paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
c3fe9d83-260a-4119-91ad-9f56f23582e0 | inter-beat-interval-estimation-with-tiramisu | 2107.00693 | null | https://arxiv.org/abs/2107.00693v1 | https://arxiv.org/pdf/2107.00693v1.pdf | Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced Error | Inter-beat interval (IBI) measurement enables estimation of heart-rate variability (HRV) which, in turns, can provide early indication of potential cardiovascular diseases. However, extracting IBIs from noisy signals is challenging since the morphology of the signal is distorted in the presence of the noise. Electrocar... | ['Hassan Ghasemzadeh', 'Behrooz A. Shirazi', 'Roozbeh Jafari', 'Seyed Iman Mirzadeh', 'Ali Akbari', 'Asiful Arefeen'] | 2021-07-01 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 3.14562649e-01 -3.96713823e-01 2.17536598e-01 -1.13719717e-01
-6.00889683e-01 -3.40324283e-01 -1.88589498e-01 1.61971465e-01
-3.31647247e-01 9.02452767e-01 2.14611441e-01 1.16319679e-01
-3.06278616e-01 -5.47297716e-01 -1.93713397e-01 -1.00592291e+00
-3.22873056e-01 -1.70983911e-01 -4.54349667e-01 -2.68965424... | [14.259604454040527, 3.177182912826538] |
29266f30-49c8-40f0-b5ac-97617e885311 | image-to-image-translation-for-autonomous | 2209.11673 | null | https://arxiv.org/abs/2209.11673v1 | https://arxiv.org/pdf/2209.11673v1.pdf | Image-to-Image Translation for Autonomous Driving from Coarsely-Aligned Image Pairs | A self-driving car must be able to reliably handle adverse weather conditions (e.g., snowy) to operate safely. In this paper, we investigate the idea of turning sensor inputs (i.e., images) captured in an adverse condition into a benign one (i.e., sunny), upon which the downstream tasks (e.g., semantic segmentation) ca... | ['Mark Campbell', 'Kilian Q Weinberger', 'Bharath Hariharan', 'Wei-Lun Chao', 'Josephine Monica', 'Youya Xia'] | 2022-09-23 | null | null | null | null | ['visual-localization'] | ['computer-vision'] | [ 5.91059566e-01 -4.09465730e-02 -1.30357891e-01 -6.23569906e-01
-6.77222788e-01 -7.89489388e-01 5.22156179e-01 -3.66951317e-01
-1.74560770e-01 5.96149385e-01 -2.77233154e-01 -5.05318940e-01
2.92769194e-01 -8.57134044e-01 -1.29542482e+00 -5.87364733e-01
4.87373620e-01 3.98985177e-01 1.40444905e-01 -1.47649139... | [8.468131065368652, -2.138725996017456] |
1b548b8b-dc5a-42b2-ba5e-f7ae619146df | boosting-rgb-d-saliency-detection-by | 2201.001 | null | https://arxiv.org/abs/2201.00100v1 | https://arxiv.org/pdf/2201.00100v1.pdf | Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images | Training deep models for RGB-D salient object detection (SOD) often requires a large number of labeled RGB-D images. However, RGB-D data is not easily acquired, which limits the development of RGB-D SOD techniques. To alleviate this issue, we present a Dual-Semi RGB-D Salient Object Detection Network (DS-Net) to levera... | ['Yueting Zhuang', 'Yi Yang', 'Fei Wu', 'Ping Li', 'Huazhu Fu', 'Siliang Tang', 'Lei Zhu', 'Xiaoqiang Wang'] | 2022-01-01 | null | null | null | null | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 1.69812620e-01 3.36705863e-01 -3.73539597e-01 -4.86153007e-01
-7.54141510e-01 -1.38018116e-01 1.99548259e-01 -2.44836528e-02
-2.69491673e-01 2.94922739e-01 7.39625171e-02 -1.40871331e-01
4.01395857e-01 -6.45928741e-01 -8.09204400e-01 -8.87029707e-01
6.07878625e-01 6.20639622e-02 9.25225079e-01 -1.11936450... | [9.67101764678955, -0.7761887907981873] |
c46ae9d2-246b-4564-9060-7515b59586d6 | scalable-algorithms-for-string-kernels-with | null | null | http://papers.nips.cc/paper/3441-scalable-algorithms-for-string-kernels-with-inexact-matching | http://papers.nips.cc/paper/3441-scalable-algorithms-for-string-kernels-with-inexact-matching.pdf | Scalable Algorithms for String Kernels with Inexact Matching | We present a new family of linear time algorithms based on sufficient statistics for string comparison with mismatches under the string kernels framework. Our algorithms improve theoretical complexity bounds of existing approaches while scaling well with respect to the sequence alphabet size, the number of allowed mism... | ['Pai-Hsi Huang', 'Vladimir Pavlovic', 'Pavel P. Kuksa'] | 2008-12-01 | null | null | null | neurips-2008-12 | ['genre-classification'] | ['computer-vision'] | [ 6.42693341e-01 -4.97496188e-01 -1.26468703e-01 -1.74740762e-01
-7.92704582e-01 -1.00635529e+00 2.39152580e-01 7.83838391e-01
-5.79944849e-01 6.61605716e-01 -2.59721279e-01 -3.60659838e-01
-1.11085892e-01 -6.19333565e-01 -9.30006444e-01 -6.60552204e-01
-2.67359436e-01 7.21546173e-01 7.53317237e-01 -3.45158279... | [4.860790729522705, 5.202752590179443] |
588caeb7-e1d5-4b8a-afb6-339b7959de3d | sparse-gaussian-process-temporal-difference | 1810.01217 | null | http://arxiv.org/abs/1810.01217v1 | http://arxiv.org/pdf/1810.01217v1.pdf | Sparse Gaussian Process Temporal Difference Learning for Marine Robot Navigation | We present a method for Temporal Difference (TD) learning that addresses
several challenges faced by robots learning to navigate in a marine
environment. For improved data efficiency, our method reduces TD updates to
Gaussian Process regression. To make predictions amenable to online settings,
we introduce a sparse app... | ['John Martin', 'Jinkun Wang', 'Brendan Englot'] | 2018-10-02 | null | null | null | null | ['marine-robot-navigation'] | ['robots'] | [ 1.04133487e-01 2.20677346e-01 7.52495751e-02 -8.56427327e-02
-1.18342721e+00 -3.08899432e-01 2.99909920e-01 -5.65068088e-02
-6.32521272e-01 1.07822633e+00 5.67869507e-02 -3.75427663e-01
-3.65404814e-01 -4.94271070e-01 -1.12994981e+00 -1.01957214e+00
-7.66222179e-01 6.41938567e-01 1.51471317e-01 -1.59037858... | [4.163841247558594, 2.332655191421509] |
e11bce48-5f55-4b32-a3c1-6f9dcabdf881 | continuous-mdp-homomorphisms-and-homomorphic | 2209.07364 | null | https://arxiv.org/abs/2209.07364v1 | https://arxiv.org/pdf/2209.07364v1.pdf | Continuous MDP Homomorphisms and Homomorphic Policy Gradient | Abstraction has been widely studied as a way to improve the efficiency and generalization of reinforcement learning algorithms. In this paper, we study abstraction in the continuous-control setting. We extend the definition of MDP homomorphisms to encompass continuous actions in continuous state spaces. We derive a pol... | ['Doina Precup', 'David Meger', 'Prakash Panangaden', 'Rosie Zhao', 'Sahand Rezaei-Shoshtari'] | 2022-09-15 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-1.88507468e-01 1.57964766e-01 -7.23378897e-01 1.02657929e-01
-5.55015564e-01 -6.94994450e-01 9.49579477e-01 4.43653800e-02
-4.69218194e-01 9.06403959e-01 3.95194024e-01 -5.08581698e-01
-1.08053647e-01 -7.72835255e-01 -9.63823199e-01 -7.69086480e-01
-3.48164767e-01 2.14484587e-01 3.93666625e-02 -3.95694971... | [4.173600673675537, 2.044827938079834] |
d074922d-177e-42be-b3ff-5703ac9126f8 | iterative-patch-selection-for-high-resolution | 2210.13007 | null | https://arxiv.org/abs/2210.13007v2 | https://arxiv.org/pdf/2210.13007v2.pdf | Iterative Patch Selection for High-Resolution Image Recognition | High-resolution images are prevalent in various applications, such as autonomous driving and computer-aided diagnosis. However, training neural networks on such images is computationally challenging and easily leads to out-of-memory errors even on modern GPUs. We propose a simple method, Iterative Patch Selection (IPS)... | ['Aravindh Mahendran', 'Christoph Lippert', 'Benjamin Bergner'] | 2022-10-24 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 4.58998203e-01 5.74493501e-03 -9.21363160e-02 -1.68245718e-01
-8.59071910e-01 -2.97140509e-01 3.36182684e-01 5.10954022e-01
-7.17901945e-01 4.10921723e-01 -4.14611101e-01 -3.99477094e-01
-4.72798124e-02 -9.97455537e-01 -8.70021880e-01 -7.60786235e-01
1.85045600e-01 3.70814830e-01 4.84544337e-01 -4.73993504... | [9.467338562011719, 0.296062707901001] |
45632730-e93e-4a51-a261-0cfd674be36f | continuous-episodic-control | 2211.15183 | null | https://arxiv.org/abs/2211.15183v3 | https://arxiv.org/pdf/2211.15183v3.pdf | Continuous Episodic Control | Non-parametric episodic memory can be used to quickly latch onto high-rewarded experience in reinforcement learning tasks. In contrast to parametric deep reinforcement learning approaches in which reward signals need to be back-propagated slowly, these methods only need to discover the solution once, and may then repea... | ['Aske Plaat', 'Mike Preuss', 'Thomas M. Moerland', 'Zhao Yang'] | 2022-11-28 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-6.26154989e-02 -8.65770318e-03 -4.66286659e-01 7.58479908e-02
-7.82113314e-01 -2.64223546e-01 6.66117132e-01 2.17098683e-01
-8.06525409e-01 1.45309997e+00 9.70934778e-02 9.70574915e-02
-4.19739276e-01 -9.47378635e-01 -8.00960183e-01 -7.98976600e-01
-3.01771402e-01 9.59770441e-01 2.08653778e-01 -1.50049224... | [4.087830066680908, 1.7945263385772705] |
119816f4-3efa-4894-8a87-a5f59c5a7a3d | do-deep-learning-models-really-outperform | 2302.07134 | null | https://arxiv.org/abs/2302.07134v3 | https://arxiv.org/pdf/2302.07134v3.pdf | Do Deep Learning Models Really Outperform Traditional Approaches in Molecular Docking? | Molecular docking, given a ligand molecule and a ligand binding site (called ``pocket'') on a protein, predicting the binding mode of the protein-ligand complex, is a widely used technique in drug design. Many deep learning models have been developed for molecular docking, while most existing deep learning models perfo... | ['Guolin Ke', 'Hang Zheng', 'Zhifeng Gao', 'Shuqi Lu', 'Yuejiang Yu'] | 2023-02-14 | null | null | null | null | ['molecular-docking'] | ['medical'] | [-3.65817696e-01 -2.97885388e-01 -4.13684636e-01 -1.83456138e-01
-7.54580975e-01 -7.60551035e-01 -2.74683554e-02 1.83852434e-01
-3.39643538e-01 1.08245707e+00 -7.25424737e-02 -8.81182313e-01
2.58978784e-01 -6.91041291e-01 -1.06469691e+00 -1.00512660e+00
-1.91583201e-01 6.02103829e-01 1.31158784e-01 -3.22302341... | [4.916896343231201, 5.6805620193481445] |
c5024753-d946-4ebe-b729-3dbcff4b6e32 | fr-net-a-light-weight-fft-residual-net-for | 2305.11875 | null | https://arxiv.org/abs/2305.11875v1 | https://arxiv.org/pdf/2305.11875v1.pdf | FR-Net:A Light-weight FFT Residual Net For Gaze Estimation | Gaze estimation is a crucial task in computer vision, however, existing methods suffer from high computational costs, which limit their practical deployment in resource-limited environments. In this paper, we propose a novel lightweight model, FR-Net, for accurate gaze angle estimation while significantly reducing comp... | ['Di Huang', 'Yun Zhou', 'Ruilong Fan', 'Bo Wu', 'Tao Xu'] | 2023-05-04 | null | null | null | null | ['gaze-estimation'] | ['computer-vision'] | [ 1.12278700e-01 -2.31358469e-01 1.91327259e-02 -4.39402461e-01
-2.43160620e-01 -2.06103697e-01 3.83422971e-02 -1.86520040e-01
-7.25301027e-01 4.26440954e-01 -3.69387537e-01 -4.90206689e-01
-7.61883482e-02 -1.98019594e-01 -4.65753406e-01 -6.56827152e-01
2.96376854e-01 -5.05998552e-01 4.78289187e-01 -8.50124508... | [14.110392570495605, 0.10402733832597733] |
3d9127c9-8c82-42d0-b293-5491946fa71a | milestones-in-autonomous-driving-and-2 | 2306.0198 | null | https://arxiv.org/abs/2306.01980v1 | https://arxiv.org/pdf/2306.01980v1.pdf | Milestones in Autonomous Driving and Intelligent Vehicles Part II: Perception and Planning | Growing interest in autonomous driving (AD) and intelligent vehicles (IVs) is fueled by their promise for enhanced safety, efficiency, and economic benefits. While previous surveys have captured progress in this field, a comprehensive and forward-looking summary is needed. Our work fills this gap through three distinct... | ['Fei-Yue Wang', 'Nanning Zheng', 'Dongpu Cao', 'Jinjun Wang', 'Zixuan Li', 'Yuchen Li', 'Xiaoxiang Na', 'Bai Li', 'Siyu Teng', 'Long Chen'] | 2023-06-03 | null | null | null | null | ['ethics'] | ['miscellaneous'] | [-1.18053183e-01 1.82953939e-01 -4.06474710e-01 -5.36268413e-01
-2.45540235e-02 -4.99406427e-01 6.43296599e-01 -1.00734644e-02
-2.75981098e-01 4.03334081e-01 -1.48315921e-01 -8.38853598e-01
1.54087916e-01 -8.09864163e-01 -6.29117310e-01 -3.21641505e-01
8.86290297e-02 6.16054647e-02 4.56428587e-01 -6.81317985... | [5.690428733825684, 1.0457582473754883] |
2197e1c1-6164-4c2a-bdf3-b92e624eca08 | amuse-multilingual-semantic-parsing-for | 1802.09296 | null | http://arxiv.org/abs/1802.09296v1 | http://arxiv.org/pdf/1802.09296v1.pdf | AMUSE: Multilingual Semantic Parsing for Question Answering over Linked Data | The task of answering natural language questions over RDF data has received
wide interest in recent years, in particular in the context of the series of
QALD benchmarks. The task consists of mapping a natural language question to an
executable form, e.g. SPARQL, so that answers from a given KB can be extracted.
So far,... | ['Soufian Jebbara', 'Sherzod Hakimov', 'Philipp Cimiano'] | 2018-02-26 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [-1.02431573e-01 6.86746895e-01 -2.77220964e-01 -6.84356451e-01
-1.14731336e+00 -7.64868498e-01 1.05596375e+00 6.50437236e-01
-5.18376470e-01 7.68575191e-01 6.82374477e-01 -4.49323386e-01
-4.05074507e-01 -1.32332253e+00 -1.12019479e+00 6.83844686e-02
2.79496193e-01 1.13911295e+00 4.83421534e-01 -6.25736058... | [10.2982816696167, 7.896028995513916] |
269939d9-615f-4174-b449-d39b8cf8e9f0 | a-discourse-aware-graph-neural-network-for | null | null | https://aclanthology.org/2021.findings-emnlp.252 | https://aclanthology.org/2021.findings-emnlp.252.pdf | A Discourse-Aware Graph Neural Network for Emotion Recognition in Multi-Party Conversation | Emotion recognition in multi-party conversation (ERMC) is becoming increasingly popular as an emerging research topic in natural language processing. Prior research focuses on exploring sequential information but ignores the discourse structures of conversations. In this paper, we investigate the importance of discours... | ['Guohong Fu', 'Nan Yu', 'Yang Sun'] | null | null | null | null | findings-emnlp-2021-11 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 1.43472642e-01 2.78324157e-01 -1.40620759e-02 -6.94632947e-01
-4.46294338e-01 -5.47589302e-01 7.12923169e-01 2.27083847e-01
-2.84271091e-01 4.30247962e-01 1.03297246e+00 -2.55317301e-01
2.08662316e-01 -3.90558779e-01 -2.23377302e-01 -3.24868888e-01
-3.25835228e-01 -2.51205694e-02 -2.77531713e-01 -6.43921018... | [12.93421745300293, 6.374436378479004] |
0c150f54-ac0e-4e56-973d-8395b48776ef | low-resource-neural-machine-translation-a | 2003.14402 | null | https://arxiv.org/abs/2003.14402v1 | https://arxiv.org/pdf/2003.14402v1.pdf | Low Resource Neural Machine Translation: A Benchmark for Five African Languages | Recent advents in Neural Machine Translation (NMT) have shown improvements in low-resource language (LRL) translation tasks. In this work, we benchmark NMT between English and five African LRL pairs (Swahili, Amharic, Tigrigna, Oromo, Somali [SATOS]). We collected the available resources on the SATOS languages to evalu... | ['Matteo Negri', 'Marco Turchi', 'Surafel M. Lakew'] | 2020-03-31 | null | null | null | null | ['low-resource-neural-machine-translation'] | ['natural-language-processing'] | [ 9.15639028e-02 -3.00680131e-01 -6.05943978e-01 -3.79055530e-01
-1.73313475e+00 -8.44869077e-01 8.80432069e-01 -3.35945964e-01
-5.17046332e-01 1.29754210e+00 2.67063588e-01 -9.76612151e-01
2.49362439e-01 -1.45056173e-01 -9.15077567e-01 -2.82864630e-01
3.50160003e-01 1.14234698e+00 -4.42444414e-01 -6.39557421... | [11.542854309082031, 10.390960693359375] |
598c2b33-a2e0-41cf-b340-7908e7e0538c | inducing-semantic-grouping-of-latent-concepts | 2108.11761 | null | https://arxiv.org/abs/2108.11761v2 | https://arxiv.org/pdf/2108.11761v2.pdf | A Framework for Learning Ante-hoc Explainable Models via Concepts | Self-explaining deep models are designed to learn the latent concept-based explanations implicitly during training, which eliminates the requirement of any post-hoc explanation generation technique. In this work, we propose one such model that appends an explanation generation module on top of any basic network and joi... | ['Vineeth N Balasubramanian', 'Anindya Sarkar', 'Deepak Vijaykeerthy', 'Anirban Sarkar'] | 2021-08-25 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Sarkar_A_Framework_for_Learning_Ante-Hoc_Explainable_Models_via_Concepts_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Sarkar_A_Framework_for_Learning_Ante-Hoc_Explainable_Models_via_Concepts_CVPR_2022_paper.pdf | cvpr-2022-1 | ['explainable-models'] | ['computer-vision'] | [ 1.21638231e-01 9.16474283e-01 -2.48883530e-01 -5.99613607e-01
-2.96173275e-01 -9.52221230e-02 7.02698946e-01 1.14969738e-01
-5.12634590e-02 8.47614884e-01 1.49538785e-01 -5.64190328e-01
-2.46099874e-01 -7.03880847e-01 -9.25229371e-01 -2.65432924e-01
-1.01243801e-01 7.01929688e-01 -3.83189991e-02 -1.74519420... | [9.02340316772461, 5.703380107879639] |
0db958ae-4354-42f5-890f-b9fcf6c2baa7 | s2gan-share-aging-factors-across-ages-and | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/He_S2GAN_Share_Aging_Factors_Across_Ages_and_Share_Aging_Trends_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/He_S2GAN_Share_Aging_Factors_Across_Ages_and_Share_Aging_Trends_ICCV_2019_paper.pdf | S2GAN: Share Aging Factors Across Ages and Share Aging Trends Among Individuals | Generally, we human follow the roughly common aging trends, e.g., the wrinkles only tend to be more, longer or deeper. However, the aging process of each individual is more dominated by his/her personalized factors, including the invariant factors such as identity and mole, as well as the personalized aging patterns, e... | [' Xilin Chen', ' Shiguang Shan', ' Meina Kan', 'Zhenliang He'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['face-age-editing'] | ['computer-vision'] | [-1.54118448e-01 -1.80709392e-01 -2.17081860e-01 -6.47541583e-02
4.24334586e-01 -2.58334398e-01 1.23644508e-01 -3.16840746e-02
-1.90405976e-02 8.74216735e-01 3.20700556e-01 2.87677169e-01
8.97737667e-02 -8.80845487e-01 -4.79980439e-01 -8.23579550e-01
-3.03340815e-02 -8.53199586e-02 1.60334513e-01 -3.61132413... | [13.146327018737793, 0.43954065442085266] |
1c704b56-414d-47bf-b3d5-d41d8b78664b | neural-inventory-control-in-networks-via | 2306.11246 | null | https://arxiv.org/abs/2306.11246v1 | https://arxiv.org/pdf/2306.11246v1.pdf | Neural Inventory Control in Networks via Hindsight Differentiable Policy Optimization | Inventory management offers unique opportunities for reliably evaluating and applying deep reinforcement learning (DRL). Rather than evaluate DRL algorithms by comparing against one another or against human experts, we can compare to the optimum itself in several problem classes with hidden structure. Our DRL methods c... | ['Yash Kanoria', 'Daniel Russo', 'Matias Alvo'] | 2023-06-20 | null | null | null | null | ['management'] | ['miscellaneous'] | [-1.20173067e-01 -2.05191299e-02 -6.80825830e-01 -2.17945844e-01
-7.41884053e-01 -8.45669985e-01 3.04233819e-01 1.31000236e-01
-6.02051318e-01 1.04246294e+00 1.89818531e-01 -7.67196476e-01
-4.79844064e-01 -6.00877464e-01 -1.07673371e+00 -7.05758333e-01
-5.20269096e-01 9.53515351e-01 -1.01569660e-01 -2.41534784... | [4.215375900268555, 2.3922066688537598] |
a2be6792-a350-4652-8e00-3d7832c6e067 | audio-transformers-transformer-architectures | 2105.00335 | null | https://arxiv.org/abs/2105.00335v1 | https://arxiv.org/pdf/2105.00335v1.pdf | Audio Transformers:Transformer Architectures For Large Scale Audio Understanding. Adieu Convolutions | Over the past two decades, CNN architectures have produced compelling models of sound perception and cognition, learning hierarchical organizations of features. Analogous to successes in computer vision, audio feature classification can be optimized for a particular task of interest, over a wide variety of datasets and... | ['Jonathan Berger', 'Prateek Verma'] | 2021-05-01 | null | null | null | null | ['unsupervised-pre-training'] | ['methodology'] | [ 1.33098543e-01 5.08214300e-03 4.10441071e-01 -3.90825123e-01
-7.48498380e-01 -5.61523974e-01 4.62393016e-01 1.33532807e-01
-5.43342173e-01 1.29398674e-01 5.45513332e-01 -5.46855479e-02
-1.70204416e-01 -8.00821126e-01 -6.22650266e-01 -4.87902194e-01
-5.74994028e-01 -1.09702908e-01 3.40751112e-01 -3.63196224... | [15.423295021057129, 5.328564167022705] |
56915359-8b11-43bf-8ae9-98f1ac989305 | label-relation-graphs-enhanced-hierarchical | 2201.03194 | null | https://arxiv.org/abs/2201.03194v2 | https://arxiv.org/pdf/2201.03194v2.pdf | Label Relation Graphs Enhanced Hierarchical Residual Network for Hierarchical Multi-Granularity Classification | Hierarchical multi-granularity classification (HMC) assigns hierarchical multi-granularity labels to each object and focuses on encoding the label hierarchy, e.g., ["Albatross", "Laysan Albatross"] from coarse-to-fine levels. However, the definition of what is fine-grained is subjective, and the image quality may affec... | ['Yuntao Qian', 'Jian Liu', 'Peng Wang', 'Jingzhou Chen'] | 2022-01-10 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Chen_Label_Relation_Graphs_Enhanced_Hierarchical_Residual_Network_for_Hierarchical_Multi-Granularity_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_Label_Relation_Graphs_Enhanced_Hierarchical_Residual_Network_for_Hierarchical_Multi-Granularity_CVPR_2022_paper.pdf | cvpr-2022-1 | ['fine-grained-image-classification'] | ['computer-vision'] | [ 3.47916245e-01 2.56280303e-01 -2.96794981e-01 -4.06038195e-01
-5.68489850e-01 -4.01994437e-01 3.76895458e-01 3.62259597e-01
-1.07466191e-01 8.94409716e-01 -1.09741770e-01 1.69908166e-01
-5.38038850e-01 -1.16896498e+00 -7.80614257e-01 -9.28764045e-01
-2.50474542e-01 3.40220124e-01 3.55363071e-01 3.79903555... | [9.601076126098633, 2.3980371952056885] |
ec9160a0-2ec3-475a-b9b7-3337ce13e687 | generative-entity-typing-with-curriculum | 2210.02914 | null | https://arxiv.org/abs/2210.02914v2 | https://arxiv.org/pdf/2210.02914v2.pdf | Generative Entity Typing with Curriculum Learning | Entity typing aims to assign types to the entity mentions in given texts. The traditional classification-based entity typing paradigm has two unignorable drawbacks: 1) it fails to assign an entity to the types beyond the predefined type set, and 2) it can hardly handle few-shot and zero-shot situations where many long-... | ['Yanghua Xiao', 'Jingyue Huang', 'Jinxi Liu', 'Zhixu Li', 'Jiaqing Liang', 'Deqing Yang', 'Siyu Yuan'] | 2022-10-06 | null | null | null | null | ['entity-typing'] | ['natural-language-processing'] | [-1.12056389e-01 1.89094007e-01 -3.53751779e-01 -3.98484558e-01
-6.03944242e-01 -6.27447605e-01 6.49360418e-01 2.47329399e-01
-6.98625863e-01 9.85196471e-01 -3.96923721e-02 -3.49573106e-01
2.17693835e-01 -1.15457606e+00 -7.37271249e-01 -4.37950999e-01
3.07893097e-01 8.69488299e-01 2.72328287e-01 -3.47642422... | [9.644502639770508, 8.758882522583008] |
b1a9f1b7-f0a1-4db4-ba08-03cc1da24902 | reinforcement-learning-finetuned-vision-code | 2305.14637 | null | https://arxiv.org/abs/2305.14637v1 | https://arxiv.org/pdf/2305.14637v1.pdf | Reinforcement Learning finetuned Vision-Code Transformer for UI-to-Code Generation | Automated HTML/CSS code generation from screenshots is an important yet challenging problem with broad applications in website development and design. In this paper, we present a novel vision-code transformer approach that leverages an Encoder-Decoder architecture as well as explore actor-critic fine-tuning as a method... | ['Tianyi Zhou', 'Khalid Saifullah', 'Davit Soselia'] | 2023-05-24 | null | null | null | null | ['code-generation'] | ['computer-code'] | [ 3.85940403e-01 1.21314332e-01 2.52668291e-01 -2.60064781e-01
-1.32293105e+00 -8.97094250e-01 5.60110807e-01 -2.51108199e-01
1.81377809e-02 3.76625001e-01 2.15432689e-01 -4.74861622e-01
3.00044656e-01 -5.37580729e-01 -1.07618451e+00 6.31019920e-02
4.40559864e-01 1.61894038e-03 1.87799662e-01 -5.21899275... | [7.783567905426025, 7.795492172241211] |
c009cbf6-3779-483b-98bf-487ced354785 | correcting-discount-factor-mismatch-in-on | 2306.13284 | null | https://arxiv.org/abs/2306.13284v1 | https://arxiv.org/pdf/2306.13284v1.pdf | Correcting discount-factor mismatch in on-policy policy gradient methods | The policy gradient theorem gives a convenient form of the policy gradient in terms of three factors: an action value, a gradient of the action likelihood, and a state distribution involving discounting called the \emph{discounted stationary distribution}. But commonly used on-policy methods based on the policy gradien... | ['A. Rupam Mahmood', 'Gautham Vasan', 'Fengdi Che'] | 2023-06-23 | null | null | null | null | ['policy-gradient-methods', 'openai-gym'] | ['methodology', 'playing-games'] | [-9.10883695e-02 -1.56497121e-01 -5.79787314e-01 -4.31941450e-01
-4.52479005e-01 -4.98283565e-01 5.36944449e-01 1.76244915e-01
-1.03474605e+00 1.18583429e+00 1.82008758e-01 -6.97194219e-01
-7.33587295e-02 -3.83435398e-01 -7.67381608e-01 -7.56949663e-01
5.35894781e-02 8.01946521e-02 5.59219956e-01 -1.94922820... | [4.110219955444336, 2.373169183731079] |
349e37e4-ff18-4855-a0f5-ccf1f88dc9a0 | neural-rankers-for-effective-screening | 2212.09017 | null | https://arxiv.org/abs/2212.09017v1 | https://arxiv.org/pdf/2212.09017v1.pdf | Neural Rankers for Effective Screening Prioritisation in Medical Systematic Review Literature Search | Medical systematic reviews typically require assessing all the documents retrieved by a search. The reason is two-fold: the task aims for ``total recall''; and documents retrieved using Boolean search are an unordered set, and thus it is unclear how an assessor could examine only a subset. Screening prioritisation is t... | ['Guido Zuccon', 'Bevan Koopman', 'Harrisen Scells', 'Shuai Wang'] | 2022-12-18 | null | null | null | null | ['document-ranking'] | ['natural-language-processing'] | [ 6.47104621e-01 3.81299049e-01 -7.41374731e-01 -3.33215445e-01
-1.41989064e+00 -6.19506836e-01 6.40096188e-01 7.54570842e-01
-6.90374553e-01 6.82348788e-01 4.94422525e-01 -6.01242006e-01
-6.57574713e-01 -7.71739900e-01 -3.63704115e-01 -2.78027385e-01
-8.80555660e-02 9.50635135e-01 3.03677022e-01 -9.97900069... | [8.796343803405762, 8.569063186645508] |
66706eb2-fa30-4295-8df9-c64dfc1267c0 | imagenet-pretrained-cnns-for-jpeg | null | null | http://www.ws.binghamton.edu/Fridrich/Research/Alaska-2-Revised.pdf | http://www.ws.binghamton.edu/Fridrich/Research/Alaska-2-Revised.pdf | ImageNet Pretrained CNNs for JPEG Steganalysis | In this paper, we investigate pre-trained computervision deep architectures, such as the EfficientNet, MixNet, and
ResNet for steganalysis. These models pre-trained on ImageNet
can be rather quickly refined for JPEG steganalysis while offering
significantly better performance than CNNs designed purposely
for stegan... | ['Jessica Fridrich', 'Eugene Khvedchenya', 'Jan Butora', 'Yassine Yousfi'] | 2020-11-24 | null | null | null | null | ['steganalysis', 'image-steganography'] | ['computer-vision', 'computer-vision'] | [ 4.63553280e-01 4.91108119e-01 2.66155154e-01 1.87624231e-01
-4.21628088e-01 -3.51860136e-01 8.45156133e-01 -6.81053400e-01
-6.71263933e-01 3.24454993e-01 2.85919398e-01 -7.14267313e-01
5.51909924e-01 -7.76778162e-01 -8.88619065e-01 -5.92087626e-01
-1.80603206e-01 -7.73719996e-02 3.72288644e-01 -6.15194201... | [4.334000587463379, 8.04135513305664] |
bc5d3d30-8948-42ab-aa0f-13c9f97495fc | consistent-and-elastic-registration-of | null | null | https://link.springer.com/chapter/10.1007/11889762_8 | https://repositorio.uam.es/bitstream/handle/10486/666430/consistent_arganda-carreras_LNCS_2006_ps.pdf | Consistent and elastic registration of histological sections using vector-spline regularization | Here we present a new image registration algorithm for the alignment of histological sections that combines the ideas of B-spline based elastic registration and consistent image registration, to allow simultaneous registration of images in two directions (direct and inverse). In principle, deformations based on B-splin... | ['Carlos Ortiz-de-Solorzano', 'José María Carazo', 'Ignacio Arganda-Carreras', 'Roberto Marabini', 'Jan Kybic', 'Carlos O. S. Sorzano'] | 2006-05-12 | null | null | null | computer-vision-approaches-to-medical-image | ['birl-cima'] | ['medical'] | [ 8.79950672e-02 2.76348572e-02 -1.53181306e-03 -4.20913219e-01
-6.27552927e-01 -4.35017884e-01 3.34822029e-01 2.67757148e-01
-8.00951600e-01 5.97115576e-01 -3.61334607e-02 -4.10542898e-02
-3.97371233e-01 -6.94349527e-01 -3.11374158e-01 -1.05298042e+00
-1.94098845e-01 5.24214268e-01 6.19329393e-01 -3.65653157... | [13.97729778289795, -2.595978021621704] |
afe6140e-896b-412c-af4f-2aaf3f7dcea9 | predictive-process-model-monitoring-using | 2011.02819 | null | https://arxiv.org/abs/2011.02819v3 | https://arxiv.org/pdf/2011.02819v3.pdf | Predictive Process Model Monitoring using Recurrent Neural Networks | The field of predictive process monitoring focuses on case-level models to predict a single specific outcome such as a particular objective, (remaining) time, or next activity/remaining sequence. Recently, a longer-horizon, model-wide approach has been proposed in the form of process model forecasting, which predicts t... | ['Jochen De Weerdt', 'Johannes De Smedt'] | 2020-11-05 | null | null | null | null | ['predictive-process-monitoring'] | ['time-series'] | [ 6.97811902e-01 5.27110603e-03 -1.33674592e-01 -3.97535890e-01
-3.57963890e-02 -1.55875355e-01 1.21383786e+00 5.79387605e-01
8.26018378e-02 1.82277188e-01 3.87598336e-01 -2.19086006e-01
-6.69533134e-01 -1.03010786e+00 -2.50427336e-01 -3.56295466e-01
-4.29897845e-01 5.79925358e-01 1.36379704e-01 2.78044432... | [8.58228588104248, 5.946831703186035] |
267c9704-1a9a-47ca-9efa-2d640b36297f | segment-everything-everywhere-all-at-once | 2304.06718 | null | https://arxiv.org/abs/2304.06718v3 | https://arxiv.org/pdf/2304.06718v3.pdf | Segment Everything Everywhere All at Once | Despite the growing demand for interactive AI systems, there have been few comprehensive studies on human-AI interaction in visual understanding e.g. segmentation. Inspired by the development of prompt-based universal interfaces for LLMs, this paper presents SEEM, a promptable, interactive model for Segmenting Everythi... | ['Yong Jae Lee', 'Jianfeng Gao', 'Linjie Li', 'Feng Li', 'Hao Zhang', 'Jianwei Yang', 'Xueyan Zou'] | 2023-04-13 | null | null | null | null | ['personalized-segmentation'] | ['computer-vision'] | [ 4.65957642e-01 6.51061833e-01 -5.84531836e-02 -5.60550272e-01
-3.72739673e-01 -9.19807076e-01 9.69369173e-01 2.46329159e-01
-3.48921418e-01 2.15814933e-01 5.49524307e-01 -3.76148015e-01
1.32178932e-01 -2.97415167e-01 -6.52124524e-01 -1.73342019e-01
2.28532508e-01 8.93396854e-01 5.75076818e-01 -2.16935620... | [10.915609359741211, 1.6670310497283936] |
b774c4c0-56bd-49bf-880f-9ef7489dbd9e | deep-vfx-deep-action-recognition-driven-vfx | 2007.11257 | null | https://arxiv.org/abs/2007.11257v1 | https://arxiv.org/pdf/2007.11257v1.pdf | Deep-VFX: Deep Action Recognition Driven VFX for Short Video | Human motion is a key function to communicate information. In the application, short-form mobile video is so popular all over the world such as Tik Tok. The users would like to add more VFX so as to pursue creativity and personlity. Many special effects are added on the short video platform. These gives the users more ... | ['Feng Jiang', 'Ning Xie', 'Ao Luo', 'Zhijia Tao'] | 2020-07-22 | null | null | null | null | ['template-matching'] | ['computer-vision'] | [ 1.81950003e-01 -1.01176724e-01 -7.58600011e-02 -1.21772595e-01
1.81691200e-02 -1.60629645e-01 3.45217168e-01 -6.87623024e-01
-3.08422834e-01 4.38848913e-01 1.36996880e-01 -9.42475814e-03
5.57581000e-02 -7.90809333e-01 -4.75633532e-01 -4.48084503e-01
2.71213055e-01 -1.44808784e-01 3.14091146e-01 -4.25471038... | [10.783562660217285, -0.7933686971664429] |
560515f5-f55a-4600-b5fc-892769722b28 | generalizing-interactive-backpropagating | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Lin_Generalizing_Interactive_Backpropagating_Refinement_for_Dense_Prediction_Networks_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lin_Generalizing_Interactive_Backpropagating_Refinement_for_Dense_Prediction_Networks_CVPR_2022_paper.pdf | Generalizing Interactive Backpropagating Refinement for Dense Prediction Networks | As deep neural networks become the state-of-the-art approach in the field of computer vision for dense prediction tasks, many methods have been developed for automatic estimation of the target outputs given the visual inputs. Although the estimation accuracy of the proposed automatic methods continues to improve, i... | ['Tony Martinez', 'Brian Price', 'Fanqing Lin'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['image-matting'] | ['computer-vision'] | [ 3.44215184e-01 2.90944695e-01 -5.70323355e-02 -6.54229164e-01
-5.93463898e-01 -1.16388485e-01 4.86916929e-01 -9.26156119e-02
-6.07789457e-01 5.08563459e-01 -1.74513862e-01 -5.68428747e-02
3.44746441e-01 -8.07495713e-01 -8.74535620e-01 -5.44298291e-01
2.34870911e-01 6.25678420e-01 8.23676586e-01 5.30251339... | [9.526975631713867, 0.009137987159192562] |
f37604a2-cd9f-4572-9e8a-0d0c960b4b74 | local-relighting-of-real-scenes | 2207.02774 | null | https://arxiv.org/abs/2207.02774v1 | https://arxiv.org/pdf/2207.02774v1.pdf | Local Relighting of Real Scenes | We introduce the task of local relighting, which changes a photograph of a scene by switching on and off the light sources that are visible within the image. This new task differs from the traditional image relighting problem, as it introduces the challenge of detecting light sources and inferring the pattern of light ... | ['David Bau', 'Rohit Kumar', 'Shahin Mahdizadehaghdam', 'Antonio Torralba', 'Agata Lapedriza', 'Ali Jahanian', 'Audrey Cui'] | 2022-07-06 | null | null | null | null | ['image-relighting'] | ['computer-vision'] | [ 8.95174026e-01 -1.31727681e-01 3.51424754e-01 -4.57642645e-01
-7.16997683e-01 -7.15456545e-01 7.96085835e-01 -5.69209158e-01
-1.34913996e-01 8.50370228e-01 1.61507219e-01 -1.10887110e-01
5.18921137e-01 -9.62596416e-01 -1.35783851e+00 -8.33528399e-01
6.25127792e-01 2.24123344e-01 6.09377883e-02 -1.83894157... | [9.83217716217041, -2.894676446914673] |
afb395e4-6749-49a1-8412-90f647a99e9e | knee-osteoarthritis-severity-prediction-using | 2106.14292 | null | https://arxiv.org/abs/2106.14292v1 | https://arxiv.org/pdf/2106.14292v1.pdf | Knee Osteoarthritis Severity Prediction using an Attentive Multi-Scale Deep Convolutional Neural Network | Knee Osteoarthritis (OA) is a destructive joint disease identified by joint stiffness, pain, and functional disability concerning millions of lives across the globe. It is generally assessed by evaluating physical symptoms, medical history, and other joint screening tests like radiographs, Magnetic Resonance Imaging (M... | ['Palash Ghosh', 'Arijit Sur', 'Sibaji Gaj', 'Prasen Kumar Sharma', 'Rohit Kumar Jain'] | 2021-06-27 | null | null | null | null | ['severity-prediction'] | ['computer-vision'] | [-2.53673047e-01 -1.35210723e-01 -4.33970690e-01 -4.14233916e-02
-7.68311262e-01 2.92474717e-01 2.69312024e-01 -9.64785963e-02
-4.86020595e-01 8.64994407e-01 5.03235877e-01 8.13907534e-02
-5.61469138e-01 -7.47440696e-01 -1.52494714e-01 -5.56559443e-01
-6.30412161e-01 5.40964603e-01 3.18262309e-01 -2.27144003... | [14.626235008239746, -1.8331093788146973] |
d1f46ff6-6720-45db-a35f-7692f6966f82 | capsnet-for-medical-image-segmentation | 2203.08948 | null | https://arxiv.org/abs/2203.08948v1 | https://arxiv.org/pdf/2203.08948v1.pdf | CapsNet for Medical Image Segmentation | Convolutional Neural Networks (CNNs) have been successful in solving tasks in computer vision including medical image segmentation due to their ability to automatically extract features from unstructured data. However, CNNs are sensitive to rotation and affine transformation and their success relies on huge-scale label... | ['Ngan Le', 'Khoa Luu', 'Hien Nguyen', 'Kyle Quinn', 'Viet-Khoa Vo-Ho', 'Minh Tran'] | 2022-03-16 | null | null | null | null | ['volumetric-medical-image-segmentation'] | ['medical'] | [ 1.65332586e-01 2.65499264e-01 -4.44055974e-01 -5.80310345e-01
-1.37620389e-01 -5.96537590e-01 1.01631917e-01 1.40265509e-01
-5.36941350e-01 4.51950699e-01 4.07719091e-02 -2.50084460e-01
1.29723445e-01 -8.00873518e-01 -5.25938511e-01 -5.38705707e-01
-2.30976149e-01 2.08465621e-01 3.07526350e-01 -1.42913550... | [14.69187068939209, -2.627533197402954] |
d443fec6-f341-4f39-a0d5-1b2291b15482 | seq-u-net-a-one-dimensional-causal-u-net-for | 1911.06393 | null | https://arxiv.org/abs/1911.06393v1 | https://arxiv.org/pdf/1911.06393v1.pdf | Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence Modelling | Convolutional neural networks (CNNs) with dilated filters such as the Wavenet or the Temporal Convolutional Network (TCN) have shown good results in a variety of sequence modelling tasks. However, efficiently modelling long-term dependencies in these sequences is still challenging. Although the receptive field of these... | ['Daniel Stoller', 'Simon Dixon', 'Sebastian Ewert', 'Mi Tian'] | 2019-11-14 | null | null | null | null | ['audio-generation', 'music-modeling'] | ['audio', 'music'] | [ 2.02210054e-01 -3.29707444e-01 3.47201079e-01 -3.78169745e-01
-2.80440629e-01 -4.97260839e-01 8.17125142e-01 -1.27341077e-01
-6.31580114e-01 6.64332569e-01 3.86551768e-01 -4.88315016e-01
5.34859076e-02 -7.63679266e-01 -8.34779680e-01 -5.97111642e-01
-4.49388444e-01 -1.26394883e-01 2.78038949e-01 -2.94140071... | [10.95485782623291, 6.541244983673096] |
7da4482e-1170-4537-b7e4-4f7c079f8b4c | efficient-unsupervised-sentence-compression-1 | 2205.08221 | null | https://arxiv.org/abs/2205.08221v1 | https://arxiv.org/pdf/2205.08221v1.pdf | Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning | Sentence compression reduces the length of text by removing non-essential content while preserving important facts and grammaticality. Unsupervised objective driven methods for sentence compression can be used to create customized models without the need for ground-truth training data, while allowing flexibility in the... | ['Georgiana Ifrim', 'Chris Hokamp', 'Demian Gholipour Ghalandari'] | 2022-05-17 | efficient-unsupervised-sentence-compression | https://aclanthology.org/2022.acl-long.90 | https://aclanthology.org/2022.acl-long.90.pdf | acl-2022-5 | ['sentence-compression', 'unsupervised-abstractive-sentence-compression'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.67314887e-01 5.79833567e-01 -3.68948460e-01 -6.51458025e-01
-9.74022627e-01 -3.58870685e-01 5.64108551e-01 5.52429140e-01
-7.07431197e-01 1.08547771e+00 5.79559028e-01 -5.96133113e-01
1.62793901e-02 -9.46463287e-01 -8.37843478e-01 -2.78736383e-01
3.17051351e-01 8.64821792e-01 -1.85984448e-02 -3.26011151... | [12.117677688598633, 9.266711235046387] |
022eda6f-1a50-4f96-b43e-e55e4d95dc85 | x-scitldr-cross-lingual-extreme-summarization | 2205.15051 | null | https://arxiv.org/abs/2205.15051v1 | https://arxiv.org/pdf/2205.15051v1.pdf | X-SCITLDR: Cross-Lingual Extreme Summarization of Scholarly Documents | The number of scientific publications nowadays is rapidly increasing, causing information overload for researchers and making it hard for scholars to keep up to date with current trends and lines of work. Consequently, recent work on applying text mining technologies for scholarly publications has investigated the appl... | ['Simone Paolo Ponzetto', 'Kai Eckert', 'Niklas Friedrich', 'Tommaso Green', 'Sotaro Takeshita'] | 2022-05-30 | null | null | null | null | ['extreme-summarization'] | ['natural-language-processing'] | [ 1.44095331e-01 3.64621915e-02 -4.53944981e-01 -4.47786674e-02
-1.51440167e+00 -6.89668477e-01 7.51573384e-01 5.65153301e-01
-4.31383699e-01 1.23664391e+00 7.37618685e-01 -5.05215466e-01
2.64495939e-01 -2.55614221e-01 -5.99189520e-01 -1.31964639e-01
4.45822746e-01 7.96868563e-01 -3.00298259e-02 -3.48537385... | [12.405743598937988, 9.55396556854248] |
acc16a74-f180-49e4-bb4c-4a7d373aa5be | low-rank-quaternion-matrix-completion-based | 2211.12793 | null | https://arxiv.org/abs/2211.12793v1 | https://arxiv.org/pdf/2211.12793v1.pdf | Low Rank Quaternion Matrix Completion Based on Quaternion QR Decomposition and Sparse Regularizer | Matrix completion is one of the most challenging problems in computer vision. Recently, quaternion representations of color images have achieved competitive performance in many fields. Because it treats the color image as a whole, the coupling information between the three channels of the color image is better utilized... | ['LiZhi Liu', 'Jifei Miao', 'Kit Ian Kou', 'Liqiao Yang', 'Juan Han'] | 2022-11-23 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [-2.28018016e-01 -5.03633559e-01 2.53894925e-01 1.17653802e-01
-5.63901544e-01 -3.46556120e-02 1.59404978e-01 -8.23983178e-02
-7.35389233e-01 5.40376484e-01 -5.79006299e-02 -1.11934826e-01
3.68216373e-02 -5.20544767e-01 -4.48989600e-01 -7.96446919e-01
-4.30211332e-03 -1.33289158e-01 1.75724164e-01 -6.30156755... | [10.823116302490234, -1.6898629665374756] |
8e1bedbb-eec4-4ff6-a09b-ed6cf88f4cca | neural-laplace-control-for-continuous-time | 2302.12604 | null | https://arxiv.org/abs/2302.12604v2 | https://arxiv.org/pdf/2302.12604v2.pdf | Neural Laplace Control for Continuous-time Delayed Systems | Many real-world offline reinforcement learning (RL) problems involve continuous-time environments with delays. Such environments are characterized by two distinctive features: firstly, the state x(t) is observed at irregular time intervals, and secondly, the current action a(t) only affects the future state x(t + g) wi... | ['Mihaela van der Schaar', 'Hao Sun', 'Zhaozhi Qian', 'Alihan Hüyük', 'Samuel Holt'] | 2023-02-24 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [ 1.51218340e-01 2.41922781e-01 -2.24779144e-01 2.14972079e-01
-3.83727401e-01 -7.10290253e-01 7.64873207e-01 5.51131189e-01
-5.75306773e-01 1.21774805e+00 -4.04034436e-01 -4.78113353e-01
-5.29696107e-01 -7.54513502e-01 -9.15451348e-01 -8.78749728e-01
-9.79902387e-01 7.94967651e-01 8.58249515e-02 -3.15425247... | [4.548150062561035, 2.2391626834869385] |
273cca4a-1486-4733-9b88-18b30ca49b30 | counterfactual-multihop-qa-a-cause-effect | 2210.07138 | null | https://arxiv.org/abs/2210.07138v1 | https://arxiv.org/pdf/2210.07138v1.pdf | Counterfactual Multihop QA: A Cause-Effect Approach for Reducing Disconnected Reasoning | Multi-hop QA requires reasoning over multiple supporting facts to answer the question. However, the existing QA models always rely on shortcuts, e.g., providing the true answer by only one fact, rather than multi-hop reasoning, which is referred as $\textit{disconnected reasoning}$ problem. To alleviate this issue, we ... | ['Hanjiang Lai', 'Qinkang Gong', 'Wangzhen Guo'] | 2022-10-13 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [-4.81827632e-02 5.94413221e-01 -4.94615674e-01 -5.03071487e-01
-1.10249507e+00 -5.20394683e-01 3.06861132e-01 8.24678838e-02
5.40434420e-02 1.27542830e+00 4.68315274e-01 -6.78940833e-01
-6.28774762e-01 -1.41437733e+00 -9.20441031e-01 -5.22900224e-01
1.79426745e-01 4.42817360e-01 3.71787697e-01 -5.39782941... | [9.951370239257812, 7.842447757720947] |
1b42c65f-17de-4f93-892b-31d54c6beef3 | causal-augmentation-for-causal-sentence | null | null | https://aclanthology.org/2021.cinlp-1.1 | https://aclanthology.org/2021.cinlp-1.1.pdf | Causal Augmentation for Causal Sentence Classification | Scarcity of annotated causal texts leads to poor robustness when training state-of-the-art language models for causal sentence classification. In particular, we found that models misclassify on augmented sentences that have been negated or strengthened with respect to its causal meaning. This is worrying since minor li... | ['Roger Zimmermann', 'Soujanya Poria', 'See-Kiong Ng', 'Devamanyu Hazarika', 'Fiona Anting Tan'] | null | null | null | null | emnlp-cinlp-2021-11 | ['sentence-classification'] | ['natural-language-processing'] | [ 5.36572158e-01 6.28514051e-01 -2.93834984e-01 -8.11847985e-01
-6.65937006e-01 -6.55201077e-01 1.21330094e+00 5.40009916e-01
-5.12595952e-01 1.28878248e+00 8.37183118e-01 -5.89573264e-01
-7.16883838e-02 -6.52962327e-01 -9.49951768e-01 -2.31754750e-01
-2.52971619e-01 2.96344161e-01 1.17932022e-01 -3.49929631... | [9.943744659423828, 8.109054565429688] |
96230801-e78b-4385-9b2e-5e2f227ff6b2 | multiple-riemannian-manifold-valued | 1908.0195 | null | https://arxiv.org/abs/1908.01950v1 | https://arxiv.org/pdf/1908.01950v1.pdf | Multiple Riemannian Manifold-valued Descriptors based Image Set Classification with Multi-Kernel Metric Learning | The importance of wild video based image set recognition is becoming monotonically increasing. However, the contents of these collected videos are often complicated, and how to efficiently perform set modeling and feature extraction is a big challenge for set-based classification algorithms. In recent years, some propo... | ['Xiao-Jun Wu', 'Rui Wang', 'Josef Kittler'] | 2019-08-06 | null | null | null | null | ['object-categorization'] | ['computer-vision'] | [-1.85561981e-02 -7.16509044e-01 4.48606648e-02 -4.69664574e-01
-4.97416437e-01 -4.01182353e-01 2.86977530e-01 -2.75131553e-01
-2.17308462e-01 2.04818204e-01 -2.16146678e-01 2.22745419e-01
-6.04377866e-01 -5.37133217e-01 -4.02834207e-01 -1.14563227e+00
1.30770132e-01 -1.56502426e-01 -1.60714149e-01 -1.86270759... | [7.946136474609375, 4.074167728424072] |
b26bbd1b-8351-4a2d-ad31-54ba14676806 | opental-towards-open-set-temporal-action | 2203.05114 | null | https://arxiv.org/abs/2203.05114v1 | https://arxiv.org/pdf/2203.05114v1.pdf | OpenTAL: Towards Open Set Temporal Action Localization | Temporal Action Localization (TAL) has experienced remarkable success under the supervised learning paradigm. However, existing TAL methods are rooted in the closed set assumption, which cannot handle the inevitable unknown actions in open-world scenarios. In this paper, we, for the first time, step toward the Open Set... | ['Yu Kong', 'Qi Yu', 'Wentao Bao'] | 2022-03-10 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Bao_OpenTAL_Towards_Open_Set_Temporal_Action_Localization_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Bao_OpenTAL_Towards_Open_Set_Temporal_Action_Localization_CVPR_2022_paper.pdf | cvpr-2022-1 | ['action-localization'] | ['computer-vision'] | [ 2.36894011e-01 1.20398059e-01 -8.24123383e-01 -4.04418051e-01
-1.02847850e+00 -2.35675290e-01 6.43271327e-01 -3.29303980e-01
-7.59817883e-02 8.57302725e-01 3.94110143e-01 2.95625310e-02
-3.40779573e-01 -2.83437312e-01 -7.53942430e-01 -7.52403140e-01
-1.46447226e-01 2.58776367e-01 3.55583102e-01 1.89851269... | [8.561345100402832, 0.7451103925704956] |
c703ea97-a5c2-4304-b355-d21d5c1984c1 | joint-bayesian-inference-of-graphical | 2305.19366 | null | https://arxiv.org/abs/2305.19366v1 | https://arxiv.org/pdf/2305.19366v1.pdf | Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network | Generative Flow Networks (GFlowNets), a class of generative models over discrete and structured sample spaces, have been previously applied to the problem of inferring the marginal posterior distribution over the directed acyclic graph (DAG) of a Bayesian Network, given a dataset of observations. Based on recent advanc... | ['Yoshua Bengio', 'Laurent Charlin', 'Nikolay Malkin', 'Jithendaraa Subramanian', 'Mizu Nishikawa-Toomey', 'Tristan Deleu'] | 2023-05-30 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [ 1.30465090e-01 3.86714756e-01 -9.49577689e-02 -3.83381337e-01
-2.83363461e-01 -4.92714345e-01 1.06303811e+00 -1.16932757e-01
-2.64570475e-01 8.17583263e-01 2.75109589e-01 -2.12734684e-01
-5.37606359e-01 -1.13849902e+00 -7.43583679e-01 -7.59425044e-01
-3.90619814e-01 1.18923044e+00 4.44831222e-01 5.59058785... | [6.925859451293945, 4.280847072601318] |
66def7bd-3e10-4cca-aa14-956b02ee78e4 | guaranteed-non-convex-optimization-submodular | 1606.05615 | null | https://arxiv.org/abs/1606.05615v5 | https://arxiv.org/pdf/1606.05615v5.pdf | Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains | Submodular continuous functions are a category of (generally) non-convex/non-concave functions with a wide spectrum of applications. We characterize these functions and demonstrate that they can be maximized efficiently with approximation guarantees. Specifically, i) We introduce the weak DR property that gives a unifi... | ['Baharan Mirzasoleiman', 'Andreas Krause', 'Joachim M. Buhmann', 'Andrew An Bian'] | 2016-06-17 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 1.51432574e-01 3.32819611e-01 -5.45387506e-01 -4.87327397e-01
-9.34133530e-01 -1.04529858e+00 -4.62667346e-01 2.59336293e-01
-5.46373315e-02 1.19609261e+00 2.31376722e-01 1.16255119e-01
-8.78717244e-01 -8.77172232e-01 -1.14997172e+00 -8.99511278e-01
-4.86997247e-01 8.05642068e-01 -2.97159255e-01 -3.20941448... | [6.579762935638428, 4.918125629425049] |
c8c08455-99ee-4f8d-ab0b-18432391b931 | active-source-free-domain-adaptation | 2205.10711 | null | https://arxiv.org/abs/2205.10711v1 | https://arxiv.org/pdf/2205.10711v1.pdf | Active Source Free Domain Adaptation | Source free domain adaptation (SFDA) aims to transfer a trained source model to the unlabeled target domain without accessing the source data. However, the SFDA setting faces an effect bottleneck due to the absence of source data and target supervised information, as evidenced by the limited performance gains of newest... | ['Yilong Yin', 'Zhiyan Zhang', 'Zhongyi Han', 'Fan Wang'] | 2022-05-22 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [-1.96863934e-02 1.63049594e-01 -7.51212895e-01 -4.44924682e-01
-1.26805031e+00 -6.34598076e-01 6.03816986e-01 -2.27932930e-02
-1.60085693e-01 8.36903691e-01 2.18032226e-01 4.14886922e-02
-1.89459994e-01 -5.76340973e-01 -6.69724703e-01 -9.37704563e-01
1.89579621e-01 6.67614639e-01 3.33068430e-01 -2.04427149... | [10.344001770019531, 3.101552963256836] |
b0216dfb-2b0c-4b53-999f-d2a1349c96f0 | tupa-at-mrp-2019-a-multi-task-baseline-system | null | null | https://aclanthology.org/K19-2002 | https://aclanthology.org/K19-2002.pdf | TUPA at MRP 2019: A Multi-Task Baseline System | This paper describes the TUPA system submission to the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL). Because it was prepared by one of the task co-organizers, TUPA provides a baseline point of comparison and is not considered in t... | ['Daniel Hershcovich', 'Ofir Arviv'] | 2019-11-01 | null | null | null | conll-2019-11 | ['ucca-parsing'] | ['natural-language-processing'] | [ 4.7898299e-01 5.8247954e-01 -3.1645378e-01 -3.8282838e-01
-1.4751624e+00 -6.3894677e-01 6.1059588e-01 1.9379853e-01
-4.8311278e-01 5.3806180e-01 5.7622308e-01 -6.8271106e-01
2.0127875e-01 -3.6527613e-01 -6.2643951e-01 -2.7767128e-01
5.0763838e-02 5.9220588e-01 1.4302313e-01 -7.4695848e-02
-9.1707855e-02... | [10.361757278442383, 9.494482040405273] |
821d5fb3-4731-4e72-97d1-c15fc4294168 | improving-simultaneous-machine-translation | 2212.01188 | null | https://arxiv.org/abs/2212.01188v1 | https://arxiv.org/pdf/2212.01188v1.pdf | Improving Simultaneous Machine Translation with Monolingual Data | Simultaneous machine translation (SiMT) is usually done via sequence-level knowledge distillation (Seq-KD) from a full-sentence neural machine translation (NMT) model. However, there is still a significant performance gap between NMT and SiMT. In this work, we propose to leverage monolingual data to improve SiMT, which... | ['Min Zhang', 'DaCheng Tao', 'Meishan Zhang', 'Xuebo Liu', 'Liang Ding', 'Hexuan Deng'] | 2022-12-02 | null | null | null | null | ['nmt'] | ['computer-code'] | [-4.53547947e-03 9.51221958e-02 -5.93075395e-01 -1.67975813e-01
-1.61748743e+00 -6.69016302e-01 7.23298311e-01 -2.47256413e-01
-5.63164294e-01 1.32235634e+00 5.07918239e-01 -8.59717488e-01
3.12271178e-01 -3.95462692e-01 -1.04664564e+00 -3.92923176e-01
4.77918029e-01 9.97802258e-01 -3.16255510e-01 -3.78163248... | [11.599058151245117, 10.285199165344238] |
86564557-7b6f-494c-94be-48a0fcb5ddaa | the-curse-of-dimensionality-in-operator | 2306.15924 | null | https://arxiv.org/abs/2306.15924v1 | https://arxiv.org/pdf/2306.15924v1.pdf | The curse of dimensionality in operator learning | Neural operator architectures employ neural networks to approximate operators mapping between Banach spaces of functions; they may be used to accelerate model evaluations via emulation, or to discover models from data. Consequently, the methodology has received increasing attention over recent years, giving rise to the... | ['Andrew M. Stuart', 'Samuel Lanthaler'] | 2023-06-28 | null | null | null | null | ['operator-learning'] | ['miscellaneous'] | [ 1.97441027e-01 3.77757519e-01 4.56115194e-02 1.31633624e-01
-1.85867772e-01 -1.47391200e-01 1.54469088e-01 -1.69358198e-02
-5.07133424e-01 6.51770949e-01 -8.75245333e-02 -3.22530806e-01
-5.82405388e-01 -6.08888328e-01 -7.71054804e-01 -7.67363787e-01
-4.05813038e-01 3.75439785e-02 -2.10287273e-01 -3.73822689... | [7.526030540466309, 3.7129886150360107] |
193b167a-e1d3-4989-bf9b-faf60f3a77e3 | ref-rotation-equivariant-features-for-local | 2203.05206 | null | https://arxiv.org/abs/2203.05206v1 | https://arxiv.org/pdf/2203.05206v1.pdf | ReF -- Rotation Equivariant Features for Local Feature Matching | Sparse local feature matching is pivotal for many computer vision and robotics tasks. To improve their invariance to challenging appearance conditions and viewing angles, and hence their usefulness, existing learning-based methods have primarily focused on data augmentation-based training. In this work, we propose an a... | ['K. Madhava Krishna', 'Sourav Garg', 'Michael Milford', 'Avneesh Mishra', 'Kinal Mehta', 'Abhishek Peri'] | 2022-03-10 | null | null | null | null | ['visual-place-recognition'] | ['computer-vision'] | [ 1.19602151e-01 -9.35091749e-02 -1.93172559e-01 -5.44021130e-01
-5.25464892e-01 -4.50558275e-01 1.01439619e+00 -2.78032601e-01
-4.04379517e-01 5.29854536e-01 3.11967254e-01 -9.76303741e-02
-9.08600315e-02 -6.40158832e-01 -1.00345993e+00 -6.71424031e-01
4.72672936e-03 2.24011704e-01 2.10152194e-01 -5.09376347... | [7.795804023742676, -1.972265362739563] |
2e64de5f-c3c1-4efd-bde6-7c5913b544f6 | qursim-a-corpus-for-evaluation-of-relatedness | null | null | https://aclanthology.org/L12-1051 | https://aclanthology.org/L12-1051.pdf | QurSim: A corpus for evaluation of relatedness in short texts | This paper presents a large corpus created from the original Quranic text, where semantically similar or related verses are linked together. This corpus will be a valuable evaluation resource for computational linguists investigating similarity and relatedness in short texts. Furthermore, this dataset can be used for e... | ['Abdul-Baquee Sharaf', 'Eric Atwell'] | 2012-05-01 | null | null | null | lrec-2012-5 | ['text-clustering'] | ['natural-language-processing'] | [-7.75703117e-02 5.25246840e-03 -3.68557900e-01 -4.45300192e-02
-7.77937949e-01 -1.05623853e+00 8.76049101e-01 6.85098112e-01
-4.72152084e-01 7.83659220e-01 7.31904626e-01 -1.14481449e-01
-5.76828778e-01 -8.71275306e-01 -1.87174052e-01 -3.31579328e-01
7.36805424e-02 8.04935098e-01 3.53693575e-01 -1.08777773... | [10.868712425231934, 9.353446006774902] |
ee860e6f-c3ab-4aa5-89aa-b6fc33ca7a85 | a-general-framework-for-information | 1904.03296 | null | http://arxiv.org/abs/1904.03296v1 | http://arxiv.org/pdf/1904.03296v1.pdf | A General Framework for Information Extraction using Dynamic Span Graphs | We introduce a general framework for several information extraction tasks
that share span representations using dynamically constructed span graphs. The
graphs are constructed by selecting the most confident entity spans and linking
these nodes with confidence-weighted relation types and coreferences. The
dynamic span ... | ['Mari Ostendorf', 'Yi Luan', 'Hannaneh Hajishirzi', 'Dave Wadden', 'Amy Shah', 'Luheng He'] | 2019-04-05 | a-general-framework-for-information-1 | https://aclanthology.org/N19-1308 | https://aclanthology.org/N19-1308.pdf | naacl-2019-6 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 1.04594275e-01 9.83974636e-01 -6.28211379e-01 -3.03186655e-01
-1.02975321e+00 -7.36900508e-01 4.09716368e-01 7.26009429e-01
-4.11858439e-01 1.05736208e+00 5.36469340e-01 -6.97934031e-02
-4.20473695e-01 -9.26509380e-01 -7.52307773e-01 1.21929727e-01
-6.89449370e-01 7.33476162e-01 2.69418478e-01 -7.46759698... | [9.33401870727539, 8.968188285827637] |
6e8372cc-71be-4ae6-ac05-251f8cfaa642 | active-learning-with-gaussian-processes-for | 1901.06803 | null | http://arxiv.org/abs/1901.06803v1 | http://arxiv.org/pdf/1901.06803v1.pdf | Active Learning with Gaussian Processes for High Throughput Phenotyping | A looming question that must be solved before robotic plant phenotyping
capabilities can have significant impact to crop improvement programs is
scalability. High Throughput Phenotyping (HTP) uses robotic technologies to
analyze crops in order to determine species with favorable traits, however, the
current practices r... | ['Katia Sycara', 'Sumit Kumar', 'George Kantor', 'Wenhao Luo'] | 2019-01-21 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 2.96213269e-01 1.43852979e-01 -3.42469096e-01 -1.89428285e-01
4.48205806e-02 -1.02848208e+00 -3.42966676e-01 5.34131050e-01
2.86944360e-02 8.01585138e-01 -4.69819635e-01 -5.43297827e-01
-5.82730711e-01 -1.15582454e+00 -4.65904176e-01 -1.00034904e+00
-1.90926984e-01 7.27111518e-01 2.06966162e-01 -1.83676526... | [9.119287490844727, -1.5927984714508057] |
ea855aa2-82b4-4273-9aee-05e8087abc90 | park-detect-towards-efficient-multi-task | 2302.13263 | null | https://arxiv.org/abs/2302.13263v1 | https://arxiv.org/pdf/2302.13263v1.pdf | PaRK-Detect: Towards Efficient Multi-Task Satellite Imagery Road Extraction via Patch-Wise Keypoints Detection | Automatically extracting roads from satellite imagery is a fundamental yet challenging computer vision task in the field of remote sensing. Pixel-wise semantic segmentation-based approaches and graph-based approaches are two prevailing schemes. However, prior works show the imperfections that semantic segmentation-base... | ['Ming Wu', 'Chuang Zhang', 'Junli Yang', 'Zhenglin Xian', 'Wanfeng Zheng', 'Shenwei Xie'] | 2023-02-26 | null | null | null | null | ['road-segementation', 'keypoint-detection'] | ['computer-vision', 'computer-vision'] | [ 4.59758013e-01 1.10008689e-02 -9.94201973e-02 -3.62382531e-01
-7.88095713e-01 -5.56493521e-01 5.65543830e-01 -2.42603421e-02
-3.86763722e-01 5.66613734e-01 -1.79884449e-01 -7.24111557e-01
-3.95969301e-01 -1.45898044e+00 -7.96759605e-01 -4.57748622e-01
-2.53069490e-01 4.28726196e-01 8.15326333e-01 -2.74303496... | [8.980910301208496, -1.4865748882293701] |
73e820bf-44ec-4ec9-82e8-53a6fde6bb21 | efficient-regional-memory-network-for-video | 2103.12934 | null | https://arxiv.org/abs/2103.12934v2 | https://arxiv.org/pdf/2103.12934v2.pdf | Efficient Regional Memory Network for Video Object Segmentation | Recently, several Space-Time Memory based networks have shown that the object cues (e.g. video frames as well as the segmented object masks) from the past frames are useful for segmenting objects in the current frame. However, these methods exploit the information from the memory by global-to-global matching between th... | ['Wenxiu Sun', 'Shengping Zhang', 'Shangchen Zhou', 'Hongxun Yao', 'Haozhe Xie'] | 2021-03-24 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Xie_Efficient_Regional_Memory_Network_for_Video_Object_Segmentation_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Xie_Efficient_Regional_Memory_Network_for_Video_Object_Segmentation_CVPR_2021_paper.pdf | cvpr-2021-1 | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [-1.26718611e-01 -4.81045008e-01 -5.70664227e-01 -2.49864861e-01
-3.26442838e-01 -1.66467890e-01 2.02147007e-01 -2.83814445e-02
-6.12828195e-01 6.46788359e-01 3.15582976e-02 4.68871623e-01
5.96140735e-02 -7.64926970e-01 -6.03745580e-01 -6.23603642e-01
-4.91666794e-02 9.32104141e-03 1.07073236e+00 1.93769440... | [9.28734016418457, -0.2322259396314621] |
dbf3e1ab-2e11-4b0b-a306-55275bec8d89 | image-differential-invariants | 1911.05327 | null | https://arxiv.org/abs/1911.05327v2 | https://arxiv.org/pdf/1911.05327v2.pdf | Rotation Differential Invariants of Images Generated by Two Fundamental Differential Operators | In this paper, we design two fundamental differential operators for the derivation of rotation differential invariants of images. Each differential invariant obtained by using the new method can be expressed as a homogeneous polynomial of image partial derivatives, which preserve their values when the image is rotated ... | ['Hanlin Mo', 'Hua Li'] | 2019-11-13 | null | null | null | null | ['texture-classification'] | ['computer-vision'] | [-9.06277969e-02 -3.65529180e-01 -3.68191093e-01 -2.47612983e-01
2.69802734e-02 -4.14599776e-01 4.70793009e-01 -3.85235727e-01
-3.44881833e-01 3.59180897e-01 -3.62040013e-01 -1.22969776e-01
-3.61852229e-01 -4.63962615e-01 -1.89373538e-01 -9.14778233e-01
-4.02544349e-01 1.31394908e-01 4.62624401e-01 -3.76912832... | [9.5503511428833, -1.671940803527832] |
4d10f362-936c-4b6b-b15e-671387fee453 | swin-unet-unet-like-pure-transformer-for | 2105.05537 | null | https://arxiv.org/abs/2105.05537v1 | https://arxiv.org/pdf/2105.05537v1.pdf | Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation | In the past few years, convolutional neural networks (CNNs) have achieved milestones in medical image analysis. Especially, the deep neural networks based on U-shaped architecture and skip-connections have been widely applied in a variety of medical image tasks. However, although CNN has achieved excellent performance,... | ['Manning Wang', 'Qi Tian', 'Xiaopeng Zhang', 'Dongsheng Jiang', 'Joy Chen', 'Yueyue Wang', 'Hu Cao'] | 2021-05-12 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 1.01556897e-01 -1.62021741e-02 -5.15922531e-02 -4.47152436e-01
-5.84899247e-01 -1.28132492e-01 8.92042965e-02 -9.30353999e-02
-2.98915803e-01 4.47784573e-01 1.70467213e-01 -3.09594780e-01
4.81139794e-02 -9.51786995e-01 -7.10051179e-01 -7.68446565e-01
1.64063185e-01 -3.44895981e-02 5.88490248e-01 -6.07900135... | [14.549463272094727, -2.5909957885742188] |
46a26cf1-24fc-4046-aa71-766db36f87a0 | mrn-a-locally-and-globally-mention-based | null | null | https://aclanthology.org/2021.findings-acl.117 | https://aclanthology.org/2021.findings-acl.117.pdf | MRN: A Locally and Globally Mention-Based Reasoning Network for Document-Level Relation Extraction | null | ['Donghong Ji', 'Yafeng Ren', 'Hao Fei', 'Fei Li', 'Kang Xu', 'Jingye Li'] | null | null | null | null | findings-acl-2021-8 | ['document-level-relation-extraction'] | ['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... | [-7.357705593109131, 3.7358553409576416] |
e46f73ca-26bc-44e9-8e04-85d8a56071b0 | logical-tasks-for-measuring-extrapolation-and | 2211.07727 | null | https://arxiv.org/abs/2211.07727v1 | https://arxiv.org/pdf/2211.07727v1.pdf | Logical Tasks for Measuring Extrapolation and Rule Comprehension | Logical reasoning is essential in a variety of human activities. A representative example of a logical task is mathematics. Recent large-scale models trained on large datasets have been successful in various fields, but their reasoning ability in arithmetic tasks is limited, which we reproduce experimentally. Here, we ... | ['Ryota Kanai', 'Ippei Fujisawa'] | 2022-11-14 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 1.29481256e-01 4.05861676e-01 -7.07569271e-02 -5.82177579e-01
-2.19988599e-01 -4.85483766e-01 7.48125136e-01 2.71265626e-01
-1.22152641e-01 8.71678948e-01 1.78951502e-01 -7.03865647e-01
-6.55147135e-01 -1.01380694e+00 -9.17264640e-01 -2.19227687e-01
-7.89881200e-02 6.09800994e-01 -8.99201259e-02 -3.32940310... | [9.416400909423828, 7.257997989654541] |
1724840a-c545-4cb3-935e-65a9b8257bba | beyond-statistical-similarity-rethinking | 2302.02913 | null | https://arxiv.org/abs/2302.02913v3 | https://arxiv.org/pdf/2302.02913v3.pdf | Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design | Deep generative models, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models, and Transformers, have shown great promise in a variety of applications, including image and speech synthesis, natural language processing, and drug discovery. However, when applied to engineering ... | ['Faez Ahmed', 'Dan Gutfreund', 'Akash Srivastava', 'Lyle Regenwetter'] | 2023-02-06 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [ 1.09597392e-01 -7.17855478e-03 -1.21663019e-01 1.21939415e-02
-4.77004915e-01 -6.16173029e-01 6.21743083e-01 -3.89384985e-01
2.20553741e-01 8.76197934e-01 1.14284150e-01 -2.94538945e-01
-5.44838071e-01 -7.25212514e-01 -5.12697935e-01 -7.38700509e-01
1.83076844e-01 2.92457551e-01 -2.68287599e-01 -1.62636355... | [5.833241939544678, 3.3213672637939453] |
5532328a-d07b-4274-8d44-e036aa0202c6 | nowcasting-the-2022-mpox-outbreak-in-england | 2302.09076 | null | https://arxiv.org/abs/2302.09076v1 | https://arxiv.org/pdf/2302.09076v1.pdf | Nowcasting the 2022 mpox outbreak in England | In May 2022, a cluster of mpox cases were detected in the UK that could not be traced to recent travel history from an endemic region. Over the coming months, the outbreak grew, with over 3000 total cases reported in the UK, and similar outbreaks occurring worldwide. These outbreaks appeared linked to sexual contact ne... | ['Thomas Ward', 'Charlie Turner', 'Rob Paton', 'Owen Jones', 'Julie Day', 'Fergus Cumming', 'Rachel Christie', 'Sam Abbott', 'Christopher E. Overton'] | 2023-02-17 | null | null | null | null | ['additive-models'] | ['methodology'] | [ 1.93856955e-01 4.65963706e-02 5.29034296e-03 -1.75670177e-01
-4.42614913e-01 -6.05681539e-01 9.55768287e-01 8.48001063e-01
-7.04583049e-01 7.27136433e-01 6.32241666e-01 -4.97895598e-01
-6.21421874e-01 -9.37215686e-01 -3.16246986e-01 -5.00812471e-01
-8.89794707e-01 8.63145709e-01 2.50324845e-01 -3.02476227... | [5.954069137573242, 4.386684894561768] |
60e3c5be-b782-463d-be27-f6312ee47be1 | advancements-in-arabic-grammatical-error | 2305.14734 | null | https://arxiv.org/abs/2305.14734v1 | https://arxiv.org/pdf/2305.14734v1.pdf | Advancements in Arabic Grammatical Error Detection and Correction: An Empirical Investigation | Grammatical error correction (GEC) is a well-explored problem in English with many existing models and datasets. However, research on GEC in morphologically rich languages has been limited due to challenges such as data scarcity and language complexity. In this paper, we present the first results on Arabic GEC by using... | ['Nizar Habash', 'Christian Khairallah', 'Go Inoue', 'Bashar Alhafni'] | 2023-05-24 | null | null | null | null | ['grammatical-error-detection', 'grammatical-error-correction'] | ['natural-language-processing', 'natural-language-processing'] | [ 9.65224206e-02 -3.33042651e-01 4.73977685e-01 -4.61098403e-01
-1.00535500e+00 -7.09806442e-01 1.31753981e-01 5.58422387e-01
-7.84660578e-01 4.99957561e-01 6.38453737e-02 -4.29365098e-01
3.70666683e-01 -5.65874517e-01 -9.41366315e-01 -2.50832647e-01
-2.42208734e-01 8.29770863e-01 9.78557095e-02 -1.03305364... | [11.072563171386719, 10.721488952636719] |
256bc072-7859-41a8-b0ff-15bf2a183ef2 | improving-speech-emotion-recognition | 2305.14402 | null | https://arxiv.org/abs/2305.14402v1 | https://arxiv.org/pdf/2305.14402v1.pdf | Improving Speech Emotion Recognition Performance using Differentiable Architecture Search | Speech Emotion Recognition (SER) is a critical enabler of emotion-aware communication in human-computer interactions. Deep Learning (DL) has improved the performance of SER models by improving model complexity. However, designing DL architectures requires prior experience and experimental evaluations. Encouragingly, Ne... | ['Björn Schuller', 'Berrak Sisman', 'Sara Khalifa', 'Rajib Rana', 'Thejan Rajapakshe'] | 2023-05-23 | null | null | null | null | ['architecture-search', 'speech-emotion-recognition'] | ['methodology', 'speech'] | [ 2.71330010e-02 1.12806924e-01 -1.47685325e-02 -3.63007396e-01
-6.76645041e-01 -3.82411003e-01 4.20508802e-01 -2.18827873e-01
-4.68855321e-01 2.82335222e-01 3.60178798e-01 -3.72326732e-01
2.26790622e-01 -7.53166005e-02 -4.70238417e-01 -3.19531947e-01
-2.79639155e-01 1.41590521e-01 -4.66164798e-01 -3.33129853... | [14.117558479309082, 6.089045524597168] |
cea6d348-df1f-49e3-8525-0b0d9ac37008 | a-large-scale-japanese-dataset-for-aspect | null | null | https://aclanthology.org/2022.lrec-1.758 | https://aclanthology.org/2022.lrec-1.758.pdf | A Large-Scale Japanese Dataset for Aspect-based Sentiment Analysis | There has been significant progress in the field of sentiment analysis. However, aspect-based sentiment analysis (ABSA) has not been explored in the Japanese language even though it has a huge scope in many natural language processing applications such as 1) tracking sentiment towards products, movies, politicians etc;... | ['Ikuko Hardaway', 'Sudha Bhingardive', 'Gautam Kumar', 'Koji Murakami', 'Yuki Nakayama'] | null | null | null | null | lrec-2022-6 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 3.92062450e-03 -1.23785459e-01 -1.94110379e-01 -8.48595619e-01
-7.74760246e-01 -5.53710163e-01 5.41866839e-01 3.47314298e-01
-5.41991234e-01 6.59490943e-01 3.39610636e-01 -5.49669445e-01
1.06136329e-01 -6.66221082e-01 -3.23543698e-01 -6.38669431e-01
4.52280939e-01 4.62879360e-01 3.14188540e-01 -6.42093956... | [11.341278076171875, 6.772342681884766] |
cd3da91e-d7bc-42f8-8898-ee5c355b0e7a | learning-combinatorial-prompts-for-universal | 2303.06338 | null | https://arxiv.org/abs/2303.06338v2 | https://arxiv.org/pdf/2303.06338v2.pdf | Learning Combinatorial Prompts for Universal Controllable Image Captioning | Controllable Image Captioning (CIC) -- generating natural language descriptions about images under the guidance of given control signals -- is one of the most promising directions towards next-generation captioning systems. Till now, various kinds of control signals for CIC have been proposed, ranging from content-rela... | ['Long Chen', 'Jian Shao', 'Fei Gao', 'Lei Chen', 'Jun Xiao', 'Zhen Wang'] | 2023-03-11 | null | null | null | null | ['controllable-image-captioning'] | ['computer-vision'] | [ 5.39256990e-01 -1.76468298e-01 -2.42831498e-01 -4.71338391e-01
-6.90295219e-01 -5.41154802e-01 7.70775318e-01 -3.64331543e-01
-4.33319807e-02 4.98084158e-01 6.17723763e-01 -2.96149760e-01
1.50946364e-01 -7.38223612e-01 -8.29224110e-01 -6.28478885e-01
5.85705638e-01 1.24258481e-01 2.20464319e-01 -5.38646042... | [10.883955001831055, 0.9230377078056335] |
77542786-3730-42e6-92b3-f0de35ca6b47 | nfresnet-multi-scale-and-u-shaped-networks | 2212.05909 | null | https://arxiv.org/abs/2212.05909v1 | https://arxiv.org/pdf/2212.05909v1.pdf | NFResNet: Multi-scale and U-shaped Networks for Deblurring | Multi-Scale and U-shaped Networks are widely used in various image restoration problems, including deblurring. Keeping in mind the wide range of applications, we present a comparison of these architectures and their effects on image deblurring. We also introduce a new block called as NFResblock. It consists of a Fast F... | ['Aarya Makwana', 'Esha Pahwa', 'Preyansh Agrawal', 'Tanish Mittal'] | 2022-12-12 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 3.77994686e-01 -4.97662634e-01 1.31242722e-01 -8.49645659e-02
-5.35154581e-01 -4.07482907e-02 4.14513707e-01 -5.20780623e-01
-3.67242128e-01 6.96525395e-01 5.83467305e-01 -1.04217075e-01
1.20248817e-01 -4.15543079e-01 -7.53418505e-01 -9.21680689e-01
-2.42279708e-01 -7.05644190e-01 3.15485567e-01 -2.77671248... | [11.44458293914795, -2.4874396324157715] |
66e2ade7-aea3-437a-b09b-e80e7cb0414b | syntax-aware-hybrid-prompt-model-for-few-shot | 2306.01312 | null | https://arxiv.org/abs/2306.01312v1 | https://arxiv.org/pdf/2306.01312v1.pdf | Syntax-aware Hybrid prompt model for Few-shot multi-modal sentiment analysis | Multimodal Sentiment Analysis (MSA) has been a popular topic in natural language processing nowadays, at both sentence and aspect level. However, the existing approaches almost require large-size labeled datasets, which bring about large consumption of time and resources. Therefore, it is practical to explore the metho... | ['Zikai Zhou'] | 2023-06-02 | null | null | null | null | ['multimodal-sentiment-analysis', 'sentiment-analysis', 'multimodal-sentiment-analysis'] | ['computer-vision', 'natural-language-processing', 'natural-language-processing'] | [ 2.06905231e-01 -1.89592123e-01 -1.86659947e-01 -5.81809163e-01
-1.22890401e+00 -5.59618473e-01 5.85643768e-01 1.29616439e-01
-6.43802881e-01 4.63343322e-01 4.19466525e-01 -2.34759733e-01
2.38300219e-01 -5.87314367e-01 -3.32782924e-01 -6.71148300e-01
5.62246323e-01 -3.27836201e-02 2.46761620e-01 -5.50961614... | [12.984673500061035, 5.494215488433838] |
829e319c-34cb-42b3-9745-69f988165688 | improving-diffusion-based-image-translation | 2306.04396 | null | https://arxiv.org/abs/2306.04396v1 | https://arxiv.org/pdf/2306.04396v1.pdf | Improving Diffusion-based Image Translation using Asymmetric Gradient Guidance | Diffusion models have shown significant progress in image translation tasks recently. However, due to their stochastic nature, there's often a trade-off between style transformation and content preservation. Current strategies aim to disentangle style and content, preserving the source image's structure while successfu... | ['Jong Chul Ye', 'Gihyun Kwon'] | 2023-06-07 | null | null | null | null | ['image-manipulation'] | ['computer-vision'] | [ 6.96267009e-01 -1.06038839e-01 -3.38270158e-01 -2.27050737e-01
-6.39353335e-01 -7.22076595e-01 9.83272612e-01 -1.60259247e-01
-4.59061861e-01 5.15413821e-01 2.30182499e-01 -3.92987877e-01
3.15895259e-01 -6.25502884e-01 -4.98290718e-01 -5.92187464e-01
5.26537180e-01 4.08850104e-01 2.81116396e-01 -1.61579043... | [11.383232116699219, -0.3142123222351074] |
4db3cb4c-a2b1-46d8-9166-1751cd44cb45 | prior-guided-dropout-for-robust-visual | null | null | http://openaccess.thecvf.com/content_ICCV_2019/html/Huang_Prior_Guided_Dropout_for_Robust_Visual_Localization_in_Dynamic_Environments_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Huang_Prior_Guided_Dropout_for_Robust_Visual_Localization_in_Dynamic_Environments_ICCV_2019_paper.pdf | Prior Guided Dropout for Robust Visual Localization in Dynamic Environments | Camera localization from monocular images has been a long-standing problem, but its robustness in dynamic environments is still not adequately addressed. Compared with classic geometric approaches, modern CNN-based methods (e.g. PoseNet) have manifested the reliability against illumination or viewpoint variations, but ... | [' Guofeng Zhang', ' Hujun Bao', ' Xiaowei Zhou', ' Jianping Shi', ' Yan Xu', 'Zhaoyang Huang'] | 2019-10-01 | null | null | null | iccv-2019-10 | ['camera-localization'] | ['computer-vision'] | [-1.49228215e-01 -1.01804480e-01 7.94001855e-03 -2.67845809e-01
-6.14987671e-01 -4.96778607e-01 3.67030650e-01 -4.26890671e-01
-5.77280819e-01 6.10248327e-01 -2.87175745e-01 -1.09669760e-01
2.14309722e-01 -5.70019960e-01 -1.22865200e+00 -8.60488296e-01
3.77060264e-01 1.48294449e-01 4.78921682e-01 1.32654130... | [7.978815078735352, -2.15317702293396] |
926c5294-0686-4b73-b73b-e876cbc47050 | 3dn-3d-deformation-network | 1903.03322 | null | http://arxiv.org/abs/1903.03322v1 | http://arxiv.org/pdf/1903.03322v1.pdf | 3DN: 3D Deformation Network | Applications in virtual and augmented reality create a demand for rapid
creation and easy access to large sets of 3D models. An effective way to
address this demand is to edit or deform existing 3D models based on a
reference, e.g., a 2D image which is very easy to acquire. Given such a source
3D model and a target whi... | ['Weiyue Wang', 'Duygu Ceylan', 'Ulrich Neumann', 'Radomir Mech'] | 2019-03-08 | 3dn-3d-deformation-network-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_3DN_3D_Deformation_Network_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_3DN_3D_Deformation_Network_CVPR_2019_paper.pdf | cvpr-2019-6 | ['3d-shape-generation'] | ['computer-vision'] | [ 3.30500811e-01 4.25820053e-01 2.86920458e-01 -2.51362622e-01
-6.39411271e-01 -5.52381754e-01 4.35650885e-01 -3.12836505e-02
1.67035669e-01 4.78341848e-01 -1.18765414e-01 -2.35989764e-01
9.49333310e-02 -1.15226972e+00 -1.04597688e+00 -1.10665821e-01
4.66528125e-02 7.87560940e-01 3.62776339e-01 -2.73213178... | [8.78897762298584, -3.574474811553955] |
ba4b4a7a-3974-43a4-9e7f-9abc51cf3265 | exploiting-method-names-to-improve-code | 2103.11448 | null | https://arxiv.org/abs/2103.11448v2 | https://arxiv.org/pdf/2103.11448v2.pdf | Exploiting Method Names to Improve Code Summarization: A Deliberation Multi-Task Learning Approach | Code summaries are brief natural language descriptions of source code pieces. The main purpose of code summarization is to assist developers in understanding code and to reduce documentation workload. In this paper, we design a novel multi-task learning (MTL) approach for code summarization through mining the relations... | ['Shikun Zhang', 'Jinan Sun', 'Wei Ye', 'Rui Xie'] | 2021-03-21 | null | null | null | null | ['code-summarization'] | ['computer-code'] | [ 3.72708619e-01 1.55691102e-01 -5.20334542e-01 -4.44660664e-01
-9.45449889e-01 -3.47191006e-01 3.11058581e-01 3.90988886e-01
-7.28113949e-03 5.82003295e-01 6.88453853e-01 -3.62300158e-01
2.60976821e-01 -3.94327730e-01 -7.38647521e-01 -2.13444501e-01
1.95828229e-01 -5.93005531e-02 2.12163180e-01 8.30237120... | [7.628077983856201, 7.941091537475586] |
63a7e85f-588c-4fa9-a813-989e767fded2 | unsupervised-foreign-object-detection-based | 2104.05326 | null | https://arxiv.org/abs/2104.05326v1 | https://arxiv.org/pdf/2104.05326v1.pdf | Unsupervised foreign object detection based on dual-energy absorptiometry in the food industry | X-ray imaging is a widely used technique for non-destructive inspection of agricultural food products. One application of X-ray imaging is the autonomous, in-line detection of foreign objects in food samples. Examples of such inclusions are bone fragments in meat products, plastic and metal debris in fish, fruit infest... | ['Kees Joost Batenburg', 'Tristan van Leeuwen', 'Robert van Liere', 'Vladyslav Andriiashen'] | 2021-04-12 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 2.93798357e-01 -4.11638543e-02 1.87332078e-03 -1.41632214e-01
-2.10808650e-01 -3.30165356e-01 -1.71194226e-02 8.19247961e-01
-7.34178424e-02 8.43922049e-02 -5.01842976e-01 1.82345137e-02
-2.19849810e-01 -1.19952750e+00 -8.13611329e-01 -9.56910968e-01
-5.91904717e-03 7.86995173e-01 6.22014105e-01 -8.06964859... | [7.379330635070801, 1.7867361307144165] |
7ee7aad1-697d-41e6-b107-be8ef96b49a2 | intrinsic-relationship-reasoning-for-small | 2009.00833 | null | https://arxiv.org/abs/2009.00833v1 | https://arxiv.org/pdf/2009.00833v1.pdf | Intrinsic Relationship Reasoning for Small Object Detection | The small objects in images and videos are usually not independent individuals. Instead, they more or less present some semantic and spatial layout relationships with each other. Modeling and inferring such intrinsic relationships can thereby be beneficial for small object detection. In this paper, we propose a novel c... | ['Lin Ma', 'Yonghong Tian', 'Kui Fu', 'Kai Mu', 'Jia Li'] | 2020-09-02 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 6.86886385e-02 -2.37066790e-01 -7.62946308e-02 -4.24154460e-01
1.84610542e-02 -3.23103130e-01 3.12704355e-01 5.19269407e-01
9.66560915e-02 2.99689740e-01 2.21895456e-01 2.83698589e-01
-3.65358263e-01 -8.19962919e-01 -6.36504233e-01 -6.45974398e-01
-8.81552026e-02 1.77091673e-01 6.49301171e-01 6.67438805... | [10.0711669921875, 1.6962300539016724] |
1ee84cdf-ba70-46cd-a623-9aa88d3ecf8c | automatic-road-crack-detection-using-random | null | null | https://ieeexplore.ieee.org/document/7471507/similar#similar | https://ieeexplore.ieee.org/document/7471507/similar#similar | Automatic Road Crack Detection Using Random Structured Forests | Cracks are a growing threat to road conditions and
have drawn much attention to the construction of intelligent
transportation systems. However, as the key part of an intelli-
gent transportation system, automatic road crack detection has
been challenged because of the intense inhomogeneity along the
cracks, the t... | ['and Zhensong Chen', 'Fan Meng', 'Zhiquan Qi', 'Limeng Cui', 'Yong Shi'] | 2016-05-18 | null | null | null | ieee-transactions-on-intelligent-17 | ['crack-segmentation'] | ['computer-vision'] | [ 1.19499221e-01 -4.25676167e-01 8.47844779e-02 9.35539417e-03
-4.81310457e-01 -3.70960444e-01 2.81491816e-01 -6.59305006e-02
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3.41559172e-01 2.83873022e-01 1.06831491e+00 -3.09742957... | [7.501223564147949, 1.3390380144119263] |
be8626a4-e288-4f30-8027-84aff949aef0 | siamese-contrastive-embedding-network-for-1 | 2206.14475 | null | https://arxiv.org/abs/2206.14475v1 | https://arxiv.org/pdf/2206.14475v1.pdf | Siamese Contrastive Embedding Network for Compositional Zero-Shot Learning | Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions formed from seen state and object during training. Since the same state may be various in the visual appearance while entangled with different objects, CZSL is still a challenging task. Some methods recognize state and object with two trained... | ['Muli Yang', 'Cheng Deng', 'Kun Wei', 'Xu Yang', 'Xiangyu Li'] | 2022-06-29 | siamese-contrastive-embedding-network-for | http://openaccess.thecvf.com//content/CVPR2022/html/Li_Siamese_Contrastive_Embedding_Network_for_Compositional_Zero-Shot_Learning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Li_Siamese_Contrastive_Embedding_Network_for_Compositional_Zero-Shot_Learning_CVPR_2022_paper.pdf | cvpr-2022-1 | ['compositional-zero-shot-learning'] | ['computer-vision'] | [ 1.23374298e-01 -4.18148428e-01 -1.68498382e-01 9.31394622e-02
-3.94961566e-01 -5.90742826e-01 8.27175677e-01 -2.53256977e-01
-5.07002473e-02 3.13784331e-01 2.48367414e-01 1.29303992e-01
7.02084675e-02 -5.15256643e-01 -7.54923820e-01 -1.23588502e+00
3.14361751e-01 5.01397789e-01 2.14367554e-01 -6.32072613... | [10.217557907104492, 2.302574872970581] |
8f3d6532-0894-4600-a0b1-a76566245e78 | formulation-graphs-for-mapping-structure | 2307.03811 | null | https://arxiv.org/abs/2307.03811v1 | https://arxiv.org/pdf/2307.03811v1.pdf | Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance | Advanced computational methods are being actively sought for addressing the challenges associated with discovery and development of new combinatorial material such as formulations. A widely adopted approach involves domain informed high-throughput screening of individual components that can be combined into a formulati... | ['Young-Hye La', 'Daniele Congiu', 'Linda Sundberg', 'Khanh Nugyuen', 'Andy Tek', 'Dmitry Zubarev', 'Maxwell Giammona', 'Vidushi Sharma'] | 2023-07-07 | null | null | null | null | ['transfer-learning'] | ['miscellaneous'] | [ 3.73723537e-01 -3.37443024e-01 -3.51178348e-01 -3.73751372e-01
-6.56875014e-01 -7.85496414e-01 5.15089631e-01 9.85814810e-01
-3.83556187e-01 1.32670355e+00 -1.36512116e-01 -4.16596383e-01
-5.05325258e-01 -9.77818489e-01 -9.80277479e-01 -1.19330561e+00
-1.35237500e-01 7.91083872e-01 -3.29448611e-01 -3.32063973... | [5.118278980255127, 5.577910900115967] |
8680fd58-34c4-4dd8-a05b-8775fcf4804a | bidirectional-generative-framework-for-cross | 2305.09509 | null | https://arxiv.org/abs/2305.09509v1 | https://arxiv.org/pdf/2305.09509v1.pdf | Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis | Cross-domain aspect-based sentiment analysis (ABSA) aims to perform various fine-grained sentiment analysis tasks on a target domain by transferring knowledge from a source domain. Since labeled data only exists in the source domain, a model is expected to bridge the domain gap for tackling cross-domain ABSA. Though do... | ['Lidong Bing', 'Sinno Jialin Pan', 'Wenxuan Zhang', 'Yue Deng'] | 2023-05-16 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 1.69116467e-01 -2.35748291e-01 -7.95159638e-02 -7.99593210e-01
-1.27888763e+00 -7.34582365e-01 6.21614337e-01 -1.65068373e-01
-1.10577092e-01 7.03923702e-01 1.65148169e-01 -6.82951137e-02
1.31552666e-01 -8.39600444e-01 -5.62865794e-01 -7.81655610e-01
6.20154977e-01 5.86898446e-01 -1.10937946e-01 -4.76819426... | [11.409918785095215, 6.688283443450928] |
c89320be-12f3-4377-96c4-8c26ca0b17da | applade-adjustable-plug-and-play-audio | 2202.08028 | null | https://arxiv.org/abs/2202.08028v1 | https://arxiv.org/pdf/2202.08028v1.pdf | APPLADE: Adjustable Plug-and-play Audio Declipper Combining DNN with Sparse Optimization | In this paper, we propose an audio declipping method that takes advantages of both sparse optimization and deep learning. Since sparsity-based audio declipping methods have been developed upon constrained optimization, they are adjustable and well-studied in theory. However, they always uniformly promote sparsity and i... | ['Yasuhiro Oikawa', 'Masahiro Yasuda', 'Kohei Yatabe', 'Tomoro Tanaka'] | 2022-02-16 | null | null | null | null | ['audio-declipping'] | ['audio'] | [ 1.70234248e-01 -2.61424780e-01 -4.06709731e-01 -2.09704831e-01
-3.48402977e-01 -1.63481340e-01 1.83260784e-01 -2.15682402e-01
-2.50984550e-01 6.28667653e-01 3.86760324e-01 1.35867625e-01
-3.72011930e-01 -5.62796414e-01 -6.23879611e-01 -8.12322497e-01
4.71422449e-02 9.07612741e-02 -4.41389084e-02 -1.56839028... | [15.41474723815918, 5.530331611633301] |
f24ef454-23c4-4353-a239-77834edc74d7 | winogavil-gamified-association-benchmark-to | 2207.12576 | null | https://arxiv.org/abs/2207.12576v2 | https://arxiv.org/pdf/2207.12576v2.pdf | WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models | While vision-and-language models perform well on tasks such as visual question answering, they struggle when it comes to basic human commonsense reasoning skills. In this work, we introduce WinoGAViL: an online game of vision-and-language associations (e.g., between werewolves and a full moon), used as a dynamic evalua... | ['Roy Schwartz', 'Gabriel Stanovsky', 'Mohit Bansal', 'Yuval Elovici', 'Ron Yosef', 'Nitzan Bitton Guetta', 'Yonatan Bitton'] | 2022-07-25 | null | null | null | null | ['visual-reasoning', 'general-knowledge', 'visual-reasoning', 'multimodal-association'] | ['computer-vision', 'miscellaneous', 'reasoning', 'time-series'] | [-3.08838665e-01 1.77344412e-01 6.25441596e-02 1.66762710e-01
-3.57703209e-01 -9.31413412e-01 6.12249374e-01 3.03017706e-01
-5.28685093e-01 4.65638995e-01 7.38263205e-02 -4.22683030e-01
-6.76942170e-02 -5.40773034e-01 -6.84967041e-01 -1.62041515e-01
-3.23654599e-02 8.57490182e-01 3.63163978e-01 -6.42238259... | [10.74134349822998, 1.9822757244110107] |
88797494-0dc3-4003-90d5-9f1b8b9bda9a | query2doc-query-expansion-with-large-language | 2303.07678 | null | https://arxiv.org/abs/2303.07678v1 | https://arxiv.org/pdf/2303.07678v1.pdf | Query2doc: Query Expansion with Large Language Models | This paper introduces a simple yet effective query expansion approach, denoted as query2doc, to improve both sparse and dense retrieval systems. The proposed method first generates pseudo-documents by few-shot prompting large language models (LLMs), and then expands the query with generated pseudo-documents. LLMs are t... | ['Furu Wei', 'Nan Yang', 'Liang Wang'] | 2023-03-14 | null | null | null | null | ['memorization'] | ['natural-language-processing'] | [-1.61446646e-01 -1.44585773e-01 -5.28267443e-01 -5.15226722e-02
-1.69721806e+00 -5.92105746e-01 1.14944983e+00 4.35477585e-01
-8.56985927e-01 7.95272231e-01 5.96633375e-01 1.22047193e-01
-3.40278178e-01 -7.18109727e-01 -5.98638952e-01 -2.06426129e-01
-8.26191902e-02 1.17557180e+00 6.75872147e-01 -7.56594300... | [11.518173217773438, 7.665882110595703] |
f3c3b3b8-0290-4517-951e-40d145bb5164 | dynamic-character-graph-via-online-face | 2007.14913 | null | https://arxiv.org/abs/2007.14913v1 | https://arxiv.org/pdf/2007.14913v1.pdf | Dynamic Character Graph via Online Face Clustering for Movie Analysis | An effective approach to automated movie content analysis involves building a network (graph) of its characters. Existing work usually builds a static character graph to summarize the content using metadata, scripts or manual annotations. We propose an unsupervised approach to building a dynamic character graph that ca... | ['Prakhar Kulshreshtha', 'Tanaya Guha'] | 2020-07-29 | null | null | null | null | ['face-clustering'] | ['computer-vision'] | [ 8.95754546e-02 -1.91516742e-01 -1.40600428e-01 -3.26166064e-01
-3.92846107e-01 -1.06777132e+00 8.89606416e-01 3.53306532e-01
-4.46665147e-03 1.02887705e-01 4.73275095e-01 1.82195693e-01
-9.05183479e-02 -6.08934999e-01 -2.93841422e-01 -4.93337035e-01
-3.02342236e-01 3.67394209e-01 5.82364082e-01 1.95410520... | [10.583479881286621, 0.6939753890037537] |
3d8051bd-5891-4bfa-86bd-c103bbb1b50a | osvidcap-a-framework-for-the-simultaneous | null | null | https://ieeexplore.ieee.org/abstract/document/9552885 | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9552885 | OSVidCap: A Framework for the Simultaneous Recognition and Description of Concurrent Actions in Videos in an Open-Set Scenario | Automatically understanding and describing the visual content of videos in natural language is a challenging task in computer vision. Existing approaches are often designed to describe single events in a closed-set setting. However, in real-world scenarios, concurrent activities and previously unseen actions may appear... | ['Heitor Silvério Lopes', 'André Eugênio Lazzaretti', 'Matheus Gutoski', 'Andrei De Souza Inácio'] | 2021-09-29 | null | null | null | ieee-access-2021-9 | ['open-set-video-captioning'] | ['computer-vision'] | [ 5.31748354e-01 -9.62441489e-02 -2.24001840e-01 -2.48547763e-01
-5.26674688e-01 -4.53606695e-01 8.57392848e-01 -2.32595325e-01
-3.01355869e-01 6.96969092e-01 4.52663243e-01 2.63584740e-02
2.37796292e-01 -3.08655709e-01 -1.00705099e+00 -7.13935196e-01
-3.26533139e-01 2.89356768e-01 6.07526302e-01 6.94689453... | [8.690948486328125, 0.6696100831031799] |
af356f70-0286-4366-86a7-10f70271c9b6 | adas-a-direct-adaptation-strategy-for-multi | 2203.06811 | null | https://arxiv.org/abs/2203.06811v1 | https://arxiv.org/pdf/2203.06811v1.pdf | ADAS: A Direct Adaptation Strategy for Multi-Target Domain Adaptive Semantic Segmentation | In this paper, we present a direct adaptation strategy (ADAS), which aims to directly adapt a single model to multiple target domains in a semantic segmentation task without pretrained domain-specific models. To do so, we design a multi-target domain transfer network (MTDT-Net) that aligns visual attributes across doma... | ['Sunghoon Im', 'Minwoo Choi', 'Changjae Kim', 'Wonhyeok Choi', 'Seunghun Lee'] | 2022-03-14 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Lee_ADAS_A_Direct_Adaptation_Strategy_for_Multi-Target_Domain_Adaptive_Semantic_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lee_ADAS_A_Direct_Adaptation_Strategy_for_Multi-Target_Domain_Adaptive_Semantic_CVPR_2022_paper.pdf | cvpr-2022-1 | ['multi-target-domain-adaptation'] | ['computer-vision'] | [ 4.73562300e-01 8.02325532e-02 -9.28375274e-02 -6.56648695e-01
-9.10814047e-01 -6.63556337e-01 4.26871330e-01 -1.80088162e-01
-4.19587702e-01 5.09345114e-01 -1.60042301e-01 9.21704574e-04
3.56798284e-02 -7.61148691e-01 -9.19455886e-01 -5.71103990e-01
5.66718996e-01 9.10419524e-01 7.71592557e-01 -2.54177362... | [9.740579605102539, 1.3624237775802612] |
705c1451-625c-46e1-9f3c-46d0250b8cec | node-centric-graph-learning-from-data-for | 2011.02179 | null | https://arxiv.org/abs/2011.02179v1 | https://arxiv.org/pdf/2011.02179v1.pdf | Node-Centric Graph Learning from Data for Brain State Identification | Data-driven graph learning models a network by determining the strength of connections between its nodes. The data refers to a graph signal which associates a value with each graph node. Existing graph learning methods either use simplified models for the graph signal, or they are prohibitively expensive in terms of co... | ['Stark C. Draper', 'Taufik A. Valiante', 'Roman Genov', 'David M. Groppe', 'Nafiseh Ghoroghchian'] | 2020-11-04 | null | null | null | null | ['graph-similarity'] | ['graphs'] | [ 4.17997241e-01 4.85548854e-01 3.62765715e-02 -3.11488926e-01
-1.68409407e-01 -3.20589781e-01 6.08129680e-01 7.12178111e-01
-1.75181434e-01 4.65188473e-01 1.78518176e-01 -4.15819436e-01
-6.17723703e-01 -8.81082833e-01 -2.26676762e-01 -5.98568797e-01
-9.24883068e-01 3.20204824e-01 1.39157623e-01 -9.13342014... | [12.30441951751709, 3.437166452407837] |
18717bb5-fbb5-49a5-8a1e-5433a46b1ff0 | bloom-a-176b-parameter-open-access | 2211.051 | null | https://arxiv.org/abs/2211.05100v4 | https://arxiv.org/pdf/2211.05100v4.pdf | BLOOM: A 176B-Parameter Open-Access Multilingual Language Model | Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democra... | ['Nikolaus Muellner', 'Nicholas Michio Broad', 'Nathan Dahlberg', 'Helena U. Vrabec', 'Gully Burns', 'Giyaseddin Bayrak', 'Gabriel Altay', 'Florian Fuhrimann', 'Alfredo Palasciano', 'Abhinav Ramesh Kashyap', 'Zach Nguyen', 'Yoyo Yang', 'Trieu Le', 'Tobi Oyebade', 'Ryan Hao', 'Rasmus Kromann', 'Ran An', 'Olanrewaju Samu... | 2022-11-09 | null | null | null | null | ['multilingual-nlp'] | ['natural-language-processing'] | [ 2.98066717e-02 1.32226884e-01 -6.24141753e-01 -2.88979977e-01
-1.08707190e+00 -6.57280803e-01 8.54784727e-01 -4.75242995e-02
-5.36374927e-01 5.33224523e-01 4.71827328e-01 -7.43008494e-01
4.12053972e-01 -4.08134162e-01 -9.00333762e-01 -1.11243263e-01
-1.82780907e-01 7.58250833e-01 3.74049067e-01 -4.45738643... | [10.632351875305176, 8.347243309020996] |
43ce812a-fd4c-4afe-864b-60377cb85487 | self-supervised-learning-for-organs-at-risk | 2305.02491 | null | https://arxiv.org/abs/2305.02491v1 | https://arxiv.org/pdf/2305.02491v1.pdf | Self-Supervised Learning for Organs At Risk and Tumor Segmentation with Uncertainty Quantification | In this study, our goal is to show the impact of self-supervised pre-training of transformers for organ at risk (OAR) and tumor segmentation as compared to costly fully-supervised learning. The proposed algorithm is called Monte Carlo Transformer based U-Net (MC-Swin-U). Unlike many other available models, our approach... | ['Ulas Bagci', 'Damla Turgut', 'Mohamed Abazeed', 'Bulent Aydogan', 'Patrick Kelly', 'Justin Rineer', 'Curtis Lisle', 'Debesh Jha', 'Ilkin Isler'] | 2023-05-04 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [-3.77167873e-02 7.74886072e-01 -5.67363203e-01 -3.90659809e-01
-1.60254264e+00 -4.82267946e-01 4.89331007e-01 5.46374083e-01
-2.92196363e-01 1.20401835e+00 4.47744250e-01 -6.37893021e-01
-2.90057026e-02 -9.40222263e-01 -9.63721454e-01 -5.65261304e-01
-1.37669519e-01 6.99744403e-01 3.77337903e-01 4.64451700... | [14.671676635742188, -2.3034050464630127] |
46fbaa29-ab08-4516-a65b-82caebe7724f | interacting-hand-object-pose-estimation-via | 2211.08805 | null | https://arxiv.org/abs/2211.08805v1 | https://arxiv.org/pdf/2211.08805v1.pdf | Interacting Hand-Object Pose Estimation via Dense Mutual Attention | 3D hand-object pose estimation is the key to the success of many computer vision applications. The main focus of this task is to effectively model the interaction between the hand and an object. To this end, existing works either rely on interaction constraints in a computationally-expensive iterative optimization, or ... | ['Hongdong Li', 'Wei Mao', 'Rong Wang'] | 2022-11-16 | null | null | null | null | ['hand-object-pose'] | ['computer-vision'] | [-2.92480677e-01 1.00083221e-02 -9.70880315e-02 -2.40544267e-02
-4.94943082e-01 -4.07539010e-01 5.59315503e-01 -1.47326529e-01
-1.20145805e-01 4.62534726e-01 6.31235391e-02 1.81730330e-01
-3.49800646e-01 -7.18213737e-01 -9.30103898e-01 -6.77778542e-01
1.99471921e-01 1.05803359e+00 2.93693811e-01 6.53131083... | [6.688773155212402, -1.0679092407226562] |
305114c9-1a5f-4a06-95c9-8f4cd6920469 | decentralised-approach-for-multi-agent-path | 2106.05188 | null | https://arxiv.org/abs/2106.05188v1 | https://arxiv.org/pdf/2106.05188v1.pdf | Decentralised Approach for Multi Agent Path Finding | Multi Agent Path Finding (MAPF) requires identification of conflict free paths for agents which could be point-sized or with dimensions. In this paper, we propose an approach for MAPF for spatially-extended agents. These find application in real world problems like Convoy Movement Problem, Train Scheduling etc. Our pro... | ['M. Narasimha Murty', 'Shyni Thomas'] | 2021-06-03 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-2.16830239e-01 2.26341560e-01 2.29149029e-01 8.49555358e-02
-2.94583946e-01 -8.75027657e-01 5.78361273e-01 6.12615764e-01
-7.89825201e-01 1.53334427e+00 -3.05864125e-01 -2.68852830e-01
-1.01859438e+00 -1.11524045e+00 -3.07959765e-01 -6.15597069e-01
-7.32186735e-01 1.49501526e+00 1.01899779e+00 -3.94150347... | [4.976676940917969, 1.7382606267929077] |
c5025001-f985-4abf-a3f6-12cee205f04b | rerender-a-video-zero-shot-text-guided-video | 2306.07954 | null | https://arxiv.org/abs/2306.07954v1 | https://arxiv.org/pdf/2306.07954v1.pdf | Rerender A Video: Zero-Shot Text-Guided Video-to-Video Translation | Large text-to-image diffusion models have exhibited impressive proficiency in generating high-quality images. However, when applying these models to video domain, ensuring temporal consistency across video frames remains a formidable challenge. This paper proposes a novel zero-shot text-guided video-to-video translatio... | ['Chen Change Loy', 'Ziwei Liu', 'Yifan Zhou', 'Shuai Yang'] | 2023-06-13 | null | null | null | null | ['patch-matching'] | ['computer-vision'] | [ 2.87077546e-01 -3.34871978e-01 -6.92327917e-02 -1.93515703e-01
-6.08644366e-01 -4.85690087e-01 6.49783731e-01 -5.18100679e-01
-1.89601824e-01 5.77648103e-01 1.28470510e-01 4.13210429e-02
8.01849589e-02 -6.69911146e-01 -9.20803845e-01 -6.61525548e-01
5.25645949e-02 3.90157215e-02 7.50545144e-01 -2.52224624... | [11.02837085723877, -0.8771497011184692] |
c0daa635-5b41-470a-aab9-a69ccf88ee63 | wind-turbine-blade-surface-damage-detection | 2108.08636 | null | https://arxiv.org/abs/2108.08636v2 | https://arxiv.org/pdf/2108.08636v2.pdf | Wind Turbine Blade Surface Damage Detection based on Aerial Imagery and VGG16-RCNN Framework | In this manuscript, an image analytics based deep learning framework for wind turbine blade surface damage detection is proposed. Turbine blade(s) which carry approximately one-third of a turbine weight are susceptible to damage and can cause sudden malfunction of a grid-connected wind energy conversion system. The sur... | ['Harsh S. Dhiman', 'Lagan Sharma', 'Juhi Patel'] | 2021-08-19 | null | null | null | null | ['image-augmentation'] | ['computer-vision'] | [-3.16729635e-01 -1.93307057e-01 4.84044194e-01 4.37793016e-01
-4.85891700e-02 -9.03057337e-01 4.67643403e-02 -3.27620693e-02
1.66718096e-01 3.06380183e-01 1.34320538e-02 -3.47579777e-01
1.76667109e-01 -9.10891294e-01 -3.80080752e-02 -9.25526261e-01
-3.51501346e-01 -2.55248874e-01 -1.98155027e-02 -3.38683754... | [6.959629535675049, 2.190608501434326] |
fc73558a-23c6-4051-b5c5-0f2353041b58 | source-free-domain-adaptation-for-real-world | 2207.06644 | null | https://arxiv.org/abs/2207.06644v1 | https://arxiv.org/pdf/2207.06644v1.pdf | Source-Free Domain Adaptation for Real-world Image Dehazing | Deep learning-based source dehazing methods trained on synthetic datasets have achieved remarkable performance but suffer from dramatic performance degradation on real hazy images due to domain shift. Although certain Domain Adaptation (DA) dehazing methods have been presented, they inevitably require access to the sou... | ['Feng Zhao', 'Man Zhou', 'Qi Zhu', 'Yajing Liu', 'Jie Huang', 'Hu Yu'] | 2022-07-14 | null | null | null | null | ['image-dehazing', 'source-free-domain-adaptation'] | ['computer-vision', 'computer-vision'] | [ 3.81972998e-01 -1.90988332e-01 1.62293464e-01 -3.41967314e-01
-6.09924197e-01 -1.35425761e-01 5.41008174e-01 -3.61493349e-01
-2.43054375e-01 7.76947975e-01 8.43304768e-02 1.05836079e-01
1.33175969e-01 -1.04556262e+00 -8.54250371e-01 -1.19209898e+00
5.56968033e-01 5.51627912e-02 5.50438821e-01 -5.32002687... | [10.938694953918457, -3.1066362857818604] |
3eda3762-97b4-4255-b264-7820e27d165f | spatiotemporal-representation-learning-on | null | null | https://openreview.net/forum?id=Jh9VxCkrEZn | https://openreview.net/pdf?id=Jh9VxCkrEZn | Spatiotemporal Representation Learning on Time Series with Dynamic Graph ODEs | Spatiotemporal representation learning on multivariate time series has received tremendous attention in forecasting traffic and energy data. Recent works either rely on complicated discrete neural architectures or graph priors, hindering their effectiveness and applications in the real world. In this paper, inspired by... | ['Shirui Pan', 'Bin Yang', 'Yu Zheng', 'Yuan-Fang Li', 'Ming Jin'] | 2021-09-29 | null | null | null | null | ['graph-structure-learning'] | ['graphs'] | [-1.53899819e-01 -2.51668483e-01 -1.32032380e-01 -7.33176023e-02
-4.90401611e-02 -4.52250123e-01 6.31483018e-01 -7.86981806e-02
-1.87616423e-01 5.79632938e-01 2.55927950e-01 -4.53072637e-01
-3.70486379e-01 -7.38989234e-01 -6.67329073e-01 -7.68409550e-01
-3.82122546e-01 -9.30502266e-02 3.28237653e-01 -1.91292256... | [6.755388259887695, 2.7834079265594482] |
0511fedf-050e-4fce-ada4-35cf2b787322 | deep-bayesian-video-frame-interpolation | null | null | https://www.ecva.net/papers.php | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136750141.pdf | Deep Bayesian Video Frame Interpolation | Abstract. We present deep Bayesian video frame interpolation, a novel approach for upsampling a low frame-rate video temporally to its higher frame-rate counterpart. Our approach learns posterior distributions of optical flows and frames to be interpolated, which is optimized via learned gradient descent for fast conve... | ['Jimmy S. Ren', 'Xijun Chen', 'Dongqing Zou', 'Xujie Xiang', 'Yu Zhang', 'ZHIYANG YU'] | 2022-10-23 | null | null | null | conference-2022-10 | ['video-frame-interpolation'] | ['computer-vision'] | [-3.70182358e-02 -3.66990594e-03 -5.58121085e-01 -6.01034462e-01
-5.34631431e-01 -1.89276814e-01 6.57989740e-01 -5.44996202e-01
-5.06900668e-01 1.17153430e+00 5.28968990e-01 -2.06018344e-01
3.99848551e-01 -5.14440179e-01 -1.23071086e+00 -3.41043621e-01
-6.28767610e-01 2.85504818e-01 4.51192409e-01 2.06492007... | [10.633267402648926, -1.300772786140442] |
a93a6920-f90f-499e-879b-6ba6addb8fb3 | you-can-mask-more-for-extremely-low-bitrate | 2306.15561 | null | https://arxiv.org/abs/2306.15561v1 | https://arxiv.org/pdf/2306.15561v1.pdf | You Can Mask More For Extremely Low-Bitrate Image Compression | Learned image compression (LIC) methods have experienced significant progress during recent years. However, these methods are primarily dedicated to optimizing the rate-distortion (R-D) performance at medium and high bitrates (> 0.1 bits per pixel (bpp)), while research on extremely low bitrates is limited. Besides, ex... | ['Yao Zhao', 'Weisi Lin', 'Meng Wang', 'Chunjie Zhang', 'Runmin Cong', 'Huihui Bai', 'Jiaxin Han', 'Feng Li', 'Anqi Li'] | 2023-06-27 | null | null | null | null | ['image-compression'] | ['computer-vision'] | [ 6.13313973e-01 3.11014932e-02 -3.87761503e-01 -7.89542273e-02
-6.89755380e-01 -1.60607472e-01 5.86587667e-01 -1.73512354e-01
-1.87236890e-01 4.77815509e-01 4.06149417e-01 -1.78568721e-01
-2.99391858e-02 -8.45596254e-01 -7.72782207e-01 -8.78385842e-01
-3.79322991e-02 -8.44351724e-02 1.59895316e-01 -8.16273093... | [11.31421184539795, -1.6715164184570312] |
7de23d99-a3fb-48e1-8991-9d5a9cbdc196 | visually-aware-audio-captioning-with-adaptive | 2210.16428 | null | https://arxiv.org/abs/2210.16428v3 | https://arxiv.org/pdf/2210.16428v3.pdf | Visually-Aware Audio Captioning With Adaptive Audio-Visual Attention | Audio captioning aims to generate text descriptions of audio clips. In the real world, many objects produce similar sounds. How to accurately recognize ambiguous sounds is a major challenge for audio captioning. In this work, inspired by inherent human multimodal perception, we propose visually-aware audio captioning, ... | ['Wenwu Wang', 'Volkan Kılıç', 'Mark D. Plumbley', 'Lilian H. Tang', 'Yu Zhang', 'Tom Ko', 'Shengchen Li', 'Jianyuan Sun', 'Qiuqiang Kong', 'Haohe Liu', 'Xinhao Mei', 'Qiushi Huang', 'Xubo Liu'] | 2022-10-28 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 5.14021814e-01 -2.14870319e-01 2.37792265e-02 -6.55644909e-02
-1.19754505e+00 -6.90628648e-01 3.82807702e-01 1.90592781e-02
-4.88983542e-02 5.32672644e-01 7.39418566e-01 1.19764367e-02
4.55249429e-01 -2.25968778e-01 -9.65309083e-01 -4.11818951e-01
1.88536435e-01 1.47136718e-01 -1.98996463e-03 -4.67317142... | [15.193921089172363, 4.909128665924072] |
a7443b8a-f758-4080-93f5-95d3cf1b9f98 | lifting-2d-human-pose-to-3d-with-domain | 2111.11969 | null | https://arxiv.org/abs/2111.11969v1 | https://arxiv.org/pdf/2111.11969v1.pdf | Lifting 2D Human Pose to 3D with Domain Adapted 3D Body Concept | Lifting the 2D human pose to the 3D pose is an important yet challenging task. Existing 3D pose estimation suffers from 1) the inherent ambiguity between the 2D and 3D data, and 2) the lack of well labeled 2D-3D pose pairs in the wild. Human beings are able to imagine the human 3D pose from a 2D image or a set of 2D bo... | ['Yunhui Liu', 'Ziwei Liu', 'Qiang Nie'] | 2021-11-23 | null | null | null | null | ['3d-pose-estimation'] | ['computer-vision'] | [ 1.74649451e-02 3.98681343e-01 -4.55724657e-01 -2.76474506e-01
-3.77898335e-01 -5.53384662e-01 4.23633635e-01 -4.76787746e-01
-2.63681948e-01 4.53831702e-01 6.10227406e-01 2.44957358e-01
-5.27540110e-02 -5.50853908e-01 -7.78670609e-01 -5.07935703e-01
4.76252548e-02 6.99775696e-01 -2.22239375e-01 -4.57976371... | [7.001377582550049, -1.0002002716064453] |
9daf56d0-e4cc-477e-aa1f-101659cb16e4 | one-shot-high-fidelity-talking-head-synthesis | 2304.05097 | null | https://arxiv.org/abs/2304.05097v1 | https://arxiv.org/pdf/2304.05097v1.pdf | One-Shot High-Fidelity Talking-Head Synthesis with Deformable Neural Radiance Field | Talking head generation aims to generate faces that maintain the identity information of the source image and imitate the motion of the driving image. Most pioneering methods rely primarily on 2D representations and thus will inevitably suffer from face distortion when large head rotations are encountered. Recent works... | ['Xuelong Li', 'Liefeng Bo', 'Zhongjian Wang', 'Bang Zhang', 'Mulin Chen', 'Zhigang Wang', 'Bin Zhao', 'Dong Wang', 'Longhao Zhang', 'Weichuang Li'] | 2023-04-11 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_One-Shot_High-Fidelity_Talking-Head_Synthesis_With_Deformable_Neural_Radiance_Field_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_One-Shot_High-Fidelity_Talking-Head_Synthesis_With_Deformable_Neural_Radiance_Field_CVPR_2023_paper.pdf | cvpr-2023-1 | ['talking-head-generation', 'neural-rendering'] | ['computer-vision', 'computer-vision'] | [-1.65890772e-02 2.54077226e-01 1.21823981e-01 -7.58049786e-01
-4.24678892e-01 -4.83581930e-01 6.29511535e-01 -7.24405706e-01
1.85186133e-01 4.98879701e-01 5.94856679e-01 3.42442185e-01
2.42343456e-01 -5.67577899e-01 -7.06917822e-01 -7.82948494e-01
4.40909803e-01 1.36211589e-01 -3.85540396e-01 -4.12562042... | [12.8928861618042, -0.3339753746986389] |
dd575ad3-ec39-461e-a4cc-170ea266fafc | rhythm-controllable-attention-with-high | 2306.02593 | null | https://arxiv.org/abs/2306.02593v1 | https://arxiv.org/pdf/2306.02593v1.pdf | Rhythm-controllable Attention with High Robustness for Long Sentence Speech Synthesis | Regressive Text-to-Speech (TTS) system utilizes attention mechanism to generate alignment between text and acoustic feature sequence. Alignment determines synthesis robustness (e.g, the occurence of skipping, repeating, and collapse) and rhythm via duration control. However, current attention algorithms used in speech ... | ['Binghuai Lin', 'Jiaen Liang', 'Jianqing Sun', 'Ya Li', 'Qi Luo', 'Jinlong Xue', 'Yukang Jia', 'Yayue Deng', 'Dengfeng Ke'] | 2023-06-05 | null | null | null | null | ['speech-synthesis'] | ['speech'] | [ 1.68283656e-01 2.01143082e-02 1.32871866e-01 -5.27812019e-02
-7.25494146e-01 -4.57526863e-01 4.20588911e-01 -3.62800866e-01
-2.41613105e-01 6.41693294e-01 7.25889146e-01 -3.14192802e-01
2.43347332e-01 -4.45869982e-01 -3.64859104e-01 -8.07482123e-01
4.93925601e-01 -1.12585418e-01 1.99832007e-01 -4.75008935... | [14.95374584197998, 6.629697799682617] |
dce898a0-f44f-4fa4-9446-d98ffb774f8a | three-branches-detecting-actions-with-richer | 1908.04519 | null | https://arxiv.org/abs/1908.04519v1 | https://arxiv.org/pdf/1908.04519v1.pdf | Three Branches: Detecting Actions With Richer Features | We present our three branch solutions for International Challenge on Activity Recognition at CVPR2019. This model seeks to fuse richer information of global video clip, short human attention and long-term human activity into a unified model. We have participated in two tasks: Task A, the Kinetics challenge and Task B, ... | ['Jiajun Tang', 'Cewu Lu', 'Jin Xia'] | 2019-08-13 | null | null | null | null | ['spatio-temporal-action-localization'] | ['computer-vision'] | [ 9.49022099e-02 -3.17192852e-01 -4.37354267e-01 1.53375477e-01
-9.87789452e-01 -5.15205562e-01 7.40569353e-01 -3.29118371e-01
-7.52979100e-01 7.13555872e-01 8.68978620e-01 4.27817583e-01
2.46328026e-01 -1.05744578e-01 -6.87708020e-01 -6.59689307e-01
-4.45994020e-01 -4.22928818e-02 4.08775628e-01 1.11565232... | [8.321216583251953, 0.4723582863807678] |
4762f01a-a16c-467f-85a1-920f67917965 | learning-disentangling-and-fusing-networks | 1712.04646 | null | http://arxiv.org/abs/1712.04646v1 | http://arxiv.org/pdf/1712.04646v1.pdf | Learning Disentangling and Fusing Networks for Face Completion Under Structured Occlusions | Face completion aims to generate semantically new pixels for missing facial
components. It is a challenging generative task due to large variations of face
appearance. This paper studies generative face completion under structured
occlusions. We treat the face completion and corruption as disentangling and
fusing proce... | ['Ran He', 'Yibo Hu', 'Zhihang Li'] | 2017-12-13 | null | null | null | null | ['facial-inpainting'] | ['computer-vision'] | [ 3.69703323e-01 2.44099304e-01 3.79986018e-01 -4.89633113e-01
-7.84489036e-01 -5.30314922e-01 7.80125022e-01 -8.84134710e-01
1.94723368e-01 7.51125813e-01 2.99016148e-01 1.63671657e-01
1.21504582e-01 -8.14268947e-01 -1.00827241e+00 -1.17672503e+00
3.66476834e-01 4.29196060e-01 -6.94555581e-01 2.79951058... | [12.855669975280762, 0.07993996143341064] |
4cf25f2f-12aa-40fc-8c6a-65e092bf5b45 | when-does-bottom-up-beat-top-down-in | 2306.00833 | null | https://arxiv.org/abs/2306.00833v1 | https://arxiv.org/pdf/2306.00833v1.pdf | When Does Bottom-up Beat Top-down in Hierarchical Community Detection? | Hierarchical clustering of networks consists in finding a tree of communities, such that lower levels of the hierarchy reveal finer-grained community structures. There are two main classes of algorithms tackling this problem. Divisive ($\textit{top-down}$) algorithms recursively partition the nodes into two communities... | ['Patrick Thiran', 'Matthias Grossglauser', 'Daichi Kuroda', 'Maximilien Dreveton'] | 2023-06-01 | null | null | null | null | ['stochastic-block-model', 'community-detection'] | ['graphs', 'graphs'] | [ 2.78614640e-01 2.60932803e-01 -6.87583238e-02 2.43063532e-02
-3.22872192e-01 -7.99230218e-01 1.19276129e-01 6.19507968e-01
-6.33689016e-02 4.55454350e-01 -8.93638507e-02 -4.49229211e-01
-7.58371949e-01 -1.23210418e+00 -4.78440225e-01 -9.47426260e-01
-7.43076026e-01 8.26889813e-01 5.35090923e-01 -4.08904813... | [6.936427593231201, 5.119372844696045] |
6de12f31-1ed1-4481-adf4-5e3614389d39 | virtual-sparse-convolution-for-multimodal-3d | 2303.02314 | null | https://arxiv.org/abs/2303.02314v1 | https://arxiv.org/pdf/2303.02314v1.pdf | Virtual Sparse Convolution for Multimodal 3D Object Detection | Recently, virtual/pseudo-point-based 3D object detection that seamlessly fuses RGB images and LiDAR data by depth completion has gained great attention. However, virtual points generated from an image are very dense, introducing a huge amount of redundant computation during detection. Meanwhile, noises brought by inacc... | ['Cheng Wang', 'Xin Li', 'Shaoshuai Shi', 'Chenglu Wen', 'Hai Wu'] | 2023-03-04 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wu_Virtual_Sparse_Convolution_for_Multimodal_3D_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_Virtual_Sparse_Convolution_for_Multimodal_3D_Object_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['depth-completion'] | ['computer-vision'] | [-1.16604269e-01 -2.33970582e-01 2.80721337e-01 -4.86294366e-02
-1.15955174e+00 -3.82471085e-01 4.97737259e-01 4.94058318e-02
-4.92141247e-01 8.69467184e-02 -4.08047467e-01 -1.53618783e-01
1.98852405e-01 -9.52785373e-01 -7.76788533e-01 -5.40365756e-01
2.34855562e-01 6.75772727e-01 7.46073008e-01 5.42296446... | [7.803004741668701, -2.687835454940796] |
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