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09acc494-a429-48bc-9fad-ff60e69000d3 | unified-emulation-simulation-training | 2304.01244 | null | https://arxiv.org/abs/2304.01244v1 | https://arxiv.org/pdf/2304.01244v1.pdf | Unified Emulation-Simulation Training Environment for Autonomous Cyber Agents | Autonomous cyber agents may be developed by applying reinforcement and deep reinforcement learning (RL/DRL), where agents are trained in a representative environment. The training environment must simulate with high-fidelity the network Cyber Operations (CyOp) that the agent aims to explore. Given the complexity of net... | ['Thomas Kunz', 'James Hailing Rao', 'Adrian Taylor', 'Jean-Pierre S. El Rami', 'Li Li'] | 2023-04-03 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-2.73265868e-01 7.00822294e-01 1.72952175e-01 1.55207902e-01
-9.35752988e-02 -4.99123484e-01 6.97034419e-01 -1.57877102e-01
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-2.94015855e-01 -1.16191673e+00 -8.56384695e-01 -5.40041149e-01
-6.29573703e-01 9.45913196e-01 1.77784041e-01 -6.85358524... | [4.187369346618652, 1.5389838218688965] |
d4dfb29a-59b4-4518-96d2-13f5d1ecdd0c | electromyography-signal-classification-using | 2305.04006 | null | https://arxiv.org/abs/2305.04006v1 | https://arxiv.org/pdf/2305.04006v1.pdf | Electromyography Signal Classification Using Deep Learning | We have implemented a deep learning model with L2 regularization and trained it on Electromyography (EMG) data. The data comprises of EMG signals collected from control group, myopathy and ALS patients. Our proposed deep neural network consists of eight layers; five fully connected, two batch normalization and one drop... | ['Abdulhamit Subasi', 'Selcuk Cankurt', 'Mekia Shigute Gaso'] | 2023-05-06 | null | null | null | null | ['electromyography-emg', 'l2-regularization'] | ['medical', 'methodology'] | [ 3.00598592e-01 2.85540193e-01 -3.32311839e-01 -2.63567597e-01
-6.78174198e-01 2.96222746e-01 -5.49947731e-02 -5.84931135e-01
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4a659a4e-ab71-4236-839f-d45221bbab72 | multi-relational-embedding-for-knowledge | null | null | https://ir.soken.ac.jp/?action=pages_view_main&active_action=repository_view_main_item_detail&item_id=6334&item_no=1&page_id=29&block_id=155 | https://ir.soken.ac.jp/?action=pages_view_main&active_action=repository_view_main_item_detail&item_id=6334&item_no=1&page_id=29&block_id=155 | Multi-Relational Embedding for Knowledge Graph Representation and Analysis | Multi-relational data, such as knowledge graphs, bibliographic data, and information networks are prevalent in real-world datasets. Managing, exploring, and utilizing these large and complex datasets effectively are challenging. In recent years, multi-relational embedding methods have emerged as a new effective approac... | ['Hung Nghiep Tran'] | 2020-09-28 | null | null | null | phd-dissertation-the-graduate-university-for | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-2.60561347e-01 3.25738341e-01 -5.48878133e-01 -9.28325951e-03
5.90382982e-03 -4.89967883e-01 5.00784457e-01 2.39097208e-01
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-3.36346209e-01 5.73655963e-01 1.00638799e-01 -4.18746084... | [8.676801681518555, 7.7932634353637695] |
b1fe7d3e-abea-46b1-b347-7726aafc8669 | easy-guided-decoding-in-providing-suggestions | 2211.07093 | null | https://arxiv.org/abs/2211.07093v2 | https://arxiv.org/pdf/2211.07093v2.pdf | Easy Guided Decoding in Providing Suggestions for Interactive Machine Translation | Machine translation technology has made great progress in recent years, but it cannot guarantee error free results. Human translators perform post editing on machine translations to correct errors in the scene of computer aided translation. In favor of expediting the post editing process, many works have investigated m... | ['Yuqi Zhang', 'Jiayi Wang', 'Yu Zhao', 'Xin Ge', 'Ke Wang'] | 2022-11-14 | null | null | null | null | ['nmt'] | ['computer-code'] | [ 8.88319969e-01 -6.64400980e-02 -3.07653844e-01 -4.23435718e-01
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5.89902103e-01 1.00331557e+00 -2.33108595e-01 -4.97315168... | [11.67475414276123, 10.27749252319336] |
52e76f88-c452-4470-aaef-750d42e953a1 | tail-dependence-structure-and-extreme-risk | 2303.11030 | null | https://arxiv.org/abs/2303.11030v1 | https://arxiv.org/pdf/2303.11030v1.pdf | Tail dependence structure and extreme risk spillover effects between the international agricultural futures and spot markets | This paper combines the Copula-CoVaR approach with the ARMA-GARCH-skewed Student-t model to investigate the tail dependence structure and extreme risk spillover effects between the international agricultural futures and spot markets, taking four main agricultural commodities, namely soybean, maize, wheat, and rice as e... | ['Wei-Xing Zhou', 'Peng-Fei Dai', 'Yun-Shi Dai'] | 2023-03-20 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-6.69479072e-01 -1.97964460e-01 -2.42255673e-01 -2.25047588e-01
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-5.02866983e-01 -7.45216310e-02 -9.67425555e-02 -4.58833396... | [5.218739986419678, 3.9969170093536377] |
863c7ece-43b1-485d-94ea-f3d824aa7c04 | sceneformer-indoor-scene-generation-with | 2012.09793 | null | https://arxiv.org/abs/2012.09793v2 | https://arxiv.org/pdf/2012.09793v2.pdf | SceneFormer: Indoor Scene Generation with Transformers | We address the task of indoor scene generation by generating a sequence of objects, along with their locations and orientations conditioned on a room layout. Large-scale indoor scene datasets allow us to extract patterns from user-designed indoor scenes, and generate new scenes based on these patterns. Existing methods... | ['Matthias Nießner', 'Chandan Yeshwanth', 'Xinpeng Wang'] | 2020-12-17 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 1.96711138e-01 2.37911865e-01 7.47902572e-01 -4.38510269e-01
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2.24486411e-01 4.34888065e-01 1.74908414e-01 -3.44664127... | [9.243432998657227, -3.0228514671325684] |
32241292-91d0-42e5-9e36-0f47f35147f4 | cross-task-knowledge-transfer-for-visually | null | null | https://openreview.net/forum?id=ByGq7hRqKX | https://openreview.net/pdf?id=ByGq7hRqKX | Cross-Task Knowledge Transfer for Visually-Grounded Navigation | Recent efforts on training visual navigation agents conditioned on language using deep reinforcement learning have been successful in learning policies for two different tasks: learning to follow navigational instructions and embodied question answering. In this paper, we aim to learn a multitask model capable of joint... | ['Lisa Lee', 'Ruslan Salakhutdinov', 'Dhruv Batra', 'Devendra Singh Chaplot', 'Devi Parikh'] | 2019-05-01 | null | null | null | iclr-2019-5 | ['embodied-question-answering'] | ['computer-vision'] | [-9.92803648e-03 2.45683402e-01 1.09384447e-01 -2.21183285e-01
-5.57420850e-01 -8.22399199e-01 1.07647395e+00 4.16528322e-02
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-2.32914895e-01 6.19652867e-01 1.32130712e-01 -3.29657316... | [4.55482816696167, 0.5253642201423645] |
0945ddd0-7d44-45fb-a18f-1b16ce7e7667 | selective-experience-replay-compression-using | 2302.11510 | null | https://arxiv.org/abs/2302.11510v4 | https://arxiv.org/pdf/2302.11510v4.pdf | Selective experience replay compression using coresets for lifelong deep reinforcement learning in medical imaging | Selective experience replay is a popular strategy for integrating lifelong learning with deep reinforcement learning. Selective experience replay aims to recount selected experiences from previous tasks to avoid catastrophic forgetting. Furthermore, selective experience replay based techniques are model agnostic and al... | ['Vishwa S. Parekh', 'Michael A. Jacobs', 'Vladimir Braverman', 'Samson Zhou', 'Guangyao Zheng'] | 2023-02-22 | null | null | null | null | ['tumor-segmentation', 'brain-tumor-segmentation'] | ['computer-vision', 'medical'] | [-1.43500581e-01 -1.52205080e-02 -4.45030332e-01 -1.80735931e-01
-1.07541883e+00 -6.80662170e-02 2.29429096e-01 5.11952937e-01
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-1.74468845e-01 -3.42504233e-01 -7.34195948e-01 -9.08718765e-01
-7.62611032e-01 3.20671856e-01 1.32636994e-01 4.36609179... | [13.55036735534668, -2.3724308013916016] |
3b53b1aa-1602-4ce5-bf31-0ed7d3bbe79d | weakly-supervised-text-instance-segmentation | 2303.10848 | null | https://arxiv.org/abs/2303.10848v2 | https://arxiv.org/pdf/2303.10848v2.pdf | Weakly-Supervised Text Instance Segmentation | Text segmentation is a challenging vision task with many downstream applications. Current text segmentation methods require pixel-level annotations, which are expensive in the cost of human labor and limited in application scenarios. In this paper, we take the first attempt to perform weakly-supervised text instance se... | ['xiangyang xue', 'Bin Li', 'Haiyang Yu', 'Xinyan Zu'] | 2023-03-20 | null | null | null | null | ['weakly-supervised-instance-segmentation'] | ['computer-vision'] | [ 7.75448143e-01 2.11075664e-01 -2.71864623e-01 -5.35651386e-01
-8.83205056e-01 -3.08387071e-01 4.18704391e-01 -5.93288708e-03
-5.29650748e-01 3.67178261e-01 -8.70694891e-02 -2.30865628e-01
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7.20850587e-01 7.11505532e-01 4.13817376e-01 3.23298007... | [12.093706130981445, 2.323106050491333] |
2621839e-a33d-458b-88d9-98309a501697 | neurosymbolic-ai-why-what-and-how | 2305.00813 | null | https://arxiv.org/abs/2305.00813v1 | https://arxiv.org/pdf/2305.00813v1.pdf | Neurosymbolic AI - Why, What, and How | Humans interact with the environment using a combination of perception - transforming sensory inputs from their environment into symbols, and cognition - mapping symbols to knowledge about the environment for supporting abstraction, reasoning by analogy, and long-term planning. Human perception-inspired machine percept... | ['Manas Gaur', 'Kaushik Roy', 'Amit Sheth'] | 2023-05-01 | null | null | null | null | ['object-recognition'] | ['computer-vision'] | [ 6.27300799e-01 5.68442464e-01 -7.92162959e-03 -4.26336110e-01
2.66098291e-01 -4.87745851e-01 8.81728590e-01 5.39560974e-01
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-3.47212106e-01 -1.08844233e+00 -7.51923025e-01 -8.93904045e-02
-1.14095584e-02 5.58458567e-01 1.36122197e-01 -6.65053010... | [4.467419624328613, 1.166722297668457] |
06303e2c-c7dd-4ed4-927d-fa4761ee0582 | efficient-high-resolution-template-matching | 2306.15010 | null | https://arxiv.org/abs/2306.15010v1 | https://arxiv.org/pdf/2306.15010v1.pdf | Efficient High-Resolution Template Matching with Vector Quantized Nearest Neighbour Fields | Template matching is a fundamental problem in computer vision and has applications in various fields, such as object detection, image registration, and object tracking. The current state-of-the-art methods rely on nearest-neighbour (NN) matching in which the query feature space is converted to NN space by representing ... | ['Ida-Maria Sintorn', 'Ankit Gupta'] | 2023-06-26 | null | null | null | null | ['template-matching', 'object-tracking', 'image-registration', 'quantization'] | ['computer-vision', 'computer-vision', 'computer-vision', 'methodology'] | [ 6.15060270e-01 -5.37124634e-01 -1.65973008e-02 -5.67907870e-01
-9.14125085e-01 -4.56825823e-01 7.09499598e-01 -4.63601984e-02
-5.69038570e-01 1.42847419e-01 -3.46767530e-02 3.51961702e-01
-2.86396056e-01 -9.22259510e-01 -5.11178493e-01 -6.85133755e-01
1.88434988e-01 4.15571481e-01 9.53806281e-01 1.77503061... | [8.053352355957031, -2.3168656826019287] |
d22eddf7-56e7-475a-9324-fd8104491eb5 | attention-based-multimodal-fusion-for-video | 1701.03126 | null | http://arxiv.org/abs/1701.03126v2 | http://arxiv.org/pdf/1701.03126v2.pdf | Attention-Based Multimodal Fusion for Video Description | Currently successful methods for video description are based on
encoder-decoder sentence generation using recur-rent neural networks (RNNs).
Recent work has shown the advantage of integrating temporal and/or spatial
attention mechanisms into these models, in which the decoder net-work predicts
each word in the descript... | ['Kazuhiro Sumi', 'Teng-Yok Lee', 'Tim K. Marks', 'John R. Hershey', 'Takaaki Hori', 'Chiori Hori'] | 2017-01-11 | attention-based-multimodal-fusion-for-video-1 | http://openaccess.thecvf.com/content_iccv_2017/html/Hori_Attention-Based_Multimodal_Fusion_ICCV_2017_paper.html | http://openaccess.thecvf.com/content_ICCV_2017/papers/Hori_Attention-Based_Multimodal_Fusion_ICCV_2017_paper.pdf | iccv-2017-10 | ['video-description'] | ['computer-vision'] | [ 1.71096608e-01 -1.49751469e-01 -3.58191103e-01 -3.07341307e-01
-8.59091520e-01 -3.05884749e-01 8.89266908e-01 -5.65329008e-02
-3.67793024e-01 6.06032908e-01 8.86303782e-01 1.25332609e-01
2.03039810e-01 -4.98042375e-01 -7.25577533e-01 -4.12904888e-01
-6.08183667e-02 -6.55685924e-03 1.14310361e-01 -2.00459331... | [10.555021286010742, 0.6986870765686035] |
a7a21f19-2375-46b6-b8aa-6aedf8997335 | towards-social-generative-ai-for-education | 2306.10063 | null | https://arxiv.org/abs/2306.10063v1 | https://arxiv.org/pdf/2306.10063v1.pdf | Towards social generative AI for education: theory, practices and ethics | This paper explores educational interactions involving humans and artificial intelligences not as sequences of prompts and responses, but as a social process of conversation and exploration. In this conception, learners continually converse with AI language models within a dynamic computational medium of internet tools... | ['Mike Sharples'] | 2023-06-14 | null | null | null | null | ['ethics'] | ['miscellaneous'] | [ 2.73292903e-02 6.73137307e-01 -3.21767144e-02 -1.47267640e-01
1.72021985e-01 -7.56471038e-01 9.21139479e-01 3.90597105e-01
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-2.17797235e-01 -1.16016746e+00 -4.57781643e-01 -2.35246152e-01
5.23476124e-01 6.02626443e-01 3.68408114e-01 -5.50419629... | [10.23792552947998, 7.2178168296813965] |
b717181d-9f75-48ae-84e0-bd9c5da46009 | sieg-at-mediqa-2019-multi-task-neural | null | null | https://aclanthology.org/W19-5049 | https://aclanthology.org/W19-5049.pdf | Sieg at MEDIQA 2019: Multi-task Neural Ensemble for Biomedical Inference and Entailment | This paper presents a multi-task learning approach to natural language inference (NLI) and question entailment (RQE) in the biomedical domain. Recognizing textual inference relations and question similarity can address the issue of answering new consumer health questions by mapping them to Frequently Asked Questions on... | ['James Route', 'Sai Abishek Bhaskar', 'Rashi Rungta', 'Teruko Mitamura', 'Eric Nyberg'] | 2019-08-01 | null | null | null | ws-2019-8 | ['question-similarity'] | ['natural-language-processing'] | [ 4.11544472e-01 4.81799275e-01 -4.81643200e-01 -6.73941135e-01
-1.59253812e+00 -4.27256495e-01 3.79664004e-01 8.01151514e-01
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-6.15480840e-01 -3.30382079e-01 -9.76224244e-01 1.52585655e-01
3.40325385e-02 7.85172164e-01 5.54240367e-04 -2.86397427... | [8.739285469055176, 8.6454439163208] |
c3805f26-ad2a-488b-8f07-533069987f88 | depth-quality-aware-salient-object-detection | 2008.04159 | null | https://arxiv.org/abs/2008.04159v1 | https://arxiv.org/pdf/2008.04159v1.pdf | Depth Quality Aware Salient Object Detection | The existing fusion based RGB-D salient object detection methods usually adopt the bi-stream structure to strike the fusion trade-off between RGB and depth (D). The D quality usually varies from scene to scene, while the SOTA bi-stream approaches are depth quality unaware, which easily result in substantial difficultie... | ['Chenglizhao Chen', 'Jipeng Wei', 'Hong Qin', 'Chong Peng'] | 2020-08-07 | null | null | null | null | ['rgb-d-salient-object-detection', 'salient-object-detection'] | ['computer-vision', 'computer-vision'] | [ 2.01437950e-01 -2.16123983e-01 1.82954688e-02 -1.72547072e-01
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-1.16417788e-01 -1.04401731e+00 -5.57498969e-02 -7.43645191e-01
2.85344303e-01 -1.45916387e-01 7.66487718e-01 -4.19476479... | [9.634143829345703, -0.8636232614517212] |
134639a2-771a-4eec-9bf4-3e264555f289 | human-scene-network-a-novel-baseline-with | 2301.07923 | null | https://arxiv.org/abs/2301.07923v1 | https://arxiv.org/pdf/2301.07923v1.pdf | Human-Scene Network: A Novel Baseline with Self-rectifying Loss for Weakly supervised Video Anomaly Detection | Video anomaly detection in surveillance systems with only video-level labels (i.e. weakly-supervised) is challenging. This is due to, (i) the complex integration of human and scene based anomalies comprising of subtle and sharp spatio-temporal cues in real-world scenarios, (ii) non-optimal optimization between normal a... | ['Francois Bremond', 'Gianpiero Francesca', 'Lorenzo Garattoni', 'Quan Kong', 'Rui Dai', 'Snehashis Majhi'] | 2023-01-19 | null | null | null | null | ['video-anomaly-detection'] | ['computer-vision'] | [ 3.26861620e-01 -2.85747558e-01 3.57846953e-02 -5.10925174e-01
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5be8e567-f3cc-4f8e-84f3-e572b03fd26c | fine-grained-visual-textual-representation | 1709.00340 | null | http://arxiv.org/abs/1709.00340v4 | http://arxiv.org/pdf/1709.00340v4.pdf | Fine-grained Visual-textual Representation Learning | Fine-grained visual categorization is to recognize hundreds of subcategories
belonging to the same basic-level category, which is a highly challenging task
due to the quite subtle and local visual distinctions among similar
subcategories. Most existing methods generally learn part detectors to discover
discriminative r... | ['Yuxin Peng', 'Xiangteng He'] | 2017-08-31 | null | null | null | null | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 9.17732567e-02 -4.55515593e-01 -3.33935231e-01 -3.15448940e-01
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3.71930927e-01 2.47243926e-01 2.52572000e-01 -2.29025885... | [9.700980186462402, 1.999404788017273] |
463a5733-f47f-48bf-90b5-6d41c2d376ed | normalization-in-training-u-net-for-2d | 1809.03783 | null | http://arxiv.org/abs/1809.03783v3 | http://arxiv.org/pdf/1809.03783v3.pdf | Normalization in Training U-Net for 2D Biomedical Semantic Segmentation | 2D biomedical semantic segmentation is important for robotic vision in
surgery. Segmentation methods based on Deep Convolutional Neural Network (DCNN)
can out-perform conventional methods in terms of both accuracy and levels of
automation. One common issue in training a DCNN for biomedical semantic
segmentation is the ... | ['Guang-Zhong Yang', 'Xiao-Yun Zhou'] | 2018-09-11 | null | null | null | null | ['2d-semantic-segmentation'] | ['computer-vision'] | [ 1.89351514e-01 3.23109239e-01 -2.00145930e-01 -3.82223278e-01
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-1.01509616e-01 -6.97936594e-01 -3.17885190e-01 -8.38093162e-01
1.61456779e-01 2.83095688e-01 2.86884397e-01 -6.09162077... | [14.625676155090332, -2.5937044620513916] |
5c8e8362-2b7b-43bf-aafd-a2b4f08299e0 | community-detection-with-known-unknown-or | 2301.04088 | null | https://arxiv.org/abs/2301.04088v1 | https://arxiv.org/pdf/2301.04088v1.pdf | Community Detection with Known, Unknown, or Partially Known Auxiliary Latent Variables | Empirical observations suggest that in practice, community membership does not completely explain the dependency between the edges of an observation graph. The residual dependence of the graph edges are modeled in this paper, to first order, by auxiliary node latent variables that affect the statistics of the graph edg... | ['Aria Nosratinia', 'Mohammad Esmaeili'] | 2023-01-08 | null | null | null | null | ['stochastic-block-model', 'community-detection'] | ['graphs', 'graphs'] | [ 2.88555712e-01 5.52273035e-01 -5.11735678e-01 1.65278148e-02
-5.36029100e-01 -7.66389370e-01 2.84580350e-01 1.40789598e-02
6.49642125e-02 9.01143014e-01 1.23313859e-01 -1.83551684e-01
-4.88981962e-01 -6.14015818e-01 -7.41325498e-01 -1.05370212e+00
-4.03104484e-01 1.06316650e+00 -3.58549953e-01 3.78316194... | [6.904253959655762, 5.149803638458252] |
1d37e617-379a-436d-8522-1c59ea984036 | label-informed-graph-structure-learning-for | 2108.04595 | null | https://arxiv.org/abs/2108.04595v1 | https://arxiv.org/pdf/2108.04595v1.pdf | Label-informed Graph Structure Learning for Node Classification | Graph Neural Networks (GNNs) have achieved great success among various domains. Nevertheless, most GNN methods are sensitive to the quality of graph structures. To tackle this problem, some studies exploit different graph structure learning strategies to refine the original graph structure. However, these methods only ... | ['Liang Wang', 'Shu Wu', 'Fenyu Hu', 'Liping Wang'] | 2021-08-10 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 1.82905838e-01 1.91304654e-01 -8.11969161e-01 -3.56021047e-01
-1.06096320e-01 -4.39568311e-01 5.94138384e-01 3.85383785e-01
-3.27444315e-01 7.24829793e-01 -1.54684773e-02 -4.26016092e-01
-1.95518717e-01 -1.07653272e+00 -3.96389812e-01 -5.35346150e-01
-2.06813030e-02 3.64370584e-01 3.91902149e-01 -3.12700160... | [7.204887866973877, 6.320528030395508] |
62bbb4ba-6663-4067-8476-48d48f64ab4e | a-weakly-supervised-approach-to-emotion | 2306.06979 | null | https://arxiv.org/abs/2306.06979v1 | https://arxiv.org/pdf/2306.06979v1.pdf | A Weakly Supervised Approach to Emotion-change Prediction and Improved Mood Inference | Whilst a majority of affective computing research focuses on inferring emotions, examining mood or understanding the \textit{mood-emotion interplay} has received significantly less attention. Building on prior work, we (a) deduce and incorporate emotion-change ($\Delta$) information for inferring mood, without resortin... | ['Roland Goecke', 'Ramanathan Subramanian', 'Akshay Asthana', 'Iman Abbasnejad', 'Ravikiran Parameshwara', 'Ibrahim Radwan', 'Soujanya Narayana'] | 2023-06-12 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [ 5.39569795e-01 1.86744407e-02 9.49447230e-02 -9.27717626e-01
-6.97575271e-01 -6.71710491e-01 4.31579441e-01 3.36027056e-01
-2.95120776e-01 5.54646611e-01 3.59315723e-01 1.14911504e-01
-9.09868404e-02 -3.48851502e-01 -1.53306916e-01 -5.75294793e-01
-1.75172612e-01 1.91117793e-01 -8.23963761e-01 -2.65660018... | [13.296934127807617, 5.365234375] |
713feeeb-fa5f-4788-9c89-4e086c57c6fa | multimodal-contrastive-learning-for | 2304.11080 | null | https://arxiv.org/abs/2304.11080v1 | https://arxiv.org/pdf/2304.11080v1.pdf | Multimodal contrastive learning for diagnosing cardiovascular diseases from electrocardiography (ECG) signals and patient metadata | This work discusses the use of contrastive learning and deep learning for diagnosing cardiovascular diseases from electrocardiography (ECG) signals. While the ECG signals usually contain 12 leads (channels), many healthcare facilities and devices lack access to all these 12 leads. This raises the problem of how to use ... | ['Hieu Pham', 'Phi Le Nguyen', 'Nhat H. Tran', 'Tue M. Cao'] | 2023-04-18 | null | null | null | null | ['electrocardiography-ecg'] | ['methodology'] | [ 1.44166112e-01 9.98164713e-02 9.75528732e-02 -3.89131367e-01
-5.33153951e-01 -3.81124437e-01 5.50278313e-02 1.18095070e-01
-3.75004292e-01 7.34226525e-01 -3.47362049e-02 -5.96862078e-01
-4.33809280e-01 -5.00652790e-01 -3.53900433e-01 -6.36235893e-01
-6.46495044e-01 3.08727086e-01 -3.47948760e-01 -5.16388044... | [14.318079948425293, 3.298522710800171] |
990e03b6-46b6-4ca6-af58-b79a6acc2e55 | streaming-parallel-transducer-beam-search | 2203.15773 | null | https://arxiv.org/abs/2203.15773v1 | https://arxiv.org/pdf/2203.15773v1.pdf | Streaming parallel transducer beam search with fast-slow cascaded encoders | Streaming ASR with strict latency constraints is required in many speech recognition applications. In order to achieve the required latency, streaming ASR models sacrifice accuracy compared to non-streaming ASR models due to lack of future input context. Previous research has shown that streaming and non-streaming ASR ... | ['Michael L Seltzer', 'Ozlem Kalinli', 'Vikas Chandra', 'Jiedan Zhu', 'Duc Le', 'Ke Li', 'Yangyang Shi', 'Jay Mahadeokar'] | 2022-03-29 | null | null | null | null | ['low-latency-processing'] | ['robots'] | [ 6.49622083e-01 1.86421499e-01 4.48610820e-02 -4.12728041e-01
-1.20499146e+00 -5.73868871e-01 2.92408943e-01 6.17409572e-02
-5.10612130e-01 3.00939530e-01 5.19931972e-01 -6.54369533e-01
1.13392390e-01 -5.18880665e-01 -9.14264023e-01 -4.99811262e-01
1.32162318e-01 2.72100568e-01 7.05712259e-01 -2.44161282... | [14.489425659179688, 6.798915863037109] |
bb4de338-0466-4ba3-9172-e60d78d85510 | agcn-adversarial-graph-convolutional-network | null | null | https://www.bmvc2021-virtualconference.com/assets/papers/1545.pdf | https://www.bmvc2021-virtualconference.com/assets/papers/1545.pdf | AGCN: Adversarial Graph Convolutional Network for 3D Point Cloud Segmentation | 3D point cloud segmentation provides a high-level semantic understanding of object structure that is valuable in applications such as medicine, robotics and self-driving. In this paper, we propose an Adversarial Graph Convolutional Network for 3D point cloud segmentation. Many current networks encounter problems such a... | ['Daniel C. Alexander', 'Seunghoi Kim'] | 2021-11-25 | null | null | null | british-machine-vision-conference-bmvc-2021 | ['3d-part-segmentation', 'point-cloud-segmentation'] | ['computer-vision', 'computer-vision'] | [ 7.23878741e-02 4.35077310e-01 8.61399472e-02 -3.12942624e-01
-3.54485035e-01 -5.81694245e-01 2.78588623e-01 1.15093671e-01
-2.34467253e-01 2.87619174e-01 -5.43270767e-01 -2.46170774e-01
1.05272800e-01 -1.30768871e+00 -9.84995306e-01 -6.94712520e-01
-1.27562761e-01 5.67888260e-01 6.01341903e-01 -1.71691164... | [7.953258514404297, -3.469313144683838] |
66c97663-9ef6-4d41-9119-3483a6a62bdf | differential-private-stack-generalization | 1811.09491 | null | https://arxiv.org/abs/1811.09491v3 | https://arxiv.org/pdf/1811.09491v3.pdf | Differential Private Stack Generalization with an Application to Diabetes Prediction | To meet the standard of differential privacy, noise is usually added into the original data, which inevitably deteriorates the predicting performance of subsequent learning algorithms. In this paper, motivated by the success of improving predicting performance by ensemble learning, we propose to enhance privacy-preserv... | ['Quanming Yao', 'WeiWei Tu', 'Wenyuan Dai', 'Qiang Yang', 'James T. Kwok', 'Yuqiang Chen', 'Xiawei Guo'] | 2018-11-23 | null | null | null | null | ['diabetes-prediction'] | ['medical'] | [ 1.86639547e-01 5.05923294e-03 -1.04569279e-01 -5.48032284e-01
-7.33653426e-01 -4.16529715e-01 7.68834911e-03 3.88267934e-01
-4.85640496e-01 1.20339739e+00 2.91056708e-02 -3.70383114e-01
-2.14157477e-01 -9.05530930e-01 -9.70587134e-01 -9.20776725e-01
6.30524680e-02 1.17745697e-01 -2.59495258e-01 2.41645768... | [6.111204147338867, 6.628015518188477] |
ee13b7ce-29e8-416b-b413-b46c0bce608d | modelling-radiological-language-with | 1609.08409 | null | http://arxiv.org/abs/1609.08409v1 | http://arxiv.org/pdf/1609.08409v1.pdf | Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks | Motivated by the need to automate medical information extraction from
free-text radiological reports, we present a bi-directional long short-term
memory (BiLSTM) neural network architecture for modelling radiological
language. The model has been used to address two NLP tasks: medical
named-entity recognition (NER) and ... | ['Samuel Withey', 'Giovanni Montana', 'Robert Bakewell', 'Savelie Cornegruta'] | 2016-09-27 | modelling-radiological-language-with-1 | https://aclanthology.org/W16-6103 | https://aclanthology.org/W16-6103.pdf | ws-2016-11 | ['negation-detection', 'medical-named-entity-recognition'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.55496919e-01 6.43742919e-01 -2.36525118e-01 -3.87390196e-01
-1.05618227e+00 -1.43225893e-01 4.10734028e-01 7.93844819e-01
-1.28181994e+00 6.03431702e-01 6.01660669e-01 -8.45379829e-01
-9.38668028e-02 -7.83281922e-01 -4.13144171e-01 -4.15944785e-01
-2.33075723e-01 4.37203139e-01 2.72929788e-01 -3.44224125... | [8.482357025146484, 8.728422164916992] |
7db4622f-586a-4309-b0e5-1ca2fc95b6ed | towards-open-vocabulary-scene-graph | 2208.08165 | null | https://arxiv.org/abs/2208.08165v3 | https://arxiv.org/pdf/2208.08165v3.pdf | Towards Open-vocabulary Scene Graph Generation with Prompt-based Finetuning | Scene graph generation (SGG) is a fundamental task aimed at detecting visual relations between objects in an image. The prevailing SGG methods require all object classes to be given in the training set. Such a closed setting limits the practical application of SGG. In this paper, we introduce open-vocabulary scene grap... | ['Yuan-Fang Li', 'Jingkuan Song', 'Lianli Gao', 'Tao He'] | 2022-08-17 | null | null | null | null | ['scene-graph-generation'] | ['computer-vision'] | [ 6.25045180e-01 4.17840123e-01 6.46227598e-02 -3.21286261e-01
-6.00086749e-01 -6.68126464e-01 8.63199830e-01 1.75229281e-01
-8.41106400e-02 4.72914785e-01 -8.32997710e-02 -4.42610472e-01
2.51817942e-01 -9.12368774e-01 -1.15615904e+00 -5.93845248e-01
2.53204882e-01 6.12989485e-01 5.16704679e-01 -1.18629798... | [10.33988094329834, 1.6138097047805786] |
8619df05-b023-4c63-be88-c42a87dba003 | the-best-of-both-worlds-combining-human-and | 2305.12737 | null | https://arxiv.org/abs/2305.12737v1 | https://arxiv.org/pdf/2305.12737v1.pdf | The Best of Both Worlds: Combining Human and Machine Translations for Multilingual Semantic Parsing with Active Learning | Multilingual semantic parsing aims to leverage the knowledge from the high-resource languages to improve low-resource semantic parsing, yet commonly suffers from the data imbalance problem. Prior works propose to utilize the translations by either humans or machines to alleviate such issues. However, human translations... | ['Gholamreza Haffari', 'Raj V. Tumuluri', 'Philip R. Cohen', 'Lizhen Qu', 'Zhuang Li'] | 2023-05-22 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 3.22498202e-01 4.44687963e-01 -7.82229722e-01 -8.38859618e-01
-1.63336527e+00 -7.19699621e-01 1.40784115e-01 1.47349805e-01
-5.57955325e-01 8.68671417e-01 3.22553337e-01 -3.79418999e-01
3.77721459e-01 -8.22407126e-01 -8.51799726e-01 -3.69400114e-01
6.23063266e-01 7.02076495e-01 -3.48429382e-02 -2.71157086... | [11.379995346069336, 10.184192657470703] |
fa50bdaf-ca10-43b7-bbf8-c2fddb20ed6b | videomix-rethinking-data-augmentation-for | 2012.03457 | null | https://arxiv.org/abs/2012.03457v1 | https://arxiv.org/pdf/2012.03457v1.pdf | VideoMix: Rethinking Data Augmentation for Video Classification | State-of-the-art video action classifiers often suffer from overfitting. They tend to be biased towards specific objects and scene cues, rather than the foreground action content, leading to sub-optimal generalization performances. Recent data augmentation strategies have been reported to address the overfitting proble... | ['Jinhyung Kim', 'Dongyoon Han', 'Byeongho Heo', 'Seong Joon Oh', 'Sangdoo Yun'] | 2020-12-07 | null | null | null | null | ['weakly-supervised-action-localization'] | ['computer-vision'] | [ 6.61266208e-01 -1.39869079e-01 -6.14311814e-01 -1.10254191e-01
-8.37647736e-01 -3.05174857e-01 8.23649824e-01 -1.51169419e-01
-5.95867455e-01 5.58596611e-01 3.04814756e-01 1.74292848e-01
5.41565776e-01 -2.46874273e-01 -1.09472716e+00 -1.08297133e+00
-1.70387682e-02 2.98743099e-01 5.59665918e-01 1.95751056... | [8.5353422164917, 0.7424302697181702] |
681064e9-fc80-4b58-b0f2-e04242a0cf0f | ultra-low-power-and-real-time-ecg-1 | 1905.02954 | null | https://arxiv.org/abs/1905.02954v4 | https://arxiv.org/pdf/1905.02954v4.pdf | Ultra Low-Power and Real-time ECG Classification Based on STDP and R-STDP Neural Networks for Wearable Devices | This paper presents a novel ECG classification algorithm for real-time cardiac monitoring on ultra low-power wearable devices. The proposed solution is based on spiking neural networks which are the third generation of neural networks. In specific, we employ spike-timing dependent plasticity (STDP), and reward-modulate... | ['Alireza Amirshahi', 'Matin Hashemi'] | 2019-05-08 | ultra-low-power-and-real-time-ecg | null | null | arxiv190502954-2019-5 | ['ecg-classification'] | ['medical'] | [ 5.25797725e-01 -5.32828391e-01 -1.47042394e-01 -1.72479391e-01
1.51947945e-01 -2.43630037e-01 -8.18028580e-04 4.02597845e-01
-6.55983210e-01 1.15504038e+00 -3.88230801e-01 9.26870108e-02
-1.75972953e-01 -6.79005682e-01 -4.90241021e-01 -8.94121766e-01
-9.73725915e-02 -5.11183143e-02 5.47127783e-01 -1.26016378... | [8.294479370117188, 2.479668378829956] |
f030bfe6-ffc9-4dd8-80e5-112db70fb5a4 | vision-language-pre-training-for-multimodal | 2204.07955 | null | https://arxiv.org/abs/2204.07955v2 | https://arxiv.org/pdf/2204.07955v2.pdf | Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis | As an important task in sentiment analysis, Multimodal Aspect-Based Sentiment Analysis (MABSA) has attracted increasing attention in recent years. However, previous approaches either (i) use separately pre-trained visual and textual models, which ignore the crossmodal alignment or (ii) use vision-language models pre-tr... | ['Jianfei Yu', 'Rui Xia', 'Yan Ling'] | 2022-04-17 | null | https://aclanthology.org/2022.acl-long.152 | https://aclanthology.org/2022.acl-long.152.pdf | acl-2022-5 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [-2.71539409e-02 -1.64002404e-01 -9.57794115e-02 -5.80876529e-01
-1.01704657e+00 -4.79412228e-01 8.26825202e-01 -3.73756252e-02
-5.88062882e-01 2.01695323e-01 2.98505992e-01 -4.11140740e-01
5.02435744e-01 -3.66701990e-01 -6.83002174e-01 -5.35972416e-01
6.50123298e-01 3.78429443e-01 -1.14180177e-01 -2.81577080... | [10.827339172363281, 1.583335041999817] |
3ec08b76-5520-4453-ba1d-010ec27383d7 | inter-view-depth-consistency-testing-in-depth | 2301.11752 | null | https://arxiv.org/abs/2301.11752v1 | https://arxiv.org/pdf/2301.11752v1.pdf | Inter-View Depth Consistency Testing in Depth Difference Subspace | Multiview depth imagery will play a critical role in free-viewpoint television. This technology requires high quality virtual view synthesis to enable viewers to move freely in a dynamic real world scene. Depth imagery at different viewpoints is used to synthesize an arbitrary number of novel views. Usually, depth imag... | ['Markus Flierl', 'Pravin Kumar Rana'] | 2023-01-27 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 5.09056300e-02 -1.98233113e-01 2.08076835e-01 -3.45531017e-01
-5.48141301e-01 -6.41519904e-01 3.43380004e-01 -4.37442243e-01
5.78600261e-03 4.98640120e-01 8.94979015e-02 7.02541843e-02
5.49789853e-02 -8.97448003e-01 -4.19633329e-01 -6.80733383e-01
2.82456636e-01 2.70188421e-01 4.97990668e-01 -2.78622657... | [9.219407081604004, -2.5282692909240723] |
19bced65-165f-4083-9e04-1b680274cd39 | avatarbooth-high-quality-and-customizable-3d | 2306.09864 | null | https://arxiv.org/abs/2306.09864v1 | https://arxiv.org/pdf/2306.09864v1.pdf | AvatarBooth: High-Quality and Customizable 3D Human Avatar Generation | We introduce AvatarBooth, a novel method for generating high-quality 3D avatars using text prompts or specific images. Unlike previous approaches that can only synthesize avatars based on simple text descriptions, our method enables the creation of personalized avatars from casually captured face or body images, while ... | ['Xun Cao', 'Hao Zhu', 'Yao Yao', 'Xinya Ji', 'Yuanxun Lu', 'Yifei Zeng'] | 2023-06-16 | null | null | null | null | ['text-to-3d'] | ['computer-vision'] | [-9.15330555e-03 3.25554490e-01 4.21449006e-01 -3.33553761e-01
-5.05530596e-01 -4.73995209e-01 6.18053317e-01 -5.09243429e-01
1.17708929e-01 4.70048457e-01 1.92574039e-01 3.64848822e-01
2.04335049e-01 -6.06492281e-01 -5.52561283e-01 -5.33833325e-01
4.91917193e-01 6.42771065e-01 7.08360672e-02 -3.54125440... | [12.345914840698242, -0.5842046737670898] |
f35dbc0b-42c0-473d-95f7-6bc87b599ba7 | a-study-of-acoustic-features-in-arabic | 2110.12304 | null | https://arxiv.org/abs/2110.12304v1 | https://arxiv.org/pdf/2110.12304v1.pdf | A Study of Acoustic Features in Arabic Speaker Identification under Noisy Environmental Conditions | One of the major parts of the voice recognition field is the choice of acoustic features which have to be robust against the variability of the speech signal, mismatched conditions, and noisy environments. Thus, different speech feature extraction techniques have been developed. In this paper, we investigate the robust... | ['Abderrahmane Amrouche', 'Kawthar Yasmine Zergat', 'Zhor Benhafid'] | 2021-10-23 | null | null | null | null | ['speaker-identification'] | ['speech'] | [-2.46912494e-01 -6.67287827e-01 5.12918890e-01 -1.13005809e-01
-5.08268774e-01 -5.69816887e-01 7.26991177e-01 1.38720751e-01
-5.00765324e-01 6.62229121e-01 4.08049196e-01 -1.84969738e-01
-3.39446276e-01 -2.46653184e-01 8.77235457e-02 -8.72217417e-01
-2.59140104e-01 -2.55205780e-01 3.53146493e-01 -4.88848895... | [14.730911254882812, 5.928839206695557] |
cb1f583c-4de3-41e2-a016-661ff1a714e4 | call-larisa-ivanovna-code-switching-fools | 2109.14350 | null | https://arxiv.org/abs/2109.14350v2 | https://arxiv.org/pdf/2109.14350v2.pdf | Call Larisa Ivanovna: Code-Switching Fools Multilingual NLU Models | Practical needs of developing task-oriented dialogue assistants require the ability to understand many languages. Novel benchmarks for multilingual natural language understanding (NLU) include monolingual sentences in several languages, annotated with intents and slots. In such setup models for cross-lingual transfer s... | ['Ekaterina Artemova', 'Alexey Birshert'] | 2021-09-29 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [ 7.59096444e-02 5.77829480e-01 -2.49440670e-01 -3.90106916e-01
-1.10273743e+00 -7.84258425e-01 6.48439169e-01 5.46788052e-02
-1.84934884e-01 1.07815540e+00 2.40998492e-01 -8.32997978e-01
2.32640579e-01 -2.73511022e-01 -8.07933450e-01 -9.44700390e-02
-2.13824473e-02 1.00751972e+00 1.00029288e-02 -6.53476238... | [12.284607887268066, 8.51719856262207] |
dc44ffb3-768f-4d55-9911-61a5eb97e715 | braid-weaving-symbolic-and-statistical | 2011.13354 | null | https://arxiv.org/abs/2011.13354v4 | https://arxiv.org/pdf/2011.13354v4.pdf | Braid: Weaving Symbolic and Neural Knowledge into Coherent Logical Explanations | Traditional symbolic reasoning engines, while attractive for their precision and explicability, have a few major drawbacks: the use of brittle inference procedures that rely on exact matching (unification) of logical terms, an inability to deal with uncertainty, and the need for a precompiled rule-base of knowledge (th... | ['David Ferrucci', 'Tom Breloff', 'Aditya Kalyanpur'] | 2020-11-26 | null | null | null | null | ['cloze-test'] | ['natural-language-processing'] | [-7.07746819e-02 8.27995956e-01 -1.97079957e-01 -7.00228810e-01
-8.51849377e-01 -4.27913964e-01 6.08772457e-01 3.60656917e-01
1.63427740e-01 8.95081401e-01 2.42196217e-01 -6.14709556e-01
-8.28447938e-01 -9.39063609e-01 -7.88519979e-01 -1.28022926e-02
1.48358047e-01 1.01991320e+00 5.99399030e-01 -2.93274313... | [9.134002685546875, 7.133362770080566] |
141f64d8-2d29-46ab-b09b-65131b0cbde6 | progressive-upsampling-audio-synthesis-via | null | null | https://openreview.net/forum?id=Skg9jnVFvH | https://openreview.net/pdf?id=Skg9jnVFvH | Progressive Upsampling Audio Synthesis via Effective Adversarial Training | This paper proposes a novel generative model called PUGAN, which progressively synthesizes high-quality audio in a raw waveform. PUGAN leverages on the recently proposed idea of progressive generation of higher-resolution images by stacking multiple encode-decoder architectures. To effectively apply it to raw audio gen... | ['Jaegul Choo', 'Gerard Jounghyun Kim', 'Minwook Chang', 'Youngwoo Cho'] | 2019-09-25 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 4.24003035e-01 3.43113780e-01 2.27410316e-01 4.58053462e-02
-1.32607377e+00 -3.96540165e-01 5.30768812e-01 -4.70520228e-01
3.98488641e-02 7.43440807e-01 4.12917376e-01 -1.50836393e-01
4.17249203e-01 -1.00416720e+00 -9.42228496e-01 -4.13858086e-01
-5.42481095e-02 -1.88011825e-02 2.61496067e-01 -1.73100233... | [15.531429290771484, 5.848784923553467] |
645654a6-3acf-4736-ae09-83929ff6ed4a | global-universal-approximation-of-functional | 2306.03303 | null | https://arxiv.org/abs/2306.03303v1 | https://arxiv.org/pdf/2306.03303v1.pdf | Global universal approximation of functional input maps on weighted spaces | We introduce so-called functional input neural networks defined on a possibly infinite dimensional weighted space with values also in a possibly infinite dimensional output space. To this end, we use an additive family as hidden layer maps and a non-linear activation function applied to each hidden layer. Relying on St... | ['Josef Teichmann', 'Philipp Schmocker', 'Christa Cuchiero'] | 2023-06-05 | null | null | null | null | ['gaussian-processes'] | ['methodology'] | [-2.86659598e-02 4.46512043e-01 2.25925729e-01 -3.54390264e-01
-5.28841615e-01 -2.39648938e-01 5.15125930e-01 -6.26637554e-03
-6.28573179e-01 5.95581710e-01 1.27218112e-01 -3.38635802e-01
-2.74805099e-01 -1.05793357e+00 -7.56322742e-01 -1.13509822e+00
-5.06453812e-01 3.08484375e-01 7.44493902e-02 -2.18491703... | [7.473751544952393, 3.788459539413452] |
f5858e2c-5cf7-4ae4-9907-76dbfc2542eb | sensala-a-dynamic-semantics-system-for | null | null | https://aclanthology.org/C18-2027 | https://aclanthology.org/C18-2027.pdf | Sensala: a Dynamic Semantics System for Natural Language Processing | Here we describe Sensala , an open source framework for the semantic interpretation of natural language that provides the logical meaning of a given text. The framework{'}s theory is based on a lambda calculus with exception handling and uses contexts, continuations, events and dependent types to handle a wide range of... | ['Ekaterina Lebedeva', 'Daniyar Itegulov', 'Bruno Woltzenlogel Paleo'] | 2018-08-01 | sensala-a-dynamic-semantics-system-for-1 | https://aclanthology.org/C18-2027 | https://aclanthology.org/C18-2027.pdf | coling-2018-8 | ['implicatures'] | ['natural-language-processing'] | [-2.23995462e-01 5.79404354e-01 -3.79666418e-01 -4.18924868e-01
8.87642205e-02 -7.91636586e-01 9.94216919e-01 6.94507718e-01
-4.98682559e-01 1.17149091e+00 6.07104540e-01 -4.98328775e-01
-4.68767136e-01 -1.04043412e+00 -2.36175969e-01 -1.18919782e-01
3.73985269e-03 3.60778540e-01 1.03635120e+00 -9.07630503... | [10.032136917114258, 9.187586784362793] |
7300f3b4-709f-4c0d-b71d-660cf37073de | weakly-supervised-instance-segmentation-using-3 | null | null | http://papers.nips.cc/paper/8885-weakly-supervised-instance-segmentation-using-the-bounding-box-tightness-prior | http://papers.nips.cc/paper/8885-weakly-supervised-instance-segmentation-using-the-bounding-box-tightness-prior.pdf | Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior | This paper presents a weakly supervised instance segmentation method that consumes training data with tight bounding box annotations. The major difficulty lies in the uncertain figure-ground separation within each bounding box since there is no supervisory signal about it. We address the difficulty by formulating the p... | ['Yung-Yu Chuang', 'Yen-Yu Lin', 'Chung-Chi Tsai', 'Kuang-Jui Hsu', 'Cheng-Chun Hsu'] | 2019-12-01 | null | null | null | neurips-2019-12 | ['box-supervised-instance-segmentation', 'weakly-supervised-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 3.90002012e-01 5.85337400e-01 -4.96760994e-01 -6.93005979e-01
-1.08613837e+00 -6.30066514e-01 3.68151337e-01 1.60797387e-01
-3.65245491e-01 8.87269318e-01 -4.89989638e-01 -1.20835632e-01
9.60119218e-02 -6.43135428e-01 -1.08900845e+00 -1.00582576e+00
1.46193296e-01 8.01895976e-01 5.59454620e-01 1.52544513... | [9.510854721069336, 0.539635419845581] |
2bb2b33c-7952-46a2-a0f1-c38490992f1e | unified-io-a-unified-model-for-vision | 2206.08916 | null | https://arxiv.org/abs/2206.08916v2 | https://arxiv.org/pdf/2206.08916v2.pdf | Unified-IO: A Unified Model for Vision, Language, and Multi-Modal Tasks | We propose Unified-IO, a model that performs a large variety of AI tasks spanning classical computer vision tasks, including pose estimation, object detection, depth estimation and image generation, vision-and-language tasks such as region captioning and referring expression, to natural language processing tasks such a... | ['Aniruddha Kembhavi', 'Roozbeh Mottaghi', 'Rowan Zellers', 'Christopher Clark', 'Jiasen Lu'] | 2022-06-17 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [ 1.36945605e-01 -6.83213770e-02 8.60921759e-03 -5.28361261e-01
-8.64298880e-01 -7.29292870e-01 7.50552535e-01 -1.41307130e-01
-4.16291624e-01 4.65502739e-01 1.20585971e-01 -4.02340591e-01
4.23935056e-01 -7.47754037e-01 -8.88742626e-01 -3.67484510e-01
3.55267555e-01 4.61404949e-01 4.40242141e-01 -2.93598473... | [10.263580322265625, 1.3570305109024048] |
c57ab201-9e5b-4836-b50e-2f66ca25e937 | disentangling-human-dynamics-for-pedestrian | 1911.01138 | null | https://arxiv.org/abs/1911.01138v2 | https://arxiv.org/pdf/1911.01138v2.pdf | Disentangling Human Dynamics for Pedestrian Locomotion Forecasting with Noisy Supervision | We tackle the problem of Human Locomotion Forecasting, a task for jointly predicting the spatial positions of several keypoints on the human body in the near future under an egocentric setting. In contrast to the previous work that aims to solve either the task of pose prediction or trajectory forecasting in isolation,... | ['Adrien Gaidon', 'Kuan-Hui Lee', 'Juan Carlos Niebles', 'Ehsan Adeli', 'Karttikeya Mangalam'] | 2019-11-04 | null | null | null | null | ['human-dynamics'] | ['computer-vision'] | [-2.16481790e-01 -4.68198098e-02 -1.75522361e-02 -3.23676705e-01
-6.67675078e-01 -3.14988643e-01 5.88456810e-01 -3.48247081e-01
-3.59778315e-01 6.59959912e-01 6.51352882e-01 2.40926728e-01
2.85052985e-01 -7.69910812e-01 -8.94127011e-01 -8.08488667e-01
-1.79056913e-01 5.47923326e-01 3.48869324e-01 -3.32822919... | [7.171999454498291, -0.35317516326904297] |
ed7b3717-473e-47c7-9e7e-6a53edf036eb | intersection-warning-system-for-occlusion | 2303.07227 | null | https://arxiv.org/abs/2303.07227v1 | https://arxiv.org/pdf/2303.07227v1.pdf | Intersection Warning System for Occlusion Risks using Relational Local Dynamic Maps | This work addresses the task of risk evaluation in traffic scenarios with limited observability due to restricted sensorial coverage. Here, we concentrate on intersection scenarios that are difficult to access visually. To identify the area of sight, we employ ray casting on a local dynamic map providing geometrical in... | ['Julian Eggert', 'Benedict Flade', 'Tim Puphal', 'Yuda Li', 'Florian Damerow'] | 2023-03-13 | null | null | null | null | ['survival-analysis'] | ['miscellaneous'] | [ 3.55988741e-01 5.24219334e-01 1.68899357e-01 -2.20307127e-01
-7.07018733e-01 -3.48596215e-01 6.09188318e-01 7.58807361e-01
-6.99591756e-01 6.07047975e-01 9.60437134e-02 -9.01025951e-01
-4.21500683e-01 -1.09037161e+00 -4.68717337e-01 -5.37097454e-01
-2.44810045e-01 2.61171043e-01 6.01971865e-01 -3.43597025... | [5.7290940284729, 1.2664995193481445] |
83bc998d-ae91-44ee-8db8-26f3fadfb093 | exploring-phonetic-context-in-lip-movement | 2305.19556 | null | https://arxiv.org/abs/2305.19556v1 | https://arxiv.org/pdf/2305.19556v1.pdf | Exploring Phonetic Context in Lip Movement for Authentic Talking Face Generation | Talking face generation is the task of synthesizing a natural face synchronous to driving audio. Although much progress has been made in terms of visual quality, lip synchronization, and facial motion of the talking face, current works still struggle to overcome issues of crude and asynchronous lip movement, which can ... | ['Yong Man Ro', 'Jeongsoo Choi', 'Minsu Kim', 'Se Jin Park'] | 2023-05-31 | null | null | null | null | ['talking-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision'] | [ 3.09064478e-01 -5.42525761e-02 -3.83273989e-01 -8.92407522e-02
-1.06141996e+00 -4.77523804e-01 5.81492424e-01 -6.71702683e-01
2.04417944e-01 4.84322131e-01 5.87745309e-01 -3.67065280e-04
5.03266633e-01 -3.35927218e-01 -7.47233927e-01 -7.98512876e-01
2.91257739e-01 -1.94522992e-01 1.55892968e-02 8.58051237... | [13.263091087341309, -0.3988827168941498] |
b4c0ee68-6c8d-4e4a-8bcf-6bcf7f3d3708 | osu-multimodal-machine-translation-system | 1710.02718 | null | http://arxiv.org/abs/1710.02718v2 | http://arxiv.org/pdf/1710.02718v2.pdf | OSU Multimodal Machine Translation System Report | This paper describes Oregon State University's submissions to the shared
WMT'17 task "multimodal translation task I". In this task, all the sentence
pairs are image captions in different languages. The key difference between
this task and conventional machine translation is that we have corresponding
images as addition... | ['Mingbo Ma', 'Dapeng Li', 'Liang Huang', 'Kai Zhao'] | 2017-10-07 | osu-multimodal-machine-translation-system-1 | https://aclanthology.org/W17-4751 | https://aclanthology.org/W17-4751.pdf | ws-2017-9 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [ 5.05130649e-01 -1.22741777e-02 6.72370866e-02 -5.45696080e-01
-1.47726023e+00 -7.41398096e-01 7.45787263e-01 -5.58585644e-01
-8.63361716e-01 1.06373501e+00 1.70947149e-01 -4.90158886e-01
7.58675992e-01 -2.25858241e-01 -9.69207644e-01 -2.84961075e-01
4.62486118e-01 3.95536065e-01 1.76140919e-01 -3.45809728... | [11.449946403503418, 1.51760995388031] |
96140b8c-cd56-43b5-8380-c27f93ba7934 | exploring-regions-of-interest-visualizing | 2305.20058 | null | https://arxiv.org/abs/2305.20058v1 | https://arxiv.org/pdf/2305.20058v1.pdf | Exploring Regions of Interest: Visualizing Histological Image Classification for Breast Cancer using Deep Learning | Computer aided detection and diagnosis systems based on deep learning have shown promising performance in breast cancer detection. However, there are cases where the obtained results lack justification. In this study, our objective is to highlight the regions of interest used by a convolutional neural network (CNN) for... | ['Mohammed Amine Chikh', 'Khadidja Abi Ayad', 'Said Mahmoudi', 'Mohammed Brahimi', 'Imane Nedjar'] | 2023-05-31 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [-3.97998728e-02 1.68210968e-01 -3.36579569e-02 -2.87319243e-01
-2.38299370e-01 -1.67872459e-01 7.06927359e-01 6.70874178e-01
-6.28068924e-01 5.60042620e-01 -1.75373152e-01 -8.87901902e-01
-1.80573702e-01 -7.80425847e-01 -2.52799928e-01 -9.61199820e-01
-3.85625035e-01 -5.77780306e-02 2.24708915e-01 -2.26407200... | [15.26225757598877, -2.8937172889709473] |
1b17ba6d-0349-4f58-91ee-54680c60419e | shadow-neural-radiance-fields-for-multi-view | 2104.09877 | null | https://arxiv.org/abs/2104.09877v1 | https://arxiv.org/pdf/2104.09877v1.pdf | Shadow Neural Radiance Fields for Multi-view Satellite Photogrammetry | We present a new generic method for shadow-aware multi-view satellite photogrammetry of Earth Observation scenes. Our proposed method, the Shadow Neural Radiance Field (S-NeRF) follows recent advances in implicit volumetric representation learning. For each scene, we train S-NeRF using very high spatial resolution opti... | ['Dario Izzo', 'Dawa Derksen'] | 2021-04-20 | null | null | null | null | ['shadow-detection'] | ['computer-vision'] | [ 4.89877820e-01 -1.61570489e-01 2.41973132e-01 -4.74373817e-01
-2.71430224e-01 -6.95139706e-01 6.41080797e-01 -4.01244074e-01
-4.97424975e-02 8.59012067e-01 1.50860921e-01 -9.94112715e-02
1.56679109e-01 -9.51901913e-01 -9.27278578e-01 -1.08435595e+00
3.35113764e-01 4.18350995e-01 3.07118595e-01 -4.19735849... | [9.864337921142578, -3.005108594894409] |
86c1c1fd-d53a-498e-9ab1-7503cc576055 | copula-entropy-based-variable-selection-for | 2209.01561 | null | https://arxiv.org/abs/2209.01561v1 | https://arxiv.org/pdf/2209.01561v1.pdf | Copula Entropy based Variable Selection for Survival Analysis | Variable selection is an important problem in statistics and machine learning. Copula Entropy (CE) is a mathematical concept for measuring statistical independence and has been applied to variable selection recently. In this paper we propose to apply the CE-based method for variable selection to survival analysis. The ... | ['Jian Ma'] | 2022-09-04 | null | null | null | null | ['variable-selection', 'survival-analysis'] | ['methodology', 'miscellaneous'] | [ 1.11887179e-01 -2.17470735e-01 -5.14333606e-01 -4.93766665e-01
-6.05574965e-01 6.90126419e-02 1.05766758e-01 4.46984261e-01
-5.45876741e-01 1.49631500e+00 2.10267529e-01 -3.00521523e-01
-4.31224763e-01 -8.68068993e-01 -1.18717467e-02 -9.75013733e-01
-5.82757056e-01 5.55383623e-01 -1.90911219e-01 5.57684004... | [7.846938133239746, 4.902050018310547] |
ca65a0a3-b157-4fc8-bf86-59db5b64dadc | deep-ultrasound-denoising-using-diffusion | 2306.07440 | null | https://arxiv.org/abs/2306.07440v1 | https://arxiv.org/pdf/2306.07440v1.pdf | Deep Ultrasound Denoising Using Diffusion Probabilistic Models | Ultrasound images are widespread in medical diagnosis for musculoskeletal, cardiac, and obstetrical imaging due to the efficiency and non-invasiveness of the acquisition methodology. However, the acquired images are degraded by acoustic (e.g. reverberation and clutter) and electronic sources of noise. To improve the Pe... | ['Hassan Rivaz', 'Adrian Basarab', 'Sobhan Goudarzi', 'Hojat Asgariandehkordi'] | 2023-06-12 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [ 4.14571464e-01 -2.35426098e-01 5.56281090e-01 -8.22203085e-02
-5.36330700e-01 -1.76136538e-01 9.82104391e-02 3.61275412e-02
-4.65314716e-01 5.90305269e-01 2.36314446e-01 5.58924079e-02
-3.91139507e-01 -5.09077430e-01 -1.39154315e-01 -1.44074738e+00
1.80522073e-02 -2.82763422e-01 4.39790159e-01 1.83248356... | [12.201712608337402, -2.589515209197998] |
1a0fbdf9-276b-4db3-8426-097473c03835 | debiasing-stance-detection-models-with | 2212.10392 | null | https://arxiv.org/abs/2212.10392v1 | https://arxiv.org/pdf/2212.10392v1.pdf | Debiasing Stance Detection Models with Counterfactual Reasoning and Adversarial Bias Learning | Stance detection models may tend to rely on dataset bias in the text part as a shortcut and thus fail to sufficiently learn the interaction between the targets and texts. Recent debiasing methods usually treated features learned by small models or big models at earlier steps as bias features and proposed to exclude the... | ['Bing Qin', 'Yanyan Zhao', 'Jianhua Yuan'] | 2022-12-20 | null | null | null | null | ['counterfactual-inference', 'stance-detection'] | ['miscellaneous', 'natural-language-processing'] | [ 9.36877802e-02 9.62778181e-02 -8.83953512e-01 -6.23594463e-01
-5.37324250e-01 -7.16373563e-01 1.05960822e+00 5.21794967e-02
-4.00571376e-01 9.13635254e-01 7.30306089e-01 -3.30231428e-01
1.36502072e-01 -9.71309304e-01 -9.57773685e-01 -6.47047281e-01
3.91310900e-01 4.59169924e-01 1.38714775e-01 -4.61617082... | [10.170427322387695, 7.798941135406494] |
368989ce-3b85-4bec-a0ae-7a37908990f7 | inferring-community-characteristics-in | 2105.13762 | null | https://arxiv.org/abs/2105.13762v2 | https://arxiv.org/pdf/2105.13762v2.pdf | The Feature-First Block Model | Labelled networks are an important class of data, naturally appearing in numerous applications in science and engineering. A typical inference goal is to determine how the vertex labels (or features) affect the network's structure. In this work, we introduce a new generative model, the feature-first block model (FFBM),... | ['Lawrence Tray', 'Ioannis Kontoyiannis'] | 2021-05-28 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 4.18739945e-01 1.30351782e-01 -2.34807849e-01 -4.79874432e-01
-2.18822911e-01 -5.09310007e-01 1.13057768e+00 2.51837313e-01
-2.16974676e-01 8.97975445e-01 9.50318575e-02 -1.53659135e-01
-7.72813857e-01 -1.26695669e+00 -6.05285525e-01 -7.72629023e-01
-2.10872516e-01 8.92509758e-01 4.91403073e-01 1.51960582... | [7.0947442054748535, 4.71970272064209] |
60a24841-f8b1-4a7d-bfe2-fa40f7a55c54 | a-survey-on-offline-model-based-reinforcement | 2305.03360 | null | https://arxiv.org/abs/2305.03360v1 | https://arxiv.org/pdf/2305.03360v1.pdf | A Survey on Offline Model-Based Reinforcement Learning | Model-based approaches are becoming increasingly popular in the field of offline reinforcement learning, with high potential in real-world applications due to the model's capability of thoroughly utilizing the large historical datasets available with supervised learning techniques. This paper presents a literature revi... | ['Haoyang He'] | 2023-05-05 | null | null | null | null | ['model-based-reinforcement-learning'] | ['reasoning'] | [-1.61451489e-01 -2.64359176e-01 -9.06488419e-01 -3.36798429e-01
-9.89557624e-01 -6.15176976e-01 3.88202935e-01 3.29055667e-01
-8.50668311e-01 1.04558361e+00 -1.10039108e-01 -3.95012379e-01
-4.22604173e-01 -8.69803131e-01 -6.61779523e-01 -6.79140985e-01
-5.79373837e-01 7.11788118e-01 4.48045880e-02 -6.33947372... | [4.039872646331787, 2.2728967666625977] |
9930afad-656a-4641-86ac-3dad72faecef | negation-typology-and-general-representation | null | null | https://aclanthology.org/2021.naacl-srw.3 | https://aclanthology.org/2021.naacl-srw.3.pdf | Negation typology and general representation models for cross-lingual zero-shot negation scope resolution in Russian, French, and Spanish. | Negation is a linguistic universal that poses difficulties for cognitive and computational processing. Despite many advances in text analytics, negation resolution remains an acute and continuously researched question in Natural Language Processing. Reliable negation parsing affects results in biomedical text mining, s... | ['Fabio Rinaldi', 'Anastassia Shaitarova'] | 2021-06-01 | null | null | null | naacl-2021-4 | ['negation-detection', 'negation-scope-resolution'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.20240787e-01 2.14888617e-01 -3.53854954e-01 -5.16655803e-01
-7.68665016e-01 -6.11796677e-01 5.39950550e-01 8.85420144e-01
-1.00021625e+00 1.19812489e+00 4.74412471e-01 -3.73916715e-01
1.78748131e-01 -8.06822538e-01 -5.61632156e-01 -6.09785728e-02
2.53901243e-01 4.58083600e-01 1.33104652e-01 -7.83404171... | [10.425074577331543, 9.286370277404785] |
3d160552-3cf8-4d69-9d72-a2e8d9b71a2f | multi-graph-tensor-networks | 2010.13209 | null | https://arxiv.org/abs/2010.13209v4 | https://arxiv.org/pdf/2010.13209v4.pdf | Multi-Graph Tensor Networks | The irregular and multi-modal nature of numerous modern data sources poses serious challenges for traditional deep learning algorithms. To this end, recent efforts have generalized existing algorithms to irregular domains through graphs, with the aim to gain additional insights from data through the underlying graph to... | ['Danilo P. Mandic', 'Kriton Konstantinidis', 'Yao Lei Xu'] | 2020-10-25 | null | null | null | null | ['algorithmic-trading'] | ['time-series'] | [-2.97975808e-01 9.25287083e-02 -2.65344948e-01 -1.85602922e-02
-3.87858957e-01 -6.40362144e-01 7.08708167e-01 2.66392112e-01
1.31866723e-01 4.90203232e-01 4.25992996e-01 -4.07091588e-01
-6.47177637e-01 -9.80592251e-01 -6.51094437e-01 -5.33960938e-01
-5.09464800e-01 5.84438741e-01 -3.77294391e-01 -2.60348827... | [6.867447376251221, 5.995962619781494] |
4b434595-8b41-4fed-9763-2fe6c7b1b612 | a-new-sentence-ordering-method-using-bert | 2108.11994 | null | https://arxiv.org/abs/2108.11994v1 | https://arxiv.org/pdf/2108.11994v1.pdf | A New Sentence Ordering Method Using BERT Pretrained Model | Building systems with capability of natural language understanding (NLU) has been one of the oldest areas of AI. An essential component of NLU is to detect logical succession of events contained in a text. The task of sentence ordering is proposed to learn succession of events with applications in AI tasks. The perform... | ['Heshaam Faili', 'Seyedeh Zahra Razavi', 'Melika Golestani'] | 2021-08-26 | null | null | null | null | ['sentence-ordering'] | ['natural-language-processing'] | [ 4.96733725e-01 8.05554986e-02 -1.39280111e-01 -5.84980249e-01
-4.14829999e-01 -2.83893794e-01 8.86551678e-01 6.99274719e-01
-6.43091440e-01 8.26756120e-01 6.60629034e-01 -2.07628667e-01
-1.65900141e-01 -1.05883622e+00 -5.89609027e-01 -3.08870465e-01
-2.41957739e-01 5.50625384e-01 3.04745674e-01 -5.15591204... | [12.054742813110352, 9.382614135742188] |
b4e561db-aff4-41da-8605-17229c015c70 | frame-semantic-enhanced-sentence-modeling-for | null | null | https://aclanthology.org/2021.emnlp-main.331 | https://aclanthology.org/2021.emnlp-main.331.pdf | Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization | Sentence-level extractive text summarization aims to select important sentences from a given document. However, it is very challenging to model the importance of sentences. In this paper, we propose a novel Frame Semantic-Enhanced Sentence Modeling for Extractive Summarization, which leverages Frame semantics to model ... | ['Hongye Tan', 'XiaoLi Li', 'Ru Li', 'Shaoru Guo', 'Yong Guan'] | null | null | null | null | emnlp-2021-11 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 6.49699688e-01 4.85668004e-01 -5.55448532e-01 -6.32624626e-01
-9.00972307e-01 -2.15155602e-01 5.85634708e-01 7.33150005e-01
-1.75645739e-01 9.34402168e-01 1.55572820e+00 2.05734119e-01
2.43779272e-01 -6.27298474e-01 -6.25510991e-01 -1.40420943e-01
4.04152781e-01 -2.61492934e-03 2.82122046e-01 -4.33157086... | [12.53211784362793, 9.491437911987305] |
29aa008c-cdb4-4ae7-aff8-88555a03d628 | are-neural-nets-modular-inspecting-functional-1 | 2010.02066 | null | https://arxiv.org/abs/2010.02066v3 | https://arxiv.org/pdf/2010.02066v3.pdf | Are Neural Nets Modular? Inspecting Functional Modularity Through Differentiable Weight Masks | Neural networks (NNs) whose subnetworks implement reusable functions are expected to offer numerous advantages, including compositionality through efficient recombination of functional building blocks, interpretability, preventing catastrophic interference, etc. Understanding if and how NNs are modular could provide in... | ['Jürgen Schmidhuber', 'Sjoerd van Steenkiste', 'Róbert Csordás'] | 2020-10-05 | are-neural-nets-modular-inspecting-functional | https://openreview.net/forum?id=7uVcpu-gMD | https://openreview.net/pdf?id=7uVcpu-gMD | iclr-2021-1 | ['systematic-generalization'] | ['reasoning'] | [ 3.94642293e-01 3.85113120e-01 5.76537438e-02 -2.76887059e-01
3.06796938e-01 -8.28995764e-01 3.17588955e-01 -1.41507387e-01
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-6.70823753e-01 -8.74327004e-01 -7.51062751e-01 -6.20991826e-01
-4.78496999e-01 -1.88527599e-01 4.32850540e-01 -3.74315590... | [8.324400901794434, 3.4496476650238037] |
76ebf7dc-0e3c-4411-80ce-0acf08ebd890 | uvim-a-unified-modeling-approach-for-vision | 2205.10337 | null | https://arxiv.org/abs/2205.10337v3 | https://arxiv.org/pdf/2205.10337v3.pdf | UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes | We introduce UViM, a unified approach capable of modeling a wide range of computer vision tasks. In contrast to previous models, UViM has the same functional form for all tasks; it requires no task-specific modifications which require extensive human expertise. The approach involves two components: (I) a base model (fe... | ['Neil Houlsby', 'Jeremiah Harmsen', 'Xiaohua Zhai', 'Lucas Beyer', 'André Susano Pinto', 'Alexander Kolesnikov'] | 2022-05-20 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 4.31051821e-01 2.21924428e-02 -3.00829839e-02 -5.68749130e-01
-6.44051790e-01 -4.18035448e-01 9.30365324e-01 -1.14679575e-01
-3.42091173e-01 6.17365949e-02 -3.03650737e-01 -4.68024671e-01
1.91725135e-01 -6.01449728e-01 -6.68725729e-01 -7.18158484e-01
2.02368498e-01 4.65443760e-01 3.85721803e-01 5.90659082... | [10.239792823791504, 1.6016793251037598] |
fc4584e6-8afe-4fb2-a411-bad020fb2e8b | deconfounded-video-moment-retrieval-with | 2106.01534 | null | https://arxiv.org/abs/2106.01534v1 | https://arxiv.org/pdf/2106.01534v1.pdf | Deconfounded Video Moment Retrieval with Causal Intervention | We tackle the task of video moment retrieval (VMR), which aims to localize a specific moment in a video according to a textual query. Existing methods primarily model the matching relationship between query and moment by complex cross-modal interactions. Despite their effectiveness, current models mostly exploit datase... | ['Tat-Seng Chua', 'Meng Wang', 'Wei Ji', 'Fuli Feng', 'Xun Yang'] | 2021-06-03 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [ 1.01070255e-01 -3.75048101e-01 -7.93859124e-01 -7.48310462e-02
-7.57069468e-01 -6.97415590e-01 9.57335770e-01 -2.58207858e-01
-1.09407464e-02 2.53479242e-01 5.65730274e-01 -2.34102085e-01
-3.92341465e-01 -5.11484623e-01 -9.17408168e-01 -5.24020612e-01
8.31722748e-03 -1.47816017e-01 -1.19882695e-01 -7.38851354... | [10.232401847839355, 0.894092321395874] |
bc2d33cc-4dd5-4047-ab92-77e85d286777 | exploring-model-dynamics-for-accumulative | 2306.03726 | null | https://arxiv.org/abs/2306.03726v1 | https://arxiv.org/pdf/2306.03726v1.pdf | Exploring Model Dynamics for Accumulative Poisoning Discovery | Adversarial poisoning attacks pose huge threats to various machine learning applications. Especially, the recent accumulative poisoning attacks show that it is possible to achieve irreparable harm on models via a sequence of imperceptible attacks followed by a trigger batch. Due to the limited data-level discrepancy in... | ['Bo Han', 'Liang Wang', 'Tongliang Liu', 'Shuo Yuan', 'Li He', 'Chao Du', 'Jiangchao Yao', 'Xiawei Guo', 'Jianing Zhu'] | 2023-06-06 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [ 1.74524322e-01 -4.86845821e-01 2.32539866e-02 5.68661429e-02
-9.98259664e-01 -1.08468115e+00 6.69086516e-01 5.46940386e-01
-4.39940661e-01 4.71308053e-01 -9.14798975e-02 -3.50149333e-01
3.49048115e-02 -6.34779096e-01 -8.20401371e-01 -1.05395865e+00
-2.50100166e-01 1.34623617e-01 1.69319615e-01 -2.49098346... | [5.745835304260254, 7.54346227645874] |
416d876d-8b88-4a97-81a1-279c57e61d0e | radioses-mmwave-based-audioradio-speech | 2204.07092 | null | https://arxiv.org/abs/2204.07092v1 | https://arxiv.org/pdf/2204.07092v1.pdf | RadioSES: mmWave-Based Audioradio Speech Enhancement and Separation System | Speech enhancement and separation have been a long-standing problem, especially with the recent advances using a single microphone. Although microphones perform well in constrained settings, their performance for speech separation decreases in noisy conditions. In this work, we propose RadioSES, an audioradio speech en... | ['K. J. Ray Liu', 'Min Wu', 'Beibei Wang', 'Chenshu Wu', 'Muhammed Zahid Ozturk'] | 2022-04-14 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 1.07618511e-01 -2.31453910e-01 1.66260481e-01 -2.81513512e-01
-1.45241141e+00 -4.61449146e-01 2.89060891e-01 -2.82230526e-02
-3.23672712e-01 5.49291253e-01 7.24272490e-01 -1.70326993e-01
-1.70650147e-02 -4.31437522e-01 -3.62366825e-01 -9.92866457e-01
-8.59390572e-02 -1.98611245e-01 9.62913856e-02 -2.31355533... | [14.900307655334473, 5.928405284881592] |
efc6f216-0345-45a2-acda-0c551be93fcd | inclg-inpainting-for-non-cleft-lip-generation | 2305.10589 | null | https://arxiv.org/abs/2305.10589v1 | https://arxiv.org/pdf/2305.10589v1.pdf | INCLG: Inpainting for Non-Cleft Lip Generation with a Multi-Task Image Processing Network | We present a software that predicts non-cleft facial images for patients with cleft lip, thereby facilitating the understanding, awareness and discussion of cleft lip surgeries. To protect patients privacy, we design a software framework using image inpainting, which does not require cleft lip images for training, ther... | ['Hubert P. H. Shum', 'Edmond S. L. Ho', 'Amir Atapour-Abarghouei', 'Shuang Chen'] | 2023-05-17 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 5.82314953e-02 4.89593267e-01 -3.91729474e-01 -5.01438022e-01
-1.14327669e+00 -2.98823029e-01 -1.86310783e-02 -1.74243003e-01
-4.02911782e-01 2.07877606e-01 4.39041942e-01 -4.36897159e-01
3.96092504e-01 -4.89016324e-01 -6.17958188e-01 -6.63046718e-01
1.76093057e-01 7.83060715e-02 -2.92205989e-01 4.07956243... | [13.056262016296387, 0.18384987115859985] |
a849bbfb-7dcf-4c6f-832c-9459f98eb450 | sadtalker-learning-realistic-3d-motion | 2211.12194 | null | https://arxiv.org/abs/2211.12194v2 | https://arxiv.org/pdf/2211.12194v2.pdf | SadTalker: Learning Realistic 3D Motion Coefficients for Stylized Audio-Driven Single Image Talking Face Animation | Generating talking head videos through a face image and a piece of speech audio still contains many challenges. ie, unnatural head movement, distorted expression, and identity modification. We argue that these issues are mainly because of learning from the coupled 2D motion fields. On the other hand, explicitly using 3... | ['Fei Wang', 'Ying Shan', 'Yu Guo', 'Xi Shen', 'Yong Zhang', 'Xuan Wang', 'Xiaodong Cun', 'Wenxuan Zhang'] | 2022-11-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_SadTalker_Learning_Realistic_3D_Motion_Coefficients_for_Stylized_Audio-Driven_Single_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_SadTalker_Learning_Realistic_3D_Motion_Coefficients_for_Stylized_Audio-Driven_Single_CVPR_2023_paper.pdf | cvpr-2023-1 | ['talking-head-generation'] | ['computer-vision'] | [-1.02811567e-01 1.33460656e-01 2.19037294e-01 -7.18481481e-01
-9.12666142e-01 -3.73776704e-01 4.73322123e-01 -1.13504291e+00
8.17270856e-03 4.03844506e-01 6.80735469e-01 4.24528807e-01
2.97341436e-01 -2.56788224e-01 -7.95063674e-01 -8.80690038e-01
8.48663598e-02 1.33429110e-01 -4.13480759e-01 -1.90373421... | [13.159063339233398, -0.43643757700920105] |
3d4cdba6-c8f8-494f-9d30-121836f8490e | desra-detect-and-delete-the-artifacts-of-gan | 2307.02457 | null | https://arxiv.org/abs/2307.02457v1 | https://arxiv.org/pdf/2307.02457v1.pdf | DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Models | Image super-resolution (SR) with generative adversarial networks (GAN) has achieved great success in restoring realistic details. However, it is notorious that GAN-based SR models will inevitably produce unpleasant and undesirable artifacts, especially in practical scenarios. Previous works typically suppress artifacts... | ['Chao Dong', 'Jiantao Zhou', 'Ying Shan', 'Gen Li', 'Xiangyu Chen', 'Xintao Wang', 'Liangbin Xie'] | 2023-07-05 | null | null | null | null | ['image-super-resolution', 'super-resolution'] | ['computer-vision', 'computer-vision'] | [ 5.93011081e-01 6.95251068e-03 3.07857901e-01 -2.56567970e-02
-9.11223829e-01 -4.66220886e-01 1.80787489e-01 -6.60169721e-01
1.58655539e-01 9.68038678e-01 1.53414950e-01 1.56080804e-03
2.31973045e-02 -8.37780476e-01 -6.62963808e-01 -8.44736159e-01
4.05972958e-01 -9.31648761e-02 4.98135649e-02 -1.52114734... | [11.37254524230957, -1.4889882802963257] |
0a7cf5c1-9600-4ade-83bf-3eec52948d96 | open-information-extraction-via-chunks | 2305.03299 | null | https://arxiv.org/abs/2305.03299v1 | https://arxiv.org/pdf/2305.03299v1.pdf | Open Information Extraction via Chunks | Open Information Extraction (OIE) aims to extract relational tuples from open-domain sentences. Existing OIE systems split a sentence into tokens and recognize token spans as tuple relations and arguments. We instead propose Sentence as Chunk sequence (SaC) and recognize chunk spans as tuple relations and arguments. We... | ['XiaoLi Li', 'Jung-jae Kim', 'Aixin Sun', 'Kuicai Dong'] | 2023-05-05 | null | null | null | null | ['open-information-extraction', 'chunking'] | ['natural-language-processing', 'natural-language-processing'] | [-2.79808581e-01 1.02888751e+00 -7.31170952e-01 -3.57578695e-01
-7.82603681e-01 -6.20286822e-01 3.23795915e-01 6.46302521e-01
-1.96918651e-01 1.02896369e+00 6.30945802e-01 -3.44357342e-01
-1.17525697e-01 -1.19718611e+00 -9.71710205e-01 4.61264074e-01
-3.65155458e-01 9.60389912e-01 5.34547508e-01 -3.65383297... | [9.59016227722168, 8.618504524230957] |
88f44097-7046-4b9b-9611-9ffa00fa1aa4 | dermatological-diagnosis-explainability | 2302.12084 | null | https://arxiv.org/abs/2302.12084v1 | https://arxiv.org/pdf/2302.12084v1.pdf | Dermatological Diagnosis Explainability Benchmark for Convolutional Neural Networks | In recent years, large strides have been taken in developing machine learning methods for dermatological applications, supported in part by the success of deep learning (DL). To date, diagnosing diseases from images is one of the most explored applications of DL within dermatology. Convolutional neural networks (ConvNe... | ['Alfiia Galimzianova', 'Ole Winther', 'Raluca Jalaboi'] | 2023-02-23 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [-1.06613757e-02 4.44085062e-01 -4.38828439e-01 -4.04667675e-01
-2.10378096e-01 -4.16054696e-01 5.50460279e-01 1.26828685e-01
-1.41900823e-01 6.34962380e-01 1.37424260e-01 -5.22597909e-01
-6.80446863e-01 -5.72412431e-01 -3.67607206e-01 -3.89846057e-01
2.44263858e-02 4.52538788e-01 -9.55914184e-02 -1.85945868... | [15.469243049621582, -2.758995532989502] |
6aac6388-e50c-422b-9259-65dfb916a26d | boosting-text-classification-performance-on | null | null | https://aclanthology.org/W18-5114 | https://aclanthology.org/W18-5114.pdf | Boosting Text Classification Performance on Sexist Tweets by Text Augmentation and Text Generation Using a Combination of Knowledge Graphs | Text classification models have been heavily utilized for a slew of interesting natural language processing problems. Like any other machine learning model, these classifiers are very dependent on the size and quality of the training dataset. Insufficient and imbalanced datasets will lead to poor performance. An intere... | ['Stan Matwin', 'Borna Jafarpour', 'Sima Sharifirad'] | 2018-10-01 | null | null | null | ws-2018-10 | ['text-augmentation'] | ['natural-language-processing'] | [ 3.42682064e-01 4.81116027e-01 -3.69707853e-01 -3.22109044e-01
2.03129813e-01 -3.99922848e-01 8.57263088e-01 6.10459983e-01
-5.09098530e-01 1.11040306e+00 3.10625166e-01 -2.13282228e-01
9.19044111e-03 -1.33517504e+00 -2.10035771e-01 -4.43032265e-01
2.91108161e-01 8.13573420e-01 2.92102963e-01 -8.37736249... | [10.500727653503418, 7.656519412994385] |
7bde9b3c-1a29-4b3b-a367-c0021a0680d9 | rethinking-of-the-image-salient-object | 2008.05397 | null | https://arxiv.org/abs/2008.05397v1 | https://arxiv.org/pdf/2008.05397v1.pdf | Rethinking of the Image Salient Object Detection: Object-level Semantic Saliency Re-ranking First, Pixel-wise Saliency Refinement Latter | The real human attention is an interactive activity between our visual system and our brain, using both low-level visual stimulus and high-level semantic information. Previous image salient object detection (SOD) works conduct their saliency predictions in a multi-task manner, i.e., performing pixel-wise saliency regre... | ['Zhen-Yu Wu', 'Hong Qin', 'Shuai Li', 'Chenglizhao Chen', 'Aimin Hao'] | 2020-08-10 | null | null | null | null | ['salient-object-detection'] | ['computer-vision'] | [ 3.69417340e-01 1.92763761e-01 -1.54165879e-01 -1.84048876e-01
-5.87898970e-01 -2.43130215e-02 4.88525391e-01 5.17871559e-01
-4.82476324e-01 4.23654139e-01 3.79728913e-01 6.02702498e-02
5.67573421e-02 -6.23736978e-01 -8.44114065e-01 -6.24488890e-01
4.17474061e-01 8.80212858e-02 1.05445123e+00 -4.99106884... | [9.891252517700195, -0.24639928340911865] |
6ec69aec-3cec-4a3c-b078-5f3087ebc678 | synchronous-dual-network-with-cross-type | null | null | https://aclanthology.org/2021.emnlp-main.219 | https://aclanthology.org/2021.emnlp-main.219.pdf | Synchronous Dual Network with Cross-Type Attention for Joint Entity and Relation Extraction | Joint entity and relation extraction is challenging due to the complex interaction of interaction between named entity recognition and relation extraction. Although most existing works tend to jointly train these two tasks through a shared network, they fail to fully utilize the interdependence between entity types and... | ['Xiaodong Shi', 'Hui Wu'] | null | null | null | null | emnlp-2021-11 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [-8.17017555e-02 2.77374446e-01 -3.40067506e-01 -5.52163839e-01
-3.56428921e-01 -4.16996539e-01 4.91265416e-01 6.43424019e-02
-5.41663289e-01 8.33165407e-01 1.99057415e-01 -3.99149835e-01
-1.22410566e-01 -9.53348696e-01 -8.07300985e-01 -4.31111306e-01
1.43207004e-02 5.02336979e-01 1.57646909e-02 -1.57788470... | [9.245691299438477, 8.706156730651855] |
ca4f9cc7-9cf0-499a-a735-1360ab9aa46e | causal-counterfactuals-for-improving-the | 2211.05551 | null | https://arxiv.org/abs/2211.05551v3 | https://arxiv.org/pdf/2211.05551v3.pdf | Causal Counterfactuals for Improving the Robustness of Reinforcement Learning | Reinforcement learning (RL) is used in various robotic applications. RL enables agents to learn tasks autonomously by interacting with the environment. The more critical the tasks are, the higher the demand for the robustness of the RL systems. Causal RL combines RL and causal inference to make RL more robust. Causal R... | ['Ivana Dusparic', 'Jasmina Gajcin', 'Tom He'] | 2022-11-02 | null | null | null | null | ['robotic-grasping'] | ['robots'] | [-1.36956185e-01 5.45309007e-01 -3.51583004e-01 -8.14654082e-02
2.08047293e-02 -4.42697465e-01 9.48273301e-01 -1.24148875e-01
-3.37188616e-02 1.32073891e+00 4.11586881e-01 -3.01630288e-01
-6.81372941e-01 -5.75025678e-01 -1.01105380e+00 -7.71604598e-01
-6.52255177e-01 2.08571255e-01 4.78309058e-02 -3.35504919... | [4.256217002868652, 1.4753233194351196] |
a54bd962-aedf-4c2f-abab-81342474a745 | zero-shot-learners-for-natural-language | 2210.08590 | null | https://arxiv.org/abs/2210.08590v2 | https://arxiv.org/pdf/2210.08590v2.pdf | Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective | We propose a new paradigm for zero-shot learners that is format agnostic, i.e., it is compatible with any format and applicable to a list of language tasks, such as text classification, commonsense reasoning, coreference resolution, and sentiment analysis. Zero-shot learning aims to train a model on a given task such t... | ['Tetsuya Sakai', 'Jiaxing Zhang', 'Xinyu Gao', 'Ziwei Wu', 'Lin Zhang', 'Xinyu Zhu', 'Ruyi Gan', 'Junjie Wang', 'Ping Yang'] | 2022-10-16 | null | null | null | null | ['coreference-resolution'] | ['natural-language-processing'] | [ 4.25186902e-02 2.42321342e-01 -4.47812796e-01 -5.56713998e-01
-1.05357707e+00 -3.51966828e-01 8.26845884e-01 1.40052363e-01
-5.56145966e-01 7.58099139e-01 3.87531221e-01 -4.57701355e-01
7.43225589e-02 -9.42595005e-01 -6.76788151e-01 -4.48337555e-01
5.19417346e-01 8.52831185e-01 3.39751422e-01 -5.86854517... | [10.740853309631348, 7.902599811553955] |
51e1871a-f0e4-4691-9d91-b54b0843080b | unidentified-video-objects-a-benchmark-for | 2104.04691 | null | https://arxiv.org/abs/2104.04691v1 | https://arxiv.org/pdf/2104.04691v1.pdf | Unidentified Video Objects: A Benchmark for Dense, Open-World Segmentation | Current state-of-the-art object detection and segmentation methods work well under the closed-world assumption. This closed-world setting assumes that the list of object categories is available during training and deployment. However, many real-world applications require detecting or segmenting novel objects, i.e., obj... | ['Du Tran', 'Heng Wang', 'Matt Feiszli', 'Weiyao Wang'] | 2021-04-10 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Wang_Unidentified_Video_Objects_A_Benchmark_for_Dense_Open-World_Segmentation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Wang_Unidentified_Video_Objects_A_Benchmark_for_Dense_Open-World_Segmentation_ICCV_2021_paper.pdf | iccv-2021-1 | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 8.90807714e-03 -2.44255006e-01 -4.10840720e-01 -1.95891544e-01
-5.45877755e-01 -1.04034209e+00 1.83645170e-03 -8.67804140e-02
-3.95958722e-01 4.54506397e-01 -4.38696861e-01 -5.80175593e-02
2.44833216e-01 -5.44241667e-01 -9.72174168e-01 -4.90449160e-01
-3.74947369e-01 6.54972136e-01 1.04519939e+00 -1.83268450... | [9.174782752990723, -0.0637766420841217] |
52283468-f797-49b3-86eb-e60014f1e3c6 | findings-of-the-shared-task-on-multimodal | null | null | https://aclanthology.org/2022.dravidianlangtech-1.39 | https://aclanthology.org/2022.dravidianlangtech-1.39.pdf | Findings of the Shared Task on Multimodal Sentiment Analysis and Troll Meme Classification in Dravidian Languages | This paper presents the findings of the shared task on Multimodal Sentiment Analysis and Troll meme classification in Dravidian languages held at ACL 2022. Multimodal sentiment analysis deals with the identification of sentiment from video. In addition to video data, the task requires the analysis of corresponding text... | ['Prasanna Kumaresan', 'Arunaggiri Pandian', 'Sreelakshmi K', 'Dhanalakshmi V', 'Soman Kp', 'Bharathi B', 'Malliga Subramanian', 'Bharathi Raja Chakravarthi', 'Premjith B'] | null | null | null | null | dravidianlangtech-acl-2022-5 | ['meme-classification'] | ['natural-language-processing'] | [-1.11071318e-02 -2.79637098e-01 2.09349066e-01 -4.25583512e-01
-1.20454895e+00 -7.66782343e-01 6.69049442e-01 4.74828213e-01
-8.07161570e-01 3.78922969e-01 4.31990027e-01 3.87436561e-02
2.15733185e-01 -1.08736008e-01 -4.25634027e-01 -5.85024297e-01
1.84607044e-01 9.18592513e-02 -1.74496874e-01 -4.62035626... | [13.009093284606934, 5.276452541351318] |
76161387-d886-4aec-84da-706d267b2146 | eslam-efficient-dense-slam-system-based-on | 2211.11704 | null | https://arxiv.org/abs/2211.11704v2 | https://arxiv.org/pdf/2211.11704v2.pdf | ESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields | We present ESLAM, an efficient implicit neural representation method for Simultaneous Localization and Mapping (SLAM). ESLAM reads RGB-D frames with unknown camera poses in a sequential manner and incrementally reconstructs the scene representation while estimating the current camera position in the scene. We incorpora... | ['François Fleuret', 'Camilla Carta', 'Mohammad Mahdi Johari'] | 2022-11-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Johari_ESLAM_Efficient_Dense_SLAM_System_Based_on_Hybrid_Representation_of_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Johari_ESLAM_Efficient_Dense_SLAM_System_Based_on_Hybrid_Representation_of_CVPR_2023_paper.pdf | cvpr-2023-1 | ['simultaneous-localization-and-mapping', 'camera-localization'] | ['computer-vision', 'computer-vision'] | [ 5.69502525e-02 -3.32159132e-01 -2.39343598e-01 -6.24986708e-01
-8.07125747e-01 -7.31953502e-01 6.02836132e-01 -2.26817150e-02
-5.43958843e-01 5.60051799e-01 1.35551125e-01 -2.35812187e-01
2.43488878e-01 -7.11140931e-01 -1.16569281e+00 -1.44791618e-01
-1.90713517e-02 7.86563694e-01 1.84991062e-01 -4.17515896... | [7.595970630645752, -2.3314268589019775] |
895f13d8-c7f9-4104-8d53-e50d65a52bac | progression-cognition-reinforcement-learning | 2306.05016 | null | https://arxiv.org/abs/2306.05016v1 | https://arxiv.org/pdf/2306.05016v1.pdf | Progression Cognition Reinforcement Learning with Prioritized Experience for Multi-Vehicle Pursuit | Multi-vehicle pursuit (MVP) such as autonomous police vehicles pursuing suspects is important but very challenging due to its mission and safety critical nature. While multi-agent reinforcement learning (MARL) algorithms have been proposed for MVP problem in structured grid-pattern roads, the existing algorithms use ra... | ['Lin Zhang', 'Jianhua He', 'Lei LI', 'Chen Xu', 'Qinwen Wang', 'Zhe Wang', 'Zheng Yuan', 'Yiying Yang', 'Xinhang Li'] | 2023-06-08 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [-3.61388952e-01 -9.87969115e-02 -3.27857882e-01 -1.38715636e-02
-8.67335796e-01 -8.88164937e-02 4.96215641e-01 -4.51825224e-02
-4.87785637e-01 6.17684782e-01 -2.74058562e-02 -4.55532193e-01
-4.75984335e-01 -9.17957783e-01 -6.22256458e-01 -8.99707496e-01
-3.40092212e-01 6.34965360e-01 7.26281285e-01 -6.78157091... | [5.184247016906738, 1.34658682346344] |
a06677ff-4bcb-4fb0-81b7-199d83cacb96 | escl-equivariant-self-contrastive-learning | 2303.05143 | null | https://arxiv.org/abs/2303.05143v1 | https://arxiv.org/pdf/2303.05143v1.pdf | ESCL: Equivariant Self-Contrastive Learning for Sentence Representations | Previous contrastive learning methods for sentence representations often focus on insensitive transformations to produce positive pairs, but neglect the role of sensitive transformations that are harmful to semantic representations. Therefore, we propose an Equivariant Self-Contrastive Learning (ESCL) method to make fu... | ['Junlan Feng', 'Chao Deng', 'Xue Han', 'Yixuan Liu', 'Jie Liu'] | 2023-03-09 | null | null | null | null | ['semantic-textual-similarity'] | ['natural-language-processing'] | [ 3.33918780e-01 -1.09969221e-01 -1.40436471e-01 -6.93481326e-01
-8.77799988e-01 -5.22358894e-01 9.92736161e-01 2.81724781e-01
-6.19351387e-01 5.23215711e-01 4.14179057e-01 6.00415729e-02
-7.58904368e-02 -7.25184500e-01 -5.34422874e-01 -6.63995385e-01
5.04808009e-01 3.62710118e-01 2.78601855e-01 -6.40044451... | [10.902629852294922, 8.606444358825684] |
c99774fd-1818-4dd5-9eb7-39ae2e7bd435 | word-separation-in-continuous-sign-language | 2204.00923 | null | https://arxiv.org/abs/2204.00923v4 | https://arxiv.org/pdf/2204.00923v4.pdf | Word separation in continuous sign language using isolated signs and post-processing | . Continuous Sign Language Recognition (CSLR) is a long challenging task in Computer Vision due to the difficulties in detecting the explicit boundaries between the words in a sign sentence. To deal with this challenge, we propose a two-stage model. In the first stage, the predictor model, which includes a combination ... | ['Sergio Escalera', 'Kourosh Kiani', 'Razieh Rastgoo'] | 2022-04-02 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 3.96107137e-01 -2.95169234e-01 4.81065251e-02 -3.45108479e-01
-4.75084484e-01 -2.53708720e-01 4.62287843e-01 -8.48607242e-01
-8.55004072e-01 4.55591828e-01 3.90489191e-01 -6.13715015e-02
3.73237193e-01 5.55161834e-02 -5.20352423e-01 -9.31579232e-01
2.66229391e-01 -4.54442240e-02 6.25458896e-01 -5.43332584... | [9.135222434997559, -6.445216178894043] |
50b268d9-2f18-48f1-9656-a60e0ca25b94 | flow-to-control-offline-reinforcement | 2212.01105 | null | https://arxiv.org/abs/2212.01105v1 | https://arxiv.org/pdf/2212.01105v1.pdf | Flow to Control: Offline Reinforcement Learning with Lossless Primitive Discovery | Offline reinforcement learning (RL) enables the agent to effectively learn from logged data, which significantly extends the applicability of RL algorithms in real-world scenarios where exploration can be expensive or unsafe. Previous works have shown that extracting primitive skills from the recurring and temporally e... | ['Chongjie Zhang', 'Qianchuan Zhao', 'Jun Yang', 'Siyuan Li', 'Wenzhe Li', 'Hao Hu', 'Yiqin Yang'] | 2022-12-02 | null | null | null | null | ['d4rl'] | ['robots'] | [-1.01114571e-01 1.52997607e-02 -7.38199890e-01 -4.31176908e-02
-7.55325973e-01 -7.60066032e-01 6.85080767e-01 5.44022098e-02
-7.03661203e-01 1.05341268e+00 2.33583018e-01 -4.72943366e-01
-1.86012790e-01 -7.04293489e-01 -1.02578866e+00 -8.01516116e-01
-6.36867583e-01 4.38046843e-01 2.06677392e-01 -1.87688604... | [4.137388229370117, 2.0340678691864014] |
6c76cb1d-cb5f-4e47-a279-62eabead3134 | casia-iris-africa-a-large-scale-african-iris | 2302.13049 | null | https://arxiv.org/abs/2302.13049v1 | https://arxiv.org/pdf/2302.13049v1.pdf | CASIA-Iris-Africa: A Large-scale African Iris Image Database | Iris biometrics is a phenotypic biometric trait that has proven to be agnostic to human natural physiological changes. Research on iris biometrics has progressed tremendously, partly due to publicly available iris databases. Various databases have been available to researchers that address pressing iris biometric chall... | ['Zhenan Sun', 'Kunbo Zhang', 'Junxing Hu', 'Yunlong Wang', 'Jawad Muhammad'] | 2023-02-25 | null | null | null | null | ['iris-recognition'] | ['computer-vision'] | [ 5.19047584e-03 -3.51782382e-01 -3.70043486e-01 -5.93521059e-01
-5.99780306e-02 -3.53848428e-01 3.97931248e-01 -1.64609537e-01
-2.05508590e-01 8.01097214e-01 2.04129368e-01 -3.54547679e-01
-2.46899184e-02 -5.46903908e-01 -3.87936719e-02 -9.38127279e-01
-5.23474589e-02 3.35280091e-01 -4.68473077e-01 -1.40939364... | [3.7459805011749268, -3.6281657218933105] |
958f7d85-493c-46e6-b3e9-657e9efea3e8 | centerhmr-a-bottom-up-single-shot-method-for | 2008.12272 | null | https://arxiv.org/abs/2008.12272v4 | https://arxiv.org/pdf/2008.12272v4.pdf | Monocular, One-stage, Regression of Multiple 3D People | This paper focuses on the regression of multiple 3D people from a single RGB image. Existing approaches predominantly follow a multi-stage pipeline that first detects people in bounding boxes and then independently regresses their 3D body meshes. In contrast, we propose to Regress all meshes in a One-stage fashion for ... | ['Michael J. Black', 'Yu Sun', 'Qian Bao', 'Yili Fu', 'Wu Liu', 'Tao Mei'] | 2020-08-27 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Sun_Monocular_One-Stage_Regression_of_Multiple_3D_People_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Sun_Monocular_One-Stage_Regression_of_Multiple_3D_People_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-depth-estimation', '3d-multi-person-pose-estimation', '3d-multi-person-mesh-recovery'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 7.17023462e-02 -1.10039890e-01 1.24192514e-01 -4.53763127e-01
-6.26650870e-01 -1.59142151e-01 4.12557513e-01 4.62235734e-02
-4.76269424e-01 4.04609233e-01 3.18884687e-03 4.09196138e-01
4.66584593e-01 -9.31076169e-01 -7.67005324e-01 -5.59856892e-01
2.68056065e-01 1.04214728e+00 3.95463914e-01 -2.15265721... | [7.091941833496094, -1.0329508781433105] |
260dce1f-c54c-4e68-a0b4-1e8d2fbab26c | injecting-knowledge-base-information-into-end | 2107.02286 | null | https://arxiv.org/abs/2107.02286v1 | https://arxiv.org/pdf/2107.02286v1.pdf | Injecting Knowledge Base Information into End-to-End Joint Entity and Relation Extraction and Coreference Resolution | We consider a joint information extraction (IE) model, solving named entity recognition, coreference resolution and relation extraction jointly over the whole document. In particular, we study how to inject information from a knowledge base (KB) in such IE model, based on unsupervised entity linking. The used KB entity... | ['Chris Develder', 'Thomas Demeester', 'Johannes Deleu', 'Klim Zaporojets', 'Severine Verlinden'] | 2021-07-05 | null | https://aclanthology.org/2021.findings-acl.171 | https://aclanthology.org/2021.findings-acl.171.pdf | findings-acl-2021-8 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 1.41697705e-01 1.09544420e+00 -4.06409264e-01 7.14150146e-02
-9.55725670e-01 -5.01050532e-01 9.29069579e-01 6.49066806e-01
-7.50149727e-01 1.17666221e+00 7.90913284e-01 2.89114006e-02
-5.54919064e-01 -8.08020711e-01 -1.05651379e+00 -2.95898557e-01
-8.05491582e-02 9.25948679e-01 3.87684941e-01 -2.15770096... | [9.402067184448242, 8.694247245788574] |
8f9b9392-38fe-4a3c-8d2f-ef1185861924 | achieving-stable-subspace-clustering-by-post | 1605.08680 | null | http://arxiv.org/abs/1605.08680v1 | http://arxiv.org/pdf/1605.08680v1.pdf | Achieving stable subspace clustering by post-processing generic clustering results | We propose an effective subspace selection scheme as a post-processing step
to improve results obtained by sparse subspace clustering (SSC). Our method
starts by the computation of stable subspaces using a novel random sampling
scheme. Thus constructed preliminary subspaces are used to identify the
initially incorrectl... | ['Duc-Son Pham', 'Svetha Venkatesh', 'Ognjen Arandjelovic'] | 2016-05-27 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [ 4.21304822e-01 -3.24321032e-01 1.07932970e-01 -1.94687277e-01
-8.50424409e-01 -8.42159748e-01 5.40890217e-01 4.19923291e-02
-3.48634183e-01 3.95710468e-01 2.31172875e-01 -3.66402157e-02
-4.65301394e-01 -2.78791010e-01 -3.87780726e-01 -1.08234107e+00
8.18867683e-02 6.76400721e-01 2.25466952e-01 1.76712930... | [7.708306312561035, 4.452266216278076] |
ff757cbc-ba96-4e35-9dea-5d0096ca3838 | exploring-partial-intrinsic-and-extrinsic | 2003.02294 | null | https://arxiv.org/abs/2003.02294v2 | https://arxiv.org/pdf/2003.02294v2.pdf | Exploring Partial Intrinsic and Extrinsic Symmetry in 3D Medical Imaging | We present a novel methodology to detect imperfect bilateral symmetry in CT of human anatomy. In this paper, the structurally symmetric nature of the pelvic bone is explored and is used to provide interventional image augmentation for treatment of unilateral fractures in patients with traumatic injuries. The mathematic... | ['Greg Osgood', 'Mathias Unberath', 'Javad Fotouhi', 'Mehran Armand', 'Giacomo Taylor', 'Nassir Navab', 'Alex Johnson', 'Sing Chun Lee'] | 2020-03-04 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 2.82653153e-01 2.18263134e-01 -2.44135395e-01 -2.11820185e-01
-5.22305429e-01 -8.92864764e-02 1.31301254e-01 2.93280691e-01
-4.65042114e-01 5.91566682e-01 1.15984075e-01 1.88211903e-01
-6.31795526e-01 -5.27594745e-01 -5.88711560e-01 -8.33200812e-01
-1.86441779e-01 7.97047019e-01 2.45982736e-01 -1.32484660... | [13.982754707336426, -2.625553607940674] |
b4c1364f-e1ce-4297-b480-dbc86722fb17 | gabor-barcodes-for-medical-image-retrieval | 1605.04478 | null | http://arxiv.org/abs/1605.04478v1 | http://arxiv.org/pdf/1605.04478v1.pdf | Gabor Barcodes for Medical Image Retrieval | In recent years, advances in medical imaging have led to the emergence of
massive databases, containing images from a diverse range of modalities. This
has significantly heightened the need for automated annotation of the images on
one side, and fast and memory-efficient content-based image retrieval systems
on the oth... | ['Hamid. R. Tizhoosh', 'Ershad Banijamali', 'Mina Nouredanesh'] | 2016-05-14 | null | null | null | null | ['medical-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'medical'] | [ 2.94423074e-01 -4.08439845e-01 -1.61286723e-02 -3.09393853e-01
-1.22677815e+00 -2.81028986e-01 5.02272308e-01 6.67236269e-01
-6.13302290e-01 6.44746184e-01 -1.25723600e-01 7.55383149e-02
-6.16751432e-01 -9.30913925e-01 -1.84304953e-01 -1.18847120e+00
-1.21446103e-01 2.95743912e-01 6.14482880e-01 9.98966172... | [14.206510543823242, -1.4239113330841064] |
d1be6093-ce6b-4bb2-ae50-2c9684352c5f | improving-distantly-supervised-relation-3 | 2102.01156 | null | https://arxiv.org/abs/2102.01156v1 | https://arxiv.org/pdf/2102.01156v1.pdf | Improving Distantly-Supervised Relation Extraction through BERT-based Label & Instance Embeddings | Distantly-supervised relation extraction (RE) is an effective method to scale RE to large corpora but suffers from noisy labels. Existing approaches try to alleviate noise through multi-instance learning and by providing additional information, but manage to recognize mainly the top frequent relations, neglecting those... | ['Grigorios Tsoumakas', 'Despina Christou'] | 2021-02-01 | null | null | null | null | ['relationship-extraction-distant-supervised'] | ['natural-language-processing'] | [-0.05814383 0.76173955 -0.67103297 -0.52072775 -0.77769005 -0.59545517
0.68617904 0.6001395 -0.37177533 0.6760644 0.5912221 -0.20800243
-0.4148364 -0.9910722 -0.44869545 -0.51412034 -0.18972565 1.007577
0.275104 -0.2853538 -0.3379627 0.4536053 -1.208219 0.35862443
0.7359901 1.1937264 -0.38... | [9.339463233947754, 8.591050148010254] |
92bea0a2-f37c-460e-8d98-5c457cdcde9f | tuning-deep-active-learning-for-semantic-role | null | null | https://aclanthology.org/2021.iwcs-1.20 | https://aclanthology.org/2021.iwcs-1.20.pdf | Tuning Deep Active Learning for Semantic Role Labeling | Active learning has been shown to reduce annotation requirements for numerous natural language processing tasks, including semantic role labeling (SRL). SRL involves labeling argument spans for potentially multiple predicates in a sentence, which makes it challenging to aggregate the numerous decisions into a single sc... | ['Martha Palmer', 'Skatje Myers'] | null | null | null | null | iwcs-acl-2021-6 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 7.12544441e-01 8.83002520e-01 -5.05435169e-01 -8.04672599e-01
-1.65498090e+00 -8.88431668e-01 6.96510434e-01 9.21019018e-01
-1.06877732e+00 1.09139824e+00 6.00846648e-01 -1.85093477e-01
-1.21681355e-01 -5.93791425e-01 -6.00548506e-01 -6.28295302e-01
2.19725683e-01 8.44225347e-01 6.95510745e-01 2.26246864... | [10.452434539794922, 7.934598445892334] |
9b5b3341-3d1a-40ad-a48f-430266ed7c20 | self-organization-preserved-graph-structure | 2301.00015 | null | https://arxiv.org/abs/2301.00015v1 | https://arxiv.org/pdf/2301.00015v1.pdf | Self-organization Preserved Graph Structure Learning with Principle of Relevant Information | Most Graph Neural Networks follow the message-passing paradigm, assuming the observed structure depicts the ground-truth node relationships. However, this fundamental assumption cannot always be satisfied, as real-world graphs are always incomplete, noisy, or redundant. How to reveal the inherent graph structure in a u... | ['Philip S. Yu', 'Hao Peng', 'Xingcheng Fu', 'Beining Yang', 'JianXin Li', 'Qingyun Sun'] | 2022-12-30 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [ 2.56924003e-01 5.87408662e-01 -1.71825811e-01 -4.78781722e-02
3.55146490e-02 -6.85395122e-01 4.49465007e-01 4.39493418e-01
1.75964877e-01 6.91711068e-01 1.22697778e-01 -2.33448774e-01
-5.12873471e-01 -1.39303088e+00 -7.52810121e-01 -1.16844964e+00
-9.12493289e-01 2.80381471e-01 1.63071588e-01 -4.17229235... | [7.014506816864014, 6.170078277587891] |
894dbd3d-d9a4-4d42-8aab-36bb36e14a38 | dounseen-zero-shot-object-detection-for | 2304.02833 | null | https://arxiv.org/abs/2304.02833v1 | https://arxiv.org/pdf/2304.02833v1.pdf | DoUnseen: Zero-Shot Object Detection for Robotic Grasping | How can we segment varying numbers of objects where each specific object represents its own separate class? To make the problem even more realistic, how can we add and delete classes on the fly without retraining? This is the case of robotic applications where no datasets of the objects exist or application that includ... | ['Moritz Roidl', 'Anas Gouda'] | 2023-04-06 | null | null | null | null | ['template-matching', 'zero-shot-object-detection', 'robotic-grasping'] | ['computer-vision', 'computer-vision', 'robots'] | [ 4.23515588e-01 1.77755266e-01 3.39444540e-02 -4.41876113e-01
-3.39094460e-01 -8.69292438e-01 3.08600307e-01 1.90524444e-01
-4.20020193e-01 3.30784678e-01 -6.69301212e-01 -1.72179982e-01
-3.70899625e-02 -8.68502438e-01 -1.14761424e+00 -6.06937170e-01
1.67647570e-01 9.65363860e-01 9.92772460e-01 -2.57501960... | [6.192483901977539, -1.0083290338516235] |
51cd3cab-6755-445f-97f4-0042f3f254a5 | sustainability-of-collusion-and-market | 2105.02094 | null | https://arxiv.org/abs/2105.02094v1 | https://arxiv.org/pdf/2105.02094v1.pdf | Sustainability of Collusion and Market Transparency in a Sequential Search Market: a Generalization | The present work generalizes the analytical results of Petrikaite (2016) to a market where more than two firms interact. As a consequence, for a generic number of firms in the oligopoly model described by Janssen et al (2005), the relationship between the critical discount factor which sustains the monopoly collusive a... | ['Giuseppe Puleio', 'Jacopo De Tullio'] | 2021-05-05 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [-5.54777026e-01 5.20069361e-01 -5.06724954e-01 4.15126711e-01
2.02189922e-01 -1.18895459e+00 4.08714682e-01 1.12545133e-01
-5.12432694e-01 7.96581924e-01 -6.24990165e-01 -4.47325677e-01
-6.49266958e-01 -6.73083782e-01 -3.87788743e-01 -1.04697740e+00
-2.79321492e-01 2.26320252e-01 1.43705800e-01 -2.63330162... | [4.862112522125244, 3.823110580444336] |
09c0c12e-214b-408f-a70c-e6479baf8df2 | toward-dnn-of-luts-learning-efficient-image | 2303.14506 | null | https://arxiv.org/abs/2303.14506v1 | https://arxiv.org/pdf/2303.14506v1.pdf | Toward DNN of LUTs: Learning Efficient Image Restoration with Multiple Look-Up Tables | The widespread usage of high-definition screens on edge devices stimulates a strong demand for efficient image restoration algorithms. The way of caching deep learning models in a look-up table (LUT) is recently introduced to respond to this demand. However, the size of a single LUT grows exponentially with the increas... | ['Zhiwei Xiong', 'Zhen Cheng', 'Chang Chen', 'Jiacheng Li'] | 2023-03-25 | null | null | null | null | ['demosaicking'] | ['computer-vision'] | [ 2.08348170e-01 -4.47046041e-01 3.74830030e-02 -1.66001171e-01
-7.44575381e-01 -2.71970838e-01 1.99029781e-02 -1.06456399e-01
-5.40629625e-01 4.80704159e-01 2.53939211e-01 -4.11627561e-01
1.93619832e-01 -9.88915026e-01 -8.12594712e-01 -7.10234642e-01
1.53949365e-01 -4.66445982e-01 4.81596053e-01 -5.52966213... | [10.959243774414062, -2.0239431858062744] |
efd1bf90-5d18-449a-bfc0-0442a3d89379 | human-action-recognition-without-human | 1608.07876 | null | http://arxiv.org/abs/1608.07876v1 | http://arxiv.org/pdf/1608.07876v1.pdf | Human Action Recognition without Human | The objective of this paper is to evaluate "human action recognition without
human". Motion representation is frequently discussed in human action
recognition. We have examined several sophisticated options, such as dense
trajectories (DT) and the two-stream convolutional neural network (CNN).
However, some features fr... | ['Hirokatsu Kataoka', 'Soma Shirakabe', 'Yun He', 'Yutaka Satoh'] | 2016-08-29 | null | null | null | null | ['action-analysis'] | ['computer-vision'] | [ 5.08819044e-01 -2.55873144e-01 -3.38935703e-01 -2.09694415e-01
-1.11475803e-01 -1.28311306e-01 7.94548392e-01 -4.88078505e-01
-6.52527690e-01 8.52685571e-01 5.57677627e-01 -1.62229255e-01
3.30600470e-01 -7.45891333e-01 -5.05174935e-01 -8.95510793e-01
2.19816074e-01 -2.98275828e-01 5.95730007e-01 -2.27253661... | [8.153359413146973, 0.5474671125411987] |
769d69bc-375e-4ab0-b8ba-f3a64f9d8a38 | data-driven-modeling-of-time-domain-induced | 2107.14796 | null | https://arxiv.org/abs/2107.14796v1 | https://arxiv.org/pdf/2107.14796v1.pdf | Data-driven modeling of time-domain induced polarization | We present a novel approach for data-driven modeling of the time-domain induced polarization (IP) phenomenon using variational autoencoders (VAE). VAEs are Bayesian neural networks that aim to learn a latent statistical distribution to encode extensive data sets as lower dimension representations. We collected 1 600 31... | ['Pierre Bérubé', 'Charles L. Bérubé'] | 2021-07-30 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [-4.07638773e-02 -1.31831944e-01 1.87337235e-01 -3.87228936e-01
-1.02555490e+00 -4.90480840e-01 7.69843221e-01 9.95430425e-02
-3.32874835e-01 9.25307870e-01 2.04706758e-01 -4.93133605e-01
-5.02536237e-01 -8.46466780e-01 -9.42758441e-01 -1.24818528e+00
-3.36433858e-01 8.74503434e-01 1.43671244e-01 -1.35347238... | [7.056229591369629, 3.68778395652771] |
3c1de6a4-c160-467c-a572-847eff895f92 | an-empirical-assessment-of-the-qualitative | null | null | https://aclanthology.org/2021.nlp4if-1.11 | https://aclanthology.org/2021.nlp4if-1.11.pdf | An Empirical Assessment of the Qualitative Aspects of Misinformation in Health News | The explosion of online health news articles runs the risk of the proliferation of low-quality information. Within the existing work on fact-checking, however, relatively little attention has been paid to medical news. We present a health news classification task to determine whether medical news articles satisfy a set... | ['Ritwik Banerjee', 'Qi Zhang', 'Chaoyuan Zuo'] | null | null | null | null | naacl-nlp4if-2021-6 | ['news-classification'] | ['natural-language-processing'] | [ 2.59134471e-01 4.54490274e-01 -1.09261787e+00 -2.83821046e-01
-1.31438673e+00 -4.47817206e-01 7.36642659e-01 1.13617826e+00
-4.64893967e-01 8.01620483e-01 8.06008875e-01 -7.90723681e-01
-1.96338937e-01 -7.76337683e-01 -8.25215578e-01 -2.05436066e-01
1.72427505e-01 3.73688757e-01 1.60584062e-01 7.94807822... | [8.675217628479004, 9.719094276428223] |
270a83b3-3eed-491b-b3a3-fe7471b8d911 | rapid-ai-development-cycle-for-the | 2003.05037 | null | https://arxiv.org/abs/2003.05037v3 | https://arxiv.org/pdf/2003.05037v3.pdf | Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis | Purpose: Develop AI-based automated CT image analysis tools for detection, quantification, and tracking of Coronavirus; demonstrate they can differentiate coronavirus patients from non-patients. Materials and Methods: Multiple international datasets, including from Chinese disease-infected areas were included. We prese... | ['Wenbin Ji', 'Maayan Frid-Adar', 'Ophir Gozes', 'Hayit Greenspan', 'Eliot Siegel', 'Huangqi Zhang', 'Adam Bernheim', 'Patrick D. Browning'] | 2020-03-10 | null | null | null | null | ['covid-19-image-segmentation'] | ['computer-vision'] | [-2.13309135e-02 -5.75820506e-01 -6.70951381e-02 -1.29226789e-01
-5.25268555e-01 -8.42565238e-01 1.60851523e-01 5.37581325e-01
-3.10902923e-01 3.15324277e-01 1.18808866e-01 -6.61655426e-01
-1.76494062e-01 -6.30965769e-01 -2.72009164e-01 -6.27222538e-01
-8.40597570e-01 1.31429660e+00 -1.78693667e-01 3.28814417... | [15.550301551818848, -1.7347570657730103] |
0c3ee8bb-2a6e-4210-804d-7bb0a3d0a5d3 | adaptive-background-matting-using-background | 2203.05193 | null | https://arxiv.org/abs/2203.05193v2 | https://arxiv.org/pdf/2203.05193v2.pdf | Adaptive Background Matting Using Background Matching | Due to the difficulty of solving the matting problem, lots of methods use some kinds of assistance to acquire high quality alpha matte. Green screen matting methods rely on physical equipment. Trimap-based methods take manual interactions as external input. Background-based methods require a pre-captured, static backgr... | ['Jinlin Liu'] | 2022-03-10 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 2.31280953e-01 -5.40115595e-01 2.71439217e-02 -1.38136130e-02
-2.27901965e-01 -3.68677735e-01 3.01872075e-01 -3.02691489e-01
-5.12732625e-01 6.92133367e-01 -3.22200894e-01 -4.11553308e-02
2.70935923e-01 -9.75687385e-01 -7.05851316e-01 -8.67888570e-01
4.32032049e-01 4.96132165e-01 9.07931268e-01 -2.31873706... | [10.355294227600098, -1.265811562538147] |
cf11c1ab-be79-4ebd-a1a3-a3bb05183763 | on-developing-facial-stress-analysis-and | 2209.07916 | null | https://arxiv.org/abs/2209.07916v2 | https://arxiv.org/pdf/2209.07916v2.pdf | On Developing Facial Stress Analysis and Expression Recognition Platform | This work represents the experimental and development process of system facial expression recognition and facial stress analysis algorithms for an immersive digital learning platform. The system retrieves from users web camera and evaluates it using artificial neural network (ANN) algorithms. The ANN output signals can... | ['Anastasiia Archangelskaya', 'Dmitrii Grigorev', 'Sergei Nikolaev', 'Fabio Cacciatori'] | 2022-09-16 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 9.25133377e-02 -2.28435490e-02 2.61953115e-01 -7.01148450e-01
4.94488746e-01 -2.19310746e-01 9.93510475e-04 -1.90052472e-03
-7.12125421e-01 5.13440609e-01 -4.37652618e-01 8.93937889e-03
-5.13466671e-02 -7.71153927e-01 -1.56768113e-01 -3.83784413e-01
-4.12260108e-02 5.05330153e-02 -5.50817922e-02 -4.02299494... | [13.466985702514648, 2.1702709197998047] |
56d88bd1-b34b-41db-88c8-725a04586e89 | human-saliency-driven-patch-based-matching | 2208.03138 | null | https://arxiv.org/abs/2208.03138v1 | https://arxiv.org/pdf/2208.03138v1.pdf | Human Saliency-Driven Patch-based Matching for Interpretable Post-mortem Iris Recognition | Forensic iris recognition, as opposed to live iris recognition, is an emerging research area that leverages the discriminative power of iris biometrics to aid human examiners in their efforts to identify deceased persons. As a machine learning-based technique in a predominantly human-controlled task, forensic recogniti... | ['Adam Czajka', 'Kevin Bowyer', 'Andrey Kuehlkamp', 'Daniel Moreira', 'Aidan Boyd'] | 2022-08-03 | null | null | null | null | ['iris-recognition'] | ['computer-vision'] | [ 4.79766041e-01 1.69537112e-01 -1.16062872e-01 -4.01390761e-01
-8.09846938e-01 -6.25330389e-01 5.27217567e-01 4.11134422e-01
-5.35845399e-01 1.68919742e-01 1.40511826e-01 -4.99149144e-01
-2.98307598e-01 -2.13489547e-01 -4.50456113e-01 -7.24107146e-01
1.10165156e-01 4.05699164e-01 -3.96946877e-01 2.56984353... | [3.7410690784454346, -3.633040189743042] |
5de0881c-9a97-44a5-a766-747440d8de19 | msmix-an-interpolation-based-text-data | 2305.19617 | null | https://arxiv.org/abs/2305.19617v1 | https://arxiv.org/pdf/2305.19617v1.pdf | MSMix:An Interpolation-Based Text Data Augmentation Method Manifold Swap Mixup | To solve the problem of poor performance of deep neural network models due to insufficient data, a simple yet effective interpolation-based data augmentation method is proposed: MSMix (Manifold Swap Mixup). This method feeds two different samples to the same deep neural network model, and then randomly select a specifi... | ['Zheqian Chen', 'Haitao Wang', 'Mao Ye'] | 2023-05-31 | null | null | null | null | ['intent-detection'] | ['natural-language-processing'] | [ 1.65366456e-01 1.87352002e-01 -4.16170895e-01 -2.58552104e-01
-1.08445197e-01 3.99675548e-01 4.05830473e-01 -6.22463644e-01
-4.03985918e-01 7.58348346e-01 4.12375271e-01 -1.66902065e-01
2.42202654e-01 -6.85247958e-01 -4.62406605e-01 -1.04931319e+00
1.73881546e-01 1.94565207e-01 9.61379111e-02 7.17868954... | [9.438691139221191, 2.156749725341797] |
49f5c6d7-d8a1-4c95-9119-9053a0212f38 | sok-privacy-preserving-deep-learning-with | 2112.12855 | null | https://arxiv.org/abs/2112.12855v2 | https://arxiv.org/pdf/2112.12855v2.pdf | SoK: Privacy-preserving Deep Learning with Homomorphic Encryption | Outsourced computation for neural networks allows users access to state of the art models without needing to invest in specialized hardware and know-how. The problem is that the users lose control over potentially privacy sensitive data. With homomorphic encryption (HE) computation can be performed on encrypted data wi... | ['Peizhao Hu', 'Daniel Takabi', 'Robert Podschwadt'] | 2021-12-23 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [ 1.16285659e-01 2.97233671e-01 4.67470437e-02 -9.11795318e-01
-5.71251392e-01 -9.80393231e-01 1.95077449e-01 5.82033535e-03
-1.23423040e+00 5.35631418e-01 2.64699720e-02 -3.37698877e-01
9.14862100e-03 -1.01011491e+00 -9.58103895e-01 -6.86420858e-01
-1.90891296e-01 -1.48915485e-01 -9.45808366e-02 -1.70420930... | [5.8841094970703125, 6.859067916870117] |
725ef0a7-c805-451a-b8f1-5afe1119d595 | provably-efficient-offline-reinforcement-1 | 2306.08364 | null | https://arxiv.org/abs/2306.08364v1 | https://arxiv.org/pdf/2306.08364v1.pdf | Provably Efficient Offline Reinforcement Learning with Perturbed Data Sources | Existing theoretical studies on offline reinforcement learning (RL) mostly consider a dataset sampled directly from the target task. In practice, however, data often come from several heterogeneous but related sources. Motivated by this gap, this work aims at rigorously understanding offline RL with multiple datasets t... | ['Jing Yang', 'Cong Shen', 'Wei Xiong', 'Chengshuai Shi'] | 2023-06-14 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [ 9.66121182e-02 3.84790719e-01 -3.75080943e-01 2.43407011e-01
-1.30736780e+00 -7.58547783e-01 8.72574151e-02 3.11422557e-01
-3.53582442e-01 1.10653591e+00 -7.82550275e-02 -2.62107756e-02
-6.86696112e-01 -6.46892786e-01 -1.09374642e+00 -9.02709961e-01
-2.16698214e-01 4.87715811e-01 -1.73926219e-01 -6.31925166... | [4.388525009155273, 2.753044366836548] |
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