056e-01 8.27023312e-02 5.36085293e-02 4.77441102e-01 -1.76870370e+00 8.95950317e-01 6.24210715e-01 1.11522424e+00 2.21033096e-01 6.21300459e-01 5.26755443e-03 7.83602595e-01 -5.97651243e-01 -1.17340505e-01 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0da34164-e47b-4ff7-b75a-0330aa910fce	multiverse-at-the-edge-interacting-real-world	2305.10350	null	https://arxiv.org/abs/2305.10350v1	https://arxiv.org/pdf/2305.10350v1.pdf	Multiverse at the Edge: Interacting Real World and Digital Twins for Wireless Beamforming	Creating a digital world that closely mimics the real world with its many complex interactions and outcomes is possible today through advanced emulation software and ubiquitous computing power. Such a software-based emulation of an entity that exists in the real world is called a 'digital twin'. In this paper, we consider a twin of a wireless millimeter-wave band radio that is mounted on a vehicle and show how it speeds up directional beam selection in mobile environments. To achieve this, we go beyond instantiating a single twin and propose the 'Multiverse' paradigm, with several possible digital twins attempting to capture the real world at different levels of fidelity. Towards this goal, this paper describes (i) a decision strategy at the vehicle that determines which twin must be used given the computational and latency limitations, and (ii) a self-learning scheme that uses the Multiverse-guided beam outcomes to enhance DL-based decision-making in the real world over time. Our work is distinguished from prior works as follows: First, we use a publicly available RF dataset collected from an autonomous car for creating different twins. Second, we present a framework with continuous interaction between the real world and Multiverse of twins at the edge, as opposed to a one-time emulation that is completed prior to actual deployment. Results reveal that Multiverse offers up to 79.43% and 85.22% top-10 beam selection accuracy for LOS and NLOS scenarios, respectively. Moreover, we observe 52.72-85.07% improvement in beam selection time compared to 802.11ad standard.	['Kaushik Chowdhury', 'Stratis Ioannidis', 'Jennifer Dy', 'Suyash Pradhan', 'Debashri Roy', 'Utku Demir', 'Batool Salehi']	2023-05-10	null	null	null	null	['self-learning']	['natural-language-processing']	[ 1.40548553e-02 9.75828171e-02 -7.91130960e-03 -2.51652032e-01 -8.29345345e-01 -4.24820751e-01 5.01164138e-01 -3.85110199e-01 -1.35576770e-01 8.32043827e-01 -2.36967266e-01 -8.83549035e-01 -4.20625061e-01 -1.28612506e+00 -6.25118375e-01 -7.24825203e-01 -4.66539711e-01 4.93833363e-01 3.51149619e-01 -5.95834255e-01 -3.09960455e-01 5.14101088e-01 -1.47932398e+00 -1.92537069e-01 7.44196713e-01 1.10521317e+00 4.86583292e-01 8.91259670e-01 1.78540304e-01 5.07780194e-01 -7.71710098e-01 -2.05807224e-01 3.10333312e-01 1.09272286e-01 -5.26199825e-02 -4.91864175e-01 3.96025628e-01 4.50168252e-02 -3.95761460e-01 5.90278983e-01 1.01806283e+00 -3.90509665e-01 2.94161826e-01 -1.42450845e+00 2.40953207e-01 5.80706716e-01 -3.25465590e-01 -2.19039887e-01 6.30881071e-01 -2.28408240e-02 5.45865178e-01 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5927448c-93ff-472e-aa16-80da7fc769c4	transformer-based-self-supervised-multimodal	2303.17611	null	https://arxiv.org/abs/2303.17611v1	https://arxiv.org/pdf/2303.17611v1.pdf	Transformer-based Self-supervised Multimodal Representation Learning for Wearable Emotion Recognition	Recently, wearable emotion recognition based on peripheral physiological signals has drawn massive attention due to its less invasive nature and its applicability in real-life scenarios. However, how to effectively fuse multimodal data remains a challenging problem. Moreover, traditional fully-supervised based approaches suffer from overfitting given limited labeled data. To address the above issues, we propose a novel self-supervised learning (SSL) framework for wearable emotion recognition, where efficient multimodal fusion is realized with temporal convolution-based modality-specific encoders and a transformer-based shared encoder, capturing both intra-modal and inter-modal correlations. Extensive unlabeled data is automatically assigned labels by five signal transforms, and the proposed SSL model is pre-trained with signal transformation recognition as a pretext task, allowing the extraction of generalized multimodal representations for emotion-related downstream tasks. For evaluation, the proposed SSL model was first pre-trained on a large-scale self-collected physiological dataset and the resulting encoder was subsequently frozen or fine-tuned on three public supervised emotion recognition datasets. Ultimately, our SSL-based method achieved state-of-the-art results in various emotion classification tasks. Meanwhile, the proposed model proved to be more accurate and robust compared to fully-supervised methods on low data regimes.	['Ali Amad', 'Mohamed Daoudi', 'Yujin WU']	2023-03-29	null	null	null	null	['emotion-classification', 'emotion-classification']	['computer-vision', 'natural-language-processing']	[ 5.25686800e-01 -3.11572224e-01 6.35201037e-02 -7.57229686e-01 -1.11080122e+00 -2.97912657e-01 2.29293972e-01 1.51668549e-01 -5.81290424e-01 8.31104040e-01 1.35518476e-01 2.87465334e-01 -1.01761326e-01 -2.13672355e-01 -5.45698643e-01 -8.28055978e-01 1.28974626e-02 -1.01892754e-01 -5.02211273e-01 2.48350278e-02 -2.15003505e-01 1.21393517e-01 -1.61012316e+00 3.40259671e-01 1.24575031e+00 1.70904028e+00 -1.28391176e-01 3.39500725e-01 9.37449113e-02 4.95277822e-01 -2.95594215e-01 -5.61276436e-01 -2.39912555e-01 -4.70439583e-01 -3.78567070e-01 7.61481747e-02 -2.78132390e-02 1.76958308e-01 -1.55731454e-01 8.57000649e-01 9.53149796e-01 7.27431700e-02 4.93066370e-01 -1.21050215e+00 -3.78026515e-01 3.64936262e-01 -3.02880257e-01 -3.15786488e-02 4.09417093e-01 1.15057632e-01 8.14860046e-01 -1.01534963e+00 1.76848650e-01 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434006e5-db27-4122-91a1-8ff8010c2eb4	exploit-cam-by-itself-complementary-learning	2303.02449	null	https://arxiv.org/abs/2303.02449v1	https://arxiv.org/pdf/2303.02449v1.pdf	Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation	Weakly Supervised Semantic Segmentation (WSSS) with image-level labels has long been suffering from fragmentary object regions led by Class Activation Map (CAM), which is incapable of generating fine-grained masks for semantic segmentation. To guide CAM to find more non-discriminating object patterns, this paper turns to an interesting working mechanism in agent learning named Complementary Learning System (CLS). CLS holds that the neocortex builds a sensation of general knowledge, while the hippocampus specially learns specific details, completing the learned patterns. Motivated by this simple but effective learning pattern, we propose a General-Specific Learning Mechanism (GSLM) to explicitly drive a coarse-grained CAM to a fine-grained pseudo mask. Specifically, GSLM develops a General Learning Module (GLM) and a Specific Learning Module (SLM). The GLM is trained with image-level supervision to extract coarse and general localization representations from CAM. Based on the general knowledge in the GLM, the SLM progressively exploits the specific spatial knowledge from the localization representations, expanding the CAM in an explicit way. To this end, we propose the Seed Reactivation to help SLM reactivate non-discriminating regions by setting a boundary for activation values, which successively identifies more regions of CAM. Without extra refinement processes, our method is able to achieve breakthrough improvements for CAM of over 20.0% mIoU on PASCAL VOC 2012 and 10.0% mIoU on MS COCO 2014 datasets, representing a new state-of-the-art among existing WSSS methods.	['Bo Han', 'Tongliang Liu', 'Wankou Yang', 'Xian Zhang', 'Marcus Kalander', 'Junjie Ye', 'Fei Zhang', 'Jiren Mai']	2023-03-04	null	null	null	null	['general-knowledge']	['miscellaneous']	[ 5.30170023e-01 5.09170294e-01 -2.99965173e-01 -3.59150320e-01 -5.44435799e-01 -6.09041691e-01 6.69159889e-01 -1.10242464e-01 -5.40894032e-01 6.04067087e-01 -2.72289187e-01 -5.15011400e-02 1.32124633e-01 -8.17705631e-01 -9.92708623e-01 -8.37682009e-01 3.62585597e-02 4.64488328e-01 9.08875406e-01 -2.68294245e-01 3.16186279e-01 4.64596927e-01 -1.40291154e+00 7.37295568e-01 9.76645887e-01 1.15319276e+00 7.24761069e-01 3.04548949e-01 -3.49970281e-01 7.80021608e-01 -7.27202058e-01 6.96222186e-02 2.33759359e-01 -5.96415401e-01 -1.11926496e+00 8.47352222e-02 4.47523147e-01 9.33375657e-02 2.71302432e-01 1.17391336e+00 -1.08722961e-02 -2.75987424e-02 6.19401395e-01 -1.17478418e+00 -6.40314937e-01 8.05130720e-01 -6.03388071e-01 2.42596313e-01 2.28907671e-02 3.53334785e-01 8.27276587e-01 -8.44758987e-01 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