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3d0b8781-b03d-46dc-bc46-aae8e3864b4b | ris-assisted-jamming-rejection-and-path | 2302.04994 | null | https://arxiv.org/abs/2302.04994v1 | https://arxiv.org/pdf/2302.04994v1.pdf | RIS-Assisted Jamming Rejection and Path Planning for UAV-Borne IoT Platform: A New Deep Reinforcement Learning Framework | This paper presents a new deep reinforcement learning (DRL)-based approach to the trajectory planning and jamming rejection of an unmanned aerial vehicle (UAV) for the Internet-of-Things (IoT) applications. Jamming can prevent timely delivery of sensing data and reception of operation instructions. With the assistance ... | ['Abbas Jamalipour', 'Xin Wang', 'Wei Ni', 'Xin Yuan', 'Shuyan Hu'] | 2023-02-10 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [ 6.37516286e-03 2.25146398e-01 -6.33675829e-02 3.39405149e-01
1.85371581e-02 -6.62656009e-01 5.08666277e-01 -6.69192746e-02
-5.54748178e-01 8.26227605e-01 -1.95915252e-01 -6.00137889e-01
-8.26722682e-01 -1.01855195e+00 -5.52235425e-01 -1.04471660e+00
-6.37432754e-01 1.29893690e-01 1.04620837e-01 -4.93367374... | [5.697292327880859, 1.6109042167663574] |
027bafbc-2db6-4b20-afbb-f9ca0eed5f05 | verbs-in-action-improving-verb-understanding | 2304.06708 | null | https://arxiv.org/abs/2304.06708v1 | https://arxiv.org/pdf/2304.06708v1.pdf | Verbs in Action: Improving verb understanding in video-language models | Understanding verbs is crucial to modelling how people and objects interact with each other and the environment through space and time. Recently, state-of-the-art video-language models based on CLIP have been shown to have limited verb understanding and to rely extensively on nouns, restricting their performance in rea... | ['Cordelia Schmid', 'Andrew Zisserman', 'Arsha Nagrani', 'Mathilde Caron', 'Liliane Momeni'] | 2023-04-13 | null | null | null | null | ['video-classification', 'video-question-answering', 'text-matching'] | ['computer-vision', 'computer-vision', 'natural-language-processing'] | [ 3.71036470e-01 -1.74330607e-01 -4.39272791e-01 -3.37518007e-01
-6.65480137e-01 -4.99861449e-01 8.37258577e-01 1.81867585e-01
-5.85686684e-01 2.66133752e-02 2.52273619e-01 -2.28737786e-01
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-1.50478244e-01 4.17250931e-01 4.29906815e-01 -2.55631804... | [10.086840629577637, 0.8654054403305054] |
50b818fc-2c04-4f41-a102-c02cf1e546a1 | in-the-eye-of-transformer-global-local | 2208.04464 | null | https://arxiv.org/abs/2208.04464v2 | https://arxiv.org/pdf/2208.04464v2.pdf | In the Eye of Transformer: Global-Local Correlation for Egocentric Gaze Estimation | In this paper, we present the first transformer-based model to address the challenging problem of egocentric gaze estimation. We observe that the connection between the global scene context and local visual information is vital for localizing the gaze fixation from egocentric video frames. To this end, we design the tr... | ['James M. Rehg', 'Fiona Ryan', 'Miao Liu', 'Bolin Lai'] | 2022-08-08 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [-2.83809096e-01 -1.11417718e-01 -4.14860845e-01 -3.72716963e-01
-3.61105174e-01 -4.98532861e-01 5.82940996e-01 -1.37657717e-01
-1.00024931e-01 2.32823953e-01 5.36796689e-01 -1.69887871e-01
3.57305594e-02 -1.36839211e-01 -7.45092869e-01 -4.60706294e-01
-3.51621360e-02 -4.53792781e-01 3.55162323e-02 -4.73257853... | [14.009228706359863, 0.050099872052669525] |
8b78c919-3713-48bb-9e54-541a658c9d39 | event-transformer-a-sparse-aware-solution-for | 2204.03355 | null | https://arxiv.org/abs/2204.03355v2 | https://arxiv.org/pdf/2204.03355v2.pdf | Event Transformer. A sparse-aware solution for efficient event data processing | Event cameras are sensors of great interest for many applications that run in low-resource and challenging environments. They log sparse illumination changes with high temporal resolution and high dynamic range, while they present minimal power consumption. However, top-performing methods often ignore specific event-da... | ['Ana C. Murillo', 'Luis Montesano', 'Alberto Sabater'] | 2022-04-07 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 3.93092394e-01 -7.15323091e-01 9.88504197e-03 -4.16443527e-01
-7.80450404e-01 -2.69753635e-01 7.02029586e-01 3.20192397e-01
-4.71965700e-01 3.99384558e-01 7.04701021e-02 2.45907068e-01
1.27816960e-01 -8.16906154e-01 -5.53892910e-01 -5.00105798e-01
1.02999710e-01 2.57254273e-01 9.24321234e-01 2.45088324... | [8.517130851745605, -1.1752345561981201] |
47ecf9d0-a385-450d-82d1-ad66b8bb05a6 | improving-neural-machine-translation-models | 1511.06709 | null | http://arxiv.org/abs/1511.06709v4 | http://arxiv.org/pdf/1511.06709v4.pdf | Improving Neural Machine Translation Models with Monolingual Data | Neural Machine Translation (NMT) has obtained state-of-the art performance
for several language pairs, while only using parallel data for training.
Target-side monolingual data plays an important role in boosting fluency for
phrase-based statistical machine translation, and we investigate the use of
monolingual data fo... | ['Alexandra Birch', 'Barry Haddow', 'Rico Sennrich'] | 2015-11-20 | improving-neural-machine-translation-models-1 | https://aclanthology.org/P16-1009 | https://aclanthology.org/P16-1009.pdf | acl-2016-8 | ['cross-lingual-bitext-mining'] | ['natural-language-processing'] | [ 4.00207937e-02 -8.00787956e-02 -5.09606063e-01 -3.34283084e-01
-1.42365575e+00 -6.68936253e-01 7.50870407e-01 -1.82588845e-01
-8.48488212e-01 1.05642879e+00 2.88562238e-01 -1.08404291e+00
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2.16982454e-01 1.00965118e+00 -3.23367417e-01 -6.43504441... | [11.58526611328125, 10.323629379272461] |
b30b079b-81ae-4132-8261-c84713505995 | using-automatically-extracted-minimum-spans | 1906.06703 | null | https://arxiv.org/abs/1906.06703v1 | https://arxiv.org/pdf/1906.06703v1.pdf | Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection | The common practice in coreference resolution is to identify and evaluate the maximum span of mentions. The use of maximum spans tangles coreference evaluation with the challenges of mention boundary detection like prepositional phrase attachment. To address this problem, minimum spans are manually annotated in smaller... | ['Leo Born', 'Nafise Sadat Moosavi', 'Michael Strube', 'Massimo Poesio'] | 2019-06-16 | using-automatically-extracted-minimum-spans-1 | https://aclanthology.org/P19-1408 | https://aclanthology.org/P19-1408.pdf | acl-2019-7 | ['prepositional-phrase-attachment'] | ['natural-language-processing'] | [ 5.21918237e-02 2.86102444e-01 -2.55038917e-01 -3.44729841e-01
-1.27061069e+00 -1.08941984e+00 2.42313489e-01 4.88990784e-01
-6.36214495e-01 9.43447351e-01 4.88568276e-01 -1.06874201e-02
-2.52484679e-01 -3.83259863e-01 -4.91524786e-01 -3.77909690e-01
1.50015667e-01 9.27453279e-01 4.70765084e-01 -3.53142589... | [9.335786819458008, 9.514086723327637] |
16148ec4-bab6-4277-99cb-944daa50ee3e | use-of-transformer-based-models-for-word | 2205.11370 | null | https://arxiv.org/abs/2205.11370v2 | https://arxiv.org/pdf/2205.11370v2.pdf | Use of Transformer-Based Models for Word-Level Transliteration of the Book of the Dean of Lismore | The Book of the Dean of Lismore (BDL) is a 16th-century Scottish Gaelic manuscript written in a non-standard orthography. In this work, we outline the problem of transliterating the text of the BDL into a standardised orthography, and perform exploratory experiments using Transformer-based models for this task. In part... | ['Roibeard Ó Maolalaigh', 'Jade Scott', 'William Gillies', 'Mark McConville', 'Edward Gow-Smith'] | 2022-05-23 | null | https://aclanthology.org/2022.cltw-1.13 | https://aclanthology.org/2022.cltw-1.13.pdf | cltw-lrec-2022-6 | ['transliteration'] | ['natural-language-processing'] | [ 2.23741680e-01 1.38692454e-01 -3.48874144e-02 -2.25832611e-01
-1.04058075e+00 -9.00031805e-01 9.95106697e-01 4.37420867e-02
-7.89355874e-01 8.74561727e-01 8.57600510e-01 -9.20046031e-01
-4.95116003e-02 -7.03034639e-01 -9.36234355e-01 -2.77514905e-02
4.06227112e-01 9.14672315e-01 -5.46209887e-02 -3.98934126... | [11.453461647033691, 10.318253517150879] |
fd81957e-39f1-47f8-ae6e-94f917881e02 | on-training-flexible-robots-using-deep | 1907.00269 | null | https://arxiv.org/abs/1907.00269v2 | https://arxiv.org/pdf/1907.00269v2.pdf | On Training Flexible Robots using Deep Reinforcement Learning | The use of robotics in controlled environments has flourished over the last several decades and training robots to perform tasks using control strategies developed from dynamical models of their hardware have proven very effective. However, in many real-world settings, the uncertainties of the environment, the safety r... | ['Mariano Phielipp', 'Madhavun Candadai', 'Zach Dwiel'] | 2019-06-29 | null | null | null | null | ['industrial-robots'] | ['robots'] | [ 8.34904537e-02 6.70510083e-02 -2.47874737e-01 1.65888127e-02
2.39980355e-01 -7.14651644e-01 5.76882422e-01 -2.85463959e-01
-5.69323123e-01 9.36814070e-01 -2.14738354e-01 -1.87814698e-01
-7.13921666e-01 -6.24946654e-01 -7.70793438e-01 -8.02339315e-01
-2.35282943e-01 4.42139894e-01 1.08032450e-01 -5.79313040... | [4.575260639190674, 1.484718918800354] |
8060d252-cbe6-44a1-9663-98c1ee1626e9 | every-picture-tells-a-story-image-grounded | 2209.01638 | null | https://arxiv.org/abs/2209.01638v2 | https://arxiv.org/pdf/2209.01638v2.pdf | Every picture tells a story: Image-grounded controllable stylistic story generation | Generating a short story out of an image is arduous. Unlike image captioning, story generation from an image poses multiple challenges: preserving the story coherence, appropriately assessing the quality of the story, steering the generated story into a certain style, and addressing the scarcity of image-story pair ref... | ['Pascale Fung', 'Willy Chung', 'Samuel Cahyawijaya', 'Romain Barraud', 'Bryan Wilie', 'Holy Lovenia'] | 2022-09-04 | null | https://aclanthology.org/2022.latechclfl-1.6 | https://aclanthology.org/2022.latechclfl-1.6.pdf | latechclfl-coling-2022-10 | ['story-generation'] | ['natural-language-processing'] | [ 6.88057601e-01 3.16991895e-01 1.26103863e-01 -3.10759157e-01
-9.12661672e-01 -6.06561542e-01 9.61867213e-01 -2.43045107e-01
-1.04781158e-01 7.67677009e-01 5.75065613e-01 2.20842287e-02
1.76677153e-01 -6.58198237e-01 -8.64327133e-01 -3.78494680e-01
5.24138212e-01 3.94701779e-01 1.86989069e-01 -2.56200135... | [11.174444198608398, 0.555452823638916] |
c9a8bc2b-88d7-4149-9b57-165b4337689b | cosplade-contextualizing-splade-for | 2301.04413 | null | https://arxiv.org/abs/2301.04413v1 | https://arxiv.org/pdf/2301.04413v1.pdf | CoSPLADE: Contextualizing SPLADE for Conversational Information Retrieval | Conversational search is a difficult task as it aims at retrieving documents based not only on the current user query but also on the full conversation history. Most of the previous methods have focused on a multi-stage ranking approach relying on query reformulation, a critical intermediate step that might lead to a s... | ['Laure Soulier', 'Benjamin Piwowarski', 'Jian-Yun Nie', 'Thibault Formal', 'Thomas Gerald', 'Nam Le Hai'] | 2023-01-11 | null | null | null | null | ['conversational-search'] | ['natural-language-processing'] | [ 2.45476589e-01 -7.08947107e-02 -4.14023072e-01 -3.97406161e-01
-1.48662245e+00 -4.55272019e-01 1.16400945e+00 1.78692460e-01
-6.35086775e-01 7.03485966e-01 6.53916061e-01 -1.31909162e-01
-5.52877426e-01 -4.76872206e-01 -4.55914706e-01 -3.54390472e-01
3.48650776e-02 1.07033134e+00 4.33326930e-01 -5.62683046... | [11.663019180297852, 7.639759540557861] |
75cfe55d-7585-428a-b66f-a298b6eabeb6 | arabic-scene-text-recognition-in-the-deep | null | null | https://ieeexplore.ieee.org/abstract/document/9499028 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9499028 | Arabic Scene Text Recognition in the Deep Learning Era: Analysis on A Novel Dataset | The problem of scene text recognition has recently gained extra attention, being an essential part of scene
understanding systems. The broad scope of applications and the unresolved challenges has given this
problem its popularity. However, the research focus has long been on languages with Latin characters
while le... | ['Mohamed E. Hussein', 'Ahmed El-Mahdy', 'HEBA HASSAN1'] | 2021-07-27 | null | null | null | ieee-access-2021-7 | ['scene-text-recognition'] | ['computer-vision'] | [ 5.00123203e-02 -5.27469933e-01 1.15037430e-02 -3.67533803e-01
-3.26756001e-01 -6.40132964e-01 1.06907821e+00 1.50833070e-01
-6.68199658e-01 3.54250193e-01 2.49896348e-01 -2.92278558e-01
-5.69107905e-02 -8.35668147e-01 -3.56902659e-01 -7.43554533e-01
2.83388376e-01 7.41809428e-01 4.71153148e-02 -5.90206027... | [11.860979080200195, 2.3996262550354004] |
f0c9a112-c715-4385-9e07-fd583c6fe719 | adversarial-counterfactual-visual | 2303.09962 | null | https://arxiv.org/abs/2303.09962v1 | https://arxiv.org/pdf/2303.09962v1.pdf | Adversarial Counterfactual Visual Explanations | Counterfactual explanations and adversarial attacks have a related goal: flipping output labels with minimal perturbations regardless of their characteristics. Yet, adversarial attacks cannot be used directly in a counterfactual explanation perspective, as such perturbations are perceived as noise and not as actionable... | ['Frédéric Jurie', 'Loïc Simon', 'Guillaume Jeanneret'] | 2023-03-17 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Jeanneret_Adversarial_Counterfactual_Visual_Explanations_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Jeanneret_Adversarial_Counterfactual_Visual_Explanations_CVPR_2023_paper.pdf | cvpr-2023-1 | ['counterfactual-explanation'] | ['miscellaneous'] | [ 4.87450778e-01 7.94831336e-01 -2.23437145e-01 1.78223883e-03
-5.62155664e-01 -9.15914953e-01 9.36049044e-01 -1.20239966e-01
-5.24370670e-02 9.07451153e-01 3.33577245e-01 -7.11933732e-01
-3.20245981e-01 -7.90566266e-01 -1.03411734e+00 -6.37529135e-01
-4.00757074e-01 1.04119234e-01 1.64601296e-01 -1.48181424... | [5.760031700134277, 7.7129340171813965] |
4454aff7-b15d-4259-bf69-cefa5f57db13 | treating-dialogue-quality-evaluation-as-an | null | null | https://aclanthology.org/2020.lrec-1.64 | https://aclanthology.org/2020.lrec-1.64.pdf | Treating Dialogue Quality Evaluation as an Anomaly Detection Problem | Dialogue systems for interaction with humans have been enjoying increased popularity in the research and industry fields. To this day, the best way to estimate their success is through means of human evaluation and not automated approaches, despite the abundance of work done in the field. In this paper, we investigate ... | ['Rostislav Nedelchev', 'Ricardo Usbeck', 'Jens Lehmann'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['dialogue-evaluation'] | ['natural-language-processing'] | [-9.89400446e-02 5.91800630e-01 9.45816562e-02 -7.36506462e-01
-1.99760437e-01 -6.53467476e-01 9.92235065e-01 5.79062283e-01
-6.87714279e-01 6.81715131e-01 4.56668913e-01 -3.60473305e-01
2.03814700e-01 -5.36969066e-01 4.66296792e-01 -2.39000946e-01
1.20932095e-01 6.54887378e-01 2.62298703e-01 -6.56996429... | [12.89554214477539, 8.026294708251953] |
67e7b28b-bd71-47f1-8bee-fea42639f62d | the-devil-is-in-the-points-weakly-semi | 2303.15062 | null | https://arxiv.org/abs/2303.15062v1 | https://arxiv.org/pdf/2303.15062v1.pdf | The Devil is in the Points: Weakly Semi-Supervised Instance Segmentation via Point-Guided Mask Representation | In this paper, we introduce a novel learning scheme named weakly semi-supervised instance segmentation (WSSIS) with point labels for budget-efficient and high-performance instance segmentation. Namely, we consider a dataset setting consisting of a few fully-labeled images and a lot of point-labeled images. Motivated by... | ['Sung Ju Hwang', 'Dongyoon Han', 'JoonHyun Jeong', 'Beomyoung Kim'] | 2023-03-27 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Kim_The_Devil_Is_in_the_Points_Weakly_Semi-Supervised_Instance_Segmentation_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Kim_The_Devil_Is_in_the_Points_Weakly_Semi-Supervised_Instance_Segmentation_CVPR_2023_paper.pdf | cvpr-2023-1 | ['semi-supervised-instance-segmentation'] | ['computer-vision'] | [-1.21901196e-03 5.28146803e-01 -6.66918278e-01 -4.88463908e-01
-1.26850843e+00 -3.91734362e-01 2.18510687e-01 -1.28712043e-01
-6.39996886e-01 7.23780036e-01 -3.74066979e-01 -4.65935543e-02
1.10961109e-01 -5.15348375e-01 -8.70164454e-01 -7.55580485e-01
3.87720674e-01 7.70780444e-01 4.91171449e-01 2.10433438... | [9.489522933959961, 0.808192253112793] |
94f4cd23-f972-47a9-8594-a65208c4172d | learning-multi-object-tracking-and | 1912.02096 | null | https://arxiv.org/abs/1912.02096v3 | https://arxiv.org/pdf/1912.02096v3.pdf | Learning Multi-Object Tracking and Segmentation from Automatic Annotations | In this work we contribute a novel pipeline to automatically generate training data, and to improve over state-of-the-art multi-object tracking and segmentation (MOTS) methods. Our proposed track mining algorithm turns raw street-level videos into high-fidelity MOTS training data, is scalable and overcomes the need of ... | ['Samuel Rota Bulò', 'Joan Serrat', 'Peter Kontschieder', 'Lorenzo Porzi', 'Idoia Ruiz', 'Markus Hofinger'] | 2019-12-04 | learning-multi-object-tracking-and-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Porzi_Learning_Multi-Object_Tracking_and_Segmentation_From_Automatic_Annotations_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Porzi_Learning_Multi-Object_Tracking_and_Segmentation_From_Automatic_Annotations_CVPR_2020_paper.pdf | cvpr-2020-6 | ['multi-object-tracking-and-segmentation'] | ['computer-vision'] | [ 3.59865159e-01 5.89830987e-02 -8.30129236e-02 -1.97809190e-01
-9.32138205e-01 -5.16317308e-01 4.65694577e-01 -1.58365950e-01
-5.78776777e-01 1.01382446e+00 -1.98239520e-01 -5.43680862e-02
2.42407426e-01 -6.61947787e-01 -1.10595810e+00 -5.51130235e-01
-2.49296315e-02 9.28971052e-01 1.18857443e+00 -2.57971853... | [6.601358890533447, -1.9277684688568115] |
0c14ac80-8acb-46c2-840c-ea067943518d | experiments-in-language-variety-geolocation | null | null | https://aclanthology.org/2020.vardial-1.21 | https://aclanthology.org/2020.vardial-1.21.pdf | Experiments in Language Variety Geolocation and Dialect Identification | In this paper we describe the systems we used when participating in the VarDial Evaluation Campaign organized as part of the 7th workshop on NLP for similar languages, varieties and dialects. The shared tasks we participated in were the second edition of the Romanian Dialect Identification (RDI) and the first edition o... | ['Krister Lindén', 'Heidi Jauhiainen', 'Tommi Jauhiainen'] | null | null | null | null | vardial-coling-2020-12 | ['dialect-identification'] | ['natural-language-processing'] | [-5.85910439e-01 -2.88421750e-01 -1.31698981e-01 -8.48441839e-01
-8.49213839e-01 -9.92366493e-01 1.05366957e+00 1.12155311e-01
-4.54007328e-01 7.97464669e-01 7.42737353e-01 -4.21814859e-01
1.12971654e-02 -6.64213240e-01 -4.81884144e-02 -2.25308225e-01
1.40749156e-01 1.25699091e+00 7.07033724e-02 -6.43020213... | [10.177964210510254, 10.739730834960938] |
f45019fd-fae7-4d8b-9723-897a993c219c | advances-on-the-classification-of-radio-image | 2305.03435 | null | https://arxiv.org/abs/2305.03435v1 | https://arxiv.org/pdf/2305.03435v1.pdf | Advances on the classification of radio image cubes | Modern radio telescopes will daily generate data sets on the scale of exabytes for systems like the Square Kilometre Array (SKA). Massive data sets are a source of unknown and rare astrophysical phenomena that lead to discoveries. Nonetheless, this is only plausible with the exploitation of intensive machine intelligen... | ['George Azzopardi', 'Dimka Karastoyanova', 'Stefan J. Wijnholds', 'Trienko Grobler', "Steven Ndung'u"] | 2023-05-05 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [ 7.59643614e-02 -3.34764659e-01 2.27405414e-01 -6.08394854e-02
-1.82970941e-01 -7.00261950e-01 7.46772468e-01 7.20003918e-02
-5.60013890e-01 4.70449626e-01 -1.58739492e-01 -6.38754427e-01
-5.61892211e-01 -7.11002111e-01 1.17969796e-01 -8.63868713e-01
-1.40157819e-01 6.98092818e-01 2.27624759e-01 3.46751027... | [7.757811546325684, 3.0829572677612305] |
2faed590-b4e8-4d5f-b26a-cbbc9d5e605b | table-tennis-stroke-recognition-using-two | 2104.09907 | null | https://arxiv.org/abs/2104.09907v2 | https://arxiv.org/pdf/2104.09907v2.pdf | Table Tennis Stroke Recognition Using Two-Dimensional Human Pose Estimation | We introduce a novel method for collecting table tennis video data and perform stroke detection and classification. A diverse dataset containing video data of 11 basic strokes obtained from 14 professional table tennis players, summing up to a total of 22111 videos has been collected using the proposed setup. The tempo... | ['Sucheth Shenoy', 'Kaustubh Milind Kulkarni'] | 2021-04-20 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [ 1.05950728e-01 -1.96671695e-01 -3.57511401e-01 -1.61408305e-01
-3.06774467e-01 -4.04626817e-01 3.56299073e-01 2.14803964e-02
-8.01575363e-01 4.35102224e-01 2.98138320e-01 3.12548578e-01
-3.18728775e-01 -9.13199663e-01 -5.58908820e-01 -5.58438480e-01
-2.18687147e-01 6.43720448e-01 7.33782411e-01 -4.60884154... | [7.729501247406006, 0.16736744344234467] |
653d2e52-ea84-4d38-9afb-3c2f26f10c80 | perceptual-conversational-head-generation | 2206.12837 | null | https://arxiv.org/abs/2206.12837v2 | https://arxiv.org/pdf/2206.12837v2.pdf | Perceptual Conversational Head Generation with Regularized Driver and Enhanced Renderer | This paper reports our solution for ACM Multimedia ViCo 2022 Conversational Head Generation Challenge, which aims to generate vivid face-to-face conversation videos based on audio and reference images. Our solution focuses on training a generalized audio-to-head driver using regularization and assembling a high-visual ... | ['Shuchang Zhou', 'Zhewei Huang', 'Ailin Huang'] | 2022-06-26 | null | null | null | null | ['talking-head-generation'] | ['computer-vision'] | [ 6.28447309e-02 6.12776697e-01 2.99839288e-01 -4.21542257e-01
-1.36043644e+00 -9.60988253e-02 7.59241879e-01 -7.24265873e-01
7.31290877e-02 6.25038743e-01 7.29300916e-01 -2.94670671e-01
4.34150904e-01 -1.65076274e-03 -5.56179225e-01 -5.71519613e-01
1.87494040e-01 -1.19659929e-02 1.11775838e-01 -8.99025872... | [13.244309425354004, -0.42204514145851135] |
dde95574-3305-401f-8ead-0f5e9337a5eb | multimodal-emotion-recognition-with | null | null | https://ieeexplore.ieee.org/document/9206016 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9206016 | Multimodal Emotion Recognition with Transformer-Based Self Supervised Feature Fusion | Emotion Recognition is a challenging research area given its complex nature, and humans express emotional cues across various modalities such as language, facial expressions, and speech. Representation and fusion of features are the most crucial tasks in multimodal emotion recognition research. Self Supervised Learning... | ['Shamane Siriwardhana ; Tharindu Kaluarachchi ; Mark Billinghurst ; Suranga Nanayakkara'] | 2020-10-27 | null | null | null | null | ['multimodal-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech'] | [ 3.54688287e-01 -4.29951876e-01 -2.93796837e-01 -6.48192108e-01
-9.12190795e-01 -3.47236037e-01 7.39303648e-01 3.68095994e-01
-4.21966791e-01 3.73220205e-01 5.46377838e-01 3.09695542e-01
3.62153328e-03 -1.78430915e-01 -2.60852844e-01 -5.03341556e-01
9.72825289e-03 -1.09879822e-01 -4.30654556e-01 -2.92490661... | [13.256271362304688, 5.223870754241943] |
d4aa1693-80ff-4d32-88f3-f43a9504e33c | fuzzy-c-means-clustering-and-sonification-of | 1908.07107 | null | https://arxiv.org/abs/1908.07107v2 | https://arxiv.org/pdf/1908.07107v2.pdf | Fuzzy C-Means Clustering and Sonification of HRV Features | Linear and non-linear measures of heart rate variability (HRV) are widely investigated as non-invasive indicators of health. Stress has a profound impact on heart rate, and different meditation techniques have been found to modulate heartbeat rhythm. This paper aims to explore the process of identifying appropriate met... | ['Paul Batchelor', 'Victoria Grace', 'Kunal Mankodiya', 'Harishchandra Dubey', 'Debanjan Borthakur'] | 2019-08-19 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 3.06462318e-01 -1.54579610e-01 1.34424064e-02 -4.76773351e-01
-1.65375054e-01 -2.49634370e-01 -1.17551744e-01 3.35744739e-01
-1.96983516e-01 4.98249769e-01 7.43777633e-01 -1.58670098e-01
-3.10515702e-01 -5.49330294e-01 1.93302125e-01 -6.12447500e-01
-3.06105793e-01 1.67525690e-02 -7.17577040e-01 -2.95249760... | [13.768217086791992, 3.1295039653778076] |
6cb5d306-9a97-4640-b05a-976feb3466e4 | simbow-at-semeval-2017-task-3-soft-cosine | null | null | https://aclanthology.org/S17-2051 | https://aclanthology.org/S17-2051.pdf | SimBow at SemEval-2017 Task 3: Soft-Cosine Semantic Similarity between Questions for Community Question Answering | This paper describes the SimBow system submitted at SemEval2017-Task3, for the question-question similarity subtask B. The proposed approach is a supervised combination of different unsupervised textual similarities. These textual similarities rely on the introduction of a relation matrix in the classical cosine simila... | ["G{\\'e}raldine Damnati", 'Delphine Charlet'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['question-similarity'] | ['natural-language-processing'] | [ 1.26671091e-01 3.42429131e-02 2.29487151e-01 -5.26596665e-01
-4.87106651e-01 -4.76976603e-01 1.10584986e+00 1.08858645e+00
-8.70330036e-01 1.54661477e-01 5.64058483e-01 -2.72550792e-01
-6.18140519e-01 -5.84562898e-01 -2.77125090e-01 -2.43588567e-01
4.11989480e-01 5.50131321e-01 6.94104731e-01 -8.50375891... | [10.829322814941406, 9.068017959594727] |
598fcaaf-80fe-42bd-946c-27fb0e54c149 | few-shot-open-set-recognition-using | 2207.09059 | null | https://arxiv.org/abs/2207.09059v1 | https://arxiv.org/pdf/2207.09059v1.pdf | Few-shot Open-set Recognition Using Background as Unknowns | Few-shot open-set recognition aims to classify both seen and novel images given only limited training data of seen classes. The challenge of this task is that the model is required not only to learn a discriminative classifier to classify the pre-defined classes with few training data but also to reject inputs from uns... | ['Guosheng Lin', 'Chi Zhang', 'Nan Song'] | 2022-07-19 | null | null | null | null | ['open-set-learning'] | ['miscellaneous'] | [ 5.94326437e-01 -3.48453484e-02 -1.81147754e-01 -5.77868104e-01
-5.68211198e-01 -5.01500010e-01 5.25686383e-01 2.14437917e-01
-5.58358908e-01 6.48440003e-01 -2.31962904e-01 -1.67229362e-02
1.32689342e-01 -7.66495824e-01 -7.87811399e-01 -8.65894377e-01
8.19889084e-02 4.08695966e-01 6.95028663e-01 -2.64656305... | [9.699252128601074, 2.2653164863586426] |
65655ab3-bc52-4811-bc2f-26e6d13791c4 | finding-short-signals-in-long-irregular-time | 2302.04052 | null | https://arxiv.org/abs/2302.04052v1 | https://arxiv.org/pdf/2302.04052v1.pdf | Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networks | Irregularly-sampled time series (ITS) are native to high-impact domains like healthcare, where measurements are collected over time at uneven intervals. However, for many classification problems, only small portions of long time series are often relevant to the class label. In this case, existing ITS models often fail ... | ['Elke Rundensteiner', 'Xiangnan Kong', 'Jidapa Thadajarassiri', 'Thomas Hartvigsen'] | 2023-02-08 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [ 3.00555021e-01 -1.32317439e-01 -6.24721825e-01 -3.79836977e-01
-1.04397357e+00 -5.14614701e-01 5.51100314e-01 5.39129138e-01
-4.03333530e-02 7.30211079e-01 3.77399445e-01 -2.69683987e-01
-6.38040304e-01 -6.79687321e-01 -7.97529638e-01 -5.87231517e-01
-6.29549146e-01 4.55670983e-01 -1.63070604e-01 -8.60283300... | [7.164063930511475, 3.1587002277374268] |
aafdfceb-1a58-4001-9ee8-1fc04a1f6760 | joint-brain-tumor-segmentation-from-multi-mr | 2203.03338 | null | https://arxiv.org/abs/2203.03338v1 | https://arxiv.org/pdf/2203.03338v1.pdf | Joint brain tumor segmentation from multi MR sequences through a deep convolutional neural network | Brain tumor segmentation is highly contributive in diagnosing and treatment planning. The manual brain tumor delineation is a time-consuming and tedious task and varies depending on the radiologists skill. Automated brain tumor segmentation is of high importance, and does not depend on either inter or intra-observation... | ['Hossein Arabi', 'Alireza Karimian', 'Farzaneh Dehghani'] | 2022-03-07 | null | null | null | null | ['brain-tumor-segmentation'] | ['medical'] | [ 2.15788528e-01 -2.09599093e-01 2.82906163e-02 -3.28923047e-01
-5.97278535e-01 -4.29121196e-01 3.16584527e-01 2.79284030e-01
-8.65547061e-01 6.67364776e-01 -1.56375572e-01 -5.27039289e-01
-3.70039523e-01 -5.86446583e-01 1.03449926e-01 -1.01738620e+00
5.57389371e-02 8.34698915e-01 3.42795074e-01 1.13054365... | [14.694470405578613, -2.5153491497039795] |
57987970-48d7-4877-b26b-961b9b09dc41 | more-comprehensive-facial-inversion-for-more | 2211.13564 | null | https://arxiv.org/abs/2211.13564v2 | https://arxiv.org/pdf/2211.13564v2.pdf | More comprehensive facial inversion for more effective expression recognition | Facial expression recognition (FER) plays a significant role in the ubiquitous application of computer vision. We revisit this problem with a new perspective on whether it can acquire useful representations that improve FER performance in the image generation process, and propose a novel generative method based on the ... | ['Rui Xu', 'Xiaogang Peng', 'Xuesong Yin', 'Yuanqi Chang', 'Guangyi Zhao', 'Jiawei Mao'] | 2022-11-24 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 4.72992957e-01 4.93172742e-02 1.78612351e-01 -6.72692180e-01
-7.67434478e-01 -2.97858149e-01 6.11349881e-01 -9.10944521e-01
-1.33910060e-01 5.81719816e-01 -1.00938613e-02 1.91266447e-01
8.59032050e-02 -7.21936405e-01 -8.12545061e-01 -8.82675588e-01
2.31543303e-01 1.37159675e-01 -6.47990525e-01 -6.88379288... | [13.0607271194458, 0.5669262409210205] |
aa1a477c-ba1d-4af1-8d57-d1760f82258d | 3c-net-category-count-and-center-loss-for | 1908.08216 | null | https://arxiv.org/abs/1908.08216v2 | https://arxiv.org/pdf/1908.08216v2.pdf | 3C-Net: Category Count and Center Loss for Weakly-Supervised Action Localization | Temporal action localization is a challenging computer vision problem with numerous real-world applications. Most existing methods require laborious frame-level supervision to train action localization models. In this work, we propose a framework, called 3C-Net, which only requires video-level supervision (weak supervi... | ['Hisham Cholakkal', 'Fahad Shahbaz Khan', 'Sanath Narayan', 'Ling Shao'] | 2019-08-22 | 3c-net-category-count-and-center-loss-for-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Narayan_3C-Net_Category_Count_and_Center_Loss_for_Weakly-Supervised_Action_Localization_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Narayan_3C-Net_Category_Count_and_Center_Loss_for_Weakly-Supervised_Action_Localization_ICCV_2019_paper.pdf | iccv-2019-10 | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action'] | ['computer-vision', 'computer-vision'] | [ 4.45984393e-01 -3.93798321e-01 -7.60215580e-01 -2.52751201e-01
-1.08736241e+00 -2.41777748e-01 6.18537247e-01 -1.98554710e-01
-6.64438844e-01 6.33075476e-01 3.30063164e-01 2.43518576e-01
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-9.14548263e-02 -5.07247262e-03 7.82935202e-01 1.67918891... | [8.427306175231934, 0.6278786063194275] |
2e237e89-bb30-44ba-97c8-9196f1ab4aa1 | inferring-causal-effects-under-heterogeneous | 2305.17479 | null | https://arxiv.org/abs/2305.17479v1 | https://arxiv.org/pdf/2305.17479v1.pdf | Inferring Causal Effects Under Heterogeneous Peer Influence | Causal inference in networks should account for interference, which occurs when a unit's outcome is influenced by treatments or outcomes of peers. There can be heterogeneous peer influence between units when a unit's outcome is subjected to variable influence from different peers based on their attributes and relations... | ['Elena Zheleva', 'Shishir Adhikari'] | 2023-05-27 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 4.45093215e-01 3.73483628e-01 -6.06270194e-01 -2.37318471e-01
1.76706329e-01 -3.57376277e-01 6.75304055e-01 2.64802486e-01
1.59931719e-01 1.13788533e+00 7.02235699e-01 -3.37523669e-01
-8.02323043e-01 -1.49118876e+00 -1.06460440e+00 -5.16552508e-01
-6.87538981e-01 6.35930777e-01 1.55297622e-01 1.47501871... | [7.813045501708984, 5.330628395080566] |
e7e1818a-32bc-42cd-9dcb-bd99fabdd945 | two-stream-transformer-for-multi-label-image | null | null | https://dl.acm.org/doi/abs/10.1145/3503161.3548343 | https://dl.acm.org/doi/abs/10.1145/3503161.3548343 | Two-Stream Transformer for Multi-Label Image Classification | Multi-label image classification is a fundamental yet challenging task in computer vision that aims to identify multiple objects from a given image. Recent studies on this task mainly focus on learning cross-modal interactions between label semantics and high-level visual representations via an attention operation. How... | ['Bo Liu', 'Weijia Liu', 'Jiawei Ge', 'Jiuxin Cao', 'Xuelin Zhu'] | 2022-10-01 | null | null | null | acmmm-2022-10 | ['multi-label-image-classification'] | ['computer-vision'] | [ 5.83624482e-01 -4.11078244e-01 -1.91540554e-01 -4.36265737e-01
-8.37319791e-01 -3.33908319e-01 7.73866713e-01 4.32661980e-01
-3.23717594e-01 2.11378232e-01 -1.39491245e-01 6.53434843e-02
5.19092418e-02 -4.23246294e-01 -6.06028318e-01 -8.61680388e-01
6.58390880e-01 3.16578150e-01 3.11843812e-01 8.98262039... | [9.80118465423584, 3.873957395553589] |
d3f099af-79c3-48bb-8f90-cdfacca7a7f4 | non-invasive-urinary-bladder-volume | 2303.14028 | null | https://arxiv.org/abs/2303.14028v1 | https://arxiv.org/pdf/2303.14028v1.pdf | Non-invasive urinary bladder volume estimation with artefact-suppressed bio-impedance measurements | Urine output is a vital parameter to gauge kidney health. Current monitoring methods include manually written records, invasive urinary catheterization or ultrasound measurements performed by highly skilled personnel. Catheterization bears high risks of infection while intermittent ultrasound measures and manual record... | ['Michele Magno', 'Simone Schürle', 'Hugo Sax', 'Thomas Hermanns', 'Denise Franke', 'Marko Kozomara', 'Manuel Eggimann', 'Philipp Mayer', 'Stefan Walser', 'Kanika Dheman'] | 2023-03-24 | null | null | null | null | ['kidney-function'] | ['medical'] | [ 2.60632783e-01 5.52751757e-02 -2.47595042e-01 -9.55777019e-02
-4.36588466e-01 -6.90757632e-01 -2.71778494e-01 6.61915004e-01
-7.03385234e-01 9.53004241e-01 -1.82112262e-01 -4.01469231e-01
-4.82423939e-02 -6.61152661e-01 -5.89800239e-01 -5.21866977e-01
-5.89473009e-01 4.26974535e-01 -4.04625028e-01 3.10725629... | [14.051748275756836, 2.939237594604492] |
9b6d5dac-ec94-4787-a8a0-8866623f1c9d | learning-span-level-interactions-for-aspect | 2107.12214 | null | https://arxiv.org/abs/2107.12214v1 | https://arxiv.org/pdf/2107.12214v1.pdf | Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction | Aspect Sentiment Triplet Extraction (ASTE) is the most recent subtask of ABSA which outputs triplets of an aspect target, its associated sentiment, and the corresponding opinion term. Recent models perform the triplet extraction in an end-to-end manner but heavily rely on the interactions between each target word and o... | ['Lidong Bing', 'Yew Ken Chia', 'Lu Xu'] | 2021-07-26 | null | https://aclanthology.org/2021.acl-long.367 | https://aclanthology.org/2021.acl-long.367.pdf | acl-2021-5 | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [ 1.18577808e-01 -2.16144651e-01 -3.99457604e-01 -5.10439634e-01
-1.03085291e+00 -8.01563323e-01 5.27737916e-01 3.87061834e-01
-2.05710605e-01 4.26854879e-01 3.70113552e-01 -3.41099203e-01
2.56383598e-01 -8.13027442e-01 -3.98326218e-01 -5.40905595e-01
4.03926462e-01 2.96182841e-01 1.43969059e-01 -3.71270418... | [11.504034996032715, 6.642407417297363] |
3ff4b77e-550e-4787-9a08-a1a81937b91c | scalable-statistical-inference-of-photometric | 2103.16041 | null | https://arxiv.org/abs/2103.16041v2 | https://arxiv.org/pdf/2103.16041v2.pdf | Scalable Statistical Inference of Photometric Redshift via Data Subsampling | Handling big data has largely been a major bottleneck in traditional statistical models. Consequently, when accurate point prediction is the primary target, machine learning models are often preferred over their statistical counterparts for bigger problems. But full probabilistic statistical models often outperform oth... | ['Jonas Chaves-Montero', 'Stefan M. Wild', 'Arindam Fadikar'] | 2021-03-30 | null | null | null | null | ['photometric-redshift-estimation'] | ['miscellaneous'] | [-9.16051120e-02 1.41641587e-01 4.13804084e-01 -5.83709776e-01
-8.93677473e-01 -4.13124979e-01 7.82274127e-01 6.60467446e-02
-9.74651650e-02 6.92892611e-01 3.24960463e-02 -2.78490931e-01
-5.52795768e-01 -9.27430272e-01 -6.45513177e-01 -1.14393055e+00
3.58726382e-01 1.25858223e+00 4.48181331e-01 2.53920197... | [7.2766900062561035, 3.4564156532287598] |
523f3171-612c-44b1-b247-e8428e4a9d31 | k-order-graph-oriented-transformer-with | 2208.11328 | null | https://arxiv.org/abs/2208.11328v2 | https://arxiv.org/pdf/2208.11328v2.pdf | K-Order Graph-oriented Transformer with GraAttention for 3D Pose and Shape Estimation | We propose a novel attention-based 2D-to-3D pose estimation network for graph-structured data, named KOG-Transformer, and a 3D pose-to-shape estimation network for hand data, named GASE-Net. Previous 3D pose estimation methods have focused on various modifications to the graph convolution kernel, such as abandoning wei... | ['Weiqiang Wang', 'Weixi Zhao'] | 2022-08-24 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [-3.86318356e-01 2.22050488e-01 -3.62214446e-02 -3.27400535e-01
-4.13069606e-01 -3.16640645e-01 7.77566284e-02 -4.17932957e-01
-5.47973774e-02 3.34784418e-01 4.42799419e-01 -7.47402906e-02
-1.42775506e-01 -8.08725119e-01 -8.87327373e-01 -4.38142180e-01
-1.06485575e-01 1.02923775e+00 2.48591140e-01 -3.32313389... | [6.9038405418396, -0.6706165671348572] |
66ea3fd6-d99a-436e-bebf-e0880620a226 | cryptocurrency-price-prediction-using-twitter | 2303.09397 | null | https://arxiv.org/abs/2303.09397v1 | https://arxiv.org/pdf/2303.09397v1.pdf | Cryptocurrency Price Prediction using Twitter Sentiment Analysis | The cryptocurrency ecosystem has been the centre of discussion on many social media platforms, following its noted volatility and varied opinions. Twitter is rapidly being utilised as a news source and a medium for bitcoin discussion. Our algorithm seeks to use historical prices and sentiment of tweets to forecast the ... | ['Sahana N. B', 'Haritha GB'] | 2023-03-03 | null | null | null | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [-6.26667678e-01 -8.99664685e-02 -2.75099605e-01 -2.55477190e-01
-6.29008830e-01 -7.39583373e-01 9.77560878e-01 1.67224810e-01
-4.03762728e-01 6.82017505e-01 4.74961340e-01 -4.73947674e-01
5.37340224e-01 -9.65369225e-01 -5.12017846e-01 -4.31187391e-01
-3.72686833e-01 2.57250726e-01 -2.02877000e-01 -5.79279721... | [4.4959917068481445, 4.204336643218994] |
0bfcbcb7-344c-4fa0-ab89-3be8ecbe344a | directed-acyclic-graphs-with-tears | 2302.02160 | null | https://arxiv.org/abs/2302.02160v1 | https://arxiv.org/pdf/2302.02160v1.pdf | Directed Acyclic Graphs With Tears | Bayesian network is a frequently-used method for fault detection and diagnosis in industrial processes. The basis of Bayesian network is structure learning which learns a directed acyclic graph (DAG) from data. However, the search space will scale super-exponentially with the increase of process variables, which makes ... | ['Zhiqiang Ge', 'Zhichao Chen'] | 2023-02-04 | null | null | null | null | ['fault-detection'] | ['miscellaneous'] | [ 1.56682611e-01 1.97043255e-01 -7.83053134e-03 -2.70670295e-01
-2.89646447e-01 -1.23671301e-01 1.62248716e-01 1.67538851e-01
2.61232674e-01 7.22881675e-01 -2.08506480e-01 -3.95065546e-01
-7.86933005e-01 -8.09682429e-01 -7.37386107e-01 -1.10147572e+00
-3.00193131e-01 4.00663823e-01 4.94149514e-03 1.42463654... | [6.9534149169921875, 2.5327298641204834] |
389f0a5d-8256-4007-b8ba-91e89aa61693 | protecting-global-properties-of-datasets-with | 2207.08367 | null | https://arxiv.org/abs/2207.08367v2 | https://arxiv.org/pdf/2207.08367v2.pdf | Protecting Global Properties of Datasets with Distribution Privacy Mechanisms | We consider the problem of ensuring confidentiality of dataset properties aggregated over many records of a dataset. Such properties can encode sensitive information, such as trade secrets or demographic data, while involving a notion of data protection different to the privacy of individual records typically discussed... | ['Olga Ohrimenko', 'Michelle Chen'] | 2022-07-18 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [ 5.50665855e-01 2.68394500e-01 -2.48187572e-01 -7.17898428e-01
-9.79088545e-01 -1.35464716e+00 3.72355580e-01 6.29717827e-01
-4.68352020e-01 7.26334691e-01 3.82365614e-01 -5.50302446e-01
-4.92474556e-01 -1.03026152e+00 -7.87582219e-01 -1.00944650e+00
-4.83320445e-01 1.35629848e-01 2.21415423e-02 2.18873098... | [5.9926838874816895, 6.913095951080322] |
664f7c57-e48f-43f3-b2cf-20e1e6aa5957 | multi-adversarial-discriminative-deep-domain | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Shao_Multi-Adversarial_Discriminative_Deep_Domain_Generalization_for_Face_Presentation_Attack_Detection_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Shao_Multi-Adversarial_Discriminative_Deep_Domain_Generalization_for_Face_Presentation_Attack_Detection_CVPR_2019_paper.pdf | Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection | Face presentation attacks have become an increasingly critical issue in the face recognition community. Many face anti-spoofing methods have been proposed, but they cannot generalize well on "unseen" attacks. This work focuses on improving the generalization ability of face anti-spoofing methods from the perspective of... | [' Pong C. Yuen', ' Jiawei Li', ' Xiangyuan Lan', 'Rui Shao'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['face-presentation-attack-detection'] | ['computer-vision'] | [ 2.88946778e-01 -3.23356390e-01 -1.19540937e-01 -5.04746258e-01
-3.85594487e-01 -6.31548166e-01 5.96024871e-01 -4.18011904e-01
9.59157944e-02 7.20542192e-01 1.53691638e-02 -5.31572402e-02
-2.58126706e-01 -8.17248166e-01 -5.26475549e-01 -1.05814719e+00
1.21445432e-01 1.31885499e-01 6.88987449e-02 -4.56285506... | [13.07298469543457, 1.1759974956512451] |
e4f0ae14-b528-4252-a3ea-ae1926898a8f | speech-emotion-recognition-based-on-self | null | null | https://ieeexplore.ieee.org/document/9936641 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9936641 | Speech Emotion Recognition Based on Self-Attention Weight Correction for Acoustic and Text Features | Speech emotion recognition (SER) is essential for understanding a speaker’s intention. Recently, some groups have attempted to improve SER performance using a bidirectional long short-term memory (BLSTM) to extract features from speech sequences and a self-attention mechanism to focus on the important parts of the spee... | ['Shoji Makino', 'Taiichi Hashimoto', 'Kenkichi Ishizuka', 'Takeshi Yamada', 'JENNIFER SANTOSO'] | 2022-11-08 | null | null | null | ieee-access-2022-11 | ['multimodal-emotion-recognition', 'speech-emotion-recognition', 'multimodal-emotion-recognition'] | ['computer-vision', 'speech', 'speech'] | [ 1.94398552e-01 -3.64920199e-02 2.29972467e-01 -2.36365005e-01
-7.53824651e-01 3.72473411e-02 1.72207400e-01 2.16944925e-02
-7.70397186e-01 2.48279080e-01 4.20299828e-01 -1.25199273e-01
3.53110105e-01 -3.02026033e-01 -4.37168688e-01 -7.74857283e-01
1.71207517e-01 -1.46786362e-01 3.16387236e-01 -3.77097607... | [13.844273567199707, 5.866323471069336] |
032b5cbc-3426-4cf1-af5e-ed433b8a589d | examining-the-state-of-the-art-in-news | 2005.10107 | null | https://arxiv.org/abs/2005.10107v1 | https://arxiv.org/pdf/2005.10107v1.pdf | Examining the State-of-the-Art in News Timeline Summarization | Previous work on automatic news timeline summarization (TLS) leaves an unclear picture about how this task can generally be approached and how well it is currently solved. This is mostly due to the focus on individual subtasks, such as date selection and date summarization, and to the previous lack of appropriate evalu... | ['Georgiana Ifrim', 'Demian Gholipour Ghalandari'] | 2020-05-20 | examining-the-state-of-the-art-in-news-1 | https://aclanthology.org/2020.acl-main.122 | https://aclanthology.org/2020.acl-main.122.pdf | acl-2020-6 | ['timeline-summarization'] | ['natural-language-processing'] | [ 1.43380880e-01 -5.78435436e-02 -4.00377542e-01 -3.86857301e-01
-9.97217417e-01 -6.81381345e-01 9.91949379e-01 6.47594988e-01
-4.58318442e-01 1.07038057e+00 9.78087723e-01 -1.99959017e-02
-2.64223635e-01 -3.75895590e-01 -5.70450604e-01 -1.43859312e-01
-1.64722666e-01 6.80914462e-01 4.95544434e-01 -4.23397362... | [12.489384651184082, 9.474296569824219] |
46137501-86bf-42f1-b4b1-fda658b68dd7 | a-closer-look-at-geometric-temporal-dynamics | 2306.14313 | null | https://arxiv.org/abs/2306.14313v1 | https://arxiv.org/pdf/2306.14313v1.pdf | A Closer Look at Geometric Temporal Dynamics for Face Anti-Spoofing | Face anti-spoofing (FAS) is indispensable for a face recognition system. Many texture-driven countermeasures were developed against presentation attacks (PAs), but the performance against unseen domains or unseen spoofing types is still unsatisfactory. Instead of exhaustively collecting all the spoofing variations and ... | ['Trista Pei-Chun Chen', 'Shang-Hong Lai', 'Chien-Yi Wang', 'Min-Hung Chen', 'Shih-Hsuan Yao', 'Yaw-Chern Lee', 'Chih-Jung Chang'] | 2023-06-25 | null | null | null | null | ['face-recognition', 'face-anti-spoofing'] | ['computer-vision', 'computer-vision'] | [ 2.79958755e-01 -1.65934473e-01 -2.69124687e-01 -2.68177420e-01
-5.81839263e-01 -6.65849924e-01 8.15858543e-01 -2.35345647e-01
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-1.05219848e-01 -6.79532051e-01 -7.68375337e-01 -7.50053763e-01
-5.19884169e-01 1.06724352e-01 5.13499320e-01 -4.32590872... | [13.056963920593262, 1.2028383016586304] |
2b4e6378-7e09-442e-ac9f-49a6708ba8d4 | graph-generation-with-variational-recurrent | 1910.01743 | null | https://arxiv.org/abs/1910.01743v1 | https://arxiv.org/pdf/1910.01743v1.pdf | Graph Generation with Variational Recurrent Neural Network | Generating graph structures is a challenging problem due to the diverse representations and complex dependencies among nodes. In this paper, we introduce Graph Variational Recurrent Neural Network (GraphVRNN), a probabilistic autoregressive model for graph generation. Through modeling the latent variables of graph data... | ['Shih-Yang Su', 'Hossein Hajimirsadeghi', 'Greg Mori'] | 2019-10-02 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [-8.31504688e-02 7.52409995e-01 -1.27071947e-01 -1.88595399e-01
-3.64554524e-01 -3.48818392e-01 5.88523686e-01 -1.69547737e-01
5.60035646e-01 7.89624333e-01 5.89488328e-01 -2.92735696e-01
7.25435987e-02 -1.13644707e+00 -5.77827871e-01 -6.34524226e-01
-4.45547253e-02 7.07031071e-01 -1.67725027e-01 -1.51830807... | [7.210404872894287, 6.170869827270508] |
e7d9f039-a36c-4af5-9ca1-9b3a42e37190 | multimodal-classification-for-analysing | 1708.02099 | null | http://arxiv.org/abs/1708.02099v1 | http://arxiv.org/pdf/1708.02099v1.pdf | Multimodal Classification for Analysing Social Media | Classification of social media data is an important approach in understanding
user behavior on the Web. Although information on social media can be of
different modalities such as texts, images, audio or videos, traditional
approaches in classification usually leverage only one prominent modality.
Techniques that are a... | ['Remi Lebret', 'Karl Aberer', 'Chi Thang Duong'] | 2017-08-07 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 8.67436454e-02 -3.87121230e-01 -3.05964172e-01 -5.80472171e-01
-9.05834556e-01 -5.88034868e-01 8.20277274e-01 4.95100975e-01
-5.09626389e-01 5.68178058e-01 2.01383099e-01 1.63701043e-01
4.19569500e-02 -7.24280894e-01 -3.75020921e-01 -5.04470050e-01
2.15148196e-01 -1.78094178e-01 3.21916610e-01 -2.24846140... | [13.214615821838379, 5.170910358428955] |
1f829267-17ed-4f01-a03c-baa89f506cd1 | point-e-a-system-for-generating-3d-point | 2212.08751 | null | https://arxiv.org/abs/2212.08751v1 | https://arxiv.org/pdf/2212.08751v1.pdf | Point-E: A System for Generating 3D Point Clouds from Complex Prompts | While recent work on text-conditional 3D object generation has shown promising results, the state-of-the-art methods typically require multiple GPU-hours to produce a single sample. This is in stark contrast to state-of-the-art generative image models, which produce samples in a number of seconds or minutes. In this pa... | ['Mark Chen', 'Pamela Mishkin', 'Prafulla Dhariwal', 'Heewoo Jun', 'Alex Nichol'] | 2022-12-16 | null | null | null | null | ['generating-3d-point-clouds'] | ['computer-vision'] | [ 1.22797422e-01 2.28882089e-01 3.29183072e-01 -6.26351461e-02
-1.04360425e+00 -7.90391505e-01 1.03222287e+00 -3.30878198e-02
7.54920840e-02 4.57001716e-01 -2.06743300e-01 -3.77539337e-01
5.02358496e-01 -1.04603553e+00 -8.13057840e-01 -4.88217860e-01
1.29876539e-01 1.06051874e+00 5.15309930e-01 9.58993658... | [8.988665580749512, -3.6043713092803955] |
09c133b6-a3f0-49fa-9e36-3d8f3c0a6877 | speaker-profiling-in-multiparty-conversations | 2304.08801 | null | https://arxiv.org/abs/2304.08801v2 | https://arxiv.org/pdf/2304.08801v2.pdf | Speaker Profiling in Multiparty Conversations | In conversational settings, individuals exhibit unique behaviors, rendering a one-size-fits-all approach insufficient for generating responses by dialogue agents. Although past studies have aimed to create personalized dialogue agents using speaker persona information, they have relied on the assumption that the speake... | ['Tanmoy Chakraborty', 'Md Shad Akhtar', 'Rishabh Gupta', 'Shivani Kumar'] | 2023-04-18 | null | null | null | null | ['speaker-profiling'] | ['speech'] | [ 1.64069593e-01 4.51525420e-01 8.21624994e-02 -7.85153389e-01
-7.59873986e-01 -6.09208882e-01 8.95903230e-01 6.22183457e-02
-2.81212091e-01 7.79713094e-01 6.14208221e-01 -6.47807866e-02
6.62121698e-02 -5.62758327e-01 1.53509562e-03 -5.35471559e-01
2.02926993e-01 8.56647968e-01 -1.02463029e-01 -3.32991362... | [12.763581275939941, 7.919999599456787] |
e4ed8dcb-c21b-45b3-91b7-5a1ce0c2bb7f | training-free-neural-matte-extraction-for | 2306.17321 | null | https://arxiv.org/abs/2306.17321v1 | https://arxiv.org/pdf/2306.17321v1.pdf | Training-Free Neural Matte Extraction for Visual Effects | Alpha matting is widely used in video conferencing as well as in movies, television, and social media sites. Deep learning approaches to the matte extraction problem are well suited to video conferencing due to the consistent subject matter (front-facing humans), however training-based approaches are somewhat pointless... | ['Christoph Bregler', 'Nori Kanazawa', 'J. P. Lewis', 'Sharif Elcott'] | 2023-06-29 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 3.49804014e-01 -3.76126021e-02 1.68300644e-01 -4.13878471e-01
-5.77541649e-01 -3.48828167e-01 7.92539775e-01 -3.00123662e-01
-2.10384995e-01 7.61656344e-01 1.72516018e-01 -1.57413825e-01
-2.15350129e-02 -6.55969441e-01 -1.04722369e+00 -6.29752278e-01
1.17540490e-02 3.42051536e-01 -4.24287207e-02 -2.43203923... | [10.713696479797363, -0.9633006453514099] |
89f9318e-f945-4ed3-8b64-23bc80feb4c4 | bounded-future-ms-tcn-for-surgical-gesture | 2209.14647 | null | https://arxiv.org/abs/2209.14647v2 | https://arxiv.org/pdf/2209.14647v2.pdf | Bounded Future MS-TCN++ for surgical gesture recognition | In recent times there is a growing development of video based applications for surgical purposes. Part of these applications can work offline after the end of the procedure, other applications must react immediately. However, there are cases where the response should be done during the procedure but some delay is accep... | ['Shlomi Laufer', 'Carla M. Pugh', 'Netanell Avisdris', 'Adam Goldbraikh'] | 2022-09-29 | null | null | null | null | ['gesture-recognition', 'surgical-gesture-recognition'] | ['computer-vision', 'medical'] | [ 3.46425891e-01 1.96031749e-01 1.58773083e-02 -9.53201354e-02
-2.02208519e-01 -5.22854805e-01 8.01528543e-02 2.58685481e-02
-9.58327532e-01 4.27501440e-01 -8.08930919e-02 -3.34147602e-01
-2.90550411e-01 -5.68987072e-01 -6.04278922e-01 -7.49839902e-01
-3.51604104e-01 4.66127098e-02 4.49487537e-01 -4.68491800... | [14.011482238769531, -3.301952600479126] |
2fa3aa5a-1afa-4f84-b48c-e9fe72cd89ab | dmsa-dynamic-multi-scale-unsupervised | 2303.00199 | null | https://arxiv.org/abs/2303.00199v1 | https://arxiv.org/pdf/2303.00199v1.pdf | DMSA: Dynamic Multi-scale Unsupervised Semantic Segmentation Based on Adaptive Affinity | The proposed method in this paper proposes an end-to-end unsupervised semantic segmentation architecture DMSA based on four loss functions. The framework uses Atrous Spatial Pyramid Pooling (ASPP) module to enhance feature extraction. At the same time, a dynamic dilation strategy is designed to better capture multi-sca... | ['Jun Lu', 'Kun Yang'] | 2023-03-01 | null | null | null | null | ['unsupervised-semantic-segmentation'] | ['computer-vision'] | [ 2.60588944e-01 4.88298051e-02 4.61908542e-02 -4.23582137e-01
-7.17972696e-01 -1.44480824e-01 3.21023881e-01 2.31498331e-02
-8.54177773e-01 5.08573532e-01 -1.50622606e-01 3.48579526e-01
-1.09607456e-02 -6.70797229e-01 -4.89382327e-01 -9.43156481e-01
4.12819028e-01 -1.39689401e-01 9.36688364e-01 -5.17432503... | [9.581668853759766, -0.3153679370880127] |
2cec829f-dda7-4b7b-b04f-339ac136cc52 | multi-field-de-interlacing-using-deformable | 2209.10192 | null | https://arxiv.org/abs/2209.10192v1 | https://arxiv.org/pdf/2209.10192v1.pdf | Multi-Field De-interlacing using Deformable Convolution Residual Blocks and Self-Attention | Although deep learning has made significant impact on image/video restoration and super-resolution, learned deinterlacing has so far received less attention in academia or industry. This is despite deinterlacing is well-suited for supervised learning from synthetic data since the degradation model is known and fixed. I... | ['A. Murat Tekalp', 'Ronglei Ji'] | 2022-09-21 | null | null | null | null | ['video-restoration'] | ['computer-vision'] | [ 4.91677284e-01 -4.04649645e-01 -1.91670999e-01 -2.70662636e-01
-8.80349934e-01 -1.72480717e-01 4.80127245e-01 -3.59842569e-01
-2.59091854e-01 8.68344724e-01 4.25122082e-01 1.66041926e-01
-1.75943777e-01 -5.98249674e-01 -1.02193820e+00 -7.32018709e-01
-8.25960711e-02 -9.59553123e-02 3.31690133e-01 -4.89477575... | [11.106802940368652, -2.0491106510162354] |
b2ddb24e-497b-423f-8ab9-5192517cc75a | artifact-a-large-scale-dataset-with | 2302.11970 | null | https://arxiv.org/abs/2302.11970v2 | https://arxiv.org/pdf/2302.11970v2.pdf | ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image Detection | Synthetic image generation has opened up new opportunities but has also created threats in regard to privacy, authenticity, and security. Detecting fake images is of paramount importance to prevent illegal activities, and previous research has shown that generative models leave unique patterns in their synthetic images... | ['Shaikh Anowarul Fattah', 'Zaber Ibn Abdul Hakim', 'Najibul Haque Sarker', 'Bishmoy Paul', 'Md Awsafur Rahman'] | 2023-02-23 | null | null | null | null | ['synthetic-image-detection', 'synthetic-image-attribution'] | ['computer-vision', 'computer-vision'] | [ 4.95668292e-01 3.51827666e-02 3.18266332e-01 2.85235904e-02
-7.60836542e-01 -7.55118608e-01 6.64751887e-01 -1.37767255e-01
-2.13824302e-01 8.45499039e-01 -1.68568343e-01 5.65447062e-02
8.91163647e-02 -5.31572282e-01 -7.44835734e-01 -5.54630041e-01
-8.36798772e-02 7.65615776e-02 2.41843328e-01 -3.31524879... | [12.44970417022705, 1.0782544612884521] |
53606d14-50b8-4189-8476-33604134933c | sekd-self-evolving-keypoint-detection-and | 2006.05077 | null | https://arxiv.org/abs/2006.05077v1 | https://arxiv.org/pdf/2006.05077v1.pdf | SEKD: Self-Evolving Keypoint Detection and Description | Researchers have attempted utilizing deep neural network (DNN) to learn novel local features from images inspired by its recent successes on a variety of vision tasks. However, existing DNN-based algorithms have not achieved such remarkable progress that could be partly attributed to insufficient utilization of the int... | ['Mingyang Li', 'Ling Cai', 'Yonghong Tian', 'Yafei Song', 'Jia Li'] | 2020-06-09 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [-9.64946812e-04 -2.15154991e-01 -3.77959937e-01 -3.88062030e-01
-5.74431241e-01 -2.58345306e-01 7.10351110e-01 -3.83686721e-01
-2.96555012e-01 6.05349720e-01 2.74637565e-02 2.54706711e-01
-2.80101746e-01 -3.86531979e-01 -5.95096648e-01 -8.29095304e-01
-8.44659433e-02 3.37328501e-02 2.07573608e-01 -2.91562267... | [8.051566123962402, -2.0869333744049072] |
06600e94-5360-4d54-9ad6-df87025536d3 | a-multi-phase-gammatone-filterbank-for-speech | 1910.11615 | null | https://arxiv.org/abs/1910.11615v2 | https://arxiv.org/pdf/1910.11615v2.pdf | A Multi-Phase Gammatone Filterbank for Speech Separation via TasNet | In this work, we investigate if the learned encoder of the end-to-end convolutional time domain audio separation network (Conv-TasNet) is the key to its recent success, or if the encoder can just as well be replaced by a deterministic hand-crafted filterbank. Motivated by the resemblance of the trained encoder of Conv-... | ['David Ditter', 'Timo Gerkmann'] | 2019-10-25 | null | null | null | null | ['low-latency-processing'] | ['robots'] | [ 2.08848312e-01 2.74106503e-01 3.12508434e-01 -8.24515000e-02
-7.73084760e-01 -6.33023918e-01 1.90905213e-01 9.02378634e-02
-6.08263671e-01 5.86140156e-01 3.88551712e-01 -2.03785926e-01
-2.07713276e-01 -3.76149714e-01 -7.13405907e-01 -7.24687219e-01
-4.49601442e-01 -2.63277352e-01 4.20659393e-01 -1.30912229... | [15.318130493164062, 5.6565728187561035] |
7035cdda-638a-4133-b2b5-78616d165036 | counterfactual-learning-to-rank-for-additive | 1805.00065 | null | https://arxiv.org/abs/1805.00065v3 | https://arxiv.org/pdf/1805.00065v3.pdf | A General Framework for Counterfactual Learning-to-Rank | Implicit feedback (e.g., click, dwell time) is an attractive source of training data for Learning-to-Rank, but its naive use leads to learning results that are distorted by presentation bias. For the special case of optimizing average rank for linear ranking functions, however, the recently developed SVM-PropRank metho... | ['Kenta Takatsu', 'Aman Agarwal', 'Thorsten Joachims', 'Ivan Zaitsev'] | 2018-04-30 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 7.29247481e-02 1.62771091e-01 -5.62615037e-01 -5.02318919e-01
-1.07511461e+00 -6.48594379e-01 6.62327707e-01 -5.70950061e-02
-3.37290317e-01 1.18232155e+00 3.01073432e-01 -7.00243950e-01
-5.85919380e-01 -6.04566395e-01 -1.01615906e+00 -6.62843943e-01
-5.13300776e-01 2.81510681e-01 5.71789965e-02 -2.11115703... | [9.446026802062988, 5.3048224449157715] |
a91fc65a-cea3-4922-abc3-4aa9183ee07c | zero-shot-image-restoration-using-denoising | 2212.00490 | null | https://arxiv.org/abs/2212.00490v2 | https://arxiv.org/pdf/2212.00490v2.pdf | Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model | Most existing Image Restoration (IR) models are task-specific, which can not be generalized to different degradation operators. In this work, we propose the Denoising Diffusion Null-Space Model (DDNM), a novel zero-shot framework for arbitrary linear IR problems, including but not limited to image super-resolution, col... | ['Jian Zhang', 'Jiwen Yu', 'Yinhuai Wang'] | 2022-12-01 | null | null | null | null | ['colorization', 'image-inpainting'] | ['computer-vision', 'computer-vision'] | [ 5.91160297e-01 -2.73894221e-01 1.22552603e-01 8.05620197e-03
-7.68408597e-01 -2.36481860e-01 4.36096519e-01 -6.96861267e-01
-1.13966674e-01 5.34771085e-01 5.21455169e-01 6.82705119e-02
-2.16513634e-01 -4.74151850e-01 -5.79202414e-01 -8.96845162e-01
4.90140945e-01 -8.34232941e-02 5.67736626e-02 -4.68909562... | [11.401394844055176, -2.1704840660095215] |
ea5e26dd-0bc8-4fdf-b4d1-4160698e0b42 | diverse-embedding-expansion-network-and-low | 2303.14481 | null | https://arxiv.org/abs/2303.14481v1 | https://arxiv.org/pdf/2303.14481v1.pdf | Diverse Embedding Expansion Network and Low-Light Cross-Modality Benchmark for Visible-Infrared Person Re-identification | For the visible-infrared person re-identification (VIReID) task, one of the major challenges is the modality gaps between visible (VIS) and infrared (IR) images. However, the training samples are usually limited, while the modality gaps are too large, which leads that the existing methods cannot effectively mine divers... | ['Hanzi Wang', 'Yukang Zhang'] | 2023-03-25 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_Diverse_Embedding_Expansion_Network_and_Low-Light_Cross-Modality_Benchmark_for_Visible-Infrared_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_Diverse_Embedding_Expansion_Network_and_Low-Light_Cross-Modality_Benchmark_for_Visible-Infrared_CVPR_2023_paper.pdf | cvpr-2023-1 | ['person-re-identification', 'cross-view-person-re-identification'] | ['computer-vision', 'computer-vision'] | [ 2.11331435e-02 -5.88179171e-01 -3.59710231e-02 -2.43427172e-01
-4.99102294e-01 -4.56118733e-01 5.50778866e-01 -5.10508835e-01
-3.47039133e-01 5.23775578e-01 4.00310814e-01 1.94530599e-02
-1.74155179e-02 -5.79806387e-01 -3.62982303e-01 -7.92527139e-01
5.66747487e-01 -4.20314409e-02 -2.81594396e-01 -2.62276620... | [14.664911270141602, 0.9151387214660645] |
64398340-c83e-4cd6-9204-4afce7870098 | adversarial-multi-task-learning-for | 2206.11558 | null | https://arxiv.org/abs/2206.11558v1 | https://arxiv.org/pdf/2206.11558v1.pdf | Adversarial Multi-Task Learning for Disentangling Timbre and Pitch in Singing Voice Synthesis | Recently, deep learning-based generative models have been introduced to generate singing voices. One approach is to predict the parametric vocoder features consisting of explicit speech parameters. This approach has the advantage that the meaning of each feature is explicitly distinguished. Another approach is to predi... | ['Gyeong-Hoon Lee', 'Min-Su Kang', 'Tae-Woo Kim'] | 2022-06-23 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-1.02179490e-01 -1.35612473e-01 1.17041849e-01 5.89980185e-02
-9.19436038e-01 -4.69385177e-01 3.77129197e-01 -6.83610678e-01
2.11548671e-01 7.41579056e-01 3.56166035e-01 1.94991291e-01
-6.60003512e-04 -6.36582255e-01 -5.26920974e-01 -1.05950570e+00
2.37915173e-01 1.09784067e-01 -2.35390380e-01 -3.09512079... | [15.493606567382812, 6.161716461181641] |
4a430e3b-1d29-4dc6-8757-a49bcbdc1b5d | orders-of-magnitude-speedup-in-atmospheric | 1808.03874 | null | http://arxiv.org/abs/1808.03874v1 | http://arxiv.org/pdf/1808.03874v1.pdf | Orders-of-magnitude speedup in atmospheric chemistry modeling through neural network-based emulation | Chemical transport models (CTMs), which simulate air pollution transport,
transformation, and removal, are computationally expensive, largely because of
the computational intensity of the chemical mechanisms: systems of coupled
differential equations representing atmospheric chemistry. Here we investigate
the potential... | ['Christopher W. Tessum', 'Julian D. Marshall', 'Makoto M. Kelp'] | 2018-08-11 | null | null | null | null | ['multi-target-regression'] | ['miscellaneous'] | [ 2.16224328e-01 -3.88128757e-01 2.80136049e-01 1.44511521e-01
-4.85105723e-01 -4.56646889e-01 8.16759706e-01 5.53852677e-01
-2.67371774e-01 1.08044505e+00 -2.10589379e-01 -1.03746057e+00
-1.82381153e-01 -1.18261027e+00 -7.71738172e-01 -9.85081255e-01
-3.98593038e-01 2.70295471e-01 2.39445478e-01 -4.10503268... | [6.531403064727783, 3.1109373569488525] |
b43332e8-0724-4a5f-b149-21a368f25930 | efficient-large-scale-nonstationary-spatial | 2306.11487 | null | https://arxiv.org/abs/2306.11487v1 | https://arxiv.org/pdf/2306.11487v1.pdf | Efficient Large-scale Nonstationary Spatial Covariance Function Estimation Using Convolutional Neural Networks | Spatial processes observed in various fields, such as climate and environmental science, often occur on a large scale and demonstrate spatial nonstationarity. Fitting a Gaussian process with a nonstationary Mat\'ern covariance is challenging. Previous studies in the literature have tackled this challenge by employing s... | ['Ying Sun', 'Marc G. Genton', 'Ghulam A. Qadir', 'Sameh Abdulah', 'Yiping Hong', 'Pratik Nag'] | 2023-06-20 | null | null | null | null | ['gaussian-processes'] | ['methodology'] | [ 7.33301714e-02 -7.21804023e-01 4.16034937e-01 -3.68472427e-01
-3.46799076e-01 -8.09932411e-01 5.70710778e-01 1.10365510e-01
-4.25210565e-01 1.08777833e+00 -9.23863500e-02 -5.70209444e-01
-6.34408772e-01 -9.20241475e-01 -7.07541227e-01 -1.14102578e+00
-3.56345713e-01 7.31268585e-01 2.89645016e-01 1.00216553... | [6.64364767074585, 3.279541015625] |
3c341433-9fc9-4c56-80df-caeb5b85f733 | majority-voting-with-bidirectional-pre | 2103.06369 | null | https://arxiv.org/abs/2103.06369v2 | https://arxiv.org/pdf/2103.06369v2.pdf | Majority Voting with Bidirectional Pre-translation For Bitext Retrieval | Obtaining high-quality parallel corpora is of paramount importance for training NMT systems. However, as many language pairs lack adequate gold-standard training data, a popular approach has been to mine so-called "pseudo-parallel" sentences from paired documents in two languages. In this paper, we outline some problem... | ['Derry Tanti Wijaya', 'Alex Jones'] | 2021-03-10 | null | https://aclanthology.org/2021.bucc-1.7 | https://aclanthology.org/2021.bucc-1.7.pdf | ranlp-bucc-2021-9 | ['cross-lingual-bitext-mining'] | ['natural-language-processing'] | [ 0.2283297 -0.47464997 -0.17218296 -0.44476646 -1.341767 -0.7659033
0.9479027 0.2152818 -0.8936953 0.9916611 0.39906847 -0.6970069
-0.11995488 -0.26653153 -0.6213048 -0.6005429 0.07478647 0.9839025
0.01621246 -0.48143476 0.5650559 0.12183948 -1.2504728 0.2556566
0.9817349 0.1964072 0.7196... | [11.478873252868652, 10.314528465270996] |
f81b7926-9fdf-4e4d-afbe-5b9c05e5b6ea | knowledge-base-completion-for-long-tail | 2306.17472 | null | https://arxiv.org/abs/2306.17472v1 | https://arxiv.org/pdf/2306.17472v1.pdf | Knowledge Base Completion for Long-Tail Entities | Despite their impressive scale, knowledge bases (KBs), such as Wikidata, still contain significant gaps. Language models (LMs) have been proposed as a source for filling these gaps. However, prior works have focused on prominent entities with rich coverage by LMs, neglecting the crucial case of long-tail entities. In t... | ['Gerhard Weikum', 'Simon Razniewski', 'Lihu Chen'] | 2023-06-30 | null | null | null | null | ['knowledge-base-completion', 'knowledge-base-completion', 'retrieval'] | ['graphs', 'knowledge-base', 'methodology'] | [-5.62148154e-01 4.04013366e-01 -7.16699243e-01 -2.65052356e-02
-1.34644759e+00 -7.99209237e-01 6.98005378e-01 5.73159575e-01
-7.21811116e-01 1.08631349e+00 5.76305449e-01 -2.90833712e-01
-7.76905045e-02 -7.37551510e-01 -7.52226532e-01 6.66782085e-04
2.79713944e-02 7.41109610e-01 6.68774843e-01 -3.21397334... | [9.46032428741455, 8.81684398651123] |
f55d3b89-2fa5-4e4b-b014-0a01c59d527f | learning-cross-task-attribute-attribute | null | null | https://aclanthology.org/2021.ecnlp-1.10 | https://aclanthology.org/2021.ecnlp-1.10.pdf | Learning Cross-Task Attribute - Attribute Similarity for Multi-task Attribute-Value Extraction | Automatic extraction of product attribute-value pairs from unstructured text like product descriptions is an important problem for e-commerce companies. The attribute schema typically varies from one category of products (which will be referred as vertical) to another. This leads to extreme annotation efforts for train... | ['Muthusamy Chelliah', 'Karimulla Shaik', 'Harshit Jain', 'Sourangshu Bhattacharya', 'Mayank Jain'] | null | null | null | null | acl-ecnlp-2021-8 | ['attribute-value-extraction'] | ['natural-language-processing'] | [ 2.87465602e-01 1.99561864e-01 -2.44688362e-01 -9.82579231e-01
-9.29434061e-01 -8.81756246e-01 2.44637892e-01 8.46845210e-01
-3.49780321e-01 9.05275583e-01 -5.07244207e-02 -1.62845105e-01
-5.43170832e-02 -8.13621104e-01 -6.14311278e-01 -8.19766402e-01
1.06462641e-02 9.16492939e-01 4.25699018e-02 -1.88765407... | [9.9523344039917, 6.280230522155762] |
0f9140b2-35cf-4878-9c7a-40d63f25ce26 | classification-regression-for-chart | 2111.14792 | null | https://arxiv.org/abs/2111.14792v2 | https://arxiv.org/pdf/2111.14792v2.pdf | Classification-Regression for Chart Comprehension | Chart question answering (CQA) is a task used for assessing chart comprehension, which is fundamentally different from understanding natural images. CQA requires analyzing the relationships between the textual and the visual components of a chart, in order to answer general questions or infer numerical values. Most exi... | ['Dani Lischinski', 'Rami Ben-Ari', 'Matan Levy'] | 2021-11-29 | null | null | null | null | ['chart-question-answering', 'chart-question-answering'] | ['computer-code', 'computer-vision'] | [ 1.91445649e-01 -2.19476366e-04 2.98658669e-01 -2.37024099e-01
-1.06184435e+00 -9.97523248e-01 7.22357631e-01 4.93634343e-01
-2.39695251e-01 1.08015522e-01 4.12285417e-01 -8.36809278e-01
7.36498982e-02 -5.84061444e-01 -8.37928832e-01 -1.42377496e-01
1.15749031e-01 5.63577175e-01 3.19306515e-02 -3.00465167... | [11.127817153930664, 2.011080503463745] |
8f4cb7eb-9b31-4d9b-9951-cf2894be5786 | a-comprehensive-and-versatile-multimodal-deep | 2303.16412 | null | https://arxiv.org/abs/2303.16412v1 | https://arxiv.org/pdf/2303.16412v1.pdf | A Comprehensive and Versatile Multimodal Deep Learning Approach for Predicting Diverse Properties of Advanced Materials | We present a multimodal deep learning (MDL) framework for predicting physical properties of a 10-dimensional acrylic polymer composite material by merging physical attributes and chemical data. Our MDL model comprises four modules, including three generative deep learning models for material structure characterization ... | ['Kenji Hata', 'Yasuaki Miki', 'Shun Muroga'] | 2023-03-29 | null | null | null | null | ['multimodal-deep-learning'] | ['natural-language-processing'] | [ 4.08140928e-01 -1.99680269e-01 -2.14481145e-01 -9.86383632e-02
-8.72381628e-01 -5.97547412e-01 3.28931123e-01 4.01211023e-01
2.27615207e-01 7.65122414e-01 3.48443538e-01 -4.62192714e-01
-4.69730049e-01 -1.19904470e+00 -8.44278336e-01 -1.03776860e+00
-2.63599277e-01 8.67489100e-01 -2.57487416e-01 -1.69572070... | [5.190038204193115, 5.33453369140625] |
3ec29dff-61af-4a33-bbb9-fcc93d26739c | self-supervised-pre-training-and-contrastive | 2009.08043 | null | https://arxiv.org/abs/2009.08043v2 | https://arxiv.org/pdf/2009.08043v2.pdf | Self-supervised pre-training and contrastive representation learning for multiple-choice video QA | Video Question Answering (Video QA) requires fine-grained understanding of both video and language modalities to answer the given questions. In this paper, we propose novel training schemes for multiple-choice video question answering with a self-supervised pre-training stage and a supervised contrastive learning in th... | ['Seohyeong Jeong', 'Seonhoon Kim', 'Eunbyul Kim', 'Nojun Kwak', 'Inho Kang'] | 2020-09-17 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 5.96944451e-01 -1.50541598e-02 -1.48492306e-01 -5.72456777e-01
-1.45679832e+00 -5.20141721e-01 3.38409215e-01 -1.51851118e-01
-5.10446489e-01 6.04809344e-01 3.12290907e-01 -3.58646542e-01
2.76270390e-01 -6.09860480e-01 -1.13008428e+00 -6.36380076e-01
3.70244443e-01 3.42144042e-01 7.30404854e-01 -2.46540517... | [10.443990707397461, 1.0699769258499146] |
2556eefb-8901-4428-b2d5-6d1800d66348 | controlflag-a-self-supervised-idiosyncratic | 2011.03616 | null | https://arxiv.org/abs/2011.03616v5 | https://arxiv.org/pdf/2011.03616v5.pdf | ControlFlag: A Self-Supervised Idiosyncratic Pattern Detection System for Software Control Structures | Software debugging has been shown to utilize upwards of half of developers' time. Yet, machine programming (MP), the field concerned with the automation of software (and hardware) development, has recently made strides in both research and production-quality automated debugging systems. In this paper we present Control... | ['Justin Gottschlich', 'Niranjan Hasabnis'] | 2020-11-06 | null | null | null | null | ['programming-error-detection'] | ['computer-code'] | [-1.23593777e-01 3.51155937e-01 -2.63736963e-01 -3.46380979e-01
-4.38397199e-01 -5.85523903e-01 2.54075497e-01 5.63099802e-01
3.81959945e-01 4.39600378e-01 -3.31888169e-01 -7.40929067e-01
-6.84122592e-02 -3.01862866e-01 -7.38381803e-01 1.76353589e-01
-3.41765285e-01 -1.20547548e-01 4.43790317e-01 -3.62178087... | [7.602540016174316, 7.643655776977539] |
80ba6dd1-60e4-4fc9-97da-2590265dd21d | can-llm-already-serve-as-a-database-interface | 2305.03111 | null | https://arxiv.org/abs/2305.03111v2 | https://arxiv.org/pdf/2305.03111v2.pdf | Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs | Text-to-SQL parsing, which aims at converting natural language instructions into executable SQLs, has gained increasing attention in recent years. In particular, Codex and ChatGPT have shown impressive results in this task. However, most of the prevalent benchmarks, i.e., Spider, and WikiSQL, focus on database schema w... | ['Reynold Cheng', 'Guoliang Li', 'Chenhao Ma', 'Xuanhe Zhou', 'Yongbin Li', 'Fei Huang', 'Kevin C. C. Chang', 'Nan Huo', 'Ruiying Geng', 'Rongyu Cao', 'Bowen Qin', 'Bailin Wang', 'Bowen Li', 'Jiaxi Yang', 'Binhua Li', 'Ge Qu', 'Binyuan Hui', 'Jinyang Li'] | 2023-05-04 | null | null | null | null | ['text-to-sql', 'semantic-parsing'] | ['computer-code', 'natural-language-processing'] | [-3.26743990e-01 8.66530836e-02 -2.32494831e-01 -7.14773476e-01
-1.11136258e+00 -6.04004085e-01 1.05657220e-01 2.93593019e-01
-5.94401993e-02 5.44181645e-01 1.45288825e-01 -7.32888937e-01
1.67913482e-01 -1.46785820e+00 -1.23803008e+00 7.78472945e-02
2.98230976e-01 6.52630448e-01 2.76860893e-01 -5.22665441... | [9.863164901733398, 7.83104133605957] |
94525ccf-5bc7-412a-a59e-3e681037223e | fedselect-customized-selection-of-parameters | 2306.13264 | null | https://arxiv.org/abs/2306.13264v2 | https://arxiv.org/pdf/2306.13264v2.pdf | FedSelect: Customized Selection of Parameters for Fine-Tuning during Personalized Federated Learning | Recent advancements in federated learning (FL) seek to increase client-level performance by fine-tuning client parameters on local data or personalizing architectures for the local task. Existing methods for such personalization either prune a global model or fine-tune a global model on a local client distribution. How... | ['Andy Zhou', 'Ron Arel', 'Chengjun Lu', 'John Won', 'Rishub Tamirisa'] | 2023-06-23 | null | null | null | null | ['personalized-federated-learning'] | ['methodology'] | [-2.95310110e-01 7.39184320e-02 -3.45980197e-01 -7.73826957e-01
-1.03101981e+00 -7.25488603e-01 2.22564951e-01 -3.81970406e-01
-4.00404572e-01 1.02979350e+00 2.75088251e-01 -1.90997094e-01
-5.99340677e-01 -6.08109057e-01 -6.23935699e-01 -1.17674315e+00
-8.75499472e-02 8.78445566e-01 3.65260720e-01 4.07042503... | [5.802604675292969, 6.242997646331787] |
8ae2d849-939a-482c-8d46-e6b4d8373358 | wildly-unsupervised-domain-adaptation-and-its | null | null | https://openreview.net/forum?id=rkl2s34twS | https://openreview.net/pdf?id=rkl2s34twS | Wildly Unsupervised Domain Adaptation and Its Powerful and Efficient Solution | In unsupervised domain adaptation (UDA), classifiers for the target domain (TD) are trained with clean labeled data from the source domain (SD) and unlabeled data from TD. However, in the wild, it is hard to acquire a large amount of perfectly clean labeled data in SD given limited budget. Hence, we consider a new, mor... | ['Masashi Sugiyama', 'Guangquan Zhang', 'Gang Niu', 'Bo Han', 'Jie Lu', 'Feng Liu'] | 2019-09-25 | null | null | null | null | ['wildly-unsupervised-domain-adaptation'] | ['computer-vision'] | [ 6.39769882e-02 9.98705328e-02 -4.88653183e-02 -5.65127969e-01
-1.05933368e+00 -7.68252254e-01 3.80303174e-01 -2.91999310e-01
-2.85784334e-01 9.77243364e-01 -7.79990405e-02 -3.11447024e-01
1.53375730e-01 -6.51577175e-01 -8.11465085e-01 -9.83978450e-01
4.01527166e-01 7.24181771e-01 -7.27530122e-02 -2.26012245... | [10.388103485107422, 3.086488723754883] |
16dbf83a-234b-4ed0-92a2-9dbe87511e98 | revisiting-the-prepositional-phrase | 2102.00924 | null | https://arxiv.org/abs/2102.00924v2 | https://arxiv.org/pdf/2102.00924v2.pdf | Revisiting the Prepositional-Phrase Attachment Problem Using Explicit Commonsense Knowledge | We revisit the challenging problem of resolving prepositional-phrase (PP) attachment ambiguity. To date, proposed solutions are either rule-based, where explicit grammar rules direct how to resolve ambiguities; or statistical, where the decision is learned from a corpus of labeled examples. We argue that explicit commo... | ['Peter Chin', 'Henry Lieberman', 'Yida Xin'] | 2021-02-01 | null | null | null | null | ['prepositional-phrase-attachment'] | ['natural-language-processing'] | [ 3.41683105e-02 6.58385217e-01 -2.59556353e-01 -6.94472194e-01
-7.66623199e-01 -7.94206083e-01 4.48046684e-01 4.30625379e-01
-2.10178271e-01 9.96644497e-01 5.05076230e-01 -7.54464626e-01
-1.65296555e-01 -1.00951958e+00 -4.73308623e-01 -9.31799933e-02
3.27563822e-01 7.85739720e-01 4.59373802e-01 -8.00854146... | [10.13978099822998, 8.976828575134277] |
38e285e6-fb0e-40bf-bf58-b2934fc994cd | enhancing-crowd-flow-prediction-in-various | 2203.07372 | null | https://arxiv.org/abs/2203.07372v1 | https://arxiv.org/pdf/2203.07372v1.pdf | Enhancing crowd flow prediction in various spatial and temporal granularities | Thanks to the diffusion of the Internet of Things, nowadays it is possible to sense human mobility almost in real time using unconventional methods (e.g., number of bikes in a bike station). Due to the diffusion of such technologies, the last years have witnessed a significant growth of human mobility studies, motivate... | ['Luca Pappalardo', 'Massimiliano Luca', 'Marco Cardia'] | 2022-03-12 | null | null | null | null | ['epidemiology'] | ['medical'] | [-4.69039798e-01 1.27243415e-01 -2.03510493e-01 -1.39324471e-01
2.30519906e-01 -5.51873818e-02 6.75238967e-01 2.29540244e-01
-3.29295367e-01 9.68708754e-01 5.92426240e-01 -7.13432670e-01
-2.07028151e-01 -1.37292135e+00 -4.49089020e-01 -3.15400273e-01
-3.35215688e-01 7.01796055e-01 5.90675533e-01 -8.27879071... | [6.444059371948242, 2.0270421504974365] |
a5d69cf3-6c96-4941-8830-cd3805d5912c | recent-advances-in-text-to-sql-a-survey-of | 2208.10099 | null | https://arxiv.org/abs/2208.10099v1 | https://arxiv.org/pdf/2208.10099v1.pdf | Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect | Text-to-SQL has attracted attention from both the natural language processing and database communities because of its ability to convert the semantics in natural language into SQL queries and its practical application in building natural language interfaces to database systems. The major challenges in text-to-SQL lie i... | ['Yue Zhang', 'Yulong Chen', 'Naihao Deng'] | 2022-08-22 | null | https://aclanthology.org/2022.coling-1.190 | https://aclanthology.org/2022.coling-1.190.pdf | coling-2022-10 | ['text-to-sql'] | ['computer-code'] | [ 1.73614979e-01 -3.00382078e-02 -3.70224237e-01 -1.07663453e+00
-7.80468702e-01 -7.57795990e-01 4.95547980e-01 5.05646110e-01
-3.26856256e-01 4.98008966e-01 4.07273531e-01 -4.96497929e-01
2.77904451e-01 -1.21870172e+00 -4.60281938e-01 2.73107171e-01
1.11524738e-01 4.86385047e-01 4.89280045e-01 -3.41501713... | [9.893664360046387, 7.851883888244629] |
6eac1cde-754a-4222-8f5c-95025c019aa6 | measuring-cross-lingual-transferability-of | 2305.08800 | null | https://arxiv.org/abs/2305.08800v1 | https://arxiv.org/pdf/2305.08800v1.pdf | Measuring Cross-Lingual Transferability of Multilingual Transformers on Sentence Classification | Recent studies have exhibited remarkable capabilities of pre-trained multilingual Transformers, especially cross-lingual transferability. However, current methods do not measure cross-lingual transferability well, hindering the understanding of multilingual Transformers. In this paper, we propose IGap, a cross-lingual ... | ['Xian-Ling Mao', 'Heyan Huang', 'Zewen Chi'] | 2023-05-15 | null | null | null | null | ['sentence-classification', 'cross-lingual-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [-3.64691406e-01 -2.83408165e-01 -4.31942463e-01 -4.01598781e-01
-1.35179675e+00 -1.14814222e+00 5.00426531e-01 1.66139558e-01
-1.73526108e-01 7.34692633e-01 5.40895760e-01 -6.98921025e-01
-1.74512193e-01 -7.88419962e-01 -7.32097924e-01 -1.41054824e-01
1.70450076e-01 3.91156465e-01 2.18962338e-02 -5.39182961... | [11.021900177001953, 9.912150382995605] |
b0103a02-05c3-41bd-85e3-de6f70e835af | the-role-of-object-centric-representations | 2304.07091 | null | https://arxiv.org/abs/2304.07091v1 | https://arxiv.org/pdf/2304.07091v1.pdf | The role of object-centric representations, guided attention, and external memory on generalizing visual relations | Visual reasoning is a long-term goal of vision research. In the last decade, several works have attempted to apply deep neural networks (DNNs) to the task of learning visual relations from images, with modest results in terms of the generalization of the relations learned. In recent years, several innovations in DNNs h... | ['Jeffrey S. Bowers', 'Guillermo Puebla'] | 2023-04-14 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [ 1.37562051e-01 1.95689008e-01 1.41304076e-01 -4.60040957e-01
2.64516205e-01 -3.95405889e-01 8.42363715e-01 4.38023210e-02
-4.97234255e-01 5.92154920e-01 2.24151984e-01 -4.11061972e-01
-3.06622326e-01 -8.74232054e-01 -7.00932920e-01 -5.25507808e-01
2.04868898e-01 4.66900438e-01 4.16624993e-01 -3.33048940... | [10.574910163879395, 2.1656973361968994] |
97cf9c98-7961-4ae5-8b2d-7b6d7bddab3a | pare-part-attention-regressor-for-3d-human | 2104.08527 | null | https://arxiv.org/abs/2104.08527v2 | https://arxiv.org/pdf/2104.08527v2.pdf | PARE: Part Attention Regressor for 3D Human Body Estimation | Despite significant progress, we show that state of the art 3D human pose and shape estimation methods remain sensitive to partial occlusion and can produce dramatically wrong predictions although much of the body is observable. To address this, we introduce a soft attention mechanism, called the Part Attention REgress... | ['Michael J. Black', 'Otmar Hilliges', 'Chun-Hao P. Huang', 'Muhammed Kocabas'] | 2021-04-17 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Kocabas_PARE_Part_Attention_Regressor_for_3D_Human_Body_Estimation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Kocabas_PARE_Part_Attention_Regressor_for_3D_Human_Body_Estimation_ICCV_2021_paper.pdf | iccv-2021-1 | ['3d-human-pose-and-shape-estimation', '3d-multi-person-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-4.95395847e-02 5.12249410e-01 -3.98397028e-01 -3.04224133e-01
-6.91206574e-01 -1.94007099e-01 4.65493709e-01 -1.61800519e-01
2.50553489e-02 5.14019072e-01 7.27855146e-01 9.49950069e-02
2.32745811e-01 -5.04304767e-01 -1.02824140e+00 -2.01809391e-01
-1.54311717e-01 7.17908204e-01 7.31134638e-02 -1.88012198... | [7.037415027618408, -0.9311949610710144] |
a4127027-6f1c-497d-a011-a9f7d754f49f | retrognn-approximating-retrosynthesis-by | 2011.13042 | null | https://arxiv.org/abs/2011.13042v1 | https://arxiv.org/pdf/2011.13042v1.pdf | RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design | De novo molecule generation often results in chemically unfeasible molecules. A natural idea to mitigate this problem is to bias the search process towards more easily synthesizable molecules using a proxy for synthetic accessibility. However, using currently available proxies still results in highly unrealistic compou... | ['Marwin H. S. Segler', 'Yoshua Bengio', 'Paweł Włodarczyk-Pruszyński', 'Stanisław Jastrzębski', 'Maksym Korablyov', 'Cheng-Hao Liu'] | 2020-11-25 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 6.03717506e-01 4.64298904e-01 -2.40710720e-01 -1.10256732e-01
-6.75736487e-01 -1.04231000e+00 5.26325464e-01 6.70077145e-01
-4.57094193e-01 1.41428113e+00 7.53140599e-02 -8.03905606e-01
1.14544004e-01 -1.26077068e+00 -1.04540098e+00 -4.87696946e-01
-5.00403568e-02 5.62067568e-01 7.04494119e-02 -2.75856137... | [4.617212295532227, 6.004485607147217] |
f81634ab-9257-4279-8646-c19d17422dff | multi-view-dynamic-facial-action-unit | 1704.07863 | null | http://arxiv.org/abs/1704.07863v2 | http://arxiv.org/pdf/1704.07863v2.pdf | Multi-View Dynamic Facial Action Unit Detection | We propose a novel convolutional neural network approach to address the
fine-grained recognition problem of multi-view dynamic facial action unit
detection. We leverage recent gains in large-scale object recognition by
formulating the task of predicting the presence or absence of a specific action
unit in a still image... | ['Andres Romero', 'Juan Leon', 'Pablo Arbelaez'] | 2017-04-25 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 3.12941819e-01 -1.19397543e-01 -1.41401276e-01 -3.64254147e-01
-6.60775661e-01 -5.69783270e-01 7.98150122e-01 -5.85575640e-01
-3.27996910e-01 1.54207036e-01 3.64166528e-01 1.17637843e-01
2.96543211e-01 -4.03134346e-01 -7.88083971e-01 -8.03649068e-01
5.09990342e-02 7.33470470e-02 1.03529640e-01 4.27340120... | [13.505624771118164, 1.5637249946594238] |
35c986eb-9d01-47ea-8eba-3a3136aff237 | beqi-revitalize-the-senegalese-wolof-language | 2305.08518 | null | https://arxiv.org/abs/2305.08518v1 | https://arxiv.org/pdf/2305.08518v1.pdf | Beqi: Revitalize the Senegalese Wolof Language with a Robust Spelling Corrector | The progress of Natural Language Processing (NLP), although fast in recent years, is not at the same pace for all languages. African languages in particular are still behind and lack automatic processing tools. Some of these tools are very important for the development of these languages but also have an important role... | ['Moussa Diallo', 'Derguene Mbaye'] | 2023-05-15 | null | null | null | null | ['spelling-correction'] | ['natural-language-processing'] | [ 3.57634395e-01 -1.08588077e-01 2.24377096e-01 -3.16795677e-01
-8.34370792e-01 -6.85853064e-01 6.86408162e-01 5.50228834e-01
-8.48946512e-01 1.02754855e+00 2.66583502e-01 -5.54392457e-01
1.05082162e-01 -7.35012054e-01 -7.37078905e-01 -4.84323531e-01
4.62705970e-01 8.57933939e-01 3.18337768e-01 -5.79969347... | [10.912416458129883, 10.432378768920898] |
20034757-381a-415d-8f92-30adcf187e31 | adapting-to-skew-imputing-spatiotemporal | 2301.04233 | null | https://arxiv.org/abs/2301.04233v1 | https://arxiv.org/pdf/2301.04233v1.pdf | Adapting to Skew: Imputing Spatiotemporal Urban Data with 3D Partial Convolutions and Biased Masking | We adapt image inpainting techniques to impute large, irregular missing regions in urban settings characterized by sparsity, variance in both space and time, and anomalous events. Missing regions in urban data can be caused by sensor or software failures, data quality issues, interference from weather events, incomplet... | ['Bill Howe', 'Bin Han'] | 2023-01-10 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 4.69615698e-01 -1.38137445e-01 -1.96142599e-01 -2.69366592e-01
-7.71341562e-01 -3.90538245e-01 5.85680604e-01 2.01875791e-01
-5.53931296e-01 9.11267221e-01 5.10004163e-01 -4.82477427e-01
-6.32819161e-02 -8.17671776e-01 -1.05525625e+00 -6.36375368e-01
-5.53058326e-01 2.82504946e-01 2.10120514e-01 -7.44346157... | [9.509379386901855, -1.4262422323226929] |
a9800fbc-1da1-42ca-90bd-5e65e259ab6a | multistream-neural-architectures-for-cued | 2204.04965 | null | https://arxiv.org/abs/2204.04965v1 | https://arxiv.org/pdf/2204.04965v1.pdf | Multistream neural architectures for cued-speech recognition using a pre-trained visual feature extractor and constrained CTC decoding | This paper proposes a simple and effective approach for automatic recognition of Cued Speech (CS), a visual communication tool that helps people with hearing impairment to understand spoken language with the help of hand gestures that can uniquely identify the uttered phonemes in complement to lipreading. The proposed ... | ['Thomas Hueber', 'Denis Beautemps', 'Sanjana Sankar'] | 2022-04-11 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 1.82736367e-01 2.21542820e-01 -2.18430441e-02 -5.11008874e-02
-1.22962725e+00 -2.56931007e-01 4.25066262e-01 -2.61117548e-01
-7.18735456e-01 4.35627759e-01 6.42708123e-01 -4.02786732e-01
5.28210521e-01 2.26667970e-01 -2.83723563e-01 -7.86191821e-01
4.75102931e-01 2.18108118e-01 3.15198809e-01 6.78402185... | [14.324140548706055, 5.031707286834717] |
54ced8ac-6032-4fa3-be59-d358ce6ae757 | language-acquisition-neutral-change-and | null | null | https://aclanthology.org/2022.lchange-1.2 | https://aclanthology.org/2022.lchange-1.2.pdf | Language Acquisition, Neutral Change, and Diachronic Trends in Noun Classifiers | Languages around the world employ classifier systems as a method of semantic organization and categorization. These systems are rife with variability, violability, and ambiguity, and are prone to constant change over time. We explicitly model change in classifier systems as the population-level outcome of child languag... | ['Jordan Kodner', 'Aniket Kali'] | null | null | null | null | lchange-acl-2022-5 | ['language-acquisition'] | ['natural-language-processing'] | [ 2.02308893e-01 1.72188386e-01 -2.16163874e-01 -6.76450074e-01
-2.14092225e-01 -8.09826553e-01 9.07101154e-01 5.68906426e-01
-5.89951932e-01 3.13537568e-01 8.44368696e-01 -6.17571831e-01
-2.85551161e-01 -4.96034473e-01 -4.57425475e-01 -2.52785057e-01
2.22059116e-01 3.76978368e-01 1.21317722e-01 -5.47224641... | [10.621525764465332, 9.362996101379395] |
b55ad3e0-52e9-44a3-b1c1-f85418d17ddf | a-platform-independent-user-friendly | null | null | https://aclanthology.org/L12-1541 | https://aclanthology.org/L12-1541.pdf | A platform-independent user-friendly dictionary from Italian to LIS | The Lack of written representation for Italian Sign Language (LIS) makes it difficult to do perform tasks like looking up a new word in a dictionary. Most of the paper dictionaries show LIS signs in drawings or pictures. It's not a simple proposition to understand the meaning of sign from paper dictionaries unless one ... | ['Umar Shoaib', 'Nadeem Ahmad', 'Paolo Prinetto', 'Gabriele Tiotto'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['sign-language-translation'] | ['computer-vision'] | [-1.09997272e-01 -3.78095418e-01 -2.43435010e-01 -2.28613436e-01
7.50104189e-02 -8.93014669e-01 6.12842143e-01 -1.96194410e-01
-4.63857234e-01 5.78134656e-01 3.52228671e-01 -5.65345764e-01
-5.66262826e-02 -5.21975100e-01 -1.21726714e-01 -5.15593767e-01
1.65503994e-01 4.15810704e-01 5.00874221e-01 -7.93756962... | [9.114733695983887, -6.407386779785156] |
48677434-3c9f-45b3-90de-e7ec74ff7c13 | fine-grained-activities-of-people-worldwide | 2207.05182 | null | https://arxiv.org/abs/2207.05182v2 | https://arxiv.org/pdf/2207.05182v2.pdf | Fine-grained Activities of People Worldwide | Every day, humans perform many closely related activities that involve subtle discriminative motions, such as putting on a shirt vs. putting on a jacket, or shaking hands vs. giving a high five. Activity recognition by ethical visual AI could provide insights into our patterns of daily life, however existing activity r... | ['Gil Ettinger', 'Zhongheng Li', 'Greg Castanon', 'Jeffrey Byrne'] | 2022-07-11 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 2.77247995e-01 -5.08892059e-01 -5.93469381e-01 -1.91081509e-01
-2.46114984e-01 -9.72813189e-01 9.04230058e-01 -4.11607802e-01
-4.99552488e-01 8.01057339e-01 9.91474211e-01 1.30304396e-01
3.99378315e-02 -2.19059095e-01 -3.40947896e-01 -5.98065197e-01
-2.57267505e-01 2.45220542e-01 1.33765647e-02 3.54818046... | [8.089238166809082, 0.5310990810394287] |
c591b8b8-2345-4b93-8b3f-6081d4b8033d | creating-multi-level-skill-hierarchies-in | 2306.09980 | null | https://arxiv.org/abs/2306.09980v1 | https://arxiv.org/pdf/2306.09980v1.pdf | Creating Multi-Level Skill Hierarchies in Reinforcement Learning | What is a useful skill hierarchy for an autonomous agent? We propose an answer based on the graphical structure of an agent's interaction with its environment. Our approach uses hierarchical graph partitioning to expose the structure of the graph at varying timescales, producing a skill hierarchy with multiple levels o... | ['Özgür Şimşek', 'Joshua B. Evans'] | 2023-06-16 | null | null | null | null | ['graph-partitioning'] | ['graphs'] | [-3.76179442e-02 4.85756814e-01 5.39810248e-02 1.53098315e-01
3.98376524e-01 -8.99091423e-01 6.48690701e-01 6.41549408e-01
-3.06321770e-01 7.97132015e-01 -3.97213642e-03 -2.16793746e-01
-6.02501750e-01 -9.69765961e-01 -2.99198687e-01 -7.26224244e-01
-6.05689168e-01 6.56339347e-01 8.67529452e-01 -7.46987641... | [3.970998764038086, 1.4601434469223022] |
30ea93a0-51af-4fb5-ae45-982d908e3788 | model-blind-video-denoising-via-frame-to | 1811.12766 | null | https://arxiv.org/abs/1811.12766v3 | https://arxiv.org/pdf/1811.12766v3.pdf | Model-blind Video Denoising Via Frame-to-frame Training | Modeling the processing chain that has produced a video is a difficult reverse engineering task, even when the camera is available. This makes model based video processing a still more complex task. In this paper we propose a fully blind video denoising method, with two versions off-line and on-line. This is achieved b... | ['Gabriele Facciolo', 'Jean-Michel Morel', 'Thibaud Ehret', 'Pablo Arias', 'Axel Davy'] | 2018-11-30 | model-blind-video-denoising-via-frame-to-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Ehret_Model-Blind_Video_Denoising_via_Frame-To-Frame_Training_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Ehret_Model-Blind_Video_Denoising_via_Frame-To-Frame_Training_CVPR_2019_paper.pdf | cvpr-2019-6 | ['video-denoising'] | ['computer-vision'] | [ 4.77896541e-01 -4.25003290e-01 5.06773591e-01 8.20676796e-03
-6.23857141e-01 -5.99968433e-01 6.20498717e-01 -2.47852504e-01
-6.53198063e-01 4.10382837e-01 -4.93392721e-02 -3.61001998e-01
4.76003326e-02 -4.60405797e-01 -8.45886767e-01 -9.27726626e-01
-6.47431016e-02 1.05564483e-02 4.12076831e-01 -1.29834622... | [11.324522972106934, -2.2752373218536377] |
8aca1d13-d32c-4e1c-84cd-9bc8c089a932 | convolutional-gated-recurrent-neural-network | 1702.07787 | null | http://arxiv.org/abs/1702.07787v1 | http://arxiv.org/pdf/1702.07787v1.pdf | Convolutional Gated Recurrent Neural Network Incorporating Spatial Features for Audio Tagging | Environmental audio tagging is a newly proposed task to predict the presence
or absence of a specific audio event in a chunk. Deep neural network (DNN)
based methods have been successfully adopted for predicting the audio tags in
the domestic audio scene. In this paper, we propose to use a convolutional
neural network ... | ['Wenwu Wang', 'Yong Xu', 'Qiang Huang', 'Mark D. Plumbley', 'Qiuqiang Kong'] | 2017-02-24 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 2.22296908e-01 -2.71567672e-01 4.26908284e-01 -2.81603038e-01
-1.36248755e+00 -2.97869384e-01 4.08064798e-02 4.24525179e-02
-5.53465068e-01 2.11621687e-01 4.58315521e-01 5.90254106e-02
1.03598766e-01 -3.32980394e-01 -6.02840483e-01 -5.32432258e-01
-4.45014715e-01 -4.67190295e-01 4.63351369e-01 1.78988799... | [15.212300300598145, 5.182809829711914] |
73099a49-0abd-4f5f-9ac3-899df12530b5 | integrating-knowledge-graph-embeddings-to | null | null | https://aclanthology.org/2020.crac-1.7 | https://aclanthology.org/2020.crac-1.7.pdf | Integrating knowledge graph embeddings to improve mention representation for bridging anaphora resolution | Lexical semantics and world knowledge are crucial for interpreting bridging anaphora. Yet, existing computational methods for acquiring and injecting this type of information into bridging resolution systems suffer important limitations. Based on explicit querying of external knowledge bases, earlier approaches are com... | ['Liva Ralaivola', 'Pascal Denis', 'Onkar Pandit'] | null | null | null | null | coling-crac-2020-12 | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings', 'bridging-anaphora-resolution'] | ['graphs', 'methodology', 'natural-language-processing'] | [-8.39240029e-02 4.66292799e-01 -5.69342494e-01 -1.63161904e-01
-7.12586045e-01 -7.24015355e-01 6.36707842e-01 7.07871258e-01
-4.60264325e-01 7.16602802e-01 7.03622878e-01 -2.65261531e-01
-4.36463326e-01 -1.20370913e+00 -4.62357283e-01 -3.70458573e-01
1.54697686e-01 7.97424078e-01 3.11593860e-01 -5.48730314... | [9.835196495056152, 8.7506742477417] |
4b7f82dc-6a6a-452b-a8cf-6ea1f17c6b87 | text-editing-as-imitation-game | 2210.12276 | null | https://arxiv.org/abs/2210.12276v1 | https://arxiv.org/pdf/2210.12276v1.pdf | Text Editing as Imitation Game | Text editing, such as grammatical error correction, arises naturally from imperfect textual data. Recent works frame text editing as a multi-round sequence tagging task, where operations -- such as insertion and substitution -- are represented as a sequence of tags. While achieving good results, this encoding is limite... | ['Zhouhan Lin', 'Jie Fu', 'Yewen Pu', 'Longtao Huang', 'Bo Yuan', 'Bin Tang', 'Ning Shi'] | 2022-10-21 | null | null | null | null | ['action-generation', 'grammatical-error-correction'] | ['computer-vision', 'natural-language-processing'] | [ 7.12167561e-01 2.31791556e-01 -1.94002420e-01 -4.32443887e-01
-9.53518152e-01 -7.97811449e-01 7.86775768e-01 5.27092814e-02
-5.63479662e-01 8.86188626e-01 6.26629710e-01 -5.50075293e-01
5.42614579e-01 -4.74944711e-01 -1.26814401e+00 -3.39004934e-01
-2.87569687e-02 3.08499873e-01 -8.79970416e-02 -3.85563463... | [8.302240371704102, 7.629474639892578] |
b9fb10fd-2a62-4f4b-8502-baa0e0d874f5 | reducing-odd-generation-from-neural-headline | null | null | https://aclanthology.org/Y18-1034 | https://aclanthology.org/Y18-1034.pdf | Reducing Odd Generation from Neural Headline Generation | null | ['Masaaki Nagata', 'Kentaro Inui', 'Naoaki Okazaki', 'Jun Suzuki', 'Sho Takase', 'Shun Kiyono'] | null | null | null | null | paclic-2018-12 | ['headline-generation'] | ['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... | [-7.403275966644287, 3.7511110305786133] |
95461392-b2f2-4d79-81f1-4561ec705d20 | latent-space-explorations-of-singing-voice | 2103.07197 | null | https://arxiv.org/abs/2103.07197v1 | https://arxiv.org/pdf/2103.07197v1.pdf | Latent Space Explorations of Singing Voice Synthesis using DDSP | Machine learning based singing voice models require large datasets and lengthy training times. In this work we present a lightweight architecture, based on the Differentiable Digital Signal Processing (DDSP) library, that is able to output song-like utterances conditioned only on pitch and amplitude, after twelve hours... | ['Cumhur Erkut', 'Juan Alonso'] | 2021-03-12 | null | null | null | null | ['singing-voice-synthesis'] | ['speech'] | [-1.31125256e-01 -1.08858697e-01 2.77050048e-01 -6.73637912e-02
-1.08920813e+00 -9.32407081e-01 2.79325396e-01 -3.21175039e-01
-3.74031663e-02 1.49107516e-01 3.76581818e-01 -2.31280148e-01
-2.37770546e-02 -3.27483326e-01 -6.44675791e-01 -6.91509604e-01
-1.19262367e-01 2.88436204e-01 -3.81512679e-02 -3.65822524... | [15.481273651123047, 5.986166954040527] |
d82a4379-8570-4a19-9ef9-f175cf0b6744 | service-composition-scenarios-for-task | null | null | https://aclanthology.org/L12-1477 | https://aclanthology.org/L12-1477.pdf | Service Composition Scenarios for Task-Oriented Translation | Due to instant availability and low cost, machine translation is becoming popular. Machine translation mediated communication plays a more and more important role in international collaboration. However, machine translators cannot guarantee high quality translation. In a multilingual communication task, many in-domain ... | ['Chunqi Shi', 'Donghui Lin', 'Toru Ishida'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['service-composition'] | ['miscellaneous'] | [-3.12615216e-01 -1.64518714e-01 -5.25842488e-01 -3.38045061e-01
-1.04224515e+00 -7.28559732e-01 7.14768767e-01 -2.02882245e-01
-2.54540086e-01 9.60214376e-01 3.66647124e-01 -6.56621695e-01
-9.54598710e-02 -7.90674329e-01 -2.37358034e-01 -3.56862009e-01
6.80760622e-01 9.79182959e-01 1.68576062e-01 -8.56818616... | [11.511698722839355, 10.226911544799805] |
4203eed5-240c-4d7d-9e54-7b71069d5e07 | on-numerical-integration-in-neural-ordinary | 2206.07335 | null | https://arxiv.org/abs/2206.07335v1 | https://arxiv.org/pdf/2206.07335v1.pdf | On Numerical Integration in Neural Ordinary Differential Equations | The combination of ordinary differential equations and neural networks, i.e., neural ordinary differential equations (Neural ODE), has been widely studied from various angles. However, deciphering the numerical integration in Neural ODE is still an open challenge, as many researches demonstrated that numerical integrat... | ['Yifa Tang', 'Beibei Zhu', 'Pengzhan Jin', 'Aiqing Zhu'] | 2022-06-15 | null | null | null | null | ['numerical-integration'] | ['miscellaneous'] | [-2.60988891e-01 4.11826335e-02 1.63863018e-01 8.02532807e-02
3.28399613e-02 -4.67679977e-01 3.95904183e-01 -3.71666133e-01
-5.07790565e-01 1.09237838e+00 -4.13176954e-01 -3.16895336e-01
-1.13449462e-01 -5.65332115e-01 -8.11602831e-01 -1.01789331e+00
-1.14955381e-01 1.67079773e-02 -5.81887513e-02 -3.89461845... | [6.615228652954102, 3.489941120147705] |
8e650738-88d2-45d4-8731-446aa693dad3 | knowledge-guided-deep-reinforcement-learning | 2004.08068 | null | https://arxiv.org/abs/2004.08068v1 | https://arxiv.org/pdf/2004.08068v1.pdf | Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation | Interactive recommendation aims to learn from dynamic interactions between items and users to achieve responsiveness and accuracy. Reinforcement learning is inherently advantageous for coping with dynamic environments and thus has attracted increasing attention in interactive recommendation research. Inspired by knowle... | ['Xianzhi Wang', 'Wenjie Zhang', 'Lina Yao', 'Xiaocong Chen', 'Wei Liu', 'Chaoran Huang'] | 2020-04-17 | null | null | null | null | ['knowledge-aware-recommendation'] | ['miscellaneous'] | [-3.04119557e-01 -1.06864557e-01 -7.23747075e-01 -3.16910297e-01
-1.28503889e-01 -2.48438150e-01 3.30402136e-01 -9.81868654e-02
-2.73558229e-01 7.46336877e-01 5.72889447e-01 -2.58776397e-01
-7.04041898e-01 -1.09318221e+00 -7.90440023e-01 -2.66133636e-01
-6.60447180e-01 3.60068560e-01 1.00166991e-01 -6.38905823... | [10.126112937927246, 5.638330459594727] |
24e064f9-f672-414d-862f-e15e64a4bcc9 | semantic-answer-type-prediction-using-bert | 2109.06714 | null | https://arxiv.org/abs/2109.06714v1 | https://arxiv.org/pdf/2109.06714v1.pdf | Semantic Answer Type Prediction using BERT: IAI at the ISWC SMART Task 2020 | This paper summarizes our participation in the SMART Task of the ISWC 2020 Challenge. A particular question we are interested in answering is how well neural methods, and specifically transformer models, such as BERT, perform on the answer type prediction task compared to traditional approaches. Our main finding is tha... | ['Krisztian Balog', 'Vinay Setty'] | 2021-09-14 | null | null | null | null | ['type-prediction'] | ['computer-code'] | [-1.70894843e-02 2.33392522e-01 -4.27815408e-01 -4.25380111e-01
-9.69862401e-01 -6.18903935e-01 5.96682310e-01 5.13345003e-01
-5.79995394e-01 7.71328092e-01 3.50624382e-01 -4.33955222e-01
-2.21268907e-01 -8.93838942e-01 -4.31639433e-01 -6.93680495e-02
2.68325508e-01 8.05972934e-01 2.03629762e-01 -6.68133020... | [11.156801223754883, 8.021610260009766] |
dd82b7ed-868d-41a5-961d-7aeb1de85f09 | texture-aware-video-frame-interpolation | 2102.13520 | null | https://arxiv.org/abs/2102.13520v1 | https://arxiv.org/pdf/2102.13520v1.pdf | Texture-aware Video Frame Interpolation | Temporal interpolation has the potential to be a powerful tool for video compression. Existing methods for frame interpolation do not discriminate between video textures and generally invoke a single general model capable of interpolating a wide range of video content. However, past work on video texture analysis and s... | ['David Bull', 'Duolikun Danier'] | 2021-02-26 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [ 3.63959193e-01 -5.45699358e-01 -3.16505343e-01 -3.17766875e-01
-5.73492944e-01 -3.19390237e-01 7.71071732e-01 -2.01589510e-01
-4.89844792e-02 6.38662338e-01 1.98475435e-01 -2.91612953e-01
2.32482359e-01 -6.97829127e-01 -7.94855475e-01 -7.00176060e-01
-1.48208141e-01 9.71695632e-02 7.98950195e-01 -1.97548032... | [10.879809379577637, -1.340711236000061] |
fd78c0d8-89cb-4fea-8719-e2db441125cf | query-guided-end-to-end-person-search | 1905.01203 | null | https://arxiv.org/abs/1905.01203v1 | https://arxiv.org/pdf/1905.01203v1.pdf | Query-guided End-to-End Person Search | Person search has recently gained attention as the novel task of finding a person, provided as a cropped sample, from a gallery of non-cropped images, whereby several other people are also visible. We believe that i. person detection and re-identification should be pursued in a joint optimization framework and that ii.... | ['Federico Tombari', 'Fabio Galasso', 'Sikandar Amin', 'Bharti Munjal'] | 2019-05-03 | query-guided-end-to-end-person-search-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Munjal_Query-Guided_End-To-End_Person_Search_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Munjal_Query-Guided_End-To-End_Person_Search_CVPR_2019_paper.pdf | cvpr-2019-6 | ['person-search'] | ['computer-vision'] | [ 3.49933021e-02 -2.27619410e-01 -1.14675239e-02 -2.83791244e-01
-1.11553144e+00 -5.22582471e-01 8.49446535e-01 -2.13622093e-01
-8.92381191e-01 5.58388412e-01 3.92664045e-01 4.07236129e-01
-3.61438356e-02 -5.17515063e-01 -6.24407530e-01 -3.61219853e-01
-9.07476619e-03 8.86741757e-01 3.60011697e-01 -8.26502815... | [14.827945709228516, 0.8231736421585083] |
41af1fad-c1bc-4753-9f21-44e3ba77ce49 | adversarial-retriever-ranker-for-dense-text | 2110.03611 | null | https://arxiv.org/abs/2110.03611v5 | https://arxiv.org/pdf/2110.03611v5.pdf | Adversarial Retriever-Ranker for dense text retrieval | Current dense text retrieval models face two typical challenges. First, they adopt a siamese dual-encoder architecture to encode queries and documents independently for fast indexing and searching, while neglecting the finer-grained term-wise interactions. This results in a sub-optimal recall performance. Second, their... | ['Weizhu Chen', 'Nan Duan', 'Jiancheng Lv', 'Yelong Shen', 'Yeyun Gong', 'Hang Zhang'] | 2021-10-07 | adversarial-retriever-ranker-for-dense-text-1 | https://openreview.net/forum?id=MR7XubKUFB | https://openreview.net/pdf?id=MR7XubKUFB | iclr-2022-4 | ['triviaqa'] | ['miscellaneous'] | [-3.76517326e-02 -2.90601134e-01 -1.63741782e-01 -2.40103766e-01
-1.91253889e+00 -7.32593417e-01 7.39930153e-01 -2.36229748e-02
-7.34608352e-01 4.75234866e-01 2.00537041e-01 -2.40513752e-03
-1.23898998e-01 -9.14154589e-01 -9.63845849e-01 -6.69746757e-01
5.36825657e-02 1.07910287e+00 1.50269195e-01 -6.32710576... | [11.457436561584473, 7.611117839813232] |
a6c9dbca-2879-44ae-80f6-7651fbc9887b | an-interpretable-probabilistic-autoregressive | 2204.09640 | null | https://arxiv.org/abs/2204.09640v3 | https://arxiv.org/pdf/2204.09640v3.pdf | Probabilistic AutoRegressive Neural Networks for Accurate Long-range Forecasting | Forecasting time series data is a critical area of research with applications spanning from stock prices to early epidemic prediction. While numerous statistical and machine learning methods have been proposed, real-life prediction problems often require hybrid solutions that bridge classical forecasting approaches and... | ['Abdenour Hadid', 'Tanujit Chakraborty', 'Uttam Kumar', 'Madhurima Panja'] | 2022-04-01 | null | null | null | null | ['epidemiology', 'prediction-intervals'] | ['medical', 'miscellaneous'] | [-3.95905197e-01 -4.74238724e-01 -2.16088042e-01 -3.25618684e-01
-4.64073032e-01 -3.74127775e-01 8.80594075e-01 -1.28943995e-01
-2.89766081e-02 8.15853477e-01 2.91164398e-01 -6.81117594e-01
-4.40390706e-01 -8.83657157e-01 -5.44041932e-01 -8.53672683e-01
-6.92699790e-01 5.69424570e-01 -1.42248392e-01 -4.97775823... | [6.897970676422119, 3.0944712162017822] |
ac8ecb8c-8432-4078-b6e3-4c49eb0ea8e6 | clustering-based-identification-of-precursors | 2306.16291 | null | https://arxiv.org/abs/2306.16291v1 | https://arxiv.org/pdf/2306.16291v1.pdf | Clustering-based Identification of Precursors of Extreme Events in Chaotic Systems | Abrupt and rapid high-amplitude changes in a dynamical system's states known as extreme event appear in many processes occurring in nature, such as drastic climate patterns, rogue waves, or avalanches. These events often entail catastrophic effects, therefore their description and prediction is of great importance. How... | ['Nguyen Anh Khoa Doan', 'Urszula Golyska'] | 2023-06-20 | null | null | null | null | ['clustering'] | ['methodology'] | [-2.55517989e-01 -4.87579912e-01 6.18032515e-01 1.63385093e-01
1.78932130e-01 -7.88427830e-01 1.17787027e+00 5.59831262e-01
-1.85851213e-02 7.13177860e-01 -2.71762218e-02 -3.63022089e-01
-4.81481552e-01 -6.48253500e-01 6.07490838e-02 -1.05765462e+00
-9.50036645e-01 5.21992862e-01 2.19994828e-01 -3.58749062... | [6.682081699371338, 4.029041767120361] |
e1d69a8d-4781-4e57-9048-0843fff856a3 | on-the-perception-of-difficulty-differences | 2304.09803 | null | https://arxiv.org/abs/2304.09803v1 | https://arxiv.org/pdf/2304.09803v1.pdf | On the Perception of Difficulty: Differences between Humans and AI | With the increased adoption of artificial intelligence (AI) in industry and society, effective human-AI interaction systems are becoming increasingly important. A central challenge in the interaction of humans with AI is the estimation of difficulty for human and AI agents for single task instances.These estimations ar... | ['Niklas Kühl', 'Michael Vössing', 'Joshua Holstein', 'Philipp Spitzer'] | 2023-04-19 | null | null | null | null | ['experimental-design'] | ['methodology'] | [-6.87393770e-02 2.86994189e-01 1.05736054e-01 -9.26611871e-02
-9.90460664e-02 -5.33410013e-01 4.84816730e-01 3.97836208e-01
-6.15158916e-01 5.58650136e-01 -1.34552121e-01 -2.16607168e-01
-2.69212127e-01 -3.85097623e-01 8.16963613e-02 -3.02121878e-01
-1.23195425e-02 7.19248414e-01 1.43733539e-03 -1.95981175... | [9.050281524658203, 6.2938079833984375] |
5aa5ea2d-c8c4-45ba-9279-05ebef63c70a | standardized-max-logits-a-simple-yet | 2107.11264 | null | https://arxiv.org/abs/2107.11264v4 | https://arxiv.org/pdf/2107.11264v4.pdf | Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation | Identifying unexpected objects on roads in semantic segmentation (e.g., identifying dogs on roads) is crucial in safety-critical applications. Existing approaches use images of unexpected objects from external datasets or require additional training (e.g., retraining segmentation networks or training an extra network),... | ['Jaegul Choo', 'Sungha Choi', 'Daehoon Gwak', 'Jungsoo Lee', 'Sanghun Jung'] | 2021-07-23 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Jung_Standardized_Max_Logits_A_Simple_yet_Effective_Approach_for_Identifying_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Jung_Standardized_Max_Logits_A_Simple_yet_Effective_Approach_for_Identifying_ICCV_2021_paper.pdf | iccv-2021-1 | ['scene-segmentation'] | ['computer-vision'] | [ 2.01745033e-01 2.34438568e-01 -2.33070105e-01 -6.66247487e-01
-5.44489026e-01 -5.55157244e-01 3.63134265e-01 1.55778095e-01
-5.64378858e-01 4.51546252e-01 -1.23596452e-01 -3.99484456e-01
4.59583141e-02 -9.47014630e-01 -9.45474625e-01 -6.99642301e-01
2.77754247e-01 2.62667298e-01 7.72483528e-01 -1.37425110... | [9.448553085327148, 0.33485445380210876] |
ee4f4078-0ec7-48d0-9612-ff8eb6a0f84a | leveraging-redundancy-in-multiple-audio | 2303.00692 | null | https://arxiv.org/abs/2303.00692v1 | https://arxiv.org/pdf/2303.00692v1.pdf | Leveraging Redundancy in Multiple Audio Signals for Far-Field Speech Recognition | To achieve robust far-field automatic speech recognition (ASR), existing techniques typically employ an acoustic front end (AFE) cascaded with a neural transducer (NT) ASR model. The AFE output, however, could be unreliable, as the beamforming output in AFE is steered to a wrong direction. A promising way to address th... | ['Roland Maas', 'Brian King', 'Athanasios Mouchtaris', 'Maurizio Omologo', 'Harish Mallidi', 'Martin Radfar', 'Rupak Vignesh Swaminathan', 'Anastasios Alexandridis', 'Feng-Ju Chang'] | 2023-03-01 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 5.84398270e-01 1.51799947e-01 6.24853134e-01 -3.77703965e-01
-1.31210613e+00 -5.41342080e-01 4.47013408e-01 -3.05640161e-01
-4.95661467e-01 2.39278480e-01 5.58165908e-01 -7.17880964e-01
5.69992885e-02 -2.38012582e-01 -7.29585707e-01 -5.66876054e-01
-3.58401835e-02 -1.28154710e-01 9.40311328e-02 -3.79488319... | [14.843742370605469, 6.047713279724121] |
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