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5f1005f9-f2a0-4e94-803f-ba478b40ccac | wish-i-can-feel-what-you-feel-a-neural | 2212.02000 | null | https://arxiv.org/abs/2212.02000v1 | https://arxiv.org/pdf/2212.02000v1.pdf | Wish I Can Feel What You Feel: A Neural Approach for Empathetic Response Generation | Expressing empathy is important in everyday conversations, and exploring how empathy arises is crucial in automatic response generation. Most previous approaches consider only a single factor that affects empathy. However, in practice, empathy generation and expression is a very complex and dynamic psychological proces... | ['Chunfeng Liang', 'Yangbin Chen'] | 2022-12-05 | null | null | null | null | ['response-generation', 'empathetic-response-generation', 'emotion-cause-extraction'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.28826186e-01 1.57644302e-01 -3.30497362e-02 -3.83627683e-01
-3.55877243e-02 -2.18915388e-01 5.25389493e-01 1.77813917e-01
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4.78450298e-01 2.46534556e-01 -3.32728326e-01 -5.59081256... | [13.15926456451416, 7.5961995124816895] |
ec95ef12-68bd-4972-a61a-689e4725da1b | multi-frame-quality-enhancement-on-compressed | 2201.11389 | null | https://arxiv.org/abs/2201.11389v1 | https://arxiv.org/pdf/2201.11389v1.pdf | Multi-Frame Quality Enhancement On Compressed Video Using Quantised Data of Deep Belief Networks | In the age of streaming and surveillance compressed video enhancement has become a problem in need of constant improvement. Here, we investigate a way of improving the Multi-Frame Quality Enhancement approach. This approach consists of making use of the frames that have the peak quality in the region to improve those t... | ['Mkhuseli Ngxande', 'Dionne Takudzwa Chasi'] | 2022-01-27 | null | null | null | null | ['video-enhancement'] | ['computer-vision'] | [ 2.93603420e-01 -1.23783581e-01 1.15203097e-01 -2.58734733e-01
-6.06225550e-01 2.93719769e-03 3.97340655e-01 6.46577403e-02
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-2.08532110e-01 -3.38145047e-01 6.50821447e-01 -3.42258960... | [11.316340446472168, -1.74104642868042] |
5e015f94-4e04-4075-8eb2-d7e338c7a07c | domain-expanded-aste-rethinking | 2305.14434 | null | https://arxiv.org/abs/2305.14434v1 | https://arxiv.org/pdf/2305.14434v1.pdf | Domain-Expanded ASTE: Rethinking Generalization in Aspect Sentiment Triplet Extraction | Aspect Sentiment Triplet Extraction (ASTE) is a subtask of Aspect-Based Sentiment Analysis (ABSA) that considers each opinion term, their expressed sentiment, and the corresponding aspect targets. However, existing methods are limited to the in-domain setting with two domains. Hence, we propose a domain-expanded benchm... | ['Lidong Bing', 'Soujanya Poria', 'Sharifah Mahani Aljunied', 'Guizhen Chen', 'Wei Han', 'Hui Chen', 'Yew Ken Chia'] | 2023-05-23 | null | null | null | null | ['sentiment-analysis', 'aspect-based-sentiment-analysis', 'aspect-sentiment-triplet-extraction'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-1.36432618e-01 -1.24272585e-01 -3.57206970e-01 -7.10818529e-01
-1.17615449e+00 -1.17840052e+00 8.42586339e-01 2.60439166e-03
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4.61917549e-01 7.13417470e-01 -1.01087920e-01 -5.78255355... | [11.378053665161133, 6.759697437286377] |
1fe7c8e5-d8c4-438f-8226-eab24b59326a | action-quality-assessment-using-siamese | 2002.12096 | null | https://arxiv.org/abs/2002.12096v1 | https://arxiv.org/pdf/2002.12096v1.pdf | Action Quality Assessment using Siamese Network-Based Deep Metric Learning | Automated vision-based score estimation models can be used as an alternate opinion to avoid judgment bias. In the past works the score estimation models were learned by regressing the video representations to the ground truth score provided by the judges. However such regression-based solutions lack interpretability in... | ['Hiteshi Jain', 'Avinash Sharma', 'Gaurav Harit'] | 2020-02-27 | null | null | null | null | ['action-quality-assessment'] | ['computer-vision'] | [ 3.68907489e-02 -7.23593161e-02 -4.78847325e-02 -7.62447059e-01
-6.92331076e-01 -3.85201335e-01 4.11756307e-01 1.31483704e-01
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1.48038685e-01 3.56800377e-01 5.89640498e-01 -3.02814335... | [8.03217887878418, 0.5767263174057007] |
d3c5a102-0dfd-46bf-b204-b17a29ba9729 | trans4trans-efficient-transformer-for-1 | 2108.09174 | null | https://arxiv.org/abs/2108.09174v1 | https://arxiv.org/pdf/2108.09174v1.pdf | Trans4Trans: Efficient Transformer for Transparent Object and Semantic Scene Segmentation in Real-World Navigation Assistance | Transparent objects, such as glass walls and doors, constitute architectural obstacles hindering the mobility of people with low vision or blindness. For instance, the open space behind glass doors is inaccessible, unless it is correctly perceived and interacted with. However, traditional assistive technologies rarely ... | ['Rainer Stiefelhagen', 'Karin Müller', 'Kunyu Peng', 'Angela Constantinescu', 'Kailun Yang', 'Jiaming Zhang'] | 2021-08-20 | null | null | null | null | ['transparent-objects', 'scene-segmentation'] | ['computer-vision', 'computer-vision'] | [-3.66911255e-02 -5.42878360e-03 1.86380312e-01 -3.09151828e-01
-5.54311335e-01 -1.84123173e-01 -4.38672379e-02 -3.77963334e-01
-3.93528730e-01 4.11988229e-01 1.12460636e-01 -5.35415530e-01
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2.71757394e-01 -8.19621086e-02 5.17609000e-01 -2.45102182... | [7.958569049835205, -1.5341565608978271] |
49a7a831-5c6c-4ec6-a1b8-be862a2a2cdc | video-face-super-resolution-with-motion | 2002.06378 | null | https://arxiv.org/abs/2002.06378v1 | https://arxiv.org/pdf/2002.06378v1.pdf | Video Face Super-Resolution with Motion-Adaptive Feedback Cell | Video super-resolution (VSR) methods have recently achieved a remarkable success due to the development of deep convolutional neural networks (CNN). Current state-of-the-art CNN methods usually treat the VSR problem as a large number of separate multi-frame super-resolution tasks, at which a batch of low resolution (LR... | ['Zhifeng Li', 'Nannan Wang', 'Jie Li', 'Xinbo Gao', 'Jingwei Xin'] | 2020-02-15 | null | null | null | null | ['multi-frame-super-resolution'] | ['computer-vision'] | [ 2.98600227e-01 -5.21733642e-01 -1.32483974e-01 -3.36880200e-02
-3.73154491e-01 -4.31682020e-02 3.08859348e-01 -6.21236861e-01
-3.99136662e-01 7.63273180e-01 2.39287004e-01 2.78046548e-01
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5.89479953e-02 -4.47011948e-01 7.61056602e-01 -4.02419209... | [11.039942741394043, -1.7816123962402344] |
c6221aff-2064-4884-a033-4d1286c98380 | deep-triplet-hashing-network-for-case-based | 2101.12346 | null | https://arxiv.org/abs/2101.12346v1 | https://arxiv.org/pdf/2101.12346v1.pdf | Deep Triplet Hashing Network for Case-based Medical Image Retrieval | Deep hashing methods have been shown to be the most efficient approximate nearest neighbor search techniques for large-scale image retrieval. However, existing deep hashing methods have a poor small-sample ranking performance for case-based medical image retrieval. The top-ranked images in the returned query results ma... | ['Jiang Liu', 'Huazhu Fu', 'Jiansheng Fang'] | 2021-01-29 | null | null | null | null | ['medical-image-retrieval', 'medical-image-retrieval'] | ['computer-vision', 'medical'] | [-2.27607071e-01 -3.01321447e-01 -4.43169564e-01 -5.34371376e-01
-1.49914193e+00 6.34248108e-02 7.84427822e-02 5.12651622e-01
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-3.59457225e-01 -8.46525371e-01 -5.26626468e-01 -9.52761829e-01
-5.16360402e-01 2.54831284e-01 2.78285950e-01 3.83203439... | [11.347001075744629, 0.9283722043037415] |
51d62790-7dff-4c27-9b46-2c7bfde3871c | is-an-object-centric-video-representation | 2207.10075 | null | https://arxiv.org/abs/2207.10075v2 | https://arxiv.org/pdf/2207.10075v2.pdf | Is an Object-Centric Video Representation Beneficial for Transfer? | The objective of this work is to learn an object-centric video representation, with the aim of improving transferability to novel tasks, i.e., tasks different from the pre-training task of action classification. To this end, we introduce a new object-centric video recognition model based on a transformer architecture. ... | ['Andrew Zisserman', 'Ankush Gupta', 'Chuhan Zhang'] | 2022-07-20 | null | null | null | null | ['action-classification'] | ['computer-vision'] | [ 6.46230221e-01 -2.77945846e-01 -3.03546727e-01 -4.20014560e-01
-7.16405094e-01 -4.47170496e-01 9.31323171e-01 -2.14412883e-01
-3.39529872e-01 4.76587772e-01 6.82568431e-01 4.77626622e-02
-1.23920217e-01 -2.73567021e-01 -9.33855653e-01 -7.96493351e-01
-2.66764522e-01 1.09569535e-01 6.27294302e-01 -5.70143722... | [8.680416107177734, 0.8629804849624634] |
ea34bc69-a504-4298-af86-0421ec4b09ae | a-large-scale-film-style-dataset-for-learning | 2301.08880 | null | https://arxiv.org/abs/2301.08880v2 | https://arxiv.org/pdf/2301.08880v2.pdf | A Large-scale Film Style Dataset for Learning Multi-frequency Driven Film Enhancement | Film, a classic image style, is culturally significant to the whole photographic industry since it marks the birth of photography. However, film photography is time-consuming and expensive, necessitating a more efficient method for collecting film-style photographs. Numerous datasets that have emerged in the field of i... | ['Zinuo Li', 'Shuqiang Wang', 'Chi-Man Pun', 'Xuhang Chen'] | 2023-01-21 | null | null | null | null | ['film-simulation', 'image-stylization'] | ['computer-vision', 'computer-vision'] | [ 4.39036697e-01 -4.62865829e-01 -2.06481189e-01 -3.90383482e-01
-4.67341065e-01 -3.07345301e-01 3.10152054e-01 -2.25877464e-01
-1.59839913e-01 5.02326310e-01 4.50289041e-01 -5.50621673e-02
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4.72071141e-01 -4.25671518e-01 1.94920614e-01 -3.42555016... | [11.347829818725586, -0.9423348307609558] |
383e3e93-6649-4ece-bd66-16108793ba8b | deep-optimized-priors-for-3d-shape-modeling | 2012.07241 | null | https://arxiv.org/abs/2012.07241v1 | https://arxiv.org/pdf/2012.07241v1.pdf | Deep Optimized Priors for 3D Shape Modeling and Reconstruction | Many learning-based approaches have difficulty scaling to unseen data, as the generality of its learned prior is limited to the scale and variations of the training samples. This holds particularly true with 3D learning tasks, given the sparsity of 3D datasets available. We introduce a new learning framework for 3D mod... | ['Kui Jia', 'Yongwei Chen', 'Weikai Chen', 'Yuxin Wen', 'Mingyue Yang'] | 2020-12-14 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Yang_Deep_Optimized_Priors_for_3D_Shape_Modeling_and_Reconstruction_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Yang_Deep_Optimized_Priors_for_3D_Shape_Modeling_and_Reconstruction_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-shape-modeling'] | ['computer-vision'] | [ 2.35206597e-02 2.20762968e-01 4.60981615e-02 -2.51723409e-01
-6.90670192e-01 -4.10516530e-01 7.05114603e-01 -2.48851672e-01
-8.85700285e-02 5.24554729e-01 9.25405622e-02 -8.25150385e-02
-2.17534691e-01 -7.04027295e-01 -9.53191042e-01 -7.53982961e-01
4.26113233e-03 5.10604501e-01 3.15754473e-01 -2.51129037... | [8.697540283203125, -3.099785327911377] |
f9f46d69-1afd-4f55-b7fc-c332ff48c94f | deep-laparoscopic-stereo-matching-with | 2207.12152 | null | https://arxiv.org/abs/2207.12152v1 | https://arxiv.org/pdf/2207.12152v1.pdf | Deep Laparoscopic Stereo Matching with Transformers | The self-attention mechanism, successfully employed with the transformer structure is shown promise in many computer vision tasks including image recognition, and object detection. Despite the surge, the use of the transformer for the problem of stereo matching remains relatively unexplored. In this paper, we comprehen... | ['ZongYuan Ge', 'Zhiyong Wang', 'Tom Drummond', 'Mehrtash Harandi', 'Yiran Zhong', 'Xuelian Cheng'] | 2022-07-25 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 1.24807693e-01 -2.74258256e-02 6.90357238e-02 -3.11506152e-01
-4.44822490e-01 -2.94934034e-01 4.70659405e-01 -8.22339952e-02
-2.96863139e-01 1.66246772e-01 2.36083105e-01 -3.15286517e-01
-2.63772488e-01 -7.69684732e-01 -9.01644349e-01 -5.09824574e-01
1.24841452e-01 2.52305150e-01 1.53968394e-01 -2.55108654... | [8.82338809967041, -2.229863405227661] |
a8979c2a-0018-462d-a826-36996807594c | test-positive-at-w-nut-2020-shared-task-3-1 | null | null | https://aclanthology.org/2020.wnut-1.76 | https://aclanthology.org/2020.wnut-1.76.pdf | TEST_POSITIVE at W-NUT 2020 Shared Task-3: Cross-task modeling | The competition of extracting COVID-19 events from Twitter is to develop systems that can automatically extract related events from tweets. The built system should identify different pre-defined slots for each event, in order to answer important questions (e.g., Who is tested positive? What is the age of the person? Wh... | ['Jiaqi Wang', 'Enyan Dai', 'Yang Shi', 'Yaqi Hou', 'Chieh-Yang Huang', 'Chacha Chen'] | null | null | null | null | emnlp-wnut-2020-11 | ['extracting-covid-19-events-from-twitter'] | ['natural-language-processing'] | [-3.44452858e-02 -9.51137319e-02 -1.79236457e-01 -6.07772529e-01
-1.10899103e+00 -4.70800608e-01 6.77391827e-01 6.64580941e-01
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2.50340819e-01 6.43059790e-01 3.60129744e-01 -2.11138785... | [9.552387237548828, 9.465892791748047] |
5c6a4072-be66-41a5-bc51-825b7eacb71d | a-study-of-situational-reasoning-for-traffic | 2306.02520 | null | https://arxiv.org/abs/2306.02520v1 | https://arxiv.org/pdf/2306.02520v1.pdf | A Study of Situational Reasoning for Traffic Understanding | Intelligent Traffic Monitoring (ITMo) technologies hold the potential for improving road safety/security and for enabling smart city infrastructure. Understanding traffic situations requires a complex fusion of perceptual information with domain-specific and causal commonsense knowledge. Whereas prior work has provided... | ['Alessandro Oltramari', 'Jonathan Francis', 'Aravinda Kollaa', 'Kaixin Ma', 'Filip Ilievski', 'Jiarui Zhang'] | 2023-06-05 | null | null | null | null | ['knowledge-graphs', 'natural-language-inference'] | ['knowledge-base', 'natural-language-processing'] | [ 3.99187028e-01 3.76626700e-01 -5.67184031e-01 -4.21064019e-01
-7.12231278e-01 -2.94643044e-01 7.72287965e-01 1.78971946e-01
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-7.21660137e-01 -9.89252508e-01 -5.62437356e-01 -8.16034377e-02
2.14033946e-01 6.93457663e-01 5.00676870e-01 -7.31010199... | [9.470695495605469, 7.680737018585205] |
21c31612-a872-43c2-ac08-536631db44a5 | finnwoodlands-dataset | 2304.00793 | null | https://arxiv.org/abs/2304.00793v1 | https://arxiv.org/pdf/2304.00793v1.pdf | FinnWoodlands Dataset | While the availability of large and diverse datasets has contributed to significant breakthroughs in autonomous driving and indoor applications, forestry applications are still lagging behind and new forest datasets would most certainly contribute to achieving significant progress in the development of data-driven meth... | ['Esa Rahtu', 'Urho Lempiö', 'Juan Lagos'] | 2023-04-03 | null | null | null | null | ['panoptic-segmentation', 'depth-completion'] | ['computer-vision', 'computer-vision'] | [ 4.70114708e-01 1.69898495e-01 -3.64317633e-02 -6.06237769e-01
-3.71158689e-01 -6.78321719e-01 6.73198342e-01 3.65078390e-01
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-3.13276321e-01 -1.30198109e+00 -4.87815380e-01 -7.67169237e-01
-1.56436905e-01 8.05108428e-01 4.65887964e-01 -3.92094940... | [8.504836082458496, -2.249023914337158] |
90deb841-1ffd-4eeb-a202-9410150c21e9 | enhancing-cross-lingual-transfer-via-phonemic | 2307.04361 | null | https://arxiv.org/abs/2307.04361v1 | https://arxiv.org/pdf/2307.04361v1.pdf | Enhancing Cross-lingual Transfer via Phonemic Transcription Integration | Previous cross-lingual transfer methods are restricted to orthographic representation learning via textual scripts. This limitation hampers cross-lingual transfer and is biased towards languages sharing similar well-known scripts. To alleviate the gap between languages from different writing scripts, we propose PhoneXL... | ['Philip S. Yu', 'Eugene Rohrbaugh', 'Tao Zhang', 'Chenwei Zhang', 'Hoang H. Nguyen'] | 2023-07-10 | null | null | null | null | ['representation-learning', 'part-of-speech-tagging', 'cross-lingual-transfer'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [-3.99105949e-03 -4.43623334e-01 -6.33055568e-01 -4.43289727e-01
-1.27307963e+00 -1.11869335e+00 6.20994329e-01 -1.07967265e-01
-8.79254758e-01 7.31825590e-01 8.58745098e-01 -3.90594065e-01
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5.56794167e-01 7.17794359e-01 -3.83971244e-01 -2.80596852... | [10.984580039978027, 10.003830909729004] |
a09d34bc-8de1-4974-a3d7-5d167b84bbaa | stt4sg-350-a-speech-corpus-for-all-swiss | 2305.18855 | null | https://arxiv.org/abs/2305.18855v1 | https://arxiv.org/pdf/2305.18855v1.pdf | STT4SG-350: A Speech Corpus for All Swiss German Dialect Regions | We present STT4SG-350 (Speech-to-Text for Swiss German), a corpus of Swiss German speech, annotated with Standard German text at the sentence level. The data is collected using a web app in which the speakers are shown Standard German sentences, which they translate to Swiss German and record. We make the corpus public... | ['Mark Cieliebak', 'Manfred Vogel', 'Tanja Samardžić', 'Manuela Hürlimann', 'Christian Scheller', 'Larissa Schmidt', 'Julia Hartmann', 'Claudio Paonessa', 'Yanick Schraner', 'Jan Deriu', 'Michel Plüss'] | 2023-05-30 | null | null | null | null | ['dialect-identification', 'speaker-recognition', 'automatic-speech-recognition'] | ['natural-language-processing', 'speech', 'speech'] | [-1.83218971e-01 2.62742579e-01 -8.92390236e-02 -7.86766171e-01
-1.21240270e+00 -7.44863629e-01 7.09908307e-01 2.09819511e-01
-4.01408315e-01 3.88560086e-01 7.46649384e-01 -4.85903859e-01
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2.16067776e-01 8.23017538e-01 4.47772592e-02 -6.80743515... | [14.307137489318848, 6.768370628356934] |
d21ff505-409c-4af7-9302-d458d0044257 | a-graph-based-analysis-of-medical-queries-of | null | null | https://aclanthology.org/W14-1102 | https://aclanthology.org/W14-1102.pdf | A Graph-Based Analysis of Medical Queries of a Swedish Health Care Portal | null | ['Philippas Tsigas', 'Ann-Marie Eklund', 'Farnaz Moradi', 'Tomas Olovsson', 'Dimitrios Kokkinakis'] | 2014-04-01 | null | null | null | ws-2014-4 | ['local-community-detection'] | ['graphs'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.240964412689209, 3.6303396224975586] |
d47a207a-6877-4fae-a9a1-8da7601a7304 | mask3d-for-3d-semantic-instance-segmentation | 2210.03105 | null | https://arxiv.org/abs/2210.03105v2 | https://arxiv.org/pdf/2210.03105v2.pdf | Mask3D: Mask Transformer for 3D Semantic Instance Segmentation | Modern 3D semantic instance segmentation approaches predominantly rely on specialized voting mechanisms followed by carefully designed geometric clustering techniques. Building on the successes of recent Transformer-based methods for object detection and image segmentation, we propose the first Transformer-based approa... | ['Bastian Leibe', 'Siyu Tang', 'Or Litany', 'Alexander Hermans', 'Francis Engelmann', 'Jonas Schult'] | 2022-10-06 | null | null | null | null | ['3d-instance-segmentation-1', '3d-semantic-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 1.54748514e-01 3.09726268e-01 -4.22390103e-02 -3.91843617e-01
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-2.52054900e-01 1.33904636e+00 1.15561521e+00 -1.21194430... | [7.966867446899414, -3.1920206546783447] |
45614828-6cee-4448-bd76-31e1fe922f12 | pedestrian-3d-bounding-box-prediction | 2206.14195 | null | https://arxiv.org/abs/2206.14195v1 | https://arxiv.org/pdf/2206.14195v1.pdf | Pedestrian 3D Bounding Box Prediction | Safety is still the main issue of autonomous driving, and in order to be globally deployed, they need to predict pedestrians' motions sufficiently in advance. While there is a lot of research on coarse-grained (human center prediction) and fine-grained predictions (human body keypoints prediction), we focus on 3D bound... | ['Alexandre Alahi', 'Yi Zhou Ju', 'Saeed Saadatnejad'] | 2022-06-28 | null | null | null | null | ['action-anticipation'] | ['computer-vision'] | [-2.40308180e-01 8.25964287e-02 -4.06598747e-01 -6.10952616e-01
-5.07660449e-01 -1.35270625e-01 6.64072871e-01 -2.55850822e-01
-3.86246026e-01 6.58062220e-01 4.76860344e-01 -4.20258254e-01
4.23506379e-01 -7.87241280e-01 -8.01847816e-01 -5.57251751e-01
-2.60063857e-01 3.11972499e-01 7.08691299e-01 -5.80490053... | [6.189945697784424, 0.719791054725647] |
9d79716e-dea9-4f5e-a39e-464dd8a89d54 | efficient-multilingual-text-classification | null | null | https://aclanthology.org/2021.ranlp-main.3 | https://aclanthology.org/2021.ranlp-main.3.pdf | Efficient Multilingual Text Classification for Indian Languages | India is one of the richest language hubs on the earth and is very diverse and multilingual. But apart from a few Indian languages, most of them are still considered to be resource poor. Since most of the NLP techniques either require linguistic knowledge that can only be developed by experts and native speakers of tha... | ['Radhika Mamidi', 'Sourav Kumar', 'Salil Aggarwal'] | null | null | https://aclanthology.org/2021.ranlp-1.3 | https://aclanthology.org/2021.ranlp-1.3.pdf | ranlp-2021-9 | ['multilingual-text-classification'] | ['miscellaneous'] | [-1.68845221e-01 -1.58654422e-01 -3.75837982e-01 -1.77349716e-01
-9.10062850e-01 -9.04349327e-01 9.78251398e-01 6.28971398e-01
-7.18445778e-01 1.04227281e+00 3.13116103e-01 -5.77512920e-01
1.49426028e-01 -7.65343308e-01 -3.61214936e-01 -5.32682598e-01
1.97806999e-01 8.97480428e-01 2.39572287e-01 -7.90370107... | [10.515786170959473, 9.911823272705078] |
4008310a-3cd8-4642-9a92-758fa947283b | multi-agent-deep-reinforcement-learning-for-12 | 2306.14683 | null | https://arxiv.org/abs/2306.14683v1 | https://arxiv.org/pdf/2306.14683v1.pdf | Multi-Agent Deep Reinforcement Learning for Dynamic Avatar Migration in AIoT-enabled Vehicular Metaverses with Trajectory Prediction | Avatars, as promising digital assistants in Vehicular Metaverses, can enable drivers and passengers to immerse in 3D virtual spaces, serving as a practical emerging example of Artificial Intelligence of Things (AIoT) in intelligent vehicular environments. The immersive experience is achieved through seamless human-avat... | ['Shengli Xie', 'Abbas Jamalipour', 'Chuan Chen', 'Dusit Niyato', 'Zehui Xiong', 'Minrui Xu', 'Jiawen Kang', 'Junlong Chen'] | 2023-06-26 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [-6.27728999e-01 1.80574149e-01 -3.64664972e-01 1.91612262e-02
-3.35399449e-01 -4.02095854e-01 4.83577698e-01 -5.07839620e-01
-6.38153732e-01 8.26665878e-01 -1.78634897e-01 -6.59577549e-01
-1.52013481e-01 -8.16687107e-01 -7.70883381e-01 -6.80556595e-01
-1.62440270e-01 8.63106012e-01 4.17239517e-01 -4.19713676... | [5.619212627410889, 1.3327059745788574] |
1095b25b-50fa-4878-8941-9b59aedcf923 | implicit-occupancy-flow-fields-for-perception | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Agro_Implicit_Occupancy_Flow_Fields_for_Perception_and_Prediction_in_Self-Driving_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Agro_Implicit_Occupancy_Flow_Fields_for_Perception_and_Prediction_in_Self-Driving_CVPR_2023_paper.pdf | Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving | A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory forecasting of the detected objects, or predict dense occupancy and flow grids for the whole scene. The former p... | ['Raquel Urtasun', 'Sergio Casas', 'Quinlan Sykora', 'Ben Agro'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['trajectory-forecasting'] | ['computer-vision'] | [ 3.40160392e-02 9.49454978e-02 -2.36948803e-01 -2.85617292e-01
-3.84441704e-01 -2.63671458e-01 6.31990314e-01 4.14614156e-02
-6.12986386e-01 6.64668620e-01 1.64677426e-01 -4.05675441e-01
8.86690468e-02 -1.06404626e+00 -6.93264782e-01 -5.65666676e-01
-8.24304521e-02 4.64132428e-01 7.91845441e-01 -7.92440400... | [6.036518573760986, 0.7609910368919373] |
b1bafe3a-4064-4ab7-ae8e-b62f557278a5 | a-novel-tsk-fuzzy-system-incorporating-multi | 2111.08457 | null | https://arxiv.org/abs/2111.08457v1 | https://arxiv.org/pdf/2111.08457v1.pdf | A Novel TSK Fuzzy System Incorporating Multi-view Collaborative Transfer Learning for Personalized Epileptic EEG Detection | In clinical practice, electroencephalography (EEG) plays an important role in the diagnosis of epilepsy. EEG-based computer-aided diagnosis of epilepsy can greatly improve the ac-curacy of epilepsy detection while reducing the workload of physicians. However, there are many challenges in practical applications for pers... | ['Shitong Wang', 'Hongbin Shen', 'Kup-Sze Choi', 'Qiongdan Lou', 'Zhaohong Deng', 'Andong Li'] | 2021-11-11 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 2.24001594e-02 -3.44854027e-01 2.47326553e-01 -1.06322072e-01
-7.75450945e-01 -3.56760472e-01 1.14322796e-01 -8.13475400e-02
-2.87831724e-01 6.14943802e-01 -1.84944123e-01 2.44819745e-01
-7.42747188e-01 -5.35633683e-01 -2.85668850e-01 -9.82894063e-01
-2.94603109e-02 3.82545680e-01 6.05734736e-02 -2.49609634... | [13.107112884521484, 3.4644711017608643] |
27502aed-f297-497c-93ab-279a147089a9 | you-only-align-once-bidirectional-interaction | 2207.06345 | null | https://arxiv.org/abs/2207.06345v1 | https://arxiv.org/pdf/2207.06345v1.pdf | You Only Align Once: Bidirectional Interaction for Spatial-Temporal Video Super-Resolution | Spatial-Temporal Video Super-Resolution (ST-VSR) technology generates high-quality videos with higher resolution and higher frame rates. Existing advanced methods accomplish ST-VSR tasks through the association of Spatial and Temporal video super-resolution (S-VSR and T-VSR). These methods require two alignments and fu... | ['Zheng Wang', 'Zhixiang Nie', 'Kui Jiang', 'Mengshun Hu'] | 2022-07-13 | null | null | null | null | ['video-super-resolution'] | ['computer-vision'] | [ 2.65757531e-01 -2.56102532e-01 -4.28889960e-01 -2.96924204e-01
-9.62263763e-01 -1.20712928e-01 4.08143580e-01 -6.84460580e-01
-1.11147821e-01 8.82480979e-01 6.49047077e-01 -1.82117894e-01
-1.13589540e-01 -7.35043943e-01 -7.68783092e-01 -5.37024975e-01
-1.81176439e-01 -2.16129869e-01 7.93712735e-01 -3.45986009... | [11.04642105102539, -1.8814469575881958] |
ba81cf55-e803-4c4b-8b72-fc2fc6cf08ff | utilizing-temporal-information-in | 1909.02406 | null | https://arxiv.org/abs/1909.02406v2 | https://arxiv.org/pdf/1909.02406v2.pdf | Utilizing Temporal Information in Deep Convolutional Network for Efficient Soccer Ball Detection and Tracking | Soccer ball detection is identified as one of the critical challenges in the RoboCup competition. It requires an efficient vision system capable of handling the task of detection with high precision and recall and providing robust and low inference time. In this work, we present a novel convolutional neural network (CN... | ['Hafez Farazi', 'Anna Kukleva', 'Sven Behnke', 'Mohammad Asif Khan'] | 2019-09-05 | null | null | null | null | ['game-of-football'] | ['playing-games'] | [-9.18893889e-02 -6.56282306e-01 -9.91231129e-02 -5.84571548e-02
-4.37050879e-01 -3.24554682e-01 4.07095253e-01 -1.34611905e-01
-1.03725553e+00 5.57561278e-01 -2.00417176e-01 4.51461002e-02
9.46823657e-02 -5.70096910e-01 -9.87793088e-01 -4.78770018e-01
-1.82001397e-01 2.12742984e-01 1.10356104e+00 -3.98013353... | [8.041363716125488, 0.16857221722602844] |
1d63004a-6015-4485-b0b0-e34c88737875 | project-level-encoding-for-neural-source-code | 2103.11599 | null | https://arxiv.org/abs/2103.11599v1 | https://arxiv.org/pdf/2103.11599v1.pdf | Project-Level Encoding for Neural Source Code Summarization of Subroutines | Source code summarization of a subroutine is the task of writing a short, natural language description of that subroutine. The description usually serves in documentation aimed at programmers, where even brief phrase (e.g. "compresses data to a zip file") can help readers rapidly comprehend what a subroutine does witho... | ['Collin McMillan', 'Sakib Haque', 'Aakash Bansal'] | 2021-03-22 | null | null | null | null | ['code-summarization'] | ['computer-code'] | [ 6.13032818e-01 3.96313488e-01 -3.24285805e-01 -5.16397834e-01
-6.90932751e-01 -4.41454530e-01 3.00334573e-01 6.85896516e-01
-1.04858264e-01 2.70888925e-01 8.37326527e-01 -5.80214322e-01
8.78105164e-02 -5.76605499e-01 -8.02807033e-01 8.15146789e-02
1.93817198e-01 -6.92249909e-02 -2.84026802e-01 -2.09343940... | [7.662966251373291, 7.888943195343018] |
2784c0f4-314b-4c28-bbb4-cb73e4bef0ca | on-utilizing-relationships-for-transferable | 2212.00770 | null | https://arxiv.org/abs/2212.00770v1 | https://arxiv.org/pdf/2212.00770v1.pdf | On Utilizing Relationships for Transferable Few-Shot Fine-Grained Object Detection | State-of-the-art object detectors are fast and accurate, but they require a large amount of well annotated training data to obtain good performance. However, obtaining a large amount of training annotations specific to a particular task, i.e., fine-grained annotations, is costly in practice. In contrast, obtaining comm... | ['René Vidal', 'Amit Kumar K C', 'Arnau Ramisa', 'Ambar Pal'] | 2022-12-01 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [-3.86393219e-02 3.65326293e-02 -1.90480918e-01 -6.82661891e-01
-9.68035221e-01 -8.35665047e-01 5.52978098e-01 2.92558491e-01
-3.61781657e-01 3.72571051e-01 -1.64245635e-01 -9.98497158e-02
1.84931025e-01 -1.00985944e+00 -1.17136610e+00 -3.72755766e-01
2.64035434e-01 5.89660823e-01 7.62734532e-01 -1.73983455... | [9.545259475708008, 1.4501135349273682] |
5acfac70-786a-4ddb-80ac-c8a806f5845e | all-in-focus-imaging-from-event-focal-stack | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lou_All-in-Focus_Imaging_From_Event_Focal_Stack_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lou_All-in-Focus_Imaging_From_Event_Focal_Stack_CVPR_2023_paper.pdf | All-in-Focus Imaging From Event Focal Stack | Traditional focal stack methods require multiple shots to capture images focused at different distances of the same scene, which cannot be applied to dynamic scenes well. Generating a high-quality all-in-focus image from a single shot is challenging, due to the highly ill-posed nature of the single-image defocus an... | ['Boxin Shi', 'Yixin Yang', 'Minggui Teng', 'Hanyue Lou'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['deblurring'] | ['computer-vision'] | [ 8.25305223e-01 -6.66592419e-01 4.51461494e-01 -3.21957409e-01
-5.88726759e-01 -5.48980117e-01 4.08397198e-01 -3.19376171e-01
-3.67769510e-01 7.74753571e-01 4.04185057e-01 4.36201572e-01
-5.32770455e-01 -6.52548552e-01 -6.24594986e-01 -1.01995885e+00
1.19127966e-01 9.92989913e-03 5.78495562e-01 2.74722368... | [10.888463020324707, -2.0862011909484863] |
e364215f-ab23-4f1e-998e-7be994fb3b48 | aed-net-an-abnormal-event-detection-network | 1903.11891 | null | http://arxiv.org/abs/1903.11891v1 | http://arxiv.org/pdf/1903.11891v1.pdf | AED-Net: An Abnormal Event Detection Network | It is challenging to detect the anomaly in crowded scenes for quite a long
time. In this paper, a self-supervised framework, abnormal event detection
network (AED-Net), which is composed of PCAnet and kernel principal component
analysis (kPCA), is proposed to address this problem. Using surveillance video
sequences of ... | ['Zichen Miao', 'Yuxin Chen', 'Tian Wang', 'Hichem Snoussi', 'Guangcun Shan', 'Yi Zhou'] | 2019-03-28 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [-4.16937005e-03 -4.49958563e-01 5.80725312e-01 -2.27910101e-01
-7.56939277e-02 -2.58916039e-02 7.55781710e-01 1.27300113e-01
-5.26389360e-01 4.46755201e-01 3.57264638e-01 1.02390639e-01
9.36659500e-02 -7.06219733e-01 -4.66490924e-01 -9.87134337e-01
-2.20977411e-01 -1.59204558e-01 6.19067550e-01 -2.46655121... | [7.856836318969727, 1.5508300065994263] |
ac5d5d73-8b56-4f3e-a5b4-a63a4ab3c2a5 | heterogeneous-domain-adaptation-and-equipment | 2301.01038 | null | https://arxiv.org/abs/2301.01038v1 | https://arxiv.org/pdf/2301.01038v1.pdf | Heterogeneous Domain Adaptation and Equipment Matching: DANN-based Alignment with Cyclic Supervision (DBACS) | Process monitoring and control are essential in modern industries for ensuring high quality standards and optimizing production performance. These technologies have a long history of application in production and have had numerous positive impacts, but also hold great potential when integrated with Industry 4.0 and adv... | ['Gian Antonio Susto', 'Natalie Gentner'] | 2023-01-03 | null | null | null | null | ['multi-view-learning'] | ['computer-vision'] | [ 2.08448783e-01 -2.03912809e-01 1.70741715e-02 -1.96181014e-01
-3.73160124e-01 -5.50581336e-01 5.51272452e-01 2.76248506e-03
9.20669958e-02 4.39230412e-01 -3.24643224e-01 -1.64162982e-02
-4.61987376e-01 -6.64398372e-01 -5.53953171e-01 -8.67808938e-01
2.29468822e-01 9.66083229e-01 -2.12571532e-01 -2.81377763... | [7.282962799072266, 2.0036308765411377] |
975432be-2a59-47a8-a97b-0a8a9d04b62a | machine-reading-comprehension-using-case | 2305.14815 | null | https://arxiv.org/abs/2305.14815v1 | https://arxiv.org/pdf/2305.14815v1.pdf | Machine Reading Comprehension using Case-based Reasoning | We present an accurate and interpretable method for answer extraction in machine reading comprehension that is reminiscent of case-based reasoning (CBR) from classical AI. Our method (CBR-MRC) builds on the hypothesis that contextualized answers to similar questions share semantic similarities with each other. Given a ... | ['Andrew McCallum', 'Hannaneh Hajishirzi', 'Jay-Yoon Lee', 'Manzil Zaheer', 'Rajarshi Das', 'Mudit Chaudhary', 'Dhruv Agarwal', 'Dung Thai'] | 2023-05-24 | null | null | null | null | ['reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.59169817e-01 4.81314063e-01 -1.57724589e-01 -4.82445866e-01
-1.62324834e+00 -9.48232591e-01 7.31164992e-01 6.12482011e-01
-3.18425983e-01 9.25335646e-01 6.84453189e-01 -5.75734317e-01
-5.91969013e-01 -8.83735180e-01 -8.71028960e-01 -1.51316561e-02
2.94111550e-01 8.55246007e-01 8.33079696e-01 -6.81531370... | [11.223307609558105, 8.033442497253418] |
f68a77c1-f9ae-414a-86fa-bfef771f2fa0 | l3i-at-semeval-2022-task-11-straightforward | null | null | https://aclanthology.org/2022.semeval-1.225 | https://aclanthology.org/2022.semeval-1.225.pdf | L3i at SemEval-2022 Task 11: Straightforward Additional Context for Multilingual Named Entity Recognition | This paper summarizes the participation of the L3i laboratory of the University of La Rochelle in the SemEval-2022 Task 11, Multilingual Complex Named Entity Recognition (MultiCoNER). The task focuses on detecting semantically ambiguous and complex entities in short and low-context monolingual and multilingual settings... | ['Antoine Doucet', 'Jose Moreno', 'Carlos-Emiliano González-Gallardo', 'Emanuela Boros'] | null | null | null | null | semeval-naacl-2022-7 | ['multilingual-named-entity-recognition'] | ['natural-language-processing'] | [-5.45823276e-01 1.30826473e-01 1.12789743e-01 -2.58912027e-01
-9.94814932e-01 -1.00512242e+00 9.21778083e-01 4.01267052e-01
-1.10235775e+00 1.08787823e+00 3.93138230e-01 -4.76925194e-01
1.66623175e-01 -5.19009233e-01 -6.91324592e-01 -1.06773237e-02
1.94129989e-01 7.46764898e-01 1.03782102e-01 -4.15179074... | [9.899205207824707, 9.73526668548584] |
e3771c46-4c01-4853-a820-cbd98082276b | detecting-finger-vein-presentation-attacks | 1912.01408 | null | https://arxiv.org/abs/1912.01408v1 | https://arxiv.org/pdf/1912.01408v1.pdf | Detecting Finger-Vein Presentation Attacks Using 3D Shape & Diffuse Reflectance Decomposition | Despite the high biometric performance, finger-vein recognition systems are vulnerable to presentation attacks (aka., spoofing attacks). In this paper, we present a new and robust approach for detecting presentation attacks on finger-vein biometric systems exploiting the 3D Shape (normal-map) and material properties (d... | ['Jag Mohan Singh', 'Sushma Venkatesh', 'Kiran B. Raja', 'Christoph Busch', 'Raghavendra Ramachandra'] | 2019-12-03 | null | null | null | null | ['finger-vein-recognition'] | ['computer-vision'] | [ 5.87676823e-01 -3.75492811e-01 2.33941719e-01 -6.69692680e-02
-5.01954138e-01 -8.96536827e-01 8.76190841e-01 2.91965514e-01
-4.82028514e-01 4.27617788e-01 -1.24639191e-01 -3.26817632e-02
-5.04432082e-01 -6.99507177e-01 -1.71322748e-01 -7.96049476e-01
-1.40293211e-01 1.35528073e-01 1.93228140e-01 8.72087013... | [13.020110130310059, 1.0315479040145874] |
5c9e4513-8d4c-417a-9426-5779d62450ec | explaining-classes-through-word-attribution | 2108.13653 | null | https://arxiv.org/abs/2108.13653v1 | https://arxiv.org/pdf/2108.13653v1.pdf | Explaining Classes through Word Attribution | In recent years, several methods have been proposed for explaining individual predictions of deep learning models, yet there has been little study of how to aggregate these predictions to explain how such models view classes as a whole in text classification tasks. In this work, we propose a method for explaining class... | ['Filip Ginter', 'Veronika Laippala', 'Sampo Pyysalo', 'Aki-Juhani Kyröläinen', 'Amanda Myntti', 'Samuel Rönnqvist'] | 2021-08-31 | null | null | null | null | ['genre-classification'] | ['computer-vision'] | [ 3.10883850e-01 8.90941262e-01 -7.63034403e-01 -8.51355791e-01
-6.97307765e-01 -3.89191002e-01 1.14532185e+00 4.65667278e-01
-4.12724689e-02 5.13591051e-01 9.62176502e-01 -5.91488004e-01
-6.31588757e-01 -4.62408096e-01 -7.01044142e-01 -1.51353061e-01
1.79847226e-01 8.60165536e-01 2.57556308e-02 -2.56665707... | [9.576228141784668, 6.827260971069336] |
91f2befe-0101-424b-b0a1-a3792d4ae8de | retromae-2-duplex-masked-auto-encoder-for-pre | 2305.02564 | null | https://arxiv.org/abs/2305.02564v1 | https://arxiv.org/pdf/2305.02564v1.pdf | RetroMAE-2: Duplex Masked Auto-Encoder For Pre-Training Retrieval-Oriented Language Models | To better support information retrieval tasks such as web search and open-domain question answering, growing effort is made to develop retrieval-oriented language models, e.g., RetroMAE and many others. Most of the existing works focus on improving the semantic representation capability for the contextualized embedding... | ['Zhao Cao', 'Yingxia Shao', 'Zheng Liu', 'Shitao Xiao'] | 2023-05-04 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [ 1.27245814e-01 -2.14217491e-02 -3.53259385e-01 -3.66628855e-01
-9.13564682e-01 -2.61990041e-01 7.28457153e-01 3.65156323e-01
-4.11972791e-01 3.40276241e-01 5.45575857e-01 -2.66815811e-01
-1.27210781e-01 -8.73416066e-01 -6.37801170e-01 -6.13859117e-01
2.39992589e-01 9.95979533e-02 3.05366963e-01 -5.81537604... | [11.188477516174316, 8.155590057373047] |
ac7e69f1-7abc-47e0-8537-f9d18bb8d810 | sketchgan-joint-sketch-completion-and | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Liu_SketchGAN_Joint_Sketch_Completion_and_Recognition_With_Generative_Adversarial_Network_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_SketchGAN_Joint_Sketch_Completion_and_Recognition_With_Generative_Adversarial_Network_CVPR_2019_paper.pdf | SketchGAN: Joint Sketch Completion and Recognition With Generative Adversarial Network | Hand-drawn sketch recognition is a fundamental problem in computer vision, widely used in sketch-based image and video retrieval, editing, and reorganization. Previous methods often assume that a complete sketch is used as input; however, hand-drawn sketches in common application scenarios are often incomplete, which m... | [' Hongan Wang', ' Cuixia Ma', ' Yong-Jin Liu', ' Yu-Kun Lai', ' Xiaoming Deng', 'Fang Liu'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['sketch-recognition'] | ['computer-vision'] | [ 2.73404866e-01 -4.53200459e-01 -1.72107741e-01 -2.77203619e-01
-6.31193876e-01 -7.44551003e-01 9.23203766e-01 -7.15536714e-01
-1.22280912e-02 3.63575846e-01 -8.27318281e-02 -3.41208816e-01
3.30632538e-01 -7.77883887e-01 -7.17563152e-01 -5.09534061e-01
5.98957658e-01 3.73059958e-01 -2.54986525e-01 -8.67221728... | [11.818328857421875, 0.34276676177978516] |
18e9ecb7-d96a-4e45-afda-91ab940b63ce | approximating-poker-probabilities-with-deep | 1808.07220 | null | http://arxiv.org/abs/1808.07220v2 | http://arxiv.org/pdf/1808.07220v2.pdf | Approximating Poker Probabilities with Deep Learning | Many poker systems, whether created with heuristics or machine learning, rely
on the probability of winning as a key input. However calculating the precise
probability using combinatorics is an intractable problem, so instead we
approximate it. Monte Carlo simulation is an effective technique that can be
used to approx... | ['Brandon Da Silva'] | 2018-08-22 | null | null | null | null | ['game-of-poker', 'card-games'] | ['playing-games', 'playing-games'] | [-4.81583893e-01 -1.53768778e-01 -2.93524861e-01 1.20704472e-01
-7.86416173e-01 -6.73752785e-01 5.97960472e-01 2.51323432e-01
-5.97022355e-01 1.14891613e+00 -3.21077198e-01 -8.19330454e-01
-8.27328488e-02 -1.36651230e+00 -8.88679445e-01 -5.28348804e-01
-1.98120624e-01 1.08157480e+00 1.40011892e-01 -7.87869543... | [3.641087293624878, 1.6340595483779907] |
8c6f0a03-41ca-4a81-9ad7-f49161a9c35a | learning-deep-features-via-congenerous-cosine | 1702.06890 | null | http://arxiv.org/abs/1702.06890v2 | http://arxiv.org/pdf/1702.06890v2.pdf | Learning Deep Features via Congenerous Cosine Loss for Person Recognition | Person recognition aims at recognizing the same identity across time and
space with complicated scenes and similar appearance. In this paper, we propose
a novel method to address this task by training a network to obtain robust and
representative features. The intuition is that we directly compare and optimize
the cosi... | ['Yu Liu', 'Hongyang Li', 'Xiaogang Wang'] | 2017-02-22 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 2.63034284e-01 -2.20550537e-01 -1.00204751e-01 -8.94168317e-01
-4.31248665e-01 -3.45401585e-01 6.39037430e-01 -1.01401046e-01
-7.37601340e-01 5.48042476e-01 1.25299627e-02 4.93961960e-01
-2.45784342e-01 -5.71495175e-01 -4.76056904e-01 -8.11584234e-01
1.86801776e-02 1.70589373e-01 -2.46350020e-01 1.62883818... | [14.634811401367188, 1.0105292797088623] |
ff25d5cb-ac0d-485c-878a-c0251f8f461a | ba-sot-boundary-aware-serialized-output | 2305.13716 | null | https://arxiv.org/abs/2305.13716v2 | https://arxiv.org/pdf/2305.13716v2.pdf | BA-SOT: Boundary-Aware Serialized Output Training for Multi-Talker ASR | The recently proposed serialized output training (SOT) simplifies multi-talker automatic speech recognition (ASR) by generating speaker transcriptions separated by a special token. However, frequent speaker changes can make speaker change prediction difficult. To address this, we propose boundary-aware serialized outpu... | ['Lei Xie', 'Qian Chen', 'Shiliang Zhang', 'Pengcheng Guo', 'Yangze Li', 'Fan Yu', 'Yuhao Liang'] | 2023-05-23 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 5.28537035e-01 8.06312561e-02 -2.27536839e-02 -5.53002298e-01
-1.41963851e+00 -4.49944973e-01 4.10392910e-01 8.77380818e-02
-4.86671597e-01 4.91247594e-01 3.93917561e-01 -5.60732365e-01
4.89802033e-01 -1.33359442e-02 -5.78328669e-01 -4.85787302e-01
2.72419274e-01 1.12937264e-01 2.54435223e-02 -5.73018156... | [14.56238079071045, 6.5145955085754395] |
8b44ba06-cefd-417b-a183-dff84f094603 | cold-start-based-multi-scenario-ranking-model | 2304.07858 | null | https://arxiv.org/abs/2304.07858v1 | https://arxiv.org/pdf/2304.07858v1.pdf | Cold-Start based Multi-Scenario Ranking Model for Click-Through Rate Prediction | Online travel platforms (OTPs), e.g., Ctrip.com or Fliggy.com, can effectively provide travel-related products or services to users. In this paper, we focus on the multi-scenario click-through rate (CTR) prediction, i.e., training a unified model to serve all scenarios. Existing multi-scenario based CTR methods struggl... | ['Chao Zhang', 'Ying Zhou', 'Wanjie Tao', 'Qijie Shen', 'Zhao Li', 'Fuyu Lv', 'Jing Zhang', 'Hong Wen', 'Peilin Chen'] | 2023-04-16 | null | null | null | null | ['click-through-rate-prediction'] | ['miscellaneous'] | [-1.10620983e-01 -4.53260362e-01 -5.79453468e-01 -7.28563666e-01
-6.92248106e-01 -2.68303126e-01 5.15289545e-01 -1.29350662e-01
-4.60979640e-01 2.82395303e-01 5.48870027e-01 -2.41537720e-01
-3.73154849e-01 -8.95306826e-01 -4.66903865e-01 -6.44885004e-01
1.76541522e-01 6.37487233e-01 1.38570398e-01 -5.99426210... | [10.163158416748047, 5.559732913970947] |
86c70782-6274-400a-ba27-7d02f5375d41 | scalable-bottom-up-hierarchical-clustering | 2010.11821 | null | https://arxiv.org/abs/2010.11821v3 | https://arxiv.org/pdf/2010.11821v3.pdf | Scalable Hierarchical Agglomerative Clustering | The applicability of agglomerative clustering, for inferring both hierarchical and flat clustering, is limited by its scalability. Existing scalable hierarchical clustering methods sacrifice quality for speed and often lead to over-merging of clusters. In this paper, we present a scalable, agglomerative method for hier... | ['Yuchen Wu', 'YuAn Wang', 'Bryon Tjanaka', 'Mert Terzihan', 'Marc Najork', 'Gokhan Mergen', 'Andrew McCallum', 'Amr Ahmed', 'Manzil Zaheer', 'Guru Guruganesh', 'Avinava Dubey', 'Nicholas Monath'] | 2020-10-22 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-5.52701354e-01 -8.78472850e-02 1.33861959e-01 -3.40141565e-01
-1.30469835e+00 -9.13695633e-01 1.14287600e-01 5.39425611e-01
-2.82986045e-01 1.62924364e-01 2.98100024e-01 -1.90702692e-01
-4.55515355e-01 -6.39832854e-01 -6.38827264e-01 -9.38568890e-01
-5.62568307e-01 1.23892653e+00 7.40345359e-01 4.23338890... | [7.20071268081665, 4.906033515930176] |
c1fa9d88-c702-4748-858a-75238ce0d383 | jbnu-at-mrp-2019-multi-level-biaffine | null | null | https://aclanthology.org/K19-2009 | https://aclanthology.org/K19-2009.pdf | JBNU at MRP 2019: Multi-level Biaffine Attention for Semantic Dependency Parsing | This paper describes Jeonbuk National University (JBNU){'}s system for the 2019 shared task on Cross-Framework Meaning Representation Parsing (MRP 2019) at the Conference on Computational Natural Language Learning. Of the five frameworks, we address only the DELPH-IN MRS Bi-Lexical Dependencies (DP), Prague Semantic De... | ['Young-Kil Kim', 'Jong-Hun Shin', 'Kwanghyeon Park', 'Jinwoon Min', 'Seung-Hoon Na'] | 2019-11-01 | null | null | null | conll-2019-11 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [ 2.11049154e-01 4.81109917e-01 -4.62777652e-02 -5.85383594e-01
-1.03964329e+00 -6.02627277e-01 3.75640750e-01 4.26677555e-01
-6.34267986e-01 4.85088378e-01 7.01294065e-01 -5.36459088e-01
1.79306403e-01 -7.26816952e-01 -6.58566535e-01 -4.03995931e-01
1.14835747e-01 2.33167306e-01 3.54776084e-02 -2.88320154... | [10.418664932250977, 9.43175220489502] |
94eec152-5513-44fa-ad24-dafb726139d5 | openvis-open-vocabulary-video-instance | 2305.16835 | null | https://arxiv.org/abs/2305.16835v1 | https://arxiv.org/pdf/2305.16835v1.pdf | OpenVIS: Open-vocabulary Video Instance Segmentation | We propose and study a new computer vision task named open-vocabulary video instance segmentation (OpenVIS), which aims to simultaneously segment, detect, and track arbitrary objects in a video according to corresponding text descriptions. Compared to the original video instance segmentation, OpenVIS enables users to i... | ['Wenqiang Zhang', 'Zhaoyu Chen', 'Tianjun Xiao', 'Xuefeng Liu', 'Peiyang He', 'Tony Huang', 'Pinxue Guo'] | 2023-05-26 | null | null | null | null | ['video-instance-segmentation'] | ['computer-vision'] | [ 3.77305388e-01 6.60093576e-02 -3.46150249e-01 -4.70288008e-01
-7.99938083e-01 -6.98906362e-01 5.43150961e-01 -2.16752440e-01
-5.91325879e-01 2.92526931e-01 -1.26898825e-01 1.55371148e-02
3.01572680e-01 -4.75134581e-01 -9.44217622e-01 -4.91931051e-01
4.06678379e-01 5.32136321e-01 6.72258615e-01 2.97982872... | [9.443870544433594, 0.27617424726486206] |
52971906-ed98-450a-ac37-74726b3f9c82 | deep-differentiable-reinforcement-learning | 2112.02944 | null | https://arxiv.org/abs/2112.02944v2 | https://arxiv.org/pdf/2112.02944v2.pdf | Deep differentiable reinforcement learning and optimal trading | In many reinforcement learning applications, the underlying environment reward and transition functions are explicitly known differentiable functions. This enables us to use recent research which applies machine learning tools to stochastic control to find optimal action functions. In this paper, we define differentiab... | ['Thibault Jaisson'] | 2021-12-06 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-2.90738732e-01 -1.74020797e-01 -1.94484398e-01 1.05302706e-01
-5.37709475e-01 -7.41997600e-01 6.59655988e-01 -2.81225126e-02
-6.56236768e-01 9.45233107e-01 -2.68834919e-01 -2.82097250e-01
-4.25471753e-01 -9.08084035e-01 -7.24262178e-01 -7.93084025e-01
-2.83897430e-01 5.13011336e-01 -1.14889368e-02 -6.37757838... | [4.321742057800293, 3.4031636714935303] |
2793a273-a21f-4ae3-9518-89e447ed03a0 | self-contained-stylization-via-steganography | 1812.03910 | null | https://arxiv.org/abs/1812.03910v3 | https://arxiv.org/pdf/1812.03910v3.pdf | Self-Contained Stylization via Steganography for Reverse and Serial Style Transfer | Style transfer has been widely applied to give real-world images a new artistic look. However, given a stylized image, the attempts to use typical style transfer methods for de-stylization or transferring it again into another style usually lead to artifacts or undesired results. We realize that these issues are origin... | ['Wei-Chen Chiu', 'I-Sheng Fang', 'Hung-Yu Chen'] | 2018-12-10 | null | null | null | null | ['reverse-style-transfer', 'serial-style-transfer'] | ['computer-vision', 'computer-vision'] | [ 8.31672668e-01 1.05921015e-01 4.84511614e-01 1.16688907e-01
-2.92262048e-01 -7.60481775e-01 7.73761213e-01 -5.09636104e-01
-1.58828616e-01 8.78767490e-01 -8.00072998e-02 -2.88068861e-01
4.76536095e-01 -8.96893501e-01 -6.57203138e-01 -5.89753330e-01
3.88318419e-01 1.06908292e-01 2.62542844e-01 -1.57477498... | [11.634356498718262, -0.585783064365387] |
4c98a899-13c0-40dd-8cb2-683b41c71367 | understanding-grounded-language-learning | 1710.09867 | null | https://arxiv.org/abs/1710.09867v2 | https://arxiv.org/pdf/1710.09867v2.pdf | Understanding Early Word Learning in Situated Artificial Agents | Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and execute symbolic instructions as first-person actors in partially-observable worlds. To achieve this so-called grounded language learning, models must overcome challenges that inf... | ['Felix Hill', 'Stephen Clark', 'Karl Moritz Hermann', 'Phil Blunsom'] | 2017-10-26 | null | null | null | iclr-2018-1 | ['grounded-language-learning'] | ['natural-language-processing'] | [ 3.95020217e-01 6.86932325e-01 -1.11282719e-02 -4.64193493e-01
-5.35067208e-02 -4.75643069e-01 8.63589525e-01 2.75559753e-01
-8.43327284e-01 3.78898889e-01 4.70563471e-01 -4.27202016e-01
-7.50916004e-02 -5.28732955e-01 -9.20389891e-01 -4.47710276e-01
-1.04083084e-01 3.72030914e-01 -3.03056035e-02 -1.60805270... | [10.19212818145752, 8.513687133789062] |
0cf6ef4a-c461-4a36-bac7-a9a99b74dd00 | 4d-stop-panoptic-segmentation-of-4d-lidar | 2209.14858 | null | https://arxiv.org/abs/2209.14858v1 | https://arxiv.org/pdf/2209.14858v1.pdf | 4D-StOP: Panoptic Segmentation of 4D LiDAR using Spatio-temporal Object Proposal Generation and Aggregation | In this work, we present a new paradigm, called 4D-StOP, to tackle the task of 4D Panoptic LiDAR Segmentation. 4D-StOP first generates spatio-temporal proposals using voting-based center predictions, where each point in the 4D volume votes for a corresponding center. These tracklet proposals are further aggregated usin... | ['Bastian Leibe', 'Francis Engelmann', 'Sabarinath Mahadevan', 'Idil Esen Zulfikar', 'Lars Kreuzberg'] | 2022-09-29 | null | null | null | null | ['panoptic-segmentation', 'object-proposal-generation'] | ['computer-vision', 'computer-vision'] | [-1.75239772e-01 -2.93246180e-01 -2.03957692e-01 -2.78761864e-01
-1.20017040e+00 -7.18714654e-01 7.88721502e-01 1.64248824e-01
-3.03777516e-01 1.65280879e-01 -1.83498971e-02 -3.26258719e-01
-3.11669707e-02 -8.70272994e-01 -7.09587991e-01 -3.76064330e-01
-2.27735445e-01 1.18956876e+00 6.42727256e-01 3.09638083... | [8.069626808166504, -2.9979124069213867] |
4d862f98-ff05-4bae-bfd3-77582bc6514c | limsis-participation-to-the-2013-shared-task | null | null | https://aclanthology.org/W13-1733 | https://aclanthology.org/W13-1733.pdf | LIMSI's participation to the 2013 shared task on Native Language Identification | null | ["Aur{\\'e}lien Max", 'Ryo Nagata', 'Gabriel Illouz', 'Thomas Lavergne'] | 2013-06-01 | null | null | null | ws-2013-6 | ['native-language-identification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.390995979309082, 3.7025389671325684] |
d71dbc10-ed09-4d78-956e-18a64c719170 | a-deep-learning-approach-using-masked-image | 2208.11472 | null | https://arxiv.org/abs/2208.11472v1 | https://arxiv.org/pdf/2208.11472v1.pdf | A Deep Learning Approach Using Masked Image Modeling for Reconstruction of Undersampled K-spaces | Magnetic Resonance Imaging (MRI) scans are time consuming and precarious, since the patients remain still in a confined space for extended periods of time. To reduce scanning time, some experts have experimented with undersampled k spaces, trying to use deep learning to predict the fully sampled result. These studies r... | ['Yogesh Rathi', 'Arghya Pal', 'Kyler Larsen'] | 2022-08-24 | null | null | null | null | ['mri-reconstruction'] | ['computer-vision'] | [ 2.13853881e-01 2.47397453e-01 -7.17305169e-02 -4.70853448e-01
-9.43433166e-01 -8.58751684e-02 3.01699072e-01 -5.37132099e-02
-6.85654879e-01 6.27342582e-01 1.74220905e-01 -2.81354219e-01
-2.86777675e-01 -3.66108537e-01 -5.79765677e-01 -9.06507909e-01
-4.24452692e-01 3.96941841e-01 4.02797252e-01 2.43977472... | [13.704834938049316, -2.415963888168335] |
2e3dc80a-0b17-4437-a341-1bc4b2984659 | multi-domain-multi-definition-landmark | 2203.10358 | null | https://arxiv.org/abs/2203.10358v3 | https://arxiv.org/pdf/2203.10358v3.pdf | Multi-Domain Multi-Definition Landmark Localization for Small Datasets | We present a novel method for multi image domain and multi-landmark definition learning for small dataset facial localization. Training a small dataset alongside a large(r) dataset helps with robust learning for the former, and provides a universal mechanism for facial landmark localization for new and/or smaller stand... | ['Gaurav Bharaj', 'David Ferman'] | 2022-03-19 | null | null | null | null | ['face-alignment'] | ['computer-vision'] | [ 2.33843192e-01 3.97027507e-02 -3.11236829e-01 -6.93609834e-01
-1.18138754e+00 -5.07315874e-01 6.92294896e-01 -3.70131969e-01
-4.33557898e-01 4.76050258e-01 8.58732089e-02 3.53077799e-01
6.81765750e-03 -4.18723494e-01 -9.06353116e-01 -5.19196033e-01
-6.62463456e-02 6.12034023e-01 5.52569740e-02 -7.54948035... | [13.539716720581055, 1.0152322053909302] |
a24fc2c3-e7cd-4974-b4f1-725cfa0cf401 | matting-anything | 2306.05399 | null | https://arxiv.org/abs/2306.05399v1 | https://arxiv.org/pdf/2306.05399v1.pdf | Matting Anything | In this paper, we propose the Matting Anything Model (MAM), an efficient and versatile framework for estimating the alpha matte of any instance in an image with flexible and interactive visual or linguistic user prompt guidance. MAM offers several significant advantages over previous specialized image matting networks:... | ['Humphrey Shi', 'Jitesh Jain', 'Jiachen Li'] | 2023-06-08 | null | null | null | null | ['image-matting', 'referring-image-matting'] | ['computer-vision', 'computer-vision'] | [ 2.18097836e-01 -5.20388484e-02 -2.49238029e-01 -4.19916958e-01
-8.11086357e-01 -4.35832739e-01 3.32746536e-01 -3.32345784e-01
-2.26716921e-01 1.97045386e-01 -1.03654869e-01 -5.07735074e-01
3.29213679e-01 -5.87563753e-01 -1.01556242e+00 -5.58227599e-01
4.34715956e-01 5.22512972e-01 6.61976589e-03 -1.92897469... | [10.68653392791748, -0.8145278692245483] |
521a576f-4d30-4a74-9231-33cf20c9c594 | variational-learning-across-domains-with | 1806.08672 | null | http://arxiv.org/abs/1806.08672v2 | http://arxiv.org/pdf/1806.08672v2.pdf | Variational learning across domains with triplet information | The work investigates deep generative models, which allow us to use training
data from one domain to build a model for another domain. We propose the
Variational Bi-domain Triplet Autoencoder (VBTA) that learns a joint
distribution of objects from different domains. We extend the VBTAs objective
function by the relativ... | ['Alexandr Ogaltsov', 'Rita Kuznetsova', 'Oleg Bakhteev'] | 2018-06-22 | null | null | null | null | ['cross-lingual-document-classification'] | ['natural-language-processing'] | [ 1.62170902e-02 -1.27881914e-01 -5.30292392e-02 -6.56424165e-01
-8.14458132e-01 -4.20535207e-01 1.22574115e+00 -8.91388118e-01
6.62974790e-02 9.78994608e-01 2.74445742e-01 1.28060356e-01
2.79514045e-02 -8.83150339e-01 -9.56065953e-01 -9.30988014e-01
8.45202446e-01 9.99903560e-01 -2.79213101e-01 -8.12655017... | [11.538188934326172, -0.17278318107128143] |
0ee1ca96-aca8-45df-8b66-1c1617603925 | compositional-embeddings-joint-perception-and | null | null | https://openreview.net/forum?id=BJx-ZeSKDB | https://openreview.net/pdf?id=BJx-ZeSKDB | Compositional Embeddings: Joint Perception and Comparison of Class Label Sets | We explore the idea of compositional set embeddings that can be used to infer not
just a single class, but the set of classes associated with the input data (e.g., image,
video, audio signal). This can be useful, for example, in multi-object detection in
images, or multi-speaker diarization (one-shot learning) in audio... | ['Jacob Whitehill', 'Zeqian Li'] | 2019-09-25 | null | null | null | null | ['one-shot-learning'] | ['methodology'] | [ 4.47134763e-01 4.63302247e-02 1.61969848e-02 -5.81502974e-01
-8.78272951e-01 -5.94206393e-01 8.16014767e-01 4.71930802e-01
-3.75779688e-01 2.48226553e-01 3.47552747e-01 -9.24783759e-03
-8.23707432e-02 -9.15474653e-01 -6.84770167e-01 -7.35685706e-01
-1.69487298e-01 3.79200637e-01 2.32902214e-01 1.71839729... | [10.007288932800293, 2.4424076080322266] |
c42bd61b-bbba-435f-bc3f-6471d93c6b32 | twin-net-descriptor-twin-negative-mining-with | null | null | https://ieeexplore.ieee.org/abstract/document/8835028 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8835028 | Twin-Net Descriptor: Twin Negative Mining With Quad Loss for Patch-Based Matching | Local keypoint matching is an important step for computer vision based tasks. In recent years, Deep Convolutional Neural Network (CNN) based strategies have been employed to learn descriptor generation to enhance keypoint matching accuracy. Recent state-of-art works in this direction primarily rely upon a triplet based... | ['Yongju Cho', 'Muhammad Faisal', 'Rehan Hafiz', 'Mohsen Ali', 'Jeongil Seo', 'Aman Irshad'] | 2019-09-19 | null | null | null | ieee-access-2019-9 | ['patch-matching'] | ['computer-vision'] | [ 1.12370566e-01 -2.89520860e-01 -1.29456669e-01 -3.02880198e-01
-8.81385267e-01 -3.88013273e-01 8.14251244e-01 4.73774135e-01
-5.38052619e-01 2.60153681e-01 -1.62906498e-01 1.30007580e-01
-4.33939725e-01 -9.24199939e-01 -6.40034258e-01 -7.89000869e-01
-2.13188648e-01 2.82905400e-01 4.26609963e-01 -4.12729740... | [10.47616958618164, 0.16054658591747284] |
8a1f7ce4-850a-498a-a9a0-e117dbe3ab42 | unsupervised-haze-removal-from-underwater | 2306.02912 | null | https://arxiv.org/abs/2306.02912v1 | https://arxiv.org/pdf/2306.02912v1.pdf | Unsupervised haze removal from underwater images | Several supervised networks exist that remove haze information from underwater images using paired datasets and pixel-wise loss functions. However, training these networks requires large amounts of paired data which is cumbersome, complex and time-consuming. Also, directly using adversarial and cycle consistency loss f... | ['A. N. Rajagopalan', 'Praveen Kandula'] | 2023-06-05 | null | null | null | null | ['disentanglement'] | ['methodology'] | [ 3.31912637e-01 -4.71137986e-02 7.31333733e-01 -2.19346702e-01
-6.96217775e-01 -6.67266250e-01 1.79581642e-01 -4.98532921e-01
-6.08499587e-01 1.04708755e+00 2.39038095e-01 -2.23135762e-02
-3.53805870e-01 -8.95334780e-01 -9.15123820e-01 -1.44854724e+00
-2.66617119e-01 -2.80284315e-01 -1.38700055e-02 -5.69299519... | [10.709670066833496, -3.516563653945923] |
29062bf8-d925-4483-8028-707c0fbd0bc9 | dipping-plms-sauce-bridging-structure-and | 2307.01709 | null | https://arxiv.org/abs/2307.01709v1 | https://arxiv.org/pdf/2307.01709v1.pdf | Dipping PLMs Sauce: Bridging Structure and Text for Effective Knowledge Graph Completion via Conditional Soft Prompting | Knowledge Graph Completion (KGC) often requires both KG structural and textual information to be effective. Pre-trained Language Models (PLMs) have been used to learn the textual information, usually under the fine-tune paradigm for the KGC task. However, the fine-tuned PLMs often overwhelmingly focus on the textual in... | ['Kwok-Yan Lam', 'Bing Li', 'Aixin Sun', 'YuFei Wang', 'Chen Chen'] | 2023-07-04 | null | null | null | null | ['knowledge-graph-completion'] | ['knowledge-base'] | [-1.85381860e-01 4.03490067e-01 -3.72446418e-01 -3.49789470e-01
-7.16418147e-01 -4.18460041e-01 7.54226625e-01 3.22884887e-01
-5.74455500e-01 6.02065802e-01 5.46119153e-01 -2.62688220e-01
-2.74178624e-01 -9.05424416e-01 -9.67422426e-01 -3.77951652e-01
-3.07596296e-01 5.67333400e-01 4.52125400e-01 -1.89753339... | [9.095812797546387, 8.046088218688965] |
5b4e6339-1633-4633-b9fe-8f70e0c70af8 | hierarchical-joint-scene-coordinate | 1909.06216 | null | https://arxiv.org/abs/1909.06216v3 | https://arxiv.org/pdf/1909.06216v3.pdf | Hierarchical Scene Coordinate Classification and Regression for Visual Localization | Visual localization is critical to many applications in computer vision and robotics. To address single-image RGB localization, state-of-the-art feature-based methods match local descriptors between a query image and a pre-built 3D model. Recently, deep neural networks have been exploited to regress the mapping between... | ['Yi Zhao', 'Xiaotian Li', 'Shuzhe Wang', 'Juho Kannala', 'Jakob Verbeek'] | 2019-09-13 | hierarchical-scene-coordinate-classification | http://openaccess.thecvf.com/content_CVPR_2020/html/Li_Hierarchical_Scene_Coordinate_Classification_and_Regression_for_Visual_Localization_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Hierarchical_Scene_Coordinate_Classification_and_Regression_for_Visual_Localization_CVPR_2020_paper.pdf | cvpr-2020-6 | ['outdoor-localization'] | ['robots'] | [ 1.88736945e-01 -3.45475644e-01 1.29118143e-02 -5.99620044e-01
-1.08253205e+00 -4.37880576e-01 5.48542023e-01 -1.41336378e-02
-8.72722566e-01 3.94749612e-01 -2.64380544e-01 -1.32475570e-01
2.74258312e-02 -6.64487183e-01 -1.11924875e+00 -7.25911796e-01
2.35646024e-01 4.08848852e-01 3.58156681e-01 -6.51752874... | [7.739001274108887, -2.1705191135406494] |
01775eda-d46f-4984-b731-607ee2def3d8 | visual-question-answering-in-remote-sensing | 2306.14264 | null | https://arxiv.org/abs/2306.14264v1 | https://arxiv.org/pdf/2306.14264v1.pdf | Visual Question Answering in Remote Sensing with Cross-Attention and Multimodal Information Bottleneck | In this research, we deal with the problem of visual question answering (VQA) in remote sensing. While remotely sensed images contain information significant for the task of identification and object detection, they pose a great challenge in their processing because of high dimensionality, volume and redundancy. Furthe... | ['Rajbabu Velmurugan', 'Biplab Banerjee', 'Shabnam Choudhury', 'Shivam Pande', 'Jayesh Songara'] | 2023-06-25 | null | null | null | null | ['visual-question-answering-1', 'question-answering'] | ['computer-vision', 'natural-language-processing'] | [ 3.34560275e-01 -3.23935658e-01 1.31574616e-01 -3.95324111e-01
-9.94096875e-01 -4.95523542e-01 5.66496134e-01 3.04326147e-01
-7.33421445e-01 4.06867683e-01 -2.15616710e-02 -3.70394081e-01
-2.52626449e-01 -9.31242883e-01 -4.40226644e-01 -6.02670491e-01
-1.14091046e-01 1.06222317e-01 -1.52298599e-01 -2.39983462... | [9.767989158630371, -1.290917158126831] |
7cfb2435-075e-4bf0-9867-61de05adc55b | the-discriminative-kalman-filter-for-bayesian | null | null | https://doi.org/10.1162/neco_a_01275 | https://direct.mit.edu/neco/article-pdf/32/5/969/1865334/neco_a_01275.pdf | The Discriminative Kalman Filter for Bayesian Filtering with Nonlinear and Nongaussian Observation Models | The Kalman filter provides a simple and efficient algorithm to compute the posterior distribution for state-space models where both the latent state and measurement models are linear and gaussian. Extensions to the Kalman filter, including the extended and unscented Kalman filters, incorporate linearizations for models... | ['Matthew T. Harrison', 'Leigh R. Hochberg', 'Brian Franco', 'David M. Brandman', 'Michael C. Burkhart'] | 2020-05-01 | null | null | null | null | ['sequential-bayesian-inference'] | ['time-series'] | [-1.60136819e-01 -1.06934614e-01 -1.28669232e-01 -4.80505824e-02
-6.39635623e-01 -5.00340760e-01 7.37271130e-01 -1.60483524e-01
-4.15642887e-01 8.75797391e-01 3.07465255e-01 -6.44922376e-01
-3.20551068e-01 -2.85396665e-01 -4.82475609e-01 -1.11144960e+00
-2.34564647e-01 5.33360302e-01 1.74461119e-02 3.61865401... | [6.705418109893799, 3.7241439819335938] |
3c6e42cb-5bf2-4cd8-9d0f-08325492ff77 | enhancing-quality-of-pose-varied-face | 2205.14377 | null | https://arxiv.org/abs/2205.14377v3 | https://arxiv.org/pdf/2205.14377v3.pdf | Enhancing Quality of Pose-varied Face Restoration with Local Weak Feature Sensing and GAN Prior | Facial semantic guidance (including facial landmarks, facial heatmaps, and facial parsing maps) and facial generative adversarial networks (GAN) prior have been widely used in blind face restoration (BFR) in recent years. Although existing BFR methods have achieved good performance in ordinary cases, these solutions ha... | ['Bin Fu', 'Gang Yu', 'Wei Lu', 'Renhe Liu', 'Yu Liu', 'Kai Hu'] | 2022-05-28 | null | null | null | null | ['blind-face-restoration'] | ['computer-vision'] | [ 1.64729461e-01 1.07120149e-01 2.19033852e-01 -3.58738154e-01
-2.87055701e-01 -2.06744939e-01 5.46143115e-01 -1.27265382e+00
1.37497514e-01 6.46508276e-01 4.35914576e-01 1.25619411e-01
1.44389078e-01 -8.25732470e-01 -6.50398791e-01 -9.53865707e-01
4.88510787e-01 4.69100103e-02 -1.37356639e-01 -5.31038404... | [12.811284065246582, -0.04115390405058861] |
29347d47-c0f4-4b2b-a512-4a7b7645aeeb | data-driven-representations-for-testing | 2110.14122 | null | https://arxiv.org/abs/2110.14122v1 | https://arxiv.org/pdf/2110.14122v1.pdf | Data-Driven Representations for Testing Independence: Modeling, Analysis and Connection with Mutual Information Estimation | This work addresses testing the independence of two continuous and finite-dimensional random variables from the design of a data-driven partition. The empirical log-likelihood statistic is adopted to approximate the sufficient statistics of an oracle test against independence (that knows the two hypotheses). It is show... | ['Marcos E. Orchard', 'Miguel Videla', 'Jorge F. Silva', 'Mauricio E. Gonzalez'] | 2021-10-27 | null | null | null | null | ['mutual-information-estimation'] | ['methodology'] | [ 4.49988097e-01 4.47573662e-01 -3.48075598e-01 -5.19574881e-01
-1.10532200e+00 -6.05197012e-01 1.19243547e-01 6.96771815e-02
-1.34850129e-01 1.10017717e+00 -8.78528319e-03 -9.07303810e-01
-6.78394973e-01 -8.70486915e-01 -6.51652932e-01 -9.70696211e-01
-4.97563809e-01 1.11019182e+00 8.11748207e-02 3.10848743... | [7.393171787261963, 4.566883563995361] |
866b0215-cef5-4018-95cc-a9f241be5001 | meta-learning-one-class-classifiers-with | 2103.00684 | null | https://arxiv.org/abs/2103.00684v1 | https://arxiv.org/pdf/2103.00684v1.pdf | Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection | Neural network-based anomaly detection methods have shown to achieve high performance. However, they require a large amount of training data for each task. We propose a neural network-based meta-learning method for supervised anomaly detection. The proposed method improves the anomaly detection performance on unseen ta... | ['Atsutoshi Kumagai', 'Tomoharu Iwata'] | 2021-03-01 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [ 1.98660031e-01 -1.35961860e-01 -8.98706540e-02 -3.48798245e-01
-5.37590742e-01 2.96458900e-01 3.94221842e-01 2.79380620e-01
-4.85923409e-01 2.54080147e-01 -3.11372101e-01 -1.16063990e-01
-1.21202826e-01 -6.44874454e-01 -4.44375187e-01 -6.07919395e-01
-2.00650081e-01 5.05850136e-01 2.60939121e-01 -2.79345065... | [7.643136024475098, 2.3998939990997314] |
9f41a319-b608-44fe-9fc3-096520c8914b | jsi-at-semeval-2022-task-1-codwoe-reverse | null | null | https://aclanthology.org/2022.semeval-1.12 | https://aclanthology.org/2022.semeval-1.12.pdf | JSI at SemEval-2022 Task 1: CODWOE - Reverse Dictionary: Monolingual and cross-lingual approaches | The reverse dictionary task is a sequence-to-vector task in which a gloss is provided as input, and the output must be a semantically matching word vector. The reverse dictionary is useful in practical applications such as solving the tip-of-the-tongue problem, helping new language learners, etc. In this paper, we eval... | ['Senja Pollak', 'Matthew Purver', 'Matej Martinc', 'Thi Hong Hanh Tran'] | null | null | null | null | semeval-naacl-2022-7 | ['reverse-dictionary'] | ['natural-language-processing'] | [-2.90008694e-01 -2.23205447e-01 -4.55936313e-01 -1.26336664e-01
-1.06848645e+00 -6.57506645e-01 5.26360035e-01 4.70337570e-02
-7.75775731e-01 5.62045813e-01 5.01322508e-01 -7.09814668e-01
2.83448756e-01 -5.46219647e-01 -6.36566222e-01 -4.29846227e-01
3.61167252e-01 5.61443508e-01 9.45212319e-02 -6.28034115... | [11.038214683532715, 9.910065650939941] |
e43ba32f-9e4b-4d5e-8a2a-5029a7153edc | streaming-video-temporal-action-segmentation | 2209.13808 | null | https://arxiv.org/abs/2209.13808v2 | https://arxiv.org/pdf/2209.13808v2.pdf | Streaming Video Temporal Action Segmentation In Real Time | Temporal action segmentation (TAS) is a critical step toward long-term video understanding. Recent studies follow a pattern that builds models based on features instead of raw video picture information. However, we claim those models are trained complicatedly and limit application scenarios. It is hard for them to segm... | ['Shenlan Liu', 'Wanxiao Yang', 'Lin Feng', 'Zhuben Dong', 'Yunheng Li', 'Wujun Wen'] | 2022-09-28 | null | null | null | null | ['action-segmentation'] | ['computer-vision'] | [ 6.01460278e-01 -2.77563725e-02 -4.87874746e-01 -1.63527265e-01
-5.76758027e-01 -4.25840020e-01 5.19527555e-01 -3.33483398e-01
-6.66037261e-01 2.66427428e-01 2.22139448e-01 -1.35266557e-01
1.65116861e-01 -4.46438581e-01 -8.09239864e-01 -4.44750071e-01
-3.14396881e-02 2.42651358e-01 8.83545101e-01 -4.66138907... | [8.519387245178223, 0.5148131847381592] |
2c859515-fc67-450e-ab60-ff4f3a30b292 | the-color-out-of-space-learning-self | 2006.12119 | null | https://arxiv.org/abs/2006.12119v1 | https://arxiv.org/pdf/2006.12119v1.pdf | The color out of space: learning self-supervised representations for Earth Observation imagery | The recent growth in the number of satellite images fosters the development of effective deep-learning techniques for Remote Sensing (RS). However, their full potential is untapped due to the lack of large annotated datasets. Such a problem is usually countered by fine-tuning a feature extractor that is previously trai... | ['Angelo Porrello', 'Marco Cipriano', 'Carla Ippoliti', 'Stefano Vincenzi', 'Pietro Fronte', 'Pietro Buzzega', 'Annamaria Conte', 'Roberto Cuccu', 'Simone Calderara'] | 2020-06-22 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [ 3.71967852e-01 -1.76569641e-01 1.70916915e-01 -4.49877292e-01
-5.78689873e-01 -7.80745924e-01 6.41006112e-01 5.26498817e-02
-6.03468299e-01 6.92603409e-01 -1.71692044e-01 -5.74507296e-01
-1.08552635e-01 -1.03628218e+00 -7.25035012e-01 -1.00212145e+00
-3.89193684e-01 1.83834597e-01 -1.07275710e-01 -5.26497006... | [9.586514472961426, -1.4993959665298462] |
8419c32b-fbc3-4302-920c-130a7151be9a | rainformer-features-extraction-balanced | null | null | https://ieeexplore.ieee.org/document/9743916 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9743916 | Rainformer: Features Extraction Balanced Network for Radar-Based Precipitation Nowcasting | Precipitation nowcasting is one of the fundamental challenges in natural hazard research. High-intensity rainfall, especially the rainstorm, will lead to the enormous loss of people’s property. Existing methods usually utilize convolution operation to extract rainfall features and increase the network depth to expand t... | ['ShengYong Chen', 'Yi Song', 'Jinglin Zhang', 'Feng Sun', 'Cong Bai'] | 2022-03-28 | null | null | null | ieee-geoscience-and-remote-sensing-letters-8 | ['weather-forecasting'] | ['miscellaneous'] | [-1.85729489e-02 -5.00265062e-01 2.57797092e-01 -6.34133697e-01
-6.08392477e-01 1.65354073e-01 4.47034240e-01 9.15217493e-03
-4.88005668e-01 8.71251822e-01 1.83817297e-01 -3.16354096e-01
3.29638600e-01 -1.23495090e+00 -4.45360929e-01 -1.40009534e+00
-2.83936411e-01 -1.12057187e-01 1.02418996e-01 -4.14439172... | [10.906089782714844, -3.259956121444702] |
1634cbe9-70ec-43fd-b466-7ddcbecbb355 | analysing-the-robustness-of-dual-encoders-for | 2205.02303 | null | https://arxiv.org/abs/2205.02303v1 | https://arxiv.org/pdf/2205.02303v1.pdf | Analysing the Robustness of Dual Encoders for Dense Retrieval Against Misspellings | Dense retrieval is becoming one of the standard approaches for document and passage ranking. The dual-encoder architecture is widely adopted for scoring question-passage pairs due to its efficiency and high performance. Typically, dense retrieval models are evaluated on clean and curated datasets. However, when deploye... | ['Evangelos Kanoulas', 'Georgios Sidiropoulos'] | 2022-05-04 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [-2.20686093e-01 -4.61947680e-01 9.00881290e-02 -1.65994763e-01
-1.51646101e+00 -6.94941998e-01 6.21476412e-01 4.47573125e-01
-6.47429287e-01 5.94821215e-01 7.84520924e-01 -2.42080307e-03
-2.90969133e-01 -6.84874058e-01 -7.58950472e-01 -4.31917846e-01
1.34896085e-01 3.76640558e-01 3.44901085e-01 -7.06129968... | [11.479982376098633, 7.656255722045898] |
d37d256e-d5ad-4f99-9790-609c94ec6f17 | hyhtm-hyperbolic-geometry-based-hierarchical | 2305.09258 | null | https://arxiv.org/abs/2305.09258v1 | https://arxiv.org/pdf/2305.09258v1.pdf | HyHTM: Hyperbolic Geometry based Hierarchical Topic Models | Hierarchical Topic Models (HTMs) are useful for discovering topic hierarchies in a collection of documents. However, traditional HTMs often produce hierarchies where lowerlevel topics are unrelated and not specific enough to their higher-level topics. Additionally, these methods can be computationally expensive. We pre... | ['Nikaash Puri', 'Balaji Krishnamurthy', 'Sumit Bhatia', 'Nikitha Srikanth', 'Tanay Anand', 'Simra Shahid'] | 2023-05-16 | null | null | null | null | ['topic-models'] | ['natural-language-processing'] | [-6.07534289e-01 6.50655746e-01 -4.01968509e-01 -3.17952454e-01
-1.29987931e+00 -4.86154228e-01 6.84113026e-01 5.81189573e-01
3.18691671e-01 5.20560265e-01 7.68820167e-01 -2.69226253e-01
-1.66996300e-01 -1.16545439e+00 -3.58928084e-01 -4.70640272e-01
-5.90934336e-01 1.13651907e+00 9.15992379e-01 1.79957077... | [10.374738693237305, 7.003764629364014] |
186e13ca-e9fb-4a92-8cba-4302beb0b8d0 | person-recognition-in-personal-photo | 1710.03224 | null | http://arxiv.org/abs/1710.03224v2 | http://arxiv.org/pdf/1710.03224v2.pdf | Person Recognition in Personal Photo Collections | People nowadays share large parts of their personal lives through social
media. Being able to automatically recognise people in personal photos may
greatly enhance user convenience by easing photo album organisation. For human
identification task, however, traditional focus of computer vision has been
face recognition ... | ['Seong Joon Oh', 'Rodrigo Benenson', 'Mario Fritz', 'Bernt Schiele'] | 2017-10-09 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 2.05730107e-02 -1.23185389e-01 3.92987251e-01 -4.84349519e-01
-4.12159830e-01 -4.55836236e-01 1.00977409e+00 -2.29052141e-01
-5.70279300e-01 5.13226748e-01 2.23615140e-01 3.67115647e-01
-4.79008593e-02 -4.09234434e-01 -4.07490313e-01 -6.79950118e-01
-1.92835867e-01 5.64478636e-01 -2.42739394e-02 -2.06607550... | [14.322311401367188, 0.9557995200157166] |
8f453e86-cd38-49b2-9707-d8b65fefdb83 | application-of-three-graph-laplacian-based | 1211.4289 | null | http://arxiv.org/abs/1211.4289v3 | http://arxiv.org/pdf/1211.4289v3.pdf | Application of three graph Laplacian based semi-supervised learning methods to protein function prediction problem | Protein function prediction is the important problem in modern biology. In
this paper, the un-normalized, symmetric normalized, and random walk graph
Laplacian based semi-supervised learning methods will be applied to the
integrated network combined from multiple networks to predict the functions of
all yeast proteins ... | ['Loc Tran'] | 2012-11-19 | null | null | null | null | ['protein-function-prediction'] | ['medical'] | [ 1.74402639e-01 1.56788975e-01 -3.66838217e-01 -1.59743562e-01
-2.00110778e-01 -4.55258071e-01 6.96432665e-02 2.31138602e-01
-4.40363020e-01 1.31414878e+00 -2.33224764e-01 -1.14564717e-01
-5.83772540e-01 -7.43516386e-01 -5.20160615e-01 -1.00096762e+00
-4.85738575e-01 6.10533059e-01 5.51588297e-01 3.45042460... | [6.696123123168945, 5.502554893493652] |
024f0efe-3701-4311-abbd-eed8e64b6588 | mutual-information-estimation-for-graph | 2203.16887 | null | https://arxiv.org/abs/2203.16887v1 | https://arxiv.org/pdf/2203.16887v1.pdf | Mutual information estimation for graph convolutional neural networks | Measuring model performance is a key issue for deep learning practitioners. However, we often lack the ability to explain why a specific architecture attains superior predictive accuracy for a given data set. Often, validation accuracy is used as a performance heuristic quantifying how well a network generalizes to uns... | ['Signe Riemer-Sørensen', 'Marius C. Landverk'] | 2022-03-31 | null | null | null | null | ['mutual-information-estimation', 'information-plane'] | ['methodology', 'methodology'] | [ 8.74724761e-02 3.80693913e-01 -2.63842911e-01 -5.11345088e-01
6.24197461e-02 -6.28636718e-01 6.75773978e-01 4.71824497e-01
-3.95893395e-01 4.27034885e-01 1.61776170e-01 -5.30196071e-01
-5.24146736e-01 -9.85089779e-01 -7.45671272e-01 -4.29429442e-01
-5.75589947e-02 4.24755484e-01 -3.81848440e-02 -1.41042774... | [6.9444451332092285, 6.025784015655518] |
a8581114-2300-48e7-a4f7-737cf99b1abc | hyperparameter-optimization-in-deep-multi | 2211.04362 | null | https://arxiv.org/abs/2211.04362v1 | https://arxiv.org/pdf/2211.04362v1.pdf | Hyperparameter optimization in deep multi-target prediction | As a result of the ever increasing complexity of configuring and fine-tuning machine learning models, the field of automated machine learning (AutoML) has emerged over the past decade. However, software implementations like Auto-WEKA and Auto-sklearn typically focus on classical machine learning (ML) tasks such as clas... | ['Willem Waegeman', 'Bernard De Baets', 'Marcel Wever', 'Dimitrios Iliadis'] | 2022-11-08 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 3.54704946e-01 -4.54555191e-02 -3.77024233e-01 -4.50221330e-01
-1.27262557e+00 -4.63728189e-01 4.96156603e-01 3.67252558e-01
-4.22402173e-01 5.88942707e-01 -7.27418885e-02 -2.05264583e-01
-4.44426626e-01 -3.76542807e-01 -6.05161309e-01 -7.98305154e-01
8.07719231e-02 1.09954762e+00 -8.05816278e-02 -8.03679749... | [9.190485000610352, 4.197889804840088] |
26fe897d-7051-48f3-8c95-b47c0a447f9e | two-step-domain-adaptation-for-mitosis-cell | 2109.00109 | null | https://arxiv.org/abs/2109.00109v2 | https://arxiv.org/pdf/2109.00109v2.pdf | Two-step Domain Adaptation for Mitosis Cell Detection in Histopathology Images | We propose a two-step domain shift-invariant mitosis cell detection method based on Faster RCNN and a convolutional neural network (CNN). We generate various domain-shifted versions of existing histopathology images using a stain augmentation technique, enabling our method to effectively learn various stain domains and... | ['Fattaneh Pourakpour', 'Ramin Nateghi'] | 2021-08-31 | null | null | null | null | ['cell-detection', 'mitosis-detection'] | ['computer-vision', 'medical'] | [ 5.41715741e-01 -9.50362347e-03 -1.69623524e-01 -3.10506880e-01
-1.12652278e+00 -4.71814662e-01 5.23440719e-01 1.17463395e-01
-7.21052885e-01 1.12118864e+00 -8.38727131e-02 -2.27253377e-01
4.11380768e-01 -6.26709402e-01 -2.37785041e-01 -1.31618631e+00
-1.22432798e-01 4.27508146e-01 5.99935949e-01 -1.38338000... | [15.120757102966309, -3.1018965244293213] |
1a5e88c6-2d31-4b6f-9655-de0d0f49169e | cross-lingual-data-augmentation-for-document | 2305.14949 | null | https://arxiv.org/abs/2305.14949v1 | https://arxiv.org/pdf/2305.14949v1.pdf | Cross-lingual Data Augmentation for Document-grounded Dialog Systems in Low Resource Languages | This paper proposes a framework to address the issue of data scarcity in Document-Grounded Dialogue Systems(DGDS). Our model leverages high-resource languages to enhance the capability of dialogue generation in low-resource languages. Specifically, We present a novel pipeline CLEM (Cross-Lingual Enhanced Model) includi... | ['Wenzhe Du', 'Zehua Xia', 'Qi Gou'] | 2023-05-24 | null | null | null | null | ['dialogue-generation', 'dialogue-generation'] | ['natural-language-processing', 'speech'] | [-1.90966174e-01 4.47638720e-01 -1.49681583e-01 -3.89820129e-01
-1.68528771e+00 -8.65391076e-01 9.90168452e-01 -4.01963592e-01
-5.07967234e-01 1.15296650e+00 7.98121810e-01 -5.91059685e-01
5.57922900e-01 -4.62448388e-01 -5.55372179e-01 3.54619622e-02
3.78032029e-01 1.07714343e+00 -3.69283468e-01 -9.20142591... | [12.36857795715332, 8.55305290222168] |
4832a5ae-2256-4d68-9580-1633ac756e91 | transfer-learning-for-video-classification | 2210.09969 | null | https://arxiv.org/abs/2210.09969v1 | https://arxiv.org/pdf/2210.09969v1.pdf | Transfer-learning for video classification: Video Swin Transformer on multiple domains | The computer vision community has seen a shift from convolutional-based to pure transformer architectures for both image and video tasks. Training a transformer from zero for these tasks usually requires a lot of data and computational resources. Video Swin Transformer (VST) is a pure-transformer model developed for vi... | ['David Martins de Matos', 'Daniel Oliveira'] | 2022-10-18 | null | null | null | null | ['video-classification'] | ['computer-vision'] | [-4.94779721e-02 -1.85925528e-01 -9.72246751e-02 -2.01821744e-01
-4.92717385e-01 -4.44252253e-01 5.46689987e-01 -2.83637404e-01
-5.32894731e-01 5.79387367e-01 -1.97245181e-01 -4.34909075e-01
2.66579892e-02 -8.48058403e-01 -1.31531656e+00 -7.72795379e-01
-1.51734203e-01 5.99514067e-01 7.36018717e-01 -2.21056551... | [9.2389497756958, 1.1606892347335815] |
6365fd4a-fa1b-4e8f-ae64-bf2b9f23a787 | cross-lingual-speech-emotion-recognition-urdu | 1812.10411 | null | https://arxiv.org/abs/1812.10411v2 | https://arxiv.org/pdf/1812.10411v2.pdf | Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages | Cross-lingual speech emotion recognition is an important task for practical applications. The performance of automatic speech emotion recognition systems degrades in cross-corpus scenarios, particularly in scenarios involving multiple languages or a previously unseen language such as Urdu for which limited or no data i... | ['Muhammad Usman', 'Adnan Qayyum', 'Junaid Qadir', 'Siddique Latif'] | 2018-12-15 | null | null | null | null | ['cross-corpus'] | ['computer-vision'] | [-2.56341904e-01 -1.45618066e-01 -3.49230394e-02 -5.92751145e-01
-1.07686710e+00 -7.40811229e-01 5.04424274e-01 -2.27561265e-01
-6.20922029e-01 7.48928905e-01 3.86295617e-01 -3.60761166e-01
7.82173574e-01 -1.18974246e-01 -2.11127400e-01 -5.18208146e-01
3.29051875e-02 2.07447648e-01 -2.57062942e-01 -3.18352103... | [13.612419128417969, 5.870246887207031] |
55092a2c-f697-4efa-9007-56a5f98b6eaa | global-minimum-for-a-finsler-elastica-minimal | 1612.00343 | null | http://arxiv.org/abs/1612.00343v3 | http://arxiv.org/pdf/1612.00343v3.pdf | Global Minimum for a Finsler Elastica Minimal Path Approach | In this paper, we propose a novel curvature-penalized minimal path model via
an orientation-lifted Finsler metric and the Euler elastica curve. The original
minimal path model computes the globally minimal geodesic by solving an Eikonal
partial differential equation (PDE). Essentially, this first-order model is
unable ... | ['Jean-Marie Mirebeau', 'Laurent D. Cohen', 'Da Chen'] | 2016-12-01 | null | null | null | null | ['contour-detection'] | ['computer-vision'] | [-1.25564002e-02 3.21060151e-01 1.70288414e-01 -2.78866708e-01
-2.05841839e-01 -5.72741807e-01 5.03634989e-01 -4.82114730e-03
-6.81541085e-01 2.90381432e-01 -2.43223444e-01 -1.96270630e-01
-6.05146587e-01 -9.31945801e-01 -4.12770212e-01 -7.50521362e-01
-1.71835169e-01 1.35383919e-01 4.27622378e-01 -4.94051278... | [7.3567681312561035, 3.8479018211364746] |
d74c098d-a635-4ac2-a190-7e5af72e7784 | few-shot-video-classification-via-temporal | 1906.11415 | null | https://arxiv.org/abs/1906.11415v1 | https://arxiv.org/pdf/1906.11415v1.pdf | Few-Shot Video Classification via Temporal Alignment | There is a growing interest in learning a model which could recognize novel classes with only a few labeled examples. In this paper, we propose Temporal Alignment Module (TAM), a novel few-shot learning framework that can learn to classify a previous unseen video. While most previous works neglect long-term temporal or... | ['Chien-Yi Chang', 'Zhangjie Cao', 'Kaidi Cao', 'Juan Carlos Niebles', 'Jingwei Ji'] | 2019-06-27 | few-shot-video-classification-via-temporal-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Cao_Few-Shot_Video_Classification_via_Temporal_Alignment_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Cao_Few-Shot_Video_Classification_via_Temporal_Alignment_CVPR_2020_paper.pdf | cvpr-2020-6 | ['few-shot-action-recognition'] | ['computer-vision'] | [ 9.28792953e-02 -4.65699166e-01 -7.34258711e-01 -6.11182332e-01
-1.06926608e+00 -4.09415424e-01 7.48858392e-01 1.00839622e-01
-6.02272332e-01 4.25343841e-01 2.95927733e-01 8.37686434e-02
-5.65623380e-02 -3.61416250e-01 -8.81764472e-01 -4.89510745e-01
-3.54230553e-01 6.85773790e-02 6.30495310e-01 1.25795558... | [8.779107093811035, 0.9000324010848999] |
bef442bd-23f7-4328-a379-b1927c3b9b0f | reinforce-attack-adversarial-attack-against | null | null | https://openreview.net/forum?id=TUsLgD-Ohfg | https://openreview.net/pdf?id=TUsLgD-Ohfg | Reinforce Attack: Adversarial Attack against BERT with Reinforcement Learning | Adversarial attacks against textual data has been drawing increasing attention in both the NLP and security domains. Current successful attack methods for text typically consist of two stages: word importance ranking and word replacement. The first stage is usually achieved by masking each word in the sentence on... | ['Anonymous'] | 2021-08-17 | null | null | null | acl-arr-august-2021-8 | ['adversarial-text'] | ['adversarial'] | [ 5.08234143e-01 1.07348278e-01 -3.41122746e-02 -3.35301533e-02
-1.03461134e+00 -7.96737134e-01 9.77238238e-01 4.50668275e-01
-5.77916682e-01 5.96306324e-01 4.28650528e-01 -4.25751626e-01
1.41366050e-01 -8.91601861e-01 -4.65823591e-01 -4.60288525e-01
9.69952196e-02 3.72407377e-01 3.72461051e-01 -6.11925960... | [6.037147045135498, 8.118565559387207] |
3ae5a597-e2c3-426e-90ca-078238eb339e | ladra-net-locally-aware-dynamic-re-read | 2108.02915 | null | https://arxiv.org/abs/2108.02915v1 | https://arxiv.org/pdf/2108.02915v1.pdf | LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matching | Sentence semantic matching requires an agent to determine the semantic relation between two sentences, which is widely used in various natural language tasks, such as Natural Language Inference (NLI), Paraphrase Identification (PI), and so on. Much recent progress has been made in this area, especially attention-based ... | ['Meng Wang', 'Qi Liu', 'Enhong Chen', 'Le Wu', 'Guangyi Lv', 'Kun Zhang'] | 2021-08-06 | null | null | null | null | ['paraphrase-identification'] | ['natural-language-processing'] | [ 2.20246658e-01 -8.68040323e-02 -1.99575111e-01 -5.40949166e-01
-3.43683600e-01 -2.24930048e-01 4.36129928e-01 3.64708602e-01
-7.11047173e-01 2.82651693e-01 6.44298613e-01 -2.92749316e-01
4.01552813e-03 -1.08377707e+00 -5.79608917e-01 -2.26241812e-01
6.96898460e-01 3.43048662e-01 3.30725998e-01 -7.73107052... | [11.055075645446777, 8.354939460754395] |
1780f1c0-68db-423f-8e6a-96c62de2c4a6 | performance-of-ris-aided-nearfield | 2303.15176 | null | https://arxiv.org/abs/2303.15176v1 | https://arxiv.org/pdf/2303.15176v1.pdf | Performance of RIS-Aided Nearfield Localization under Beams Approximation from Real Hardware Characterization | The technology of reconfigurable intelligent surfaces (RIS) has been showing promising potential in a variety of applications relying on Beyond-5G networks. Reconfigurable intelligent surface (RIS) can indeed provide fine channel flexibility to improve communication quality of service (QoS) or restore localization capa... | ['Henk Wymeersch', 'George C. Alexandropoulos', 'Bernard Uguen', 'Musa Furkan Keskin', 'Kamran Keykhosravi', 'Benoit Denis', 'Moustafa Rahal'] | 2023-03-27 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 3.93112302e-01 5.13922691e-01 2.76663005e-01 -5.97652048e-02
-5.47377646e-01 -5.88328183e-01 4.85197991e-01 -5.49061857e-02
-1.36987427e-02 6.43899560e-01 -2.76674796e-02 -5.49193978e-01
-9.44196343e-01 -8.14613879e-01 -6.23837352e-01 -1.04698813e+00
-5.03304839e-01 3.26261193e-01 -4.46841307e-02 -5.70066035... | [6.278119087219238, 1.2469143867492676] |
2be17506-ac4c-4128-9233-081862d8a1c3 | cross-modal-multi-task-learning-for-graphic | 2003.05787 | null | https://arxiv.org/abs/2003.05787v1 | https://arxiv.org/pdf/2003.05787v1.pdf | Cross-modal Multi-task Learning for Graphic Recognition of Caricature Face | Face recognition of realistic visual images has been well studied and made a significant progress in the recent decade. Unlike the realistic visual images, the face recognition of the caricatures is far from the performance of the visual images. This is largely due to the extreme non-rigid distortions of the caricature... | ['Jean-Christophe Burie', 'Zuheng Ming', 'Muhammad Muzzamil Luqman'] | 2020-03-10 | null | null | null | null | ['caricature'] | ['computer-vision'] | [-1.30615113e-02 -3.52970660e-01 2.26181239e-01 -4.33861166e-01
-6.26668215e-01 -3.74027938e-01 7.95418262e-01 -7.13667750e-01
-3.06814224e-01 4.43897814e-01 -6.73382580e-02 1.39302894e-01
-3.14259648e-01 -1.23034522e-01 -9.03522432e-01 -1.01090407e+00
2.37336472e-01 5.48814774e-01 8.58751759e-02 -7.16886073... | [13.262124061584473, 0.6641137003898621] |
c4f458f0-2dc8-4d34-ab12-5febb6b11f86 | invariant-descriptors-for-intrinsic | 2204.04076 | null | https://arxiv.org/abs/2204.04076v1 | https://arxiv.org/pdf/2204.04076v1.pdf | Invariant Descriptors for Intrinsic Reflectance Optimization | Intrinsic image decomposition aims to factorize an image into albedo (reflectance) and shading (illumination) sub-components. Being ill-posed and under-constrained, it is a very challenging computer vision problem. There are infinite pairs of reflectance and shading images that can reconstruct the same input. To addres... | ['Theo Gevers', 'Anil S. Baslamisli'] | 2022-04-08 | null | null | null | null | ['intrinsic-image-decomposition'] | ['computer-vision'] | [ 5.36309123e-01 -3.68942201e-01 2.15000987e-01 -5.90318501e-01
-5.22007108e-01 -4.45562243e-01 6.10618472e-01 -4.47410136e-01
-1.39592692e-01 6.32107377e-01 5.74615300e-02 5.35593219e-02
-3.55950855e-02 -9.03691590e-01 -5.28600097e-01 -1.04501438e+00
5.31737804e-01 5.56462467e-01 -8.88019651e-02 1.09363813... | [9.959148406982422, -2.9838624000549316] |
840b6124-240d-4975-8ed8-b253cbb961dc | it-s-a-long-way-layer-wise-relevance | 2210.09958 | null | https://arxiv.org/abs/2210.09958v2 | https://arxiv.org/pdf/2210.09958v2.pdf | Layer-wise Relevance Propagation for Echo State Networks applied to Earth System Variability | Artificial neural networks (ANNs) are known to be powerful methods for many hard problems (e.g. image classification, speech recognition or time series prediction). However, these models tend to produce black-box results and are often difficult to interpret. Layer-wise relevance propagation (LRP) is a widely used techn... | ['Willi Rath', 'Martin Claus', 'Peer Kröger', 'Marco Landt-Hayen'] | 2022-10-18 | null | null | null | null | ['time-series-prediction'] | ['time-series'] | [ 2.11676702e-01 -1.36419177e-01 3.08965817e-02 -2.06893325e-01
1.63018882e-01 -3.10558915e-01 8.90946448e-01 -4.97803017e-02
-2.45203540e-01 7.02903450e-01 -2.07191721e-01 -7.55012214e-01
4.54953946e-02 -8.17216575e-01 -8.07102621e-01 -9.30642664e-01
-5.49730599e-01 1.89059630e-01 2.84067184e-01 -5.86078227... | [6.715849876403809, 3.204244375228882] |
7b3d34cc-26e8-47b8-b59e-2e65936153e8 | self-consistency-improves-chain-of-thought | 2203.11171 | null | https://arxiv.org/abs/2203.11171v4 | https://arxiv.org/pdf/2203.11171v4.pdf | Self-Consistency Improves Chain of Thought Reasoning in Language Models | Chain-of-thought prompting combined with pre-trained large language models has achieved encouraging results on complex reasoning tasks. In this paper, we propose a new decoding strategy, self-consistency, to replace the naive greedy decoding used in chain-of-thought prompting. It first samples a diverse set of reasonin... | ['Denny Zhou', 'Aakanksha Chowdhery', 'Sharan Narang', 'Ed Chi', 'Quoc Le', 'Dale Schuurmans', 'Jason Wei', 'Xuezhi Wang'] | 2022-03-21 | null | null | null | null | ['gsm8k', 'arithmetic-reasoning', 'strategyqa'] | ['natural-language-processing', 'reasoning', 'reasoning'] | [-2.62755509e-02 2.88196832e-01 -3.13267782e-02 -5.96984029e-01
-1.20866597e+00 -6.99444294e-01 7.60087371e-01 3.11792195e-01
-3.54495227e-01 5.28605282e-01 6.91112399e-01 -7.57952809e-01
-5.43999858e-02 -7.66502440e-01 -5.98947942e-01 -2.54655600e-01
1.37541905e-01 8.30405235e-01 5.75021990e-02 -4.47683871... | [9.716938972473145, 7.458249092102051] |
c8509112-ea60-4759-95fd-28e867269e9d | contragen-effective-contrastive-learning-for | 2210.01185 | null | https://arxiv.org/abs/2210.01185v2 | https://arxiv.org/pdf/2210.01185v2.pdf | ContraCLM: Contrastive Learning For Causal Language Model | Despite exciting progress in causal language models, the expressiveness of the representations is largely limited due to poor discrimination ability. To remedy this issue, we present ContraCLM, a novel contrastive learning framework at both token-level and sequence-level. We assess ContraCLM on a variety of downstream ... | ['Bing Xiang', 'Xiaofei Ma', 'Parminder Bhatia', 'Baishakhi Ray', 'Ramesh Nallapati', 'Ming Tan', 'Xiaopeng Li', 'Feng Nan', 'Zijian Wang', 'Wasi Uddin Ahmad', 'Dejiao Zhang', 'Nihal Jain'] | 2022-10-03 | null | null | null | null | ['code-search', 'code-search'] | ['computer-code', 'computer-vision'] | [ 3.27956915e-01 2.94652253e-01 -6.38834536e-01 -2.37193212e-01
-1.07594919e+00 -4.98538584e-01 8.64754617e-01 3.93694013e-01
-1.47591516e-01 6.70843959e-01 6.04638636e-01 -5.79303205e-01
-8.97724032e-02 -9.01283324e-01 -7.61778772e-01 -1.22801818e-01
-7.40670785e-02 2.03560069e-02 -1.48339840e-02 -1.89281583... | [7.794498920440674, 7.908487319946289] |
db325f8c-632d-4fb7-8eab-69beeae17945 | adversarial-self-attack-defense-and-spatial | 2307.03903 | null | https://arxiv.org/abs/2307.03903v1 | https://arxiv.org/pdf/2307.03903v1.pdf | Adversarial Self-Attack Defense and Spatial-Temporal Relation Mining for Visible-Infrared Video Person Re-Identification | In visible-infrared video person re-identification (re-ID), extracting features not affected by complex scenes (such as modality, camera views, pedestrian pose, background, etc.) changes, and mining and utilizing motion information are the keys to solving cross-modal pedestrian identity matching. To this end, the paper... | ['Zhengtao Yu', 'Dapeng Tao', 'Yafei Zhang', 'Le Xu', 'Huafeng Li'] | 2023-07-08 | null | null | null | null | ['adversarial-attack', 'person-re-identification', 'video-based-person-re-identification'] | ['adversarial', 'computer-vision', 'computer-vision'] | [ 1.53106660e-01 -5.68946183e-01 -1.04450628e-01 -2.24538475e-01
-2.75430143e-01 -7.40535557e-01 4.90213990e-01 -5.57536721e-01
-2.99689054e-01 3.78600568e-01 5.29413998e-01 1.74768135e-01
-7.46258274e-02 -6.21711075e-01 -5.55393457e-01 -8.01242471e-01
9.08714160e-02 -2.70522565e-01 -4.78725135e-02 -4.69521344... | [14.680112838745117, 0.9423484802246094] |
c7a5883f-1950-4779-83a3-41f7cc4ac15c | btech-thesis-report-on-adversarial-attack | 2205.07859 | null | https://arxiv.org/abs/2205.07859v1 | https://arxiv.org/pdf/2205.07859v1.pdf | Btech thesis report on adversarial attack detection and purification of adverserially attacked images | This is Btech thesis report on detection and purification of adverserially attacked images. A deep learning model is trained on certain training examples for various tasks such as classification, regression etc. By training, weights are adjusted such that the model performs the task well not only on training examples j... | ['Dvij Kalaria'] | 2022-05-09 | null | null | null | null | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [ 4.56934094e-01 1.40153423e-01 2.33417988e-01 -1.29973993e-01
-1.14266284e-01 -8.85675490e-01 8.85072231e-01 2.65689231e-02
-3.02087754e-01 7.24242687e-01 -2.49887690e-01 -3.77186537e-01
2.43608832e-01 -1.02036142e+00 -8.50720167e-01 -9.41754878e-01
-3.81218866e-02 1.10531626e-02 3.42961699e-01 -2.58667380... | [5.554721355438232, 7.763762474060059] |
be700b38-698d-4723-b2eb-33bcf46c8d88 | problematic-cases-in-the-annotation-of | null | null | https://aclanthology.org/W16-5006 | https://aclanthology.org/W16-5006.pdf | Problematic Cases in the Annotation of Negation in Spanish | This paper presents the main sources of disagreement found during the annotation of the Spanish SFU Review Corpus with negation (SFU ReviewSP -NEG). Negation detection is a challenge in most of the task related to NLP, so the availability of corpora annotated with this phenomenon is essential in order to advance in tas... | ["Mariona Taul{\\'e}", "Toni Mart{\\'\\i}", "L. Alfonso Ure{\\~n}a-L{\\'o}pez", "Salud Mar{\\'\\i}a Jim{\\'e}nez-Zafra", 'Maite Martin'] | 2016-12-01 | null | null | null | ws-2016-12 | ['negation-detection'] | ['natural-language-processing'] | [ 2.37009719e-01 5.49060822e-01 -2.50168324e-01 -4.69842225e-01
-5.60630262e-01 -7.97503412e-01 6.08135641e-01 8.80890071e-01
-5.42582452e-01 1.36654794e+00 3.47084105e-01 -3.79335880e-01
1.51945725e-01 -4.02948111e-01 -5.61751306e-01 -4.86334532e-01
4.49280083e-01 5.22605538e-01 4.45068955e-01 -7.56267011... | [10.688844680786133, 9.282841682434082] |
bba86fb8-954a-4e6e-b3bd-5e859518cc3f | less-is-more-simplifying-feature-extractors | 2210.09537 | null | https://arxiv.org/abs/2210.09537v1 | https://arxiv.org/pdf/2210.09537v1.pdf | Less is More: Simplifying Feature Extractors Prevents Overfitting for Neural Discourse Parsing Models | Complex feature extractors are widely employed for text representation building. However, these complex feature extractors can lead to severe overfitting problems especially when the training datasets are small, which is especially the case for several discourse parsing tasks. Thus, we propose to remove additional feat... | ['Ruihong Huang', 'Sijing Yu', 'Ming Li'] | 2022-10-18 | null | null | null | null | ['discourse-parsing'] | ['natural-language-processing'] | [ 3.33621711e-01 6.36682391e-01 -3.31674784e-01 -5.15024662e-01
-9.10960257e-01 -5.17323554e-01 8.68518710e-01 1.23139411e-01
-6.17864668e-01 7.91171014e-01 7.44993448e-01 -5.27857184e-01
1.93137527e-01 -6.44263506e-01 -4.04220670e-01 -4.51614559e-01
2.09339097e-01 1.37582690e-01 3.93615663e-02 -4.30610031... | [10.836615562438965, 9.3319673538208] |
db7df910-5023-4bdc-b93e-0890c2a5ac3c | pit30m-a-benchmark-for-global-localization-in | 2012.12437 | null | https://arxiv.org/abs/2012.12437v1 | https://arxiv.org/pdf/2012.12437v1.pdf | Pit30M: A Benchmark for Global Localization in the Age of Self-Driving Cars | We are interested in understanding whether retrieval-based localization approaches are good enough in the context of self-driving vehicles. Towards this goal, we introduce Pit30M, a new image and LiDAR dataset with over 30 million frames, which is 10 to 100 times larger than those used in previous work. Pit30M is captu... | ['Raquel Urtasun', 'Gellért Máttyus', 'Shenlong Wang', 'Ioan Andrei Bârsan', 'Jack Fan', 'Sasha Doubov', 'Julieta Martinez'] | 2020-12-23 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [-2.47496173e-01 -6.80212200e-01 -5.00246406e-01 -6.33687377e-01
-1.19666195e+00 -8.04247797e-01 6.90119267e-01 2.33930185e-01
-5.04064262e-01 6.20812356e-01 -7.46246949e-02 -2.09245086e-01
-7.24841878e-02 -1.09901595e+00 -8.32587004e-01 -3.45838696e-01
4.39137816e-02 8.17829430e-01 4.68976498e-01 -2.00496003... | [7.768463134765625, -2.065572738647461] |
edcdd8b8-8cdc-42c3-9eb6-a3d099427d7f | syntax-aware-opinion-role-labeling-with | null | null | https://aclanthology.org/2020.acl-main.297 | https://aclanthology.org/2020.acl-main.297.pdf | Syntax-Aware Opinion Role Labeling with Dependency Graph Convolutional Networks | Opinion role labeling (ORL) is a fine-grained opinion analysis task and aims to answer {``}who expressed what kind of sentiment towards what?{''}. Due to the scarcity of labeled data, ORL remains challenging for data-driven methods. In this work, we try to enhance neural ORL models with syntactic knowledge by comparing... | ['Yue Zhang', 'Rui Wang', 'Zhenghua Li', 'Min Zhang', 'Bo Zhang'] | 2020-07-01 | null | null | null | acl-2020-6 | ['fine-grained-opinion-analysis'] | ['natural-language-processing'] | [ 7.05914795e-02 3.04031998e-01 -8.39341432e-02 -7.60173023e-01
-1.04332459e+00 -8.44471335e-01 2.17095256e-01 3.82283539e-01
-4.38383341e-01 6.02661848e-01 6.89316690e-01 -5.23050725e-01
3.72600496e-01 -7.46695399e-01 -6.67166352e-01 -4.05816823e-01
5.03713727e-01 2.83863187e-01 1.59998804e-01 -5.68398774... | [11.408956527709961, 6.8172607421875] |
b293f7b9-c7ee-43f5-af95-53a8b5a1d4a2 | full-gradient-deep-reinforcement-learning-for | 2304.03729 | null | https://arxiv.org/abs/2304.03729v1 | https://arxiv.org/pdf/2304.03729v1.pdf | Full Gradient Deep Reinforcement Learning for Average-Reward Criterion | We extend the provably convergent Full Gradient DQN algorithm for discounted reward Markov decision processes from Avrachenkov et al. (2021) to average reward problems. We experimentally compare widely used RVI Q-Learning with recently proposed Differential Q-Learning in the neural function approximation setting with F... | ['Konstantin Avrachenkov', 'Vivek Borkar', 'Tejas Pagare'] | 2023-04-07 | null | null | null | null | ['q-learning', 'multi-armed-bandits'] | ['methodology', 'miscellaneous'] | [-2.93610275e-01 5.33195473e-02 -7.40846753e-01 -3.01154554e-01
-1.15966547e+00 -6.74226224e-01 4.13571358e-01 -3.06158990e-01
-9.44672227e-01 1.57880008e+00 -1.46556059e-02 -8.17608178e-01
-4.84802395e-01 -4.61787373e-01 -8.09160233e-01 -7.72817910e-01
-1.03169136e-01 8.20972443e-01 2.70013697e-02 -8.73801038... | [4.187001705169678, 2.5582680702209473] |
c50f4c9f-68f9-4938-8a56-206c69b9d272 | flow-fields-dense-correspondence-fields-for | 1703.02563 | null | http://arxiv.org/abs/1703.02563v2 | http://arxiv.org/pdf/1703.02563v2.pdf | Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation | Modern large displacement optical flow algorithms usually use an
initialization by either sparse descriptor matching techniques or dense
approximate nearest neighbor fields. While the latter have the advantage of
being dense, they have the major disadvantage of being very outlier-prone as
they are not designed to find ... | ['Didier Stricker', 'Christian Bailer', 'Bertram Taetz'] | 2017-03-07 | null | null | null | iccv-2015 | ['patch-matching'] | ['computer-vision'] | [-3.64518672e-01 -6.98390424e-01 2.08447799e-01 1.10740632e-01
-3.42866331e-01 -7.32824028e-01 5.85597038e-01 3.58568072e-01
-4.86927927e-01 6.93792224e-01 2.02262178e-01 -7.48560503e-02
-2.21616477e-01 -7.83813119e-01 -4.61956114e-01 -3.30587685e-01
-4.61732686e-01 4.79885846e-01 7.16198802e-01 -3.05935979... | [8.80052661895752, -1.8687931299209595] |
a359e91c-0866-4de9-ad3f-b51df0eb847d | fuzzy-conditioned-diffusion-and-diffusion | 2306.14891 | null | https://arxiv.org/abs/2306.14891v2 | https://arxiv.org/pdf/2306.14891v2.pdf | Fuzzy-Conditioned Diffusion and Diffusion Projection Attention Applied to Facial Image Correction | Image diffusion has recently shown remarkable performance in image synthesis and implicitly as an image prior. Such a prior has been used with conditioning to solve the inpainting problem, but only supporting binary user-based conditioning. We derive a fuzzy-conditioned diffusion, where implicit diffusion priors can be... | ['Majed El Helou'] | 2023-06-26 | null | null | null | null | ['image-generation'] | ['computer-vision'] | [ 7.89493322e-01 5.16969979e-01 -1.04825944e-01 -4.25860167e-01
-5.02328694e-01 -3.88981879e-01 6.56708598e-01 -3.99608552e-01
-3.69557649e-01 5.52691519e-01 1.21379092e-01 2.38581508e-01
-8.59066248e-02 -6.63353205e-01 -8.01652491e-01 -7.67982244e-01
3.65890622e-01 3.48735005e-01 7.38826096e-02 -4.19275433... | [11.504144668579102, -0.4895149767398834] |
14c9a3c0-c7ca-44b9-8f5a-314a7b2b507d | adaptive-sampling-for-fast-constrained | 2102.06486 | null | https://arxiv.org/abs/2102.06486v1 | https://arxiv.org/pdf/2102.06486v1.pdf | Adaptive Sampling for Fast Constrained Maximization of Submodular Function | Several large-scale machine learning tasks, such as data summarization, can be approached by maximizing functions that satisfy submodularity. These optimization problems often involve complex side constraints, imposed by the underlying application. In this paper, we develop an algorithm with poly-logarithmic adaptivity... | ['Tobias Friedrich', 'Andreas Göbel', 'Vanja Doskoč', 'Francesco Quinzan'] | 2021-02-12 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 2.45144203e-01 3.78304720e-01 -6.53521717e-01 -3.27651918e-01
-1.25656152e+00 -9.56995308e-01 -2.25025520e-01 4.03699219e-01
-6.47718191e-01 8.91401529e-01 -8.49203169e-02 -3.31117123e-01
-3.91742021e-01 -1.03631592e+00 -1.12910068e+00 -8.28608632e-01
-3.78178239e-01 1.07387185e+00 1.38835415e-01 -2.89230198... | [6.590502738952637, 4.878420352935791] |
293d6fcc-00fc-4cb0-a032-6f20be969ffe | background-subtraction-via-generalized-fused | 1504.03707 | null | http://arxiv.org/abs/1504.03707v1 | http://arxiv.org/pdf/1504.03707v1.pdf | Background Subtraction via Generalized Fused Lasso Foreground Modeling | Background Subtraction (BS) is one of the key steps in video analysis. Many
background models have been proposed and achieved promising performance on
public data sets. However, due to challenges such as illumination change,
dynamic background etc. the resulted foreground segmentation often consists of
holes as well as... | ['Yuan Tian', 'Wen Gao', 'Bo Xin', 'Yizhou Wang'] | 2015-04-14 | background-subtraction-via-generalized-fused-1 | http://openaccess.thecvf.com/content_cvpr_2015/html/Xin_Background_Subtraction_via_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Xin_Background_Subtraction_via_2015_CVPR_paper.pdf | cvpr-2015-6 | ['foreground-segmentation'] | ['computer-vision'] | [ 5.48733771e-01 -4.11971092e-01 1.65048033e-01 -6.35658130e-02
-5.30204535e-01 -3.48032653e-01 3.97409886e-01 -2.01438755e-01
-3.53403091e-01 8.44948232e-01 -1.20784521e-01 3.35081369e-02
1.44122720e-01 -3.71051818e-01 -7.54923284e-01 -1.27130127e+00
3.25091183e-01 1.78765386e-01 2.25034416e-01 1.23947941... | [9.01704216003418, -0.8066307902336121] |
c109f4eb-8a7e-4fe1-9820-65bbf855dc0c | omni3d-a-large-benchmark-and-model-for-3d | 2207.10660 | null | https://arxiv.org/abs/2207.10660v2 | https://arxiv.org/pdf/2207.10660v2.pdf | Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild | Recognizing scenes and objects in 3D from a single image is a longstanding goal of computer vision with applications in robotics and AR/VR. For 2D recognition, large datasets and scalable solutions have led to unprecedented advances. In 3D, existing benchmarks are small in size and approaches specialize in few object c... | ['Nikhila Ravi', 'Abhinav Kumar', 'Georgia Gkioxari', 'Justin Johnson', 'Julian Straub', 'Garrick Brazil'] | 2022-07-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Brazil_Omni3D_A_Large_Benchmark_and_Model_for_3D_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Brazil_Omni3D_A_Large_Benchmark_and_Model_for_3D_Object_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['3d-object-recognition'] | ['computer-vision'] | [-6.16031652e-03 -3.82096648e-01 -3.12825739e-01 -4.01045382e-01
-5.13346970e-01 -8.19613576e-01 6.67756498e-01 -3.16136599e-01
-2.47160241e-01 7.75260404e-02 6.82634115e-02 -3.55381012e-01
3.11300140e-02 -5.39674461e-01 -1.05436301e+00 -3.22096229e-01
-2.34323964e-01 4.73761708e-01 3.61552417e-01 -4.23094667... | [7.726869106292725, -2.755718231201172] |
cfd34a2c-3800-418d-b0d9-6d3075770f31 | arabglossbert-fine-tuning-bert-on-context-1 | 2205.09685 | null | https://arxiv.org/abs/2205.09685v1 | https://arxiv.org/pdf/2205.09685v1.pdf | ArabGlossBERT: Fine-Tuning BERT on Context-Gloss Pairs for WSD | Using pre-trained transformer models such as BERT has proven to be effective in many NLP tasks. This paper presents our work to fine-tune BERT models for Arabic Word Sense Disambiguation (WSD). We treated the WSD task as a sentence-pair binary classification task. First, we constructed a dataset of labeled Arabic conte... | ['Mustafa Jarrar', 'Moustafa Al-Hajj'] | 2022-05-19 | arabglossbert-fine-tuning-bert-on-context | https://aclanthology.org/2021.ranlp-1.5 | https://aclanthology.org/2021.ranlp-1.5.pdf | ranlp-2021-9 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 1.42043959e-02 -2.43683867e-02 8.41367096e-02 -5.26053846e-01
-9.48678315e-01 -1.01746809e+00 8.20501506e-01 6.40392780e-01
-7.17995703e-01 8.61349165e-01 2.34810829e-01 -3.53258878e-01
-2.99897008e-02 -7.58087099e-01 -3.06170881e-01 -5.26653290e-01
-3.81672591e-01 1.00800776e+00 3.32844555e-01 -1.00809884... | [10.369061470031738, 9.511707305908203] |
d2e51f38-360a-423c-b10e-4d133d04e73d | pointar-efficient-lighting-estimation-for | 2004.00006 | null | https://arxiv.org/abs/2004.00006v3 | https://arxiv.org/pdf/2004.00006v3.pdf | PointAR: Efficient Lighting Estimation for Mobile Augmented Reality | We propose an efficient lighting estimation pipeline that is suitable to run on modern mobile devices, with comparable resource complexities to state-of-the-art mobile deep learning models. Our pipeline, PointAR, takes a single RGB-D image captured from the mobile camera and a 2D location in that image, and estimates 2... | ['Tian Guo', 'Yiqin Zhao'] | 2020-03-30 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4405_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123680664.pdf | eccv-2020-8 | ['lighting-estimation'] | ['computer-vision'] | [-1.64754361e-01 -1.80375844e-01 2.32939959e-01 -2.54514188e-01
-1.06543398e+00 -6.69112563e-01 6.25300705e-01 -3.23300213e-01
-3.37606370e-01 3.25050741e-01 1.30807282e-02 -4.75615084e-01
4.25551444e-01 -9.24213171e-01 -1.22270608e+00 -4.30353492e-01
2.27102593e-01 5.35824597e-01 -9.10812467e-02 9.41186026... | [9.517495155334473, -2.8985230922698975] |
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