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
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
6b23687b-6041-4dfd-b505-ef53ff342935 | cross-lingual-learning-to-rank-with-shared | null | null | https://aclanthology.org/N18-2073 | https://aclanthology.org/N18-2073.pdf | Cross-Lingual Learning-to-Rank with Shared Representations | Cross-lingual information retrieval (CLIR) is a document retrieval task where the documents are written in a language different from that of the user{'}s query. This is a challenging problem for data-driven approaches due to the general lack of labeled training data. We introduce a large-scale dataset derived from Wiki... | ['Shota Sasaki', 'Shigehiko Schamoni', 'Kevin Duh', 'Kentaro Inui', 'Shuo Sun'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['cross-lingual-information-retrieval'] | ['natural-language-processing'] | [-3.59444559e-01 -6.19033098e-01 -7.19714463e-01 -3.64551663e-01
-1.38394094e+00 -7.28936493e-01 6.99157476e-01 2.16343284e-01
-9.00473535e-01 6.45786762e-01 6.28204942e-01 -3.58196080e-01
-4.95910108e-01 -3.18029404e-01 -2.18886107e-01 -1.91296548e-01
1.87051937e-01 7.69442976e-01 -4.53249179e-02 -6.62591815... | [11.358192443847656, 9.81503677368164] |
5bf58c07-f327-46d0-bcab-44ad0cebb4c6 | hotpotqa-a-dataset-for-diverse-explainable | 1809.09600 | null | http://arxiv.org/abs/1809.09600v1 | http://arxiv.org/pdf/1809.09600v1.pdf | HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering | Existing question answering (QA) datasets fail to train QA systems to perform
complex reasoning and provide explanations for answers. We introduce HotpotQA,
a new dataset with 113k Wikipedia-based question-answer pairs with four key
features: (1) the questions require finding and reasoning over multiple
supporting docu... | ['Peng Qi', 'Saizheng Zhang', 'Yoshua Bengio', 'Ruslan Salakhutdinov', 'Zhilin Yang', 'William W. Cohen', 'Christopher D. Manning'] | 2018-09-25 | hotpotqa-a-dataset-for-diverse-explainable-1 | https://aclanthology.org/D18-1259 | https://aclanthology.org/D18-1259.pdf | emnlp-2018-10 | ['multi-hop-question-answering'] | ['knowledge-base'] | [-1.21766217e-01 9.29441035e-01 -1.66580096e-01 -6.90689564e-01
-1.45605314e+00 -7.81450450e-01 4.68076795e-01 4.23322767e-01
1.25836655e-01 1.20023429e+00 6.03837967e-01 -7.72815764e-01
-5.48138916e-01 -1.02968037e+00 -9.82934654e-01 3.55302334e-01
7.81844463e-03 1.24241352e+00 8.87918711e-01 -1.05257666... | [11.033415794372559, 7.937178611755371] |
d72ea368-e295-4833-89ef-8a3dd45e878b | quality-aware-generative-adversarial-networks | 1911.03149 | null | https://arxiv.org/abs/1911.03149v1 | https://arxiv.org/pdf/1911.03149v1.pdf | Quality Aware Generative Adversarial Networks | Generative Adversarial Networks (GANs) have become a very popular tool for implicitly learning high-dimensional probability distributions. Several improvements have been made to the original GAN formulation to address some of its shortcomings like mode collapse, convergence issues, entanglement, poor visual quality etc... | ['Sumohana S. Channappayya', 'Parimala Kancharla'] | 2019-11-08 | quality-aware-generative-adversarial-networks-1 | http://papers.nips.cc/paper/8560-quality-aware-generative-adversarial-networks | http://papers.nips.cc/paper/8560-quality-aware-generative-adversarial-networks.pdf | neurips-2019-12 | ['no-reference-image-quality-assessment'] | ['computer-vision'] | [ 1.14591703e-01 -2.78029665e-02 3.19213808e-01 -3.76805902e-01
-1.11395943e+00 -3.58590990e-01 7.84946084e-01 -3.00851017e-01
-4.60811913e-01 1.11490309e+00 2.49387190e-01 1.84779465e-01
-1.56999856e-01 -8.10474217e-01 -4.78339791e-01 -1.03549302e+00
8.06837156e-02 5.60587168e-01 -1.72327295e-01 -2.23296851... | [11.676581382751465, -0.5857764482498169] |
f6fe793b-8aed-4254-b8ec-7b481ba19c4b | fdnerf-semantics-driven-face-reconstruction | 2306.00783 | null | https://arxiv.org/abs/2306.00783v1 | https://arxiv.org/pdf/2306.00783v1.pdf | FDNeRF: Semantics-Driven Face Reconstruction, Prompt Editing and Relighting with Diffusion Models | The ability to create high-quality 3D faces from a single image has become increasingly important with wide applications in video conferencing, AR/VR, and advanced video editing in movie industries. In this paper, we propose Face Diffusion NeRF (FDNeRF), a new generative method to reconstruct high-quality Face NeRFs fr... | ['Tai Chi-Keung Tang', 'Yu-Wing', 'Tianyuan Dai', 'Yanbo Xu', 'Hao Zhang'] | 2023-06-01 | null | null | null | null | ['video-editing', '3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.43977115e-01 3.46617401e-01 3.40513617e-01 -4.92244124e-01
-7.08084166e-01 -5.99817216e-01 6.32635236e-01 -7.55424678e-01
6.39802888e-02 4.59403396e-01 1.87588573e-01 9.26891863e-02
1.65142149e-01 -6.59294307e-01 -7.25646675e-01 -2.51748413e-01
2.97371238e-01 7.10758030e-01 -3.28887969e-01 -2.84184694... | [12.695623397827148, -0.3709513247013092] |
9d3b4342-66db-43a0-b8a9-fef812b9349b | high-fidelity-3d-face-generation-from-natural | 2305.03302 | null | https://arxiv.org/abs/2305.03302v1 | https://arxiv.org/pdf/2305.03302v1.pdf | High-Fidelity 3D Face Generation from Natural Language Descriptions | Synthesizing high-quality 3D face models from natural language descriptions is very valuable for many applications, including avatar creation, virtual reality, and telepresence. However, little research ever tapped into this task. We argue the major obstacle lies in 1) the lack of high-quality 3D face data with descrip... | ['Xun Cao', 'Yuanxun Lu', 'Yiyu Zhuang', 'Linjia Huang', 'Hao Zhu', 'Menghua Wu'] | 2023-05-05 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wu_High-Fidelity_3D_Face_Generation_From_Natural_Language_Descriptions_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wu_High-Fidelity_3D_Face_Generation_From_Natural_Language_Descriptions_CVPR_2023_paper.pdf | cvpr-2023-1 | ['face-model', 'face-generation', 'text-to-3d', 'text-annotation'] | ['computer-vision', 'computer-vision', 'computer-vision', 'natural-language-processing'] | [-1.00828484e-01 1.37423009e-01 4.41102907e-02 -6.91479146e-01
-4.29697484e-01 -3.74551117e-01 7.17046797e-01 -5.57884932e-01
4.38826948e-01 4.36770320e-01 3.93572658e-01 3.93188335e-02
2.71257579e-01 -7.30868399e-01 -4.31399912e-01 -2.31809631e-01
5.06087363e-01 8.54030728e-01 -4.52157073e-02 -2.47274593... | [12.799188613891602, -0.19566722214221954] |
ce587792-ed27-4249-b944-87559c63fc7a | cross-domain-named-entity-recognition-via | null | null | https://openreview.net/forum?id=pfjbxxqih3x | https://openreview.net/pdf?id=pfjbxxqih3x | Cross-domain Named Entity Recognition via Graph Matching | Cross-domain NER is a practical yet challenging problem since the data scarcity in the real-world scenario. A common practice is first to learn a NER model in a rich-resource general domain and then adapt the model to specific domains. Due to the mismatch problem between entity types across domains, the wide knowledge ... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['cross-domain-named-entity-recognition'] | ['natural-language-processing'] | [ 2.20939949e-01 6.40711561e-02 -3.99724901e-01 -3.52442592e-01
-8.22765887e-01 -7.43555367e-01 5.72671533e-01 3.33631247e-01
-5.53844988e-01 8.23287189e-01 2.60871738e-01 1.72422752e-02
-1.20060958e-01 -1.17627585e+00 -3.90022397e-01 -4.24837232e-01
3.38027149e-01 7.51448214e-01 4.54378515e-01 -3.42685401... | [9.746838569641113, 9.613967895507812] |
8f3f742e-029a-4a07-b829-fbb773e00a4c | runtime-analysis-of-competitive-co | 2206.15238 | null | https://arxiv.org/abs/2206.15238v1 | https://arxiv.org/pdf/2206.15238v1.pdf | Runtime Analysis of Competitive co-Evolutionary Algorithms for Maximin Optimisation of a Bilinear Function | Co-evolutionary algorithms have a wide range of applications, such as in hardware design, evolution of strategies for board games, and patching software bugs. However, these algorithms are poorly understood and applications are often limited by pathological behaviour, such as loss of gradient, relative over-generalisat... | ['Per Kristian Lehre'] | 2022-06-30 | null | null | null | null | ['board-games'] | ['playing-games'] | [ 1.76090047e-01 -1.90864071e-01 1.93222135e-01 1.49887905e-01
-4.85995620e-01 -6.46967888e-01 2.27358952e-01 1.59113690e-01
-3.53377402e-01 7.74907768e-01 -6.46201253e-01 -5.98202407e-01
-3.54090512e-01 -8.05092275e-01 -8.43512952e-01 -8.64285231e-01
-4.75638449e-01 5.59168458e-01 3.24658930e-01 -3.73141110... | [5.773055076599121, 4.060076713562012] |
64b6c39a-3612-4849-a735-987891bc2550 | automatic-evaluation-of-herding-behavior-in | 2303.12016 | null | https://arxiv.org/abs/2303.12016v1 | https://arxiv.org/pdf/2303.12016v1.pdf | Automatic evaluation of herding behavior in towed fishing gear using end-to-end training of CNN and attention-based networks | This paper considers the automatic classification of herding behavior in the cluttered low-visibility environment that typically surrounds towed fishing gear. The paper compares three convolutional and attention-based deep action recognition network architectures trained end-to-end on a small set of video sequences cap... | ['Torfi Thorhallsson', 'Martin Eineborg', 'Týr Vilhjálmsson', 'Orri Steinn Guðfinnsson'] | 2023-03-21 | null | null | null | null | ['experimental-design'] | ['methodology'] | [ 4.03550923e-01 -1.89758003e-01 6.74295127e-01 -4.82656389e-01
-6.46479577e-02 -6.97439551e-01 4.37179893e-01 -2.12541908e-01
-9.46075857e-01 8.75979289e-02 -9.32244733e-02 -7.99196959e-03
4.64854278e-02 -4.94030625e-01 -9.32538986e-01 -8.37282538e-01
-4.59570646e-01 1.49081558e-01 3.47993761e-01 -7.83059001... | [8.474173545837402, -1.0724670886993408] |
14e6a65b-1ace-4c63-a174-06a7f7310a63 | rl-dwa-omnidirectional-motion-planning-for | 2211.04993 | null | https://arxiv.org/abs/2211.04993v2 | https://arxiv.org/pdf/2211.04993v2.pdf | RL-DWA Omnidirectional Motion Planning for Person Following in Domestic Assistance and Monitoring | Robot assistants are emerging as high-tech solutions to support people in everyday life. Following and assisting the user in the domestic environment requires flexible mobility to safely move in cluttered spaces. We introduce a new approach to person following for assistance and monitoring. Our methodology exploits an ... | ['Marcello Chiaberge', 'Mauro Martini', 'Andrea Eirale'] | 2022-11-09 | null | null | null | null | ['motion-planning'] | ['robots'] | [-1.81640685e-01 4.95956987e-01 9.92682427e-02 -3.22314352e-01
8.14993829e-02 -5.34604251e-01 7.51629651e-01 -4.67078835e-01
-1.24119329e+00 1.07914937e+00 3.68199736e-01 -4.69176203e-01
-3.54995310e-01 -6.26910567e-01 -2.54490674e-01 -5.60614049e-01
-2.70662189e-01 9.16688144e-01 2.12614089e-01 -8.53105605... | [4.800148963928223, 0.9056942462921143] |
8b1809a3-4416-4009-8003-f0517269182a | mapping-probability-word-problems-to | null | null | https://aclanthology.org/2021.emnlp-main.294 | https://aclanthology.org/2021.emnlp-main.294.pdf | Mapping probability word problems to executable representations | While solving math word problems automatically has received considerable attention in the NLP community, few works have addressed probability word problems specifically. In this paper, we employ and analyse various neural models for answering such word problems. In a two-step approach, the problem text is first mapped ... | ['Walter Daelemans', 'Luc De Raedt', 'Jesse Davis', 'Angelika Kimmig', 'Pietro Totis', 'Pieter Fivez', 'Simon Suster'] | null | null | null | null | emnlp-2021-11 | ['contextualised-word-representations'] | ['natural-language-processing'] | [ 4.35402840e-01 4.16152447e-01 1.27351806e-01 -4.98841554e-01
-1.03615963e+00 -6.21393800e-01 6.00269675e-01 3.80054563e-01
-5.72251141e-01 8.90322685e-01 1.69464648e-01 -6.33793533e-01
-4.68918145e-01 -1.29272604e+00 -8.42276454e-01 -1.75811812e-01
3.26395482e-01 9.52432036e-01 3.76197547e-01 -6.70494437... | [9.555259704589844, 7.5344133377075195] |
3122e6e4-fee6-47fa-97d3-702fc5faff17 | generating-videos-with-scene-dynamics | 1609.02612 | null | http://arxiv.org/abs/1609.02612v3 | http://arxiv.org/pdf/1609.02612v3.pdf | Generating Videos with Scene Dynamics | We capitalize on large amounts of unlabeled video in order to learn a model
of scene dynamics for both video recognition tasks (e.g. action classification)
and video generation tasks (e.g. future prediction). We propose a generative
adversarial network for video with a spatio-temporal convolutional architecture
that un... | ['Antonio Torralba', 'Hamed Pirsiavash', 'Carl Vondrick'] | 2016-09-08 | generating-videos-with-scene-dynamics-1 | http://papers.nips.cc/paper/6194-generating-videos-with-scene-dynamics | http://papers.nips.cc/paper/6194-generating-videos-with-scene-dynamics.pdf | neurips-2016-12 | ['self-supervised-action-recognition'] | ['computer-vision'] | [ 3.17979306e-01 4.01180416e-01 -1.17600001e-01 -2.97610939e-01
-4.34997082e-01 -6.25614643e-01 9.36740398e-01 -6.95094049e-01
4.29141782e-02 6.46272421e-01 6.94498956e-01 -4.77908254e-01
6.65628314e-01 -8.82359207e-01 -1.44051516e+00 -5.89306533e-01
-4.88650113e-01 7.86582939e-03 3.41790259e-01 -1.60452202... | [10.730000495910645, -0.6026548743247986] |
2f3f2763-4432-46f7-a671-a3b4eca50c88 | multimodal-manoeuvre-and-trajectory | 2303.16109 | null | https://arxiv.org/abs/2303.16109v1 | https://arxiv.org/pdf/2303.16109v1.pdf | Multimodal Manoeuvre and Trajectory Prediction for Autonomous Vehicles Using Transformer Networks | Predicting the behaviour (i.e. manoeuvre/trajectory) of other road users, including vehicles, is critical for the safe and efficient operation of autonomous vehicles (AVs), a.k.a. automated driving systems (ADSs). Due to the uncertain future behaviour of vehicles, multiple future behaviour modes are often plausible for... | ['Mehrdad Dianati', 'Konstantinos Koufos', 'Sajjad Mozaffari'] | 2023-03-28 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [-6.93619847e-02 1.83617603e-02 -3.64919484e-01 -7.01457262e-01
-9.28779185e-01 -1.76924959e-01 1.11164105e+00 1.20714724e-01
-1.80470139e-01 5.25437593e-01 7.48740584e-02 -8.29562545e-01
-2.46688709e-01 -8.75088215e-01 -7.40044713e-01 -7.88302779e-01
-4.30449955e-02 3.82397979e-01 6.02826416e-01 -4.78533894... | [5.826858043670654, 1.1155730485916138] |
f5f897a4-1d6f-40f0-9de8-f216dba91a49 | question-type-identification-for-academic | 2211.13727 | null | https://arxiv.org/abs/2211.13727v1 | https://arxiv.org/pdf/2211.13727v1.pdf | Question-type Identification for Academic Questions in Online Learning Platform | Online learning platforms provide learning materials and answers to students' academic questions by experts, peers, or systems. This paper explores question-type identification as a step in content understanding for an online learning platform. The aim of the question-type identifier is to categorize question types bas... | ['Saurabh Khanwalkar', "Johnson D'Souza", 'Alok Goel', 'Azam Rabiee'] | 2022-11-24 | null | null | null | null | ['type'] | ['speech'] | [-1.42720891e-02 4.55845386e-01 -3.92328113e-01 -4.74431515e-01
-1.10236418e+00 -1.07448578e+00 4.54054028e-01 9.13980186e-01
-4.07626063e-01 4.99232054e-01 -9.41420533e-03 -1.02172792e+00
-4.36419159e-01 -7.77952552e-01 -3.94973785e-01 3.53371985e-02
4.35699880e-01 1.88298464e-01 6.24720812e-01 -4.32183474... | [11.313522338867188, 8.3779935836792] |
5be7d787-0860-4577-b12d-1dbf829d81e1 | telling-stories-through-multi-user-dialogue | 2105.15054 | null | https://arxiv.org/abs/2105.15054v1 | https://arxiv.org/pdf/2105.15054v1.pdf | Telling Stories through Multi-User Dialogue by Modeling Character Relations | This paper explores character-driven story continuation, in which the story emerges through characters' first- and second-person narration as well as dialogue -- requiring models to select language that is consistent with a character's persona and their relationships with other characters while following and advancing ... | ['Mark O. Riedl', 'Prithviraj Ammanabrolu', 'Wai Man Si'] | 2021-05-31 | null | https://aclanthology.org/2021.sigdial-1.30 | https://aclanthology.org/2021.sigdial-1.30.pdf | sigdial-acl-2021-7 | ['story-continuation'] | ['computer-vision'] | [ 1.74219698e-01 2.98315138e-01 -4.99861389e-02 -4.54964459e-01
-9.12558794e-01 -8.93887758e-01 1.44481063e+00 3.31861645e-01
-3.40400159e-01 9.81161892e-01 1.29506695e+00 6.30840436e-02
1.38137892e-01 -8.16004395e-01 -1.98603570e-01 -1.64810613e-01
-1.76970020e-01 1.01160407e+00 7.26295188e-02 -8.87633145... | [12.32728385925293, 8.394868850708008] |
a7f444cd-617f-4f11-9a91-dcf691240317 | aggregated-text-transformer-for-scene-text | 2211.13984 | null | https://arxiv.org/abs/2211.13984v1 | https://arxiv.org/pdf/2211.13984v1.pdf | Aggregated Text Transformer for Scene Text Detection | This paper explores the multi-scale aggregation strategy for scene text detection in natural images. We present the Aggregated Text TRansformer(ATTR), which is designed to represent texts in scene images with a multi-scale self-attention mechanism. Starting from the image pyramid with multiple resolutions, the features... | ['Cheng Jin', 'Yingbin Zheng', 'Xiangcheng Du', 'Zhao Zhou'] | 2022-11-25 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 7.18973041e-01 -3.95271212e-01 7.93459415e-02 -3.22576165e-01
-8.11668038e-01 -1.53857186e-01 7.38000631e-01 1.89020298e-02
-1.78165391e-01 1.38422385e-01 5.77037752e-01 2.28946269e-01
3.63735944e-01 -8.67772818e-01 -7.80826211e-01 -6.68148518e-01
5.87274909e-01 1.72623545e-01 7.35441804e-01 2.57517546... | [12.034929275512695, 2.2582921981811523] |
dcb497c6-0ec7-4c42-a161-6776cbbd3d9a | semantic-role-labeling-as-dependency-parsing-1 | null | null | https://openreview.net/forum?id=bmF2qC-CUG | https://openreview.net/pdf?id=bmF2qC-CUG | Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments | Semantic role labeling (SRL) is a fundamental yet challenging task in the NLP community.
Recent works of SRL mainly fall into two lines: 1) BIO-based; 2) span-based.
Despite ubiquity, they share some intrinsic drawbacks of not explicitly considering internal argument structures, which may potentially hinder the model's... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 4.71214980e-01 5.57036757e-01 -8.17731082e-01 -3.45518529e-01
-6.44787669e-01 -9.10172701e-01 5.52700222e-01 2.20942959e-01
-1.74771503e-01 9.29217637e-01 6.20470524e-01 -5.98241806e-01
-2.98581362e-01 -6.47944808e-01 -5.25269747e-01 -6.01106882e-01
2.77647525e-01 3.37656677e-01 6.45519555e-01 -2.62823731... | [10.216400146484375, 9.264005661010742] |
4c75d41e-d769-4255-a94d-9a889b4b6813 | automatic-icd-coding-exploiting-discourse | null | null | https://aclanthology.org/2022.coling-1.254 | https://aclanthology.org/2022.coling-1.254.pdf | Automatic ICD Coding Exploiting Discourse Structure and Reconciled Code Embeddings | The International Classification of Diseases (ICD) is the foundation of global health statistics and epidemiology. The ICD is designed to translate health conditions into alphanumeric codes. A number of approaches have been proposed for automatic ICD coding, since manual coding is labor-intensive and there is a global ... | ['Wanchun Yang', 'Bo Sang', 'Fuxin Zhang', 'Bozheng Zhang', 'Shurui Zhang'] | null | null | null | null | coling-2022-10 | ['medical-code-prediction', 'epidemiology'] | ['medical', 'medical'] | [-5.79313701e-03 5.66281855e-01 -8.62481713e-01 -1.68320119e-01
-6.67649567e-01 -5.69007337e-01 2.25391746e-01 7.93357670e-01
-8.82774666e-02 5.91040373e-01 9.35311258e-01 -5.04394889e-01
-1.13282003e-01 -7.27376699e-01 -9.20944207e-04 -3.38632762e-01
9.66127515e-02 5.51527917e-01 -4.24978405e-01 1.63089439... | [8.014861106872559, 6.927388668060303] |
8ff8381e-7423-491c-9e26-f09a23e775e1 | prediction-of-the-motion-of-chest-internal | 2207.05951 | null | https://arxiv.org/abs/2207.05951v1 | https://arxiv.org/pdf/2207.05951v1.pdf | Prediction of the motion of chest internal points using a recurrent neural network trained with real-time recurrent learning for latency compensation in lung cancer radiotherapy | During the radiotherapy treatment of patients with lung cancer, the radiation delivered to healthy tissue around the tumor needs to be minimized, which is difficult because of respiratory motion and the latency of linear accelerator systems. In the proposed study, we first use the Lucas-Kanade pyramidal optical flow al... | ['Ritu Bhusal Chhatkuli', 'Kazuyuki Demachi', 'Mitsuru Uesaka', 'Michel Pohl'] | 2022-07-13 | null | null | null | null | ['respiratory-motion-forecasting', 'time-series-prediction'] | ['medical', 'time-series'] | [ 7.85085037e-02 2.90742129e-01 -2.25951940e-01 2.50981927e-01
-8.10188174e-01 -2.68267483e-01 2.45150924e-01 -1.15814403e-01
-6.71382308e-01 7.09615648e-01 8.95542055e-02 -1.04054391e-01
-3.10389131e-01 -3.78762960e-01 -4.53189671e-01 -1.20591247e+00
-3.65414955e-02 5.02450705e-01 5.42549253e-01 1.74828485... | [13.802556991577148, -2.603996992111206] |
d3fe67ab-23d8-4ea1-b373-8728e4f9bbd0 | fingers-angle-calculation-using-level-set | 1406.3418 | null | http://arxiv.org/abs/1406.3418v1 | http://arxiv.org/pdf/1406.3418v1.pdf | Fingers' Angle Calculation using Level-Set Method | In the current age, use of natural communication in human computer
interaction is a known and well installed thought. Hand gesture recognition and
gesture based applications has gained a significant amount of popularity
amongst people all over the world. It has a number of applications ranging from
security to entertai... | ['J. L. Raheja', 'Ankit Chaudhary', 'K. Das', 'S. Raheja'] | 2014-06-13 | null | null | null | null | ['fingertip-detection'] | ['computer-vision'] | [ 3.34531128e-01 -5.27748406e-01 2.27678671e-01 -2.87188321e-01
1.23397909e-01 -9.75464582e-01 4.14169729e-01 1.70336202e-01
-8.36338460e-01 6.05257928e-01 -2.40559697e-01 -3.97087216e-01
-3.80132794e-01 -6.94506228e-01 1.53896138e-01 -4.90611553e-01
2.23382652e-01 4.25356299e-01 5.89137197e-01 -1.66122913... | [6.484547138214111, -0.28094691038131714] |
8a20808a-c999-4e61-aee5-50233f1ea4a0 | chatgpt-chemistry-assistant-for-text-mining | 2306.11296 | null | https://arxiv.org/abs/2306.11296v1 | https://arxiv.org/pdf/2306.11296v1.pdf | ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesis | We use prompt engineering to guide ChatGPT in the automation of text mining of metal-organic frameworks (MOFs) synthesis conditions from diverse formats and styles of the scientific literature. This effectively mitigates ChatGPT's tendency to hallucinate information -- an issue that previously made the use of Large Lan... | ['Omar M. Yaghi', 'Jennifer T. Chayes', 'Christian Borgs', 'Oufan Zhang', 'Zhiling Zheng'] | 2023-06-20 | null | null | null | null | ['chatbot', 'prompt-engineering', 'chatbot'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [ 1.20036483e-01 1.54034555e-01 -4.23086524e-01 -1.11054271e-01
-8.77173781e-01 -8.25081170e-01 4.45131123e-01 6.64999664e-01
-8.95268470e-02 8.42029572e-01 1.01081245e-01 -1.12816370e+00
-2.38752842e-01 -6.99538171e-01 -6.48501933e-01 -3.21427613e-01
3.22170734e-01 5.51801145e-01 -2.90127218e-01 -6.92150518... | [4.795236587524414, 5.891251564025879] |
6bdf4638-50e5-4f88-8a97-949d155df4f0 | fedcut-a-spectral-analysis-framework-for | 2211.13389 | null | https://arxiv.org/abs/2211.13389v1 | https://arxiv.org/pdf/2211.13389v1.pdf | FedCut: A Spectral Analysis Framework for Reliable Detection of Byzantine Colluders | This paper proposes a general spectral analysis framework that thwarts a security risk in federated Learning caused by groups of malicious Byzantine attackers or colluders, who conspire to upload vicious model updates to severely debase global model performances. The proposed framework delineates the strong consistency... | ['Qiang Yang', 'Xingxing Tang', 'Lixin Fan', 'Hanlin Gu'] | 2022-11-24 | null | null | null | null | ['community-detection'] | ['graphs'] | [-1.12670757e-01 2.69058570e-02 -1.03813879e-01 3.78389090e-01
-5.91896892e-01 -9.96339023e-01 6.67643726e-01 1.01075247e-01
-2.35838555e-02 6.47348106e-01 -3.05414200e-01 -4.34842199e-01
-4.76219565e-01 -8.11303198e-01 -5.90787232e-01 -9.78016913e-01
-6.87313259e-01 2.46283382e-01 2.14771599e-01 -1.38733715... | [5.739081859588623, 7.110145092010498] |
90348fb7-4c19-4e93-b04a-c8864fa587f3 | diffusion-models-for-video-prediction-and | 2206.07696 | null | https://arxiv.org/abs/2206.07696v3 | https://arxiv.org/pdf/2206.07696v3.pdf | Diffusion Models for Video Prediction and Infilling | Predicting and anticipating future outcomes or reasoning about missing information in a sequence are critical skills for agents to be able to make intelligent decisions. This requires strong, temporally coherent generative capabilities. Diffusion models have shown remarkable success in several generative tasks, but hav... | ['Andrea Dittadi', 'Didrik Nielsen', 'Stefan Bauer', 'Arash Mehrjou', 'Tobias Höppe'] | 2022-06-15 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 1.52877554e-01 -8.21189955e-02 -8.71584862e-02 -2.78472573e-01
-3.16440970e-01 -3.68421793e-01 1.10139930e+00 -3.78208965e-01
-1.97295457e-01 7.84281254e-01 4.40877050e-01 -3.30565691e-01
3.36018473e-01 -8.90803635e-01 -1.00788903e+00 -7.95896709e-01
-2.70744890e-01 3.78261149e-01 3.81367594e-01 4.88022640... | [10.69731616973877, -0.5737518668174744] |
bd17cafc-cd95-4a28-ad47-66db4a33e890 | probing-inter-modality-visual-parsing-with | 2106.13488 | null | https://arxiv.org/abs/2106.13488v4 | https://arxiv.org/pdf/2106.13488v4.pdf | Probing Inter-modality: Visual Parsing with Self-Attention for Vision-Language Pre-training | Vision-Language Pre-training (VLP) aims to learn multi-modal representations from image-text pairs and serves for downstream vision-language tasks in a fine-tuning fashion. The dominant VLP models adopt a CNN-Transformer architecture, which embeds images with a CNN, and then aligns images and text with a Transformer. V... | ['Jiebo Luo', 'Houqiang Li', 'Jianlong Fu', 'Houwen Peng', 'Bei Liu', 'Yupan Huang', 'Hongwei Xue'] | 2021-06-25 | probing-inter-modality-visual-parsing-with-1 | http://proceedings.neurips.cc/paper/2021/hash/23fa71cc32babb7b91130824466d25a5-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/23fa71cc32babb7b91130824466d25a5-Paper.pdf | neurips-2021-12 | ['visual-entailment'] | ['reasoning'] | [ 6.15310743e-02 -9.19046253e-02 -3.55610102e-01 -4.07048643e-01
-5.28337181e-01 -5.09715915e-01 8.79988730e-01 -1.42671674e-01
-4.09486324e-01 1.36417150e-01 3.58389348e-01 -3.76193941e-01
3.89092416e-02 -7.48448491e-01 -8.95768285e-01 -6.53671563e-01
5.11681557e-01 -3.27949412e-02 2.01020718e-01 -2.02738330... | [10.751249313354492, 1.4415854215621948] |
23ce90fd-9e7c-427c-9f91-b7f7c5a4d6b2 | neural-network-compression-via-effective | 2206.03596 | null | https://arxiv.org/abs/2206.03596v1 | https://arxiv.org/pdf/2206.03596v1.pdf | Neural Network Compression via Effective Filter Analysis and Hierarchical Pruning | Network compression is crucial to making the deep networks to be more efficient, faster, and generalizable to low-end hardware. Current network compression methods have two open problems: first, there lacks a theoretical framework to estimate the maximum compression rate; second, some layers may get over-prunned, resul... | ['Ze Wang', 'Yilong Yin', 'Li Lian', 'Ziqi Zhou'] | 2022-06-07 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 3.44082892e-01 8.11791718e-02 -1.70675665e-01 -1.84859395e-01
1.59461156e-01 8.55072290e-02 1.88424569e-02 1.74180016e-01
-5.76527059e-01 6.30789876e-01 -2.20463574e-01 -3.24392736e-01
-4.61132109e-01 -8.47589791e-01 -4.54489112e-01 -5.48588753e-01
-2.81342119e-02 -5.56101725e-02 4.87952173e-01 6.11395948... | [8.532817840576172, 3.08404541015625] |
40e71700-b99c-4768-8a68-a143dcfc0a9c | stip-a-spatiotemporal-information-preserving | 2206.04381 | null | https://arxiv.org/abs/2206.04381v1 | https://arxiv.org/pdf/2206.04381v1.pdf | STIP: A SpatioTemporal Information-Preserving and Perception-Augmented Model for High-Resolution Video Prediction | Although significant achievements have been achieved by recurrent neural network (RNN) based video prediction methods, their performance in datasets with high resolutions is still far from satisfactory because of the information loss problem and the perception-insensitive mean square error (MSE) based loss functions. I... | ['Wen Gao', 'Siwei Ma', 'Shanshe Wang', 'Xinfeng Zhang', 'Zheng Chang'] | 2022-06-09 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 2.17259690e-01 -3.73169541e-01 -1.05729528e-01 -8.33389908e-02
-7.65215337e-01 2.25453839e-01 2.99583197e-01 -4.26781505e-01
-1.99461922e-01 7.50988126e-01 1.97570190e-01 6.86640367e-02
-8.58021900e-02 -8.84772480e-01 -9.66802537e-01 -9.87752914e-01
2.47425511e-01 -3.83256733e-01 4.19100404e-01 -1.04742616... | [11.085329055786133, -1.746423602104187] |
0e0c0dfe-6ea2-4acd-ab42-f00382730a0b | clickbait-detection-with-style-aware-title | null | null | https://aclanthology.org/2020.ccl-1.106 | https://aclanthology.org/2020.ccl-1.106.pdf | Clickbait Detection with Style-aware Title Modeling and Co-attention | Clickbait is a form of web content designed to attract attention and entice users to click on specific hyperlinks. The detection of clickbaits is an important task for online platforms to improve the quality of web content and the satisfaction of users. Clickbait detection is typically formed as a binary classification... | ['Yongfeng Huang', 'Tao Qi', 'Fangzhao Wu', 'Chuhan Wu'] | null | null | null | null | ccl-2020-10 | ['clickbait-detection'] | ['natural-language-processing'] | [-2.93327756e-02 -6.28750682e-01 -5.93000948e-01 -4.70175415e-01
-6.25028729e-01 -4.88964409e-01 6.75601840e-01 3.16174120e-01
-2.45310083e-01 2.40669683e-01 3.42802078e-01 -2.72379577e-01
-1.71301156e-01 -8.54106367e-01 -6.65365458e-01 -2.71469206e-01
2.61488527e-01 1.15575239e-01 6.06272340e-01 -2.50281513... | [7.7728190422058105, 9.650147438049316] |
1d6ad714-c37f-4016-b76d-5b8fa409d990 | chatcad-interactive-computer-aided-diagnosis | 2302.07257 | null | https://arxiv.org/abs/2302.07257v1 | https://arxiv.org/pdf/2302.07257v1.pdf | ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models | Large language models (LLMs) have recently demonstrated their potential in clinical applications, providing valuable medical knowledge and advice. For example, a large dialog LLM like ChatGPT has successfully passed part of the US medical licensing exam. However, LLMs currently have difficulty processing images, making... | ['Dinggang Shen', 'Qian Wang', 'Xi Ouyang', 'Zihao Zhao', 'Sheng Wang'] | 2023-02-14 | null | null | null | null | ['logical-reasoning'] | ['reasoning'] | [ 1.37426853e-01 7.16556847e-01 -3.54566962e-01 -7.02027798e-01
-5.64250708e-01 -1.02913462e-01 2.13141859e-01 6.67803586e-01
-1.51297122e-01 5.63735485e-01 2.95558453e-01 -6.84780419e-01
-1.45427629e-01 -9.79663134e-01 -1.45412579e-01 -3.29648882e-01
1.38976440e-01 7.31477141e-01 1.40685722e-01 -7.99995963... | [15.021306037902832, -1.5832418203353882] |
2da38865-9017-4454-9359-b3b1649b66d2 | call-attention-to-rumors-deep-attention-based | 1704.05973 | null | http://arxiv.org/abs/1704.05973v1 | http://arxiv.org/pdf/1704.05973v1.pdf | Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection | The proliferation of social media in communication and information
dissemination has made it an ideal platform for spreading rumors. Automatically
debunking rumors at their stage of diffusion is known as \textit{early rumor
detection}, which refers to dealing with sequential posts regarding disputed
factual claims with... | ['Xue Li', 'Jun Zhang', 'Lin Wu', 'Hongzhi Yin', 'Yang Wang', 'Tong Chen'] | 2017-04-20 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 6.46631643e-02 -3.17949653e-01 -2.81258434e-01 -1.58293828e-01
-4.62528497e-01 -1.53852329e-01 9.26634490e-01 1.86361447e-01
-1.95370004e-01 4.51937973e-01 5.62231481e-01 -5.21856070e-01
-6.67851567e-02 -5.10534227e-01 -3.73630852e-01 -3.91617477e-01
-2.04509348e-01 4.78315383e-01 1.82575315e-01 -6.69986188... | [8.162402153015137, 10.134195327758789] |
4d54cc7e-47f0-4be4-b273-89e47ff99086 | two-baselines-for-unsupervised-dependency | null | null | https://aclanthology.org/W12-1910 | https://aclanthology.org/W12-1910.pdf | Two baselines for unsupervised dependency parsing | null | ['Anders S{\\o}gaard'] | 2012-06-01 | null | null | null | ws-2012-6 | ['unsupervised-dependency-parsing'] | ['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... | [-7.315014839172363, 3.6626956462860107] |
74ecfa65-83c6-4d29-8d61-9da797cb5c5a | dsdp-a-blind-docking-strategy-accelerated-by | 2303.09916 | null | https://arxiv.org/abs/2303.09916v1 | https://arxiv.org/pdf/2303.09916v1.pdf | DSDP: A Blind Docking Strategy Accelerated by GPUs | Virtual screening, including molecular docking, plays an essential role in drug discovery. Many traditional and machine-learning based methods are available to fulfil the docking task. The traditional docking methods are normally extensively time-consuming, and their performance in blind docking remains to be improved.... | ['Yi Qin Gao', 'Jun Zhang', 'Xiaohan Lin', 'Dajiong Yue', 'Siyuan Jiang', 'Hong Zhang', 'Yupeng Huang'] | 2023-03-16 | null | null | null | null | ['drug-discovery', 'blind-docking', 'molecular-docking'] | ['medical', 'medical', 'medical'] | [-2.98567444e-01 -5.97724617e-01 4.55416031e-02 -1.37986869e-01
-6.61863863e-01 -8.61465514e-01 1.41804606e-01 2.17446789e-01
-7.19022036e-01 1.25701141e+00 -4.06813443e-01 -6.16656184e-01
2.62511939e-01 -4.19100106e-01 -7.56302297e-01 -1.22474647e+00
-3.68110016e-02 5.98512232e-01 4.34459537e-01 -2.74116069... | [4.877192497253418, 5.533206939697266] |
7a54e472-fd56-4fea-bfec-b2304dd2901a | eye-in-the-sky-drone-based-object-tracking | 1910.08259 | null | https://arxiv.org/abs/1910.08259v1 | https://arxiv.org/pdf/1910.08259v1.pdf | Eye in the Sky: Drone-Based Object Tracking and 3D Localization | Drones, or general UAVs, equipped with a single camera have been widely deployed to a broad range of applications, such as aerial photography, fast goods delivery and most importantly, surveillance. Despite the great progress achieved in computer vision algorithms, these algorithms are not usually optimized for dealing... | ['Jenq-Neng Hwang', 'Gaoang Wang', 'Zhichao Lei', 'Haotian Zhang'] | 2019-10-18 | null | null | null | null | ['drone-based-object-tracking'] | ['computer-vision'] | [-2.51236320e-01 -8.66204977e-01 3.39615613e-01 2.88691908e-01
-1.90601349e-01 -7.96569347e-01 4.01285082e-01 3.76776569e-02
-5.28988004e-01 4.85936344e-01 -7.13437378e-01 2.80656010e-01
-2.21190080e-01 -4.54793483e-01 -5.74817121e-01 -8.32716346e-01
-2.11844221e-01 3.48747671e-01 7.91572750e-01 -2.20785215... | [6.898179531097412, -1.8734655380249023] |
d769dc41-b508-4d4b-b997-6ed7e470ffac | a-neural-network-transliteration-model-in-low | null | null | https://aclanthology.org/2017.mtsummit-papers.26 | https://aclanthology.org/2017.mtsummit-papers.26.pdf | A Neural Network Transliteration Model in Low Resource Settings | null | ['Fatiha Sadat', 'Tan Le'] | null | null | null | null | mtsummit-2017-9 | ['transliteration'] | ['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... | [-7.2482805252075195, 3.801905632019043] |
59716fd1-0e27-4c11-ab27-d58486513d1c | pyramid-stereo-matching-network | 1803.08669 | null | http://arxiv.org/abs/1803.08669v1 | http://arxiv.org/pdf/1803.08669v1.pdf | Pyramid Stereo Matching Network | Recent work has shown that depth estimation from a stereo pair of images can
be formulated as a supervised learning task to be resolved with convolutional
neural networks (CNNs). However, current architectures rely on patch-based
Siamese networks, lacking the means to exploit context information for finding
corresponde... | ['Yong-Sheng Chen', 'Jia-Ren Chang'] | 2018-03-23 | pyramid-stereo-matching-network-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Chang_Pyramid_Stereo_Matching_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Chang_Pyramid_Stereo_Matching_CVPR_2018_paper.pdf | cvpr-2018-6 | ['stereo-depth-estimation', 'stereo-lidar-fusion'] | ['computer-vision', 'computer-vision'] | [ 9.23226997e-02 1.68880939e-01 -1.23163581e-01 -3.24909925e-01
-7.94839382e-01 -4.03138787e-01 5.93870997e-01 1.17566422e-01
-4.77604479e-01 5.32517374e-01 1.45037621e-01 8.58798623e-02
-1.34461462e-01 -8.33576083e-01 -1.06485534e+00 -3.94736558e-01
-7.40546063e-02 3.74154359e-01 5.12948394e-01 -1.92253307... | [8.754830360412598, -2.376689910888672] |
466147c3-703d-41ab-8185-85c6a4c948c5 | pdanet-polarity-consistent-deep-attention | 1909.05693 | null | https://arxiv.org/abs/1909.05693v1 | https://arxiv.org/pdf/1909.05693v1.pdf | PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression | Existing methods on visual emotion analysis mainly focus on coarse-grained emotion classification, i.e. assigning an image with a dominant discrete emotion category. However, these methods cannot well reflect the complexity and subtlety of emotions. In this paper, we study the fine-grained regression problem of visual ... | ['Guiguang Ding', 'Leida Li', 'Kurt Keutzer', 'Zizhou Jia', 'Sicheng Zhao', 'Hui Chen'] | 2019-09-11 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [-2.40510508e-01 -4.12379980e-01 -1.11641608e-01 -4.78808343e-01
-2.28503883e-01 -3.47900271e-01 1.50993213e-01 -5.06991446e-02
-1.39356166e-01 5.50580561e-01 2.45347708e-01 6.63755760e-02
1.04011618e-01 -5.54337502e-01 -6.93640053e-01 -6.98227882e-01
1.98153049e-01 -1.49869844e-01 -2.46459469e-01 -2.62525439... | [13.536092758178711, 1.7609022855758667] |
d66e1393-8026-4a38-a7ea-f77b2d24558e | moving-objects-detection-with-a-moving-camera | 2001.05238 | null | https://arxiv.org/abs/2001.05238v1 | https://arxiv.org/pdf/2001.05238v1.pdf | Moving Objects Detection with a Moving Camera: A Comprehensive Review | During about 30 years, a lot of research teams have worked on the big challenge of detection of moving objects in various challenging environments. First applications concern static cameras but with the rise of the mobile sensors studies on moving cameras have emerged over time. In this survey, we propose to identify a... | ['Marie-Neige Chapel', 'Thierry Bouwmans'] | 2020-01-15 | null | null | null | null | ['motion-segmentation'] | ['computer-vision'] | [ 5.65219700e-01 -6.63867474e-01 -1.14859931e-01 1.84417367e-02
-2.92809039e-01 -1.00948942e+00 7.06589460e-01 -4.40393150e-01
-4.67152566e-01 4.32602525e-01 2.24191379e-02 -1.19701317e-02
3.12193017e-02 -2.36582488e-01 -3.03044349e-01 -8.80429864e-01
3.01362216e-01 -1.05081506e-01 9.28252459e-01 8.82139057... | [8.873080253601074, -0.8660454154014587] |
e54fff29-058d-4c32-8572-a6ec1c28fbb3 | modified-parametric-multichannel-wiener | 2306.17317 | null | https://arxiv.org/abs/2306.17317v1 | https://arxiv.org/pdf/2306.17317v1.pdf | Modified Parametric Multichannel Wiener Filter \\for Low-latency Enhancement of Speech Mixtures with Unknown Number of Speakers | This paper introduces a novel low-latency online beamforming (BF) algorithm, named Modified Parametric Multichannel Wiener Filter (Mod-PMWF), for enhancing speech mixtures with unknown and varying number of speakers. Although conventional BFs such as linearly constrained minimum variance BF (LCMV BF) can enhance a spee... | ['Takehiro Moriya', 'Shoko Araki', 'Tomohiro Nakatani', 'Ning Guo'] | 2023-06-29 | null | null | null | null | ['low-latency-processing'] | ['robots'] | [ 2.32475027e-01 -3.05397898e-01 3.65349919e-01 -2.00983062e-01
-1.00661588e+00 -6.63646996e-01 3.20515126e-01 -5.34216642e-01
-3.92244279e-01 6.12390399e-01 6.14985406e-01 -6.14309847e-01
-4.17627037e-01 -2.32622698e-01 -4.46015418e-01 -9.55100715e-01
-2.07004875e-01 -1.02183260e-01 2.55766094e-01 -4.05280627... | [15.070100784301758, 5.829063415527344] |
584f5cf1-a1f0-4a84-875c-ee620e363755 | a-novel-context-aware-multimodal-framework | 2103.02636 | null | https://arxiv.org/abs/2103.02636v1 | https://arxiv.org/pdf/2103.02636v1.pdf | A Novel Context-Aware Multimodal Framework for Persian Sentiment Analysis | Most recent works on sentiment analysis have exploited the text modality. However, millions of hours of video recordings posted on social media platforms everyday hold vital unstructured information that can be exploited to more effectively gauge public perception. Multimodal sentiment analysis offers an innovative sol... | ['Amir Hussain', 'Erik Cambria', 'Mandar Gogate', 'Kia Dashtipour'] | 2021-03-03 | null | null | null | null | ['persian-sentiment-anlysis'] | ['natural-language-processing'] | [ 3.54004949e-01 -3.87471437e-01 1.36949956e-01 -5.83256304e-01
-1.37476516e+00 -8.05056930e-01 7.72457242e-01 4.78145003e-01
-6.71328247e-01 5.25588214e-01 4.23887461e-01 2.60263920e-01
2.70997465e-01 -2.59891242e-01 -1.92835897e-01 -9.93751287e-01
1.23692274e-01 -2.89861560e-01 5.23567200e-03 -5.42093635... | [13.111824989318848, 5.219996452331543] |
526a8be4-eab3-4281-b2c1-3e489d6ee2a2 | recognizing-and-extracting-cybersecurtity | 2208.01693 | null | https://arxiv.org/abs/2208.01693v1 | https://arxiv.org/pdf/2208.01693v1.pdf | Recognizing and Extracting Cybersecurtity-relevant Entities from Text | Cyber Threat Intelligence (CTI) is information describing threat vectors, vulnerabilities, and attacks and is often used as training data for AI-based cyber defense systems such as Cybersecurity Knowledge Graphs (CKG). There is a strong need to develop community-accessible datasets to train existing AI-based cybersecur... | ['Anupam Joshi', 'Tim Finin', 'Priyanka Ranade', 'Michael Maiden', 'Casey Hanks'] | 2022-08-02 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 1.01623125e-01 3.66221815e-01 -6.09709024e-01 1.89103618e-01
-4.02287632e-01 -1.35482943e+00 1.03792000e+00 7.09964693e-01
-1.48395553e-01 7.44284570e-01 4.32928443e-01 -9.67755616e-01
-6.33408308e-01 -1.23575902e+00 -5.13304532e-01 4.15089965e-01
-5.14983177e-01 7.86451578e-01 3.95688146e-01 -3.94116014... | [6.516456604003906, 7.452075481414795] |
12991382-dbed-4e02-8acc-b35dda6c3fd9 | prise-demystifying-deep-lucas-kanade-with | 2303.11526 | null | https://arxiv.org/abs/2303.11526v1 | https://arxiv.org/pdf/2303.11526v1.pdf | PRISE: Demystifying Deep Lucas-Kanade with Strongly Star-Convex Constraints for Multimodel Image Alignment | The Lucas-Kanade (LK) method is a classic iterative homography estimation algorithm for image alignment, but often suffers from poor local optimality especially when image pairs have large distortions. To address this challenge, in this paper we propose a novel Deep Star-Convexified Lucas-Kanade (PRISE) method for mult... | ['Ziming Zhang', 'Xinming Huang', 'Yiqing Zhang'] | 2023-03-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Zhang_PRISE_Demystifying_Deep_Lucas-Kanade_With_Strongly_Star-Convex_Constraints_for_Multimodel_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Zhang_PRISE_Demystifying_Deep_Lucas-Kanade_With_Strongly_Star-Convex_Constraints_for_Multimodel_CVPR_2023_paper.pdf | cvpr-2023-1 | ['homography-estimation'] | ['computer-vision'] | [-3.04149166e-02 2.31784657e-02 -1.05203539e-01 -2.12880686e-01
-1.02714455e+00 -3.98171902e-01 3.94167900e-01 -2.20232621e-01
-3.81930262e-01 5.64021647e-01 8.46966878e-02 -1.10179275e-01
-6.62537143e-02 -4.42021102e-01 -1.09849191e+00 -8.12270641e-01
1.65182412e-01 5.09229481e-01 -2.00821593e-01 -1.16830923... | [8.64970588684082, -2.2700560092926025] |
d59e79b3-a6c3-4132-8e88-b1f0fc5292d3 | teaching-arithmetic-to-small-transformers | 2307.03381 | null | https://arxiv.org/abs/2307.03381v1 | https://arxiv.org/pdf/2307.03381v1.pdf | Teaching Arithmetic to Small Transformers | Large language models like GPT-4 exhibit emergent capabilities across general-purpose tasks, such as basic arithmetic, when trained on extensive text data, even though these tasks are not explicitly encoded by the unsupervised, next-token prediction objective. This study investigates how small transformers, trained fro... | ['Dimitris Papailiopoulos', 'Kangwook Lee', 'Jason D. Lee', 'Kartik Sreenivasan', 'Nayoung Lee'] | 2023-07-07 | null | null | null | null | ['low-rank-matrix-completion', 'matrix-completion'] | ['methodology', 'methodology'] | [ 3.66795629e-01 -1.50402039e-02 -7.61879459e-02 -1.53111830e-01
-5.43285608e-01 -4.57108289e-01 6.94722354e-01 5.53333461e-01
-4.77586418e-01 3.48358512e-01 4.68487889e-01 -5.58202147e-01
-2.00444162e-01 -1.00585008e+00 -8.11620295e-01 -3.51527989e-01
-3.72443199e-01 6.08215690e-01 -1.87225491e-01 -5.95462918... | [9.664321899414062, 7.377455711364746] |
4244b51a-7577-4c5c-98b6-b49423473ad9 | transforma-at-semeval-2019-task-6-offensive | 1903.05280 | null | http://arxiv.org/abs/1903.05280v3 | http://arxiv.org/pdf/1903.05280v3.pdf | Offensive Language Analysis using Deep Learning Architecture | SemEval-2019 Task 6 (Zampieri et al., 2019b) requires us to identify and
categorise offensive language in social media. In this paper we will describe
the process we took to tackle this challenge. Our process is heavily inspired
by Sosa (2017) where he proposed CNN-LSTM and LSTM-CNN models to conduct
twitter sentiment ... | ['Ryan Ong'] | 2019-03-12 | null | null | null | null | ['twitter-sentiment-analysis', 'abuse-detection'] | ['natural-language-processing', 'natural-language-processing'] | [-2.24308167e-02 1.37805670e-01 6.11551255e-02 -5.23798645e-01
-5.03651679e-01 -5.17954409e-01 7.61412084e-01 2.74017423e-01
-8.65916491e-01 4.09663141e-01 3.32609743e-01 -5.18085659e-01
1.85990453e-01 -7.17445850e-01 -5.04481196e-01 -3.02149266e-01
1.56345546e-01 2.73163646e-01 5.65760536e-03 -7.53517330... | [10.884710311889648, 7.503410339355469] |
7d57fc68-ec63-44d1-b24e-0ab789abd64b | class-specific-channel-attention-for-few-shot | 2209.01332 | null | https://arxiv.org/abs/2209.01332v2 | https://arxiv.org/pdf/2209.01332v2.pdf | Class-Specific Channel Attention for Few-Shot Learning | Few-Shot Learning (FSL) has attracted growing attention in computer vision due to its capability in model training without the need for excessive data. FSL is challenging because the training and testing categories (the base vs. novel sets) can be largely diversified. Conventional transfer-based solutions that aim to t... | ['Ming-Ching Chang', 'Jun-Wei Hsieh', 'Ying-Yu Chen'] | 2022-09-03 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 3.90870780e-01 -8.11802670e-02 -1.38159797e-01 -5.29128730e-01
-9.11224246e-01 -1.45605341e-01 5.81729829e-01 -1.59576043e-01
-5.58352530e-01 8.27170014e-01 7.22846836e-02 1.18086219e-01
-2.16695175e-01 -7.74548590e-01 -7.74701238e-01 -8.06815803e-01
-2.10072786e-01 2.57443130e-01 4.18867826e-01 -1.19295627... | [9.9443941116333, 2.7743747234344482] |
729c100d-c9c4-4645-9e99-e44203205516 | towards-a-robust-detection-of-language-model | 2306.05871 | null | https://arxiv.org/abs/2306.05871v1 | https://arxiv.org/pdf/2306.05871v1.pdf | Towards a Robust Detection of Language Model Generated Text: Is ChatGPT that Easy to Detect? | Recent advances in natural language processing (NLP) have led to the development of large language models (LLMs) such as ChatGPT. This paper proposes a methodology for developing and evaluating ChatGPT detectors for French text, with a focus on investigating their robustness on out-of-domain data and against common att... | ['Djamé Seddah', 'Benoît Sagot', 'Virginie Mouilleron', 'Wissam Antoun'] | 2023-06-09 | null | null | null | null | ['adversarial-text'] | ['adversarial'] | [ 1.10141234e-02 -6.61815777e-02 1.21171132e-01 -1.02873534e-01
-1.33988166e+00 -1.15980101e+00 9.92405653e-01 4.32918280e-01
-3.41930568e-01 5.66929221e-01 2.35543381e-02 -6.92784190e-01
2.52727956e-01 -8.40424061e-01 -6.50466859e-01 -3.25907588e-01
4.30493690e-02 5.43981016e-01 3.75295371e-01 -3.62105578... | [6.0574493408203125, 8.075825691223145] |
e933abff-c959-4eca-b135-19848ae45dd4 | sar-to-optical-image-synthesis-for-cloud | null | null | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-1/5/2018/ | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-1/5/2018/ | SAR TO OPTICAL IMAGE SYNTHESIS FOR CLOUD REMOVAL WITH GENERATIVE ADVERSARIAL NETWORKS | Optical imagery is often affected by the presence of clouds. Aiming to reduce their effects, different reconstruction techniques have been proposed in the last years. A common alternative is to extract data from active sensors, like Synthetic Aperture Radar (SAR), because they are almost independent on the atmospheric ... | ['R. Q. Feitosa', 'D. A. B. Oliveira', 'P. N. Happ', 'J. D. Bermudez'] | 2018-09-26 | null | null | null | isprs-annals-of-the-photogrammetry-remote-2 | ['cloud-removal'] | ['computer-vision'] | [ 7.34571218e-01 -1.34831756e-01 2.41058677e-01 -3.32846820e-01
-5.21372080e-01 -5.40075660e-01 7.79678583e-01 -1.74382716e-01
-3.78596187e-01 1.13489342e+00 -3.35251510e-01 1.11480594e-01
-1.60573319e-01 -1.04601467e+00 -6.81836665e-01 -1.09536135e+00
2.48268589e-01 4.17716742e-01 -1.86636373e-02 -2.52784073... | [10.04766845703125, -1.9665217399597168] |
04de5d55-72bd-483b-992e-0fe61dbc986e | automatic-fine-grained-glomerular-lesion | 2203.05847 | null | https://arxiv.org/abs/2203.05847v1 | https://arxiv.org/pdf/2203.05847v1.pdf | Automatic Fine-grained Glomerular Lesion Recognition in Kidney Pathology | Recognition of glomeruli lesions is the key for diagnosis and treatment planning in kidney pathology; however, the coexisting glomerular structures such as mesangial regions exacerbate the difficulties of this task. In this paper, we introduce a scheme to recognize fine-grained glomeruli lesions from whole slide images... | ['Guang Yang', 'Zhihong Liu', 'Guotong Xie', 'Caihong Zeng', 'Guyue Zhang', 'Peng Tang', 'Fengyi Li', 'Yang Nan'] | 2022-03-11 | null | null | null | null | ['fine-grained-image-classification'] | ['computer-vision'] | [ 2.51665115e-01 2.24857852e-01 9.73689370e-03 -5.27082503e-01
-8.40931535e-01 -4.65444833e-01 4.14341837e-01 3.32584441e-01
-2.27560401e-01 7.40129173e-01 -1.07732534e-01 -6.18555620e-02
-3.87482852e-01 -7.12650001e-01 -5.23762584e-01 -9.89448488e-01
-3.66951860e-02 7.01772392e-01 1.05871350e-01 4.82926250... | [15.132658958435059, -2.8118937015533447] |
56cf346e-b31f-429a-94a8-de00054d5f33 | instance-shadow-detection-with-a-single-stage | 2207.04614 | null | https://arxiv.org/abs/2207.04614v1 | https://arxiv.org/pdf/2207.04614v1.pdf | Instance Shadow Detection with A Single-Stage Detector | This paper formulates a new problem, instance shadow detection, which aims to detect shadow instance and the associated object instance that cast each shadow in the input image. To approach this task, we first compile a new dataset with the masks for shadow instances, object instances, and shadow-object associations. W... | ['Chi-Wing Fu', 'Pheng-Ann Heng', 'Xiaowei Hu', 'Tianyu Wang'] | 2022-07-11 | null | null | null | null | ['shadow-detection'] | ['computer-vision'] | [ 8.67864311e-01 4.99705791e-01 1.56005129e-01 -7.46355653e-01
-5.18725336e-01 -3.17778349e-01 6.76809907e-01 -3.94940317e-01
-1.55110639e-02 4.97297108e-01 -6.41740188e-02 -1.24712393e-01
3.98244321e-01 -6.26724899e-01 -7.95363426e-01 -6.92716360e-01
3.27186882e-01 7.21765935e-01 8.60687435e-01 3.28372389... | [10.851092338562012, -4.111710548400879] |
75f9d925-14c1-48d9-bf54-be0e8bb2bfcc | dynamic-size-message-scheduling-for-multi | 2306.10134 | null | https://arxiv.org/abs/2306.10134v1 | https://arxiv.org/pdf/2306.10134v1.pdf | Dynamic Size Message Scheduling for Multi-Agent Communication under Limited Bandwidth | Communication plays a vital role in multi-agent systems, fostering collaboration and coordination. However, in real-world scenarios where communication is bandwidth-limited, existing multi-agent reinforcement learning (MARL) algorithms often provide agents with a binary choice: either transmitting a fixed number of byt... | ['Raphaël Avalos', 'Ann Nowé', 'Yuan YAO', 'Denis Steckelmacher', 'Qingshuang Sun'] | 2023-06-16 | null | null | null | null | ['multi-agent-reinforcement-learning'] | ['methodology'] | [ 2.07899101e-02 1.45690516e-02 -4.46303278e-01 -1.28426626e-01
-5.97718596e-01 -3.23350221e-01 5.62919796e-01 7.53881156e-01
-9.53060091e-01 1.15108788e+00 6.02585124e-03 -2.08292812e-01
-3.79096210e-01 -9.66531336e-01 -2.38361433e-01 -7.72941232e-01
-7.40831614e-01 8.22224379e-01 2.09979281e-01 -3.51242095... | [3.94704008102417, 2.1835954189300537] |
04797f33-00e9-487c-b389-bed6a135e076 | attending-to-characters-in-neural-sequence | 1611.04361 | null | http://arxiv.org/abs/1611.04361v1 | http://arxiv.org/pdf/1611.04361v1.pdf | Attending to Characters in Neural Sequence Labeling Models | Sequence labeling architectures use word embeddings for capturing similarity,
but suffer when handling previously unseen or rare words. We investigate
character-level extensions to such models and propose a novel architecture for
combining alternative word representations. By using an attention mechanism,
the model is ... | ['Gamal K. O. Crichton', 'Sampo Pyysalo', 'Marek Rei'] | 2016-11-14 | attending-to-characters-in-neural-sequence-1 | https://aclanthology.org/C16-1030 | https://aclanthology.org/C16-1030.pdf | coling-2016-12 | ['grammatical-error-detection'] | ['natural-language-processing'] | [ 3.78106892e-01 -2.07239494e-01 -3.15815866e-01 -3.21323335e-01
-7.22612321e-01 -6.71053290e-01 5.49720407e-01 4.99041736e-01
-1.02010477e+00 6.82066917e-01 2.92931855e-01 -4.62044984e-01
2.82673657e-01 -7.64505088e-01 -3.13022166e-01 -5.50309479e-01
2.96848221e-03 5.47613859e-01 4.23415154e-01 -2.96360016... | [10.578577041625977, 8.68929386138916] |
8c9d1ee0-d7b7-4d70-92eb-118d9ba2189f | a-new-network-based-algorithm-for-human | 1502.06075 | null | http://arxiv.org/abs/1502.06075v1 | http://arxiv.org/pdf/1502.06075v1.pdf | A new network-based algorithm for human activity recognition in video | In this paper, a new network-transmission-based (NTB) algorithm is proposed
for human activity recognition in videos. The proposed NTB algorithm models the
entire scene as an error-free network. In this network, each node corresponds
to a patch of the scene and each edge represents the activity correlation
between the ... | ['Bin Sheng', 'Weiyao Lin', 'Hongxiang Li', 'Jianxin Wu', 'Yuanzhe Chen', 'Hanli Wang'] | 2015-02-21 | null | null | null | null | ['activity-recognition-in-videos', 'group-activity-recognition'] | ['computer-vision', 'computer-vision'] | [ 2.63947576e-01 -1.75139844e-01 -1.80542737e-01 4.19127569e-02
6.05757654e-01 -1.34618044e-01 2.46970281e-01 -4.06549051e-02
8.48073736e-02 2.24701285e-01 8.93732607e-02 1.13843277e-01
-2.52269953e-01 -1.04484355e+00 -4.59989548e-01 -7.25833476e-01
-4.63659376e-01 9.61585268e-02 5.96149862e-01 3.14011693... | [8.359685897827148, 0.4949393570423126] |
bb12f7f7-352b-4f1e-9581-abeeefb25e8b | rethinking-privacy-preserving-deep-learning | 2006.11601 | null | https://arxiv.org/abs/2006.11601v2 | https://arxiv.org/pdf/2006.11601v2.pdf | Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks | This paper investigates capabilities of Privacy-Preserving Deep Learning (PPDL) mechanisms against various forms of privacy attacks. First, we propose to quantitatively measure the trade-off between model accuracy and privacy losses incurred by reconstruction, tracing and membership attacks. Second, we formulate recons... | ['Kam Woh Ng', 'Tianyu Zhang', 'Chee Seng Chan', 'Chang Liu', 'Ce Ju', 'Qiang Yang', 'Lixin Fan'] | 2020-06-20 | null | null | null | null | ['privacy-preserving-deep-learning', 'privacy-preserving-deep-learning'] | ['methodology', 'natural-language-processing'] | [ 2.78270274e-01 3.61478239e-01 -8.46193824e-03 -4.50600743e-01
-9.50766027e-01 -1.10611463e+00 5.61731577e-01 1.73931852e-01
-5.71063817e-01 9.47846472e-01 -1.14782825e-01 -6.25303388e-01
1.42798834e-02 -7.15814114e-01 -1.09905636e+00 -9.90696192e-01
-1.61698923e-01 -2.24261478e-01 -2.07075492e-01 2.06133947... | [5.919155597686768, 6.968176364898682] |
43aa4f84-92ae-4cd4-b399-19f570c22789 | one-system-to-rule-them-all-a-universal | 2112.08261 | null | https://arxiv.org/abs/2112.08261v1 | https://arxiv.org/pdf/2112.08261v1.pdf | One System to Rule them All: a Universal Intent Recognition System for Customer Service Chatbots | Customer service chatbots are conversational systems designed to provide information to customers about products/services offered by different companies. Particularly, intent recognition is one of the core components in the natural language understating capabilities of a chatbot system. Among the different intents that... | ['Andres Felipe Tejada-Castro', 'Juan Esteban Jaramillo', 'Jose Luis Pemberty-Tamayo', 'Juan Carlos Guerrero-Sierra', 'Juan Camilo Vasquez-Correa'] | 2021-12-15 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [-1.79376096e-01 3.68926935e-02 1.11861512e-01 -5.34053028e-01
-3.22234184e-01 -5.16341627e-01 5.70731044e-01 -6.63357154e-02
-3.37580323e-01 4.06446666e-01 6.47971258e-02 -3.85060072e-01
1.86650723e-01 -7.72397101e-01 1.25339895e-01 -6.34755254e-01
4.02704060e-01 8.39582562e-01 1.23981439e-01 -8.29063654... | [12.712361335754395, 7.712646484375] |
cb01a4b3-7332-462b-8b16-810485180364 | transformers-in-medical-imaging-a-survey | 2201.09873 | null | https://arxiv.org/abs/2201.09873v1 | https://arxiv.org/pdf/2201.09873v1.pdf | Transformers in Medical Imaging: A Survey | Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as {de facto} operators. Capitalizing on these ... | ['Huazhu Fu', 'Fahad Shahbaz Khan', 'Munawar Hayat', 'Muhammad Haris Khan', 'Syed Waqas Zamir', 'Salman Khan', 'Fahad Shamshad'] | 2022-01-24 | null | null | null | null | ['medical-image-denoising', 'medical-object-detection', 'medical-report-generation'] | ['computer-vision', 'computer-vision', 'medical'] | [ 6.18365884e-01 1.75764561e-01 -1.31554082e-01 -3.71471405e-01
-6.99585438e-01 -4.55380887e-01 1.37288481e-01 6.07274137e-02
-3.64726305e-01 3.14497799e-01 1.02669127e-01 -5.62648714e-01
-2.14855015e-01 -6.14890456e-01 -3.07391882e-01 -8.41584980e-01
-2.52053708e-01 1.26407504e-01 1.19265497e-01 -3.06899995... | [14.525227546691895, -2.595310926437378] |
32ab03cd-57fd-4c33-8113-57df6a46c9d1 | psla-improving-audio-event-classification | 2102.01243 | null | https://arxiv.org/abs/2102.01243v3 | https://arxiv.org/pdf/2102.01243v3.pdf | PSLA: Improving Audio Tagging with Pretraining, Sampling, Labeling, and Aggregation | Audio tagging is an active research area and has a wide range of applications. Since the release of AudioSet, great progress has been made in advancing model performance, which mostly comes from the development of novel model architectures and attention modules. However, we find that appropriate training techniques are... | ['James Glass', 'Yu-An Chung', 'Yuan Gong'] | 2021-02-02 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 2.63091952e-01 -6.68849945e-02 -1.58997610e-01 -4.51564968e-01
-1.10005343e+00 -4.48181301e-01 4.16984946e-01 1.09571166e-01
-6.91407919e-01 3.37751031e-01 3.44512671e-01 6.68633878e-02
2.93976087e-02 -2.68801212e-01 -5.45113444e-01 -4.33602214e-01
-3.07315737e-01 5.49135745e-01 4.43015009e-01 -7.08697066... | [15.213525772094727, 5.124805927276611] |
21d342c4-ed63-465c-aa96-b7c389e20648 | motion-magnification-in-robotic-sonography | 2307.03698 | null | https://arxiv.org/abs/2307.03698v1 | https://arxiv.org/pdf/2307.03698v1.pdf | Motion Magnification in Robotic Sonography: Enabling Pulsation-Aware Artery Segmentation | Ultrasound (US) imaging is widely used for diagnosing and monitoring arterial diseases, mainly due to the advantages of being non-invasive, radiation-free, and real-time. In order to provide additional information to assist clinicians in diagnosis, the tubular structures are often segmented from US images. To improve t... | ['Zhongliang Jiang', 'Nassir Navab', 'Yuan Bi', 'Dianye Huang'] | 2023-07-07 | null | null | null | null | ['motion-magnification'] | ['computer-vision'] | [ 4.78144735e-03 9.84445959e-02 -1.97414249e-01 -2.56499708e-01
-5.03777564e-01 -5.50625384e-01 -1.55266792e-01 -4.81115341e-01
-1.39561966e-01 4.70940739e-01 3.66915427e-02 -4.33745533e-01
-2.23460466e-01 -4.46961671e-01 -4.40794677e-01 -8.90200913e-01
-3.98270249e-01 2.07083479e-01 4.26704735e-01 -1.20275110... | [14.331058502197266, -2.4940061569213867] |
5b80a0c1-a827-4a76-94df-3984eee01429 | enabling-joint-radar-communication-operation | 2305.15069 | null | https://arxiv.org/abs/2305.15069v1 | https://arxiv.org/pdf/2305.15069v1.pdf | Enabling Joint Radar-Communication Operation in Shift Register-Based PMCW Radars | This article introduces adaptations to the conventional frame structure in binary phase-modulated continuous wave (PMCW) radars with sequence generation via linear-feedbck shift registers and additional processing steps to enable joint radar-communication (RadCom) operation. In this context, a preamble structure based ... | ['Thomas Zwick', 'Akanksha Bhutani', 'Yueheng Li', 'Theresa Antes', 'Benjamin Nuss', 'Axel Diewald', 'Elizabeth Bekker', 'Lucas Giroto de Oliveira'] | 2023-05-24 | null | null | null | null | ['joint-radar-communication'] | ['robots'] | [ 6.29338801e-01 -1.47765195e-02 -3.63410823e-02 -3.62738401e-01
-5.33547997e-01 -3.48133028e-01 1.11482334e+00 -6.01555444e-02
-7.10650146e-01 1.30239534e+00 -3.77196521e-02 -7.58598447e-01
-9.05602753e-01 -4.64957267e-01 1.97235093e-01 -1.00044751e+00
-4.86622036e-01 2.42749110e-01 1.23231136e-03 -4.18050408... | [6.376824855804443, 1.2535187005996704] |
0b8431ec-88b9-4f04-b94a-93e5f7145818 | content-based-table-retrieval-for-web-queries | 1706.02427 | null | http://arxiv.org/abs/1706.02427v1 | http://arxiv.org/pdf/1706.02427v1.pdf | Content-Based Table Retrieval for Web Queries | Understanding the connections between unstructured text and semi-structured
table is an important yet neglected problem in natural language processing. In
this work, we focus on content-based table retrieval. Given a query, the task
is to find the most relevant table from a collection of tables. Further
progress toward... | ['Junwei Bao', 'Nan Duan', 'Zhao Yan', 'Yuanhua Lv', 'Zhoujun Li', 'Ming Zhou', 'Duyu Tang'] | 2017-06-08 | null | null | null | null | ['table-retrieval'] | ['natural-language-processing'] | [ 1.64966583e-01 -2.85063572e-02 -4.23691183e-01 -4.48463678e-01
-1.31874251e+00 -7.72713363e-01 4.83565986e-01 9.24647331e-01
-4.27300692e-01 7.51294971e-01 6.02748811e-01 -7.92284161e-02
-2.00511098e-01 -1.08937705e+00 -7.63350070e-01 1.35362118e-01
2.72633508e-03 8.93595695e-01 3.68437320e-01 -5.46392620... | [9.752492904663086, 7.9173903465271] |
d39aeab5-746b-4354-b41c-83b7592d0b77 | deepsolo-let-transformer-decoder-with-1 | 2305.19957 | null | https://arxiv.org/abs/2305.19957v1 | https://arxiv.org/pdf/2305.19957v1.pdf | DeepSolo++: Let Transformer Decoder with Explicit Points Solo for Text Spotting | End-to-end text spotting aims to integrate scene text detection and recognition into a unified framework. Dealing with the relationship between the two sub-tasks plays a pivotal role in designing effective spotters. Although Transformer-based methods eliminate the heuristic post-processing, they still suffer from the s... | ['DaCheng Tao', 'Bo Du', 'Tongliang Liu', 'Juhua Liu', 'Shanshan Zhao', 'Jing Zhang', 'Maoyuan Ye'] | 2023-05-31 | null | null | null | null | ['text-spotting', 'scene-text-detection'] | ['computer-vision', 'computer-vision'] | [ 1.35037050e-01 -4.82492030e-01 -1.51861861e-01 -2.74220258e-01
-1.18514121e+00 -8.97235990e-01 4.32428837e-01 -6.29524840e-03
-4.44178909e-01 2.62594163e-01 -9.56227165e-03 -5.64119875e-01
3.60170424e-01 -5.82702994e-01 -7.50997365e-01 -5.20874679e-01
6.38206303e-01 7.62800097e-01 4.67953712e-01 -1.81989789... | [11.978839874267578, 2.255518674850464] |
239ee1de-183d-435f-ae98-4ef209cb3b30 | dfr-tsd-a-deep-learning-based-framework-for | 2006.02578 | null | https://arxiv.org/abs/2006.02578v1 | https://arxiv.org/pdf/2006.02578v1.pdf | DFR-TSD: A Deep Learning Based Framework for Robust Traffic Sign Detection Under Challenging Weather Conditions | Robust traffic sign detection and recognition (TSDR) is of paramount importance for the successful realization of autonomous vehicle technology. The importance of this task has led to a vast amount of research efforts and many promising methods have been proposed in the existing literature. However, the SOTA (SOTA) met... | ['Md. Kamrul Hasan', 'Uday Kamal', 'Sabbir Ahmed'] | 2020-06-03 | null | null | null | null | ['traffic-sign-detection'] | ['computer-vision'] | [ 2.93284774e-01 -2.60703683e-01 1.37227699e-01 -3.17782551e-01
-7.52411246e-01 -1.94927827e-01 7.36489773e-01 -8.35322738e-01
-6.72728896e-01 3.79162818e-01 -2.00472355e-01 -3.82950634e-01
2.91578293e-01 -4.58448350e-01 -7.24368691e-01 -7.62306154e-01
6.07146695e-02 -1.41160995e-01 7.90608048e-01 -2.47868419... | [7.967831611633301, -0.853804886341095] |
80f0fbf1-968e-4c48-bb4a-f5f1cbcad596 | proceedings-of-the-workshop-on-computational-3 | null | null | https://aclanthology.org/W12-0400 | https://aclanthology.org/W12-0400.pdf | Proceedings of the Workshop on Computational Approaches to Deception Detection | null | [''] | 2012-04-01 | null | null | null | ws-2012-4 | ['deception-detection'] | ['miscellaneous'] | [-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... | [-7.315954685211182, 3.736192464828491] |
f5c7d0bb-38ec-4301-b213-802d31ec4fbf | robotic-navigation-autonomy-for-subretinal | 2301.07204 | null | https://arxiv.org/abs/2301.07204v1 | https://arxiv.org/pdf/2301.07204v1.pdf | Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing | In the last decade, various robotic platforms have been introduced that could support delicate retinal surgeries. Concurrently, to provide semantic understanding of the surgical area, recent advances have enabled microscope-integrated intraoperative Optical Coherent Tomography (iOCT) with high-resolution 3D imaging at ... | ['Iulian Iordachita', 'M. Ali Nasseri', 'Nassir Navab', 'Peter Gehlbach', 'Benjamin Busam', 'Alejandro Martin-Gomez', 'Peiyao Zhang', 'Michael Sommersperger', 'Shervin Dehghani'] | 2023-01-17 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-6.09281845e-02 1.28281251e-01 1.29544586e-01 -3.37074548e-02
-1.41932607e-01 -7.96599388e-01 -2.30783728e-04 -3.67693067e-01
-5.13838470e-01 2.10489810e-01 -7.69878700e-02 -3.49173576e-01
-3.05948883e-01 -3.14823568e-01 -5.60148180e-01 -4.75642413e-01
-1.22161293e-02 5.39261699e-01 2.55149752e-01 3.03846058... | [13.798877716064453, -3.0485522747039795] |
69c03413-7218-48c1-af0f-205053cc35e0 | adaptive-fine-grained-predicates-learning-for | 2207.04602 | null | https://arxiv.org/abs/2207.04602v1 | https://arxiv.org/pdf/2207.04602v1.pdf | Adaptive Fine-Grained Predicates Learning for Scene Graph Generation | The performance of current Scene Graph Generation (SGG) models is severely hampered by hard-to-distinguish predicates, e.g., woman-on/standing on/walking on-beach. As general SGG models tend to predict head predicates and re-balancing strategies prefer tail categories, none of them can appropriately handle hard-to-dist... | ['Jingkuan Song', 'Heng Tao Shen', 'Pengpeng Zeng', 'Lianli Gao', 'Xinyu Lyu'] | 2022-07-11 | null | null | null | null | ['scene-graph-generation', 'fine-grained-image-classification'] | ['computer-vision', 'computer-vision'] | [ 3.34862918e-01 4.17231858e-01 -4.05877501e-01 -3.81183743e-01
-8.25398982e-01 -4.99459922e-01 6.79004073e-01 2.13285357e-01
-5.69545738e-02 6.89441621e-01 4.64519113e-02 -2.40235776e-01
-2.36611158e-01 -1.16518211e+00 -8.38874817e-01 -6.35710418e-01
-1.70693919e-01 8.42181623e-01 6.24476790e-01 -6.02739155... | [10.300887107849121, 1.7562081813812256] |
da007d13-97dd-435b-a5e9-4b5d6e0e5f70 | a-comparative-attention-framework-for-better | 2210.13923 | null | https://arxiv.org/abs/2210.13923v1 | https://arxiv.org/pdf/2210.13923v1.pdf | A Comparative Attention Framework for Better Few-Shot Object Detection on Aerial Images | Few-Shot Object Detection (FSOD) methods are mainly designed and evaluated on natural image datasets such as Pascal VOC and MS COCO. However, it is not clear whether the best methods for natural images are also the best for aerial images. Furthermore, direct comparison of performance between FSOD methods is difficult d... | ['Anissa Mokraoui', 'Pierre Le Jeune'] | 2022-10-25 | null | null | null | null | ['few-shot-object-detection'] | ['computer-vision'] | [ 2.90585101e-01 -4.33284581e-01 -6.44361824e-02 -5.12584522e-02
-5.84003806e-01 -3.81641775e-01 6.70071959e-01 4.43959124e-02
-5.99892080e-01 2.80194879e-01 -2.06687689e-01 5.98306917e-02
-5.05821919e-03 -6.13358498e-01 -5.31645179e-01 -6.22111082e-01
9.11258236e-02 -4.39219996e-02 9.75050330e-01 -3.94857228... | [8.880888938903809, -0.05310998857021332] |
6fb5bb24-f00e-4a02-855e-873cb847748d | radformer-transformers-with-global-local | 2211.04793 | null | https://arxiv.org/abs/2211.04793v1 | https://arxiv.org/pdf/2211.04793v1.pdf | RadFormer: Transformers with Global-Local Attention for Interpretable and Accurate Gallbladder Cancer Detection | We propose a novel deep neural network architecture to learn interpretable representation for medical image analysis. Our architecture generates a global attention for region of interest, and then learns bag of words style deep feature embeddings with local attention. The global, and local feature maps are combined usi... | ['Chetan Arora', 'Pankaj Gupta', 'Pratyaksha Rana', 'Mayank Gupta', 'Soumen Basu'] | 2022-11-09 | null | null | null | null | ['gallbladder-cancer-detection'] | ['computer-vision'] | [-8.65302086e-02 6.36929750e-01 -8.79257098e-02 -3.71837765e-01
-9.48158324e-01 -2.69681841e-01 5.02075434e-01 4.17350054e-01
2.23273113e-02 3.89086306e-02 8.79011631e-01 -1.07521379e+00
-1.89558804e-01 -6.29796445e-01 -7.04847038e-01 -7.70631969e-01
-5.85102022e-01 4.32528377e-01 -3.44941527e-01 3.61565053... | [15.078195571899414, -2.4218318462371826] |
4fcaede7-0ec4-4ed0-91c0-62462208a078 | emergence-in-artificial-life | 2105.03216 | null | https://arxiv.org/abs/2105.03216v2 | https://arxiv.org/pdf/2105.03216v2.pdf | Emergence in artificial life | Even when concepts similar to emergence have been used since antiquity, we lack an agreed definition. However, emergence has been identified as one of the main features of complex systems. Most would agree on the statement ``life is complex''. Thus, understanding emergence and complexity should benefit the study of liv... | ['Carlos Gershenson'] | 2021-04-30 | null | null | null | null | ['artificial-life'] | ['miscellaneous'] | [-1.08553199e-02 1.42069399e-01 3.32297921e-01 3.07389498e-01
6.85271025e-01 -7.32564747e-01 9.05795336e-01 5.75058639e-01
-1.54323697e-01 6.90876126e-01 1.07143499e-01 -3.04170966e-01
-3.56782615e-01 -1.03816819e+00 -3.88173193e-01 -1.13139045e+00
-1.35613561e-01 2.33229563e-01 1.41390875e-01 -9.06174541... | [5.603518962860107, 4.181462287902832] |
72c6d11c-f6e2-4618-9d4c-fe34c334f1b9 | bl-research-at-semeval-2022-task-1-deep | null | null | https://aclanthology.org/2022.semeval-1.11 | https://aclanthology.org/2022.semeval-1.11.pdf | BL.Research at SemEval-2022 Task 1: Deep networks for Reverse Dictionary using embeddings and LSTM autoencoders | This paper describes our two deep learning systems that competed at SemEval-2022 Task 1 “CODWOE: Comparing Dictionaries and WOrd Embeddings”. We participated in the subtask for the reverse dictionary which consists in generating vectors from glosses. We use sequential models that integrate several neural networks, star... | ['Youssef Miloudi', 'Christophe Bortolaso', 'Mokhtar Boumedyen Billami', 'Lina Nicolaieff', 'Julien Breton', 'Nihed Bendahman'] | null | null | null | null | semeval-naacl-2022-7 | ['reverse-dictionary'] | ['natural-language-processing'] | [-1.39757976e-01 1.11346776e-02 -1.46916181e-01 -2.39850208e-01
-3.65515381e-01 -4.90187436e-01 1.00118649e+00 1.02534458e-01
-1.31875718e+00 6.72214866e-01 5.54390252e-01 -6.70023620e-01
2.37623721e-01 -8.43583524e-01 -5.20493507e-01 -3.90689224e-01
9.42700207e-02 8.22674692e-01 -1.82624310e-01 -7.08934903... | [10.685410499572754, 9.12372875213623] |
f65cb2ea-4ef7-4169-b555-fcb6d0cb3ee5 | when-more-data-hurts-a-troubling-quirk-in-1 | 2205.12228 | null | https://arxiv.org/abs/2205.12228v2 | https://arxiv.org/pdf/2205.12228v2.pdf | When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems | In natural language understanding (NLU) production systems, users' evolving needs necessitate the addition of new features over time, indexed by new symbols added to the meaning representation space. This requires additional training data and results in ever-growing datasets. We present the first systematic investigati... | ['Yu Su', 'Jason Eisner', 'Benjamin Van Durme', 'Hao Fang', 'Sam Thomson', 'Adam Pauls', 'Emmanouil Antonios Platanios', 'Elias Stengel-Eskin'] | 2022-05-24 | null | null | null | null | ['intent-recognition'] | ['natural-language-processing'] | [ 7.49638438e-01 3.01855594e-01 -4.91712332e-01 -3.89179677e-01
-4.96850550e-01 -7.71228552e-01 6.16054356e-01 4.17142242e-01
-3.39440644e-01 5.31551003e-01 5.35970509e-01 -6.39297068e-01
1.13203824e-01 -6.43015444e-01 -8.88545394e-01 -8.88405666e-02
2.48604104e-01 3.90603393e-01 2.39413772e-02 -3.39150697... | [10.672536849975586, 8.532660484313965] |
bc9001dd-847a-430f-b3d9-0e5b64d34b33 | rquge-reference-free-metric-for-evaluating | 2211.01482 | null | https://arxiv.org/abs/2211.01482v3 | https://arxiv.org/pdf/2211.01482v3.pdf | RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question | Existing metrics for evaluating the quality of automatically generated questions such as BLEU, ROUGE, BERTScore, and BLEURT compare the reference and predicted questions, providing a high score when there is a considerable lexical overlap or semantic similarity between the candidate and the reference questions. This ap... | ['Marzieh Saeidi', 'James Henderson', 'Angela Fan', 'Pouya Yanki', 'Majid Yazdani', 'Thomas Scialom', 'Alireza Mohammadshahi'] | 2022-11-02 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 6.20975904e-02 3.06233823e-01 3.13124180e-01 -3.13510150e-01
-1.61722779e+00 -8.37903738e-01 6.57177567e-01 2.83385187e-01
-4.68095183e-01 1.03700316e+00 3.92920107e-01 -2.27892146e-01
-1.71393588e-01 -7.77072906e-01 -6.20864570e-01 -4.33030501e-02
5.68841636e-01 5.61192572e-01 6.09979808e-01 -5.16803205... | [11.513933181762695, 8.227394104003906] |
44e88a45-0072-42c8-9ea9-4985027acb07 | latentforensics-towards-lighter-deepfake | 2303.17222 | null | https://arxiv.org/abs/2303.17222v1 | https://arxiv.org/pdf/2303.17222v1.pdf | LatentForensics: Towards lighter deepfake detection in the StyleGAN latent space | The classification of forged videos has been a challenge for the past few years. Deepfake classifiers can now reliably predict whether or not video frames have been tampered with. However, their performance is tied to both the dataset used for training and the analyst's computational power. We propose a deepfake classi... | ['Renaud Seguier', 'Simon Leglaive', 'Stephane Paquelet', 'Amine Kacete', 'Matthieu Delmas'] | 2023-03-30 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 2.50873566e-01 1.29535347e-01 -2.81828344e-01 -4.13620561e-01
-5.05808949e-01 -6.83565319e-01 9.22104716e-01 -7.14584768e-01
8.08236524e-02 3.71655285e-01 9.09154862e-02 -3.11173201e-01
7.92477950e-02 -7.21452653e-01 -7.62938738e-01 -8.08566749e-01
-2.10853163e-02 4.04542089e-02 -3.35853636e-01 8.98064077... | [12.569897651672363, 0.9930076003074646] |
63793b2d-e611-417b-b44e-0dccee2b644e | large-scale-spectral-clustering-using | null | null | https://aclanthology.org/W18-1705 | https://aclanthology.org/W18-1705.pdf | Large-scale spectral clustering using diffusion coordinates on landmark-based bipartite graphs | Spectral clustering has received a lot of attention due to its ability to separate nonconvex, non-intersecting manifolds, but its high computational complexity has significantly limited its applicability. Motivated by the document-term co-clustering framework by Dhillon (2001), we propose a landmark-based scalable spec... | ['Khiem Pham', 'Guangliang Chen'] | 2018-06-01 | null | null | null | ws-2018-6 | ['imagedocument-clustering'] | ['computer-vision'] | [-2.03674957e-01 -3.58864546e-01 -5.83014823e-02 3.59019451e-02
-8.93806875e-01 -7.51149595e-01 4.54820246e-01 3.33680809e-01
-4.72281605e-01 3.37786555e-01 2.46446170e-02 -1.36079803e-01
-4.14497793e-01 -6.23412967e-01 -4.49494034e-01 -9.79695499e-01
-4.42968100e-01 7.42859185e-01 4.36412364e-01 2.47006357... | [7.5108323097229, 4.702158451080322] |
d623c232-8f80-4e4d-a3e4-9ad484c38ea4 | do-multi-document-summarization-models | 2301.13844 | null | https://arxiv.org/abs/2301.13844v1 | https://arxiv.org/pdf/2301.13844v1.pdf | Do Multi-Document Summarization Models Synthesize? | Multi-document summarization entails producing concise synopses of collections of inputs. For some applications, the synopsis should accurately \emph{synthesize} inputs with respect to a key property or aspect. For example, a synopsis of film reviews all written about a particular movie should reflect the average criti... | ['Byron C. Wallace', 'Iain J. Marshall', 'Stephanie C. Martinez', 'Jay DeYoung'] | 2023-01-31 | null | null | null | null | ['document-summarization'] | ['natural-language-processing'] | [ 8.13222587e-01 4.01439786e-01 -5.23684323e-01 -4.40736145e-01
-1.22027194e+00 -9.79312778e-01 7.70741165e-01 4.65719074e-01
-1.85867473e-01 1.12185550e+00 8.41759026e-01 -3.46585602e-01
-1.02798402e-01 -5.34923136e-01 -7.57670164e-01 -3.72140050e-01
4.82711166e-01 3.79611611e-01 -1.91892505e-01 -1.19434431... | [12.255932807922363, 9.38272476196289] |
08860c5e-6199-408d-8498-f1073d9077c8 | tbn-vit-temporal-bilateral-network-with | 2112.01033 | null | https://arxiv.org/abs/2112.01033v1 | https://arxiv.org/pdf/2112.01033v1.pdf | TBN-ViT: Temporal Bilateral Network with Vision Transformer for Video Scene Parsing | Video scene parsing in the wild with diverse scenarios is a challenging and great significance task, especially with the rapid development of automatic driving technique. The dataset Video Scene Parsing in the Wild(VSPW) contains well-trimmed long-temporal, dense annotation and high resolution clips. Based on VSPW, we ... | ['Hongbin Wang', 'Leilei Cao', 'Bo Yan'] | 2021-12-02 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [ 1.58613473e-01 -1.41282022e-01 5.89223318e-02 -6.25602365e-01
-6.36609435e-01 -4.23760146e-01 5.05763948e-01 -4.80012089e-01
-4.99276072e-01 4.82872576e-01 3.82029057e-01 -2.06150785e-01
8.90311077e-02 -7.29133129e-01 -1.03263283e+00 -5.75678110e-01
1.52683705e-01 -4.66303617e-01 8.30434501e-01 -1.55217066... | [9.253369331359863, 0.02129863202571869] |
eec49a7e-4b1e-48c4-8fdd-9dc9a45b1272 | human-activity-behavioural-pattern | 2306.13374 | null | https://arxiv.org/abs/2306.13374v2 | https://arxiv.org/pdf/2306.13374v2.pdf | Human Activity Behavioural Pattern Recognition in Smarthome with Long-hour Data Collection | The research on human activity recognition has provided novel solutions to many applications like healthcare, sports, and user profiling. Considering the complex nature of human activities, it is still challenging even after effective and efficient sensors are available. The existing works on human activity recognition... | ['Geetha V', 'Ranjit Kolkar'] | 2023-06-23 | null | null | null | null | ['activity-recognition', 'human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'computer-vision', 'time-series'] | [ 1.56023145e-01 -2.49732003e-01 -4.62250262e-01 -2.20386878e-01
1.40647851e-02 4.34826650e-02 3.80595922e-01 1.92652881e-01
-4.89384055e-01 8.85014951e-01 5.75670004e-01 -1.89944059e-01
-8.82810205e-02 -9.68630195e-01 -1.40447840e-01 -7.55182266e-01
-2.50864267e-01 -1.31814957e-01 1.83082461e-01 -1.23605765... | [7.279477119445801, 0.659417450428009] |
912a3b67-2a30-491f-807b-b25e4cb5675e | a-comparative-study-on-multichannel-speaker | 2211.00511 | null | https://arxiv.org/abs/2211.00511v3 | https://arxiv.org/pdf/2211.00511v3.pdf | A Comparative Study on Multichannel Speaker-Attributed Automatic Speech Recognition in Multi-party Meetings | Speaker-attributed automatic speech recognition (SA-ASR) in multi-party meeting scenarios is one of the most valuable and challenging ASR task. It was shown that single-channel frame-level diarization with serialized output training (SC-FD-SOT), single-channel word-level diarization with SOT (SC-WD-SOT) and joint train... | ['Li-Rong Dai', 'Shiliang Zhang', 'Qian Chen', 'Fan Yu', 'Zhihao Du', 'Jie Zhang', 'Mohan Shi'] | 2022-11-01 | null | null | null | null | ['speaker-separation'] | ['speech'] | [ 4.22036380e-01 -1.82273313e-01 1.62961021e-01 -4.05528784e-01
-1.70689428e+00 -3.78497452e-01 5.95247984e-01 -1.29174232e-01
-3.00099403e-01 4.52863514e-01 6.03771865e-01 -5.92855036e-01
3.93698774e-02 3.10467035e-01 -5.51238716e-01 -7.99030602e-01
-6.78335801e-02 2.10918978e-01 -3.70641774e-03 -4.98735100... | [14.711441993713379, 5.828766345977783] |
cd875d19-a141-436a-a005-1af6520461f0 | high-resolution-image-reconstruction-with | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Takagi_High-Resolution_Image_Reconstruction_With_Latent_Diffusion_Models_From_Human_Brain_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Takagi_High-Resolution_Image_Reconstruction_With_Latent_Diffusion_Models_From_Human_Brain_CVPR_2023_paper.pdf | High-Resolution Image Reconstruction With Latent Diffusion Models From Human Brain Activity | Reconstructing visual experiences from human brain activity offers a unique way to understand how the brain represents the world, and to interpret the connection between computer vision models and our visual system. While deep generative models have recently been employed for this task, reconstructing realistic ima... | ['Shinji Nishimoto', 'Yu Takagi'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['image-reconstruction'] | ['computer-vision'] | [ 7.49342293e-02 -5.30625209e-02 2.44662672e-01 -2.82829791e-01
-2.39031062e-01 -4.91105556e-01 8.90040159e-01 -3.04201007e-01
-3.43982697e-01 5.87444663e-01 3.18764627e-01 5.07128201e-02
-2.33948991e-01 -8.20413589e-01 -7.23330140e-01 -1.06169617e+00
2.53127337e-01 3.39870691e-01 -4.36326228e-02 1.31561905... | [10.7583646774292, 2.5105717182159424] |
0cf7aeda-09af-439b-9479-f2f419db6f23 | a-graph-similarity-for-deep-learning | null | null | http://proceedings.neurips.cc/paper/2020/hash/0004d0b59e19461ff126e3a08a814c33-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/0004d0b59e19461ff126e3a08a814c33-Paper.pdf | A graph similarity for deep learning | Graph neural networks (GNNs) have been successful in learning representations from graphs. Many popular GNNs follow the pattern of aggregate-transform: they aggregate the neighbors' attributes and then transform the results of aggregation with a learnable function. Analyses of these GNNs explain which pairs of non-iden... | ['Seongmin Ok'] | 2020-12-01 | null | null | null | neurips-2020-12 | ['graph-similarity', 'graph-regression'] | ['graphs', 'graphs'] | [-7.25251883e-02 2.28921145e-01 3.56012993e-02 -2.87689924e-01
-2.42104053e-01 -6.25258148e-01 7.42576420e-01 5.21792710e-01
-4.96125557e-02 6.22451305e-01 7.72470087e-02 -4.88399655e-01
-3.30814153e-01 -1.41449153e+00 -7.92448401e-01 -4.77007031e-01
-2.17036992e-01 3.08139771e-01 3.10484469e-01 -4.65479851... | [6.885725021362305, 6.286672592163086] |
fa6d6249-bfe0-439e-ba55-b80e28a878d8 | lego-absa-a-prompt-based-task-assemblable | null | null | https://aclanthology.org/2022.coling-1.610 | https://aclanthology.org/2022.coling-1.610.pdf | LEGO-ABSA: A Prompt-based Task Assemblable Unified Generative Framework for Multi-task Aspect-based Sentiment Analysis | Aspect-based sentiment analysis (ABSA) has received increasing attention recently. ABSA can be divided into multiple tasks according to the different extracted elements. Existing generative methods usually treat the output as a whole string rather than the combination of different elements and only focus on a single ta... | ['Weipeng Yan', 'Yongjun Bao', 'Pengzhang Liu', 'Chao Liu', 'Zhiyuan Liu', 'Hanyu Liu', 'Jun Fang', 'Tianhao Gao'] | null | null | null | null | coling-2022-10 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 3.68435830e-01 -2.14536667e-01 4.16626483e-01 -5.51023841e-01
-1.34127474e+00 -5.91686606e-01 7.17810094e-01 -1.11827753e-01
-4.05040413e-01 4.42575097e-01 6.42305240e-02 -1.47864655e-01
1.01508619e-02 -6.72807574e-01 -8.33106041e-01 -8.40874195e-01
5.59660554e-01 7.77732968e-01 1.14467271e-01 -4.26225930... | [11.48651123046875, 6.680897235870361] |
cacc839d-b5ed-4355-9b2f-5f45cf3a372b | communication-efficient-tensor-factorization | 2109.01718 | null | https://arxiv.org/abs/2109.01718v2 | https://arxiv.org/pdf/2109.01718v2.pdf | Communication Efficient Generalized Tensor Factorization for Decentralized Healthcare Networks | Tensor factorization has been proved as an efficient unsupervised learning approach for health data analysis, especially for computational phenotyping, where the high-dimensional Electronic Health Records (EHRs) with patients' history of medical procedures, medications, diagnosis, lab tests, etc., are converted to mean... | ['Joyce C. Ho', 'Sivasubramanium Bhavani', 'Li Xiong', 'Jian Lou', 'Qiuchen Zhang', 'Jing Ma'] | 2021-09-03 | null | null | null | null | ['computational-phenotyping'] | ['medical'] | [-3.40702653e-01 -1.20093383e-01 5.13484627e-02 -2.31225654e-01
-4.76954639e-01 -5.33173561e-01 -4.42385733e-01 5.24737656e-01
-1.97353631e-01 5.98364770e-01 3.01221371e-01 -5.16774178e-01
-4.65107083e-01 -6.59355462e-01 -4.47970837e-01 -9.20422375e-01
-4.32379454e-01 5.15273213e-01 -2.86923379e-01 3.39339077... | [6.2257843017578125, 6.3494768142700195] |
ac93780b-8fc5-4f78-95aa-a1f90363238c | neumann-network-with-recursive-kernels-for | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Quan_Neumann_Network_With_Recursive_Kernels_for_Single_Image_Defocus_Deblurring_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Quan_Neumann_Network_With_Recursive_Kernels_for_Single_Image_Defocus_Deblurring_CVPR_2023_paper.pdf | Neumann Network With Recursive Kernels for Single Image Defocus Deblurring | Single image defocus deblurring (SIDD) refers to recovering an all-in-focus image from a defocused blurry one. It is a challenging recovery task due to the spatially-varying defocus blurring effects with significant size variation. Motivated by the strong correlation among defocus kernels of different sizes and the... | ['Hui Ji', 'Zicong Wu', 'Yuhui Quan'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['deblurring'] | ['computer-vision'] | [ 3.17609280e-01 -5.16610682e-01 4.37441856e-01 -3.53153914e-01
-4.99895006e-01 -3.26916605e-01 4.06789213e-01 -4.51778114e-01
-2.00055629e-01 8.57211351e-01 7.23575294e-01 3.68910544e-02
-7.31950581e-01 -2.26148829e-01 -8.60661089e-01 -1.35895872e+00
-1.68608904e-01 1.60533488e-01 -2.10567731e-02 1.86916396... | [11.556395530700684, -2.7897775173187256] |
ba469f4e-ec0a-4e56-a6f1-ac693bfd5f8f | analysis-of-drug-repurposing-knowledge-graphs | 2212.03911 | null | https://arxiv.org/abs/2212.03911v1 | https://arxiv.org/pdf/2212.03911v1.pdf | Analysis of Drug repurposing Knowledge graphs for Covid-19 | Knowledge graph (KG) is used to represent data in terms of entities and structural relations between the entities. This representation can be used to solve complex problems such as recommendation systems and question answering. In this study, a set of candidate drugs for COVID-19 are proposed by using Drug repurposing ... | ['Ajay Kumar Gogineni'] | 2022-12-07 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [ 1.80285722e-02 4.08112019e-01 -3.94114077e-01 -1.71251357e-01
2.07836822e-01 -4.57794458e-01 2.86441922e-01 8.89447570e-01
-2.52936184e-01 9.03962672e-01 4.26245421e-01 -4.05680895e-01
-4.84310210e-01 -1.11262643e+00 -6.71823382e-01 -5.79303563e-01
-2.63430327e-01 5.08407116e-01 1.62168935e-01 -8.90738294... | [5.610896110534668, 5.9828314781188965] |
986e161b-5319-4075-a972-96aa275f7e11 | unical-a-single-branch-transformer-based | 2304.09715 | null | https://arxiv.org/abs/2304.09715v1 | https://arxiv.org/pdf/2304.09715v1.pdf | UniCal: a Single-Branch Transformer-Based Model for Camera-to-LiDAR Calibration and Validation | We introduce a novel architecture, UniCal, for Camera-to-LiDAR (C2L) extrinsic calibration which leverages self-attention mechanisms through a Transformer-based backbone network to infer the 6-degree of freedom (DoF) relative transformation between the sensors. Unlike previous methods, UniCal performs an early fusion o... | ['Marius Bruehlmeier', 'Aaron Low', 'Mathieu Cocheteux'] | 2023-04-19 | null | null | null | null | ['camera-auto-calibration'] | ['computer-vision'] | [ 2.24204630e-01 2.27769911e-01 -4.42042053e-01 -6.29169047e-01
-1.19490027e+00 -6.73103750e-01 5.44055641e-01 -3.98835003e-01
-5.20608723e-01 3.12608302e-01 4.91522439e-02 -5.12027919e-01
3.79870594e-01 -5.00641942e-01 -1.30900264e+00 -3.08704555e-01
3.51791024e-01 5.47457516e-01 1.77865267e-01 1.90301299... | [7.918496131896973, -2.5315070152282715] |
a8b6dda8-7e74-4f73-935e-54eaea46ced6 | effects-of-mindfulness-on-perceived-stress | 1708.08006 | null | http://arxiv.org/abs/1708.08006v1 | http://arxiv.org/pdf/1708.08006v1.pdf | Effects of mindfulness on perceived stress levels and heart rate variability | Mindfulness has become increasingly popular as a method for building
resilience against stress in both clinical and healthy populations. This study
sought to investigate the effects of mindfulness training on perceived levels
of stress and heart rate variability in students. | [] | 2017-08-26 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [-9.14276913e-02 -2.80982666e-02 -8.49099338e-01 -1.92008555e-01
1.21297464e-01 1.31612360e-01 -3.37191224e-01 9.94758427e-01
-6.16462469e-01 3.11778545e-01 1.18646622e-01 -3.45625222e-01
2.66761005e-01 -3.01579714e-01 -8.14213976e-02 -4.24982309e-01
-2.08288245e-02 -4.83079404e-01 -3.66182148e-01 -4.14344043... | [13.744756698608398, 3.0804243087768555] |
538a69af-3d70-4be2-a28d-8b5db5be3669 | self-supervised-robust-scene-flow-estimation | 2203.12193 | null | https://arxiv.org/abs/2203.12193v1 | https://arxiv.org/pdf/2203.12193v1.pdf | Self-Supervised Robust Scene Flow Estimation via the Alignment of Probability Density Functions | In this paper, we present a new self-supervised scene flow estimation approach for a pair of consecutive point clouds. The key idea of our approach is to represent discrete point clouds as continuous probability density functions using Gaussian mixture models. Scene flow estimation is therefore converted into the probl... | ['Anand Rangarajan', 'Sanjay Ranka', 'Patrick Emami', 'Pan He'] | 2022-03-23 | null | null | null | null | ['scene-flow-estimation'] | ['computer-vision'] | [-2.29838893e-01 -2.80567676e-01 -1.58654481e-01 -3.24968308e-01
-6.46266282e-01 -6.95669591e-01 7.72046566e-01 1.26863018e-01
-3.57455760e-01 4.62709159e-01 -8.31264555e-02 -1.39619857e-02
-2.85672545e-01 -7.73828328e-01 -7.30040014e-01 -4.78188068e-01
-3.38245094e-01 9.16004777e-01 6.18697405e-01 -1.24919862... | [8.540793418884277, -2.0172038078308105] |
5c758a4b-eabd-4fef-821c-f3b790375d4f | multi-resolution-3d-convolutional-neural | 1805.12254 | null | https://arxiv.org/abs/1805.12254v2 | https://arxiv.org/pdf/1805.12254v2.pdf | Multi-level 3D CNN for Learning Multi-scale Spatial Features | 3D object recognition accuracy can be improved by learning the multi-scale spatial features from 3D spatial geometric representations of objects such as point clouds, 3D models, surfaces, and RGB-D data. Current deep learning approaches learn such features either using structured data representations (voxel grids and o... | ['Sambit Ghadai', 'Xian Lee', 'Adarsh Krishnamurthy', 'Aditya Balu', 'Soumik Sarkar'] | 2018-05-30 | null | null | null | null | ['3d-object-recognition'] | ['computer-vision'] | [ 7.46382922e-02 2.28000619e-02 -6.56464100e-02 -3.87270242e-01
-8.32447112e-01 -1.41697362e-01 4.84793603e-01 6.75166368e-01
-1.69564381e-01 3.56170326e-01 -1.31689206e-01 7.83631504e-02
-3.36528748e-01 -1.31036472e+00 -8.39462459e-01 -4.01361257e-01
-5.03202617e-01 8.56587529e-01 5.40548980e-01 3.65621299... | [8.171210289001465, -3.6596851348876953] |
dc84f481-9903-44de-9639-133e1e4548de | fighting-over-fitting-with-quantization-for | 2303.11803 | null | https://arxiv.org/abs/2303.11803v1 | https://arxiv.org/pdf/2303.11803v1.pdf | Fighting over-fitting with quantization for learning deep neural networks on noisy labels | The rising performance of deep neural networks is often empirically attributed to an increase in the available computational power, which allows complex models to be trained upon large amounts of annotated data. However, increased model complexity leads to costly deployment of modern neural networks, while gathering su... | ['Kevin Bailly', 'Arnaud Dapogny', 'Edouard Yvinec', 'Gauthier Tallec'] | 2023-03-21 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 5.10844588e-01 5.07025838e-01 -2.43618235e-01 -5.09506941e-01
-5.82167029e-01 -2.39529207e-01 4.36208487e-01 -5.63901626e-02
-6.21108651e-01 6.07797682e-01 1.85287565e-01 -1.00370556e-01
1.19350627e-01 -6.08822584e-01 -7.72821248e-01 -6.53827965e-01
6.80803508e-02 2.23945200e-01 -1.33287445e-01 1.78898633... | [8.997003555297852, 3.4872231483459473] |
5503382b-67fe-4327-acc4-2971f863f720 | crossing-generative-adversarial-networks-for | 1801.01760 | null | http://arxiv.org/abs/1801.01760v1 | http://arxiv.org/pdf/1801.01760v1.pdf | Crossing Generative Adversarial Networks for Cross-View Person Re-identification | Person re-identification (\textit{re-id}) refers to matching pedestrians
across disjoint yet non-overlapping camera views. The most effective way to
match these pedestrians undertaking significant visual variations is to seek
reliably invariant features that can describe the person of interest
faithfully. Most of exist... | ['Yang Wang', 'Chengyuan Zhang', 'Lin Wu'] | 2018-01-04 | null | null | null | null | ['cross-view-person-re-identification'] | ['computer-vision'] | [ 2.07388297e-01 -2.23985359e-01 5.09035960e-02 -5.35312414e-01
-6.99316859e-01 -5.74685276e-01 8.16915989e-01 -3.51978719e-01
-3.56774539e-01 5.59603333e-01 2.73134112e-01 3.89758795e-01
7.36773340e-03 -8.33910823e-01 -8.52894664e-01 -5.70715368e-01
3.42988014e-01 5.26489496e-01 -7.32505694e-02 4.50064838... | [14.643656730651855, 0.9946673512458801] |
4a2eb40d-1715-4024-b885-9770a1bb983f | asking-clarifying-questions-based-on-negative | 2107.05760 | null | https://arxiv.org/abs/2107.05760v1 | https://arxiv.org/pdf/2107.05760v1.pdf | Asking Clarifying Questions Based on Negative Feedback in Conversational Search | Users often need to look through multiple search result pages or reformulate queries when they have complex information-seeking needs. Conversational search systems make it possible to improve user satisfaction by asking questions to clarify users' search intents. This, however, can take significant effort to answer a ... | ['W. Bruce Croft', 'Qingyao Ai', 'Keping Bi'] | 2021-07-12 | null | null | null | null | ['conversational-search', 'question-selection'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.38018662e-01 6.84958175e-02 -4.10521567e-01 -5.95866799e-01
-1.15386379e+00 -6.65495157e-01 5.80092490e-01 1.83569789e-01
-6.67273283e-01 4.49780315e-01 6.29051507e-01 -7.70119429e-01
-2.73474693e-01 -2.24301443e-01 1.59375638e-01 3.21363583e-02
4.43719655e-01 5.72564006e-01 2.80815423e-01 -7.25629926... | [12.180259704589844, 7.797821044921875] |
949b80fd-3bab-49e3-b1c2-243eb64f39e7 | correlation-clustering-of-bird-sounds | 2306.09906 | null | https://arxiv.org/abs/2306.09906v1 | https://arxiv.org/pdf/2306.09906v1.pdf | Correlation Clustering of Bird Sounds | Bird sound classification is the task of relating any sound recording to those species of bird that can be heard in the recording. Here, we study bird sound clustering, the task of deciding for any pair of sound recordings whether the same species of bird can be heard in both. We address this problem by first learning,... | ['Bjoern Andres', 'David Stein'] | 2023-06-16 | null | null | null | null | ['sound-classification', 'clustering', 'classification-1'] | ['audio', 'methodology', 'methodology'] | [ 1.96656972e-01 -3.72869551e-01 7.61828959e-01 -2.56871074e-01
-3.86110187e-01 -1.01282346e+00 1.75706983e-01 4.06329185e-01
-4.88400519e-01 3.41016561e-01 8.92041922e-02 -3.30115020e-01
-3.61919105e-01 -7.95184374e-01 -5.36528528e-01 -7.59290159e-01
-5.75138390e-01 4.77566540e-01 4.15497273e-01 1.71746179... | [15.383846282958984, 5.411357879638672] |
6db66f7f-b5d5-47d3-86a3-e7ecc7f85493 | why-is-ai-hard-and-physics-simple | 2104.00008 | null | https://arxiv.org/abs/2104.00008v1 | https://arxiv.org/pdf/2104.00008v1.pdf | Why is AI hard and Physics simple? | We discuss why AI is hard and why physics is simple. We discuss how physical intuition and the approach of theoretical physics can be brought to bear on the field of artificial intelligence and specifically machine learning. We suggest that the underlying project of machine learning and the underlying project of physic... | ['Daniel A. Roberts'] | 2021-03-31 | null | null | null | null | ['physical-intuition'] | ['reasoning'] | [ 3.36252674e-02 6.43436968e-01 -3.57641250e-01 -5.08573115e-01
2.05351904e-01 -2.76913196e-01 7.34797657e-01 -9.38277096e-02
-1.90950319e-01 5.09305894e-01 1.35090277e-01 -6.12520576e-01
-3.98825318e-01 -1.10408759e+00 -7.07161486e-01 -7.27467120e-01
-5.96397407e-02 3.45103025e-01 -7.20834509e-02 -4.17869210... | [9.071990013122559, 6.422181129455566] |
067542c5-f290-4536-8a9e-ae5e319fa24a | varifocal-question-generation-for-fact | 2210.12400 | null | https://arxiv.org/abs/2210.12400v1 | https://arxiv.org/pdf/2210.12400v1.pdf | Varifocal Question Generation for Fact-checking | Fact-checking requires retrieving evidence related to a claim under investigation. The task can be formulated as question generation based on a claim, followed by question answering. However, recent question generation approaches assume that the answer is known and typically contained in a passage given as input, where... | ['Andreas Vlachos', 'Zhangdie Yuan', 'Nedjma Ousidhoum'] | 2022-10-22 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 4.06565696e-01 5.72814107e-01 -4.14633512e-01 -6.79321364e-02
-1.52250636e+00 -1.15374231e+00 1.05713463e+00 9.64578569e-01
-4.18560356e-02 9.43519950e-01 7.81842232e-01 -7.39717424e-01
-3.68657529e-01 -7.83705473e-01 -7.90555477e-01 2.34241337e-01
6.89610183e-01 2.84012020e-01 5.36186278e-01 -5.17505825... | [8.910386085510254, 9.641571998596191] |
07cde1d0-8d81-41c8-84ea-0f0e5fb1af03 | low-weight-and-learnable-image-denoising | 1911.07167 | null | https://arxiv.org/abs/1911.07167v2 | https://arxiv.org/pdf/1911.07167v2.pdf | LIDIA: Lightweight Learned Image Denoising with Instance Adaptation | Image denoising is a well studied problem with an extensive activity that has spread over several decades. Despite the many available denoising algorithms, the quest for simple, powerful and fast denoisers is still an active and vibrant topic of research. Leading classical denoising methods are typically designed to ex... | ['Michael Elad', 'Peyman Milanfar', 'Gregory Vaksman'] | 2019-11-17 | null | null | null | null | ['grayscale-image-denoising'] | ['computer-vision'] | [ 4.18540061e-01 -7.45920911e-02 2.40249336e-01 -3.28237921e-01
-7.89737821e-01 -1.98639244e-01 6.01669431e-01 2.42518917e-01
-4.87170219e-01 4.38523978e-01 3.04658145e-01 1.38545230e-01
-1.88973293e-01 -8.66459250e-01 -6.87635660e-01 -1.27888668e+00
-9.43802670e-02 2.74054632e-02 1.02479510e-01 -5.42780340... | [11.52619743347168, -2.320706605911255] |
36a4b8d6-b5d2-4713-903d-0bf226461a7b | implementation-of-the-vbm3d-video-denoising | 2001.01802 | null | https://arxiv.org/abs/2001.01802v1 | https://arxiv.org/pdf/2001.01802v1.pdf | Implementation of the VBM3D Video Denoising Method and Some Variants | VBM3D is an extension to video of the well known image denoising algorithm BM3D, which takes advantage of the sparse representation of stacks of similar patches in a transform domain. The extension is rather straightforward: the similar 2D patches are taken from a spatio-temporal neighborhood which includes neighboring... | ['Thibaud Ehret', 'Pablo Arias'] | 2020-01-06 | null | null | null | null | ['video-denoising'] | ['computer-vision'] | [ 1.97501525e-01 -2.91740090e-01 3.46025467e-01 4.18126136e-02
-5.86305320e-01 -3.88092458e-01 5.70150554e-01 -4.84226197e-02
-4.71247703e-01 6.42633796e-01 2.67746806e-01 -9.04627517e-02
-1.21839076e-01 -7.31046259e-01 -5.27030766e-01 -9.60891068e-01
-1.77733958e-01 -2.02069599e-02 8.64956975e-01 -3.97880018... | [11.325738906860352, -2.339233160018921] |
00d0cf4e-b329-430f-99bc-5488471f5ac6 | ensemble-based-offline-to-online | 2306.06871 | null | https://arxiv.org/abs/2306.06871v1 | https://arxiv.org/pdf/2306.06871v1.pdf | Ensemble-based Offline-to-Online Reinforcement Learning: From Pessimistic Learning to Optimistic Exploration | Offline reinforcement learning (RL) is a learning paradigm where an agent learns from a fixed dataset of experience. However, learning solely from a static dataset can limit the performance due to the lack of exploration. To overcome it, offline-to-online RL combines offline pre-training with online fine-tuning, which ... | ['Zhaopeng Meng', 'Yan Zheng', 'Jinyi Liu', 'Yi Ma', 'Kai Zhao'] | 2023-06-12 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-2.50311911e-01 -1.51790768e-01 -2.25688368e-01 -1.26074210e-01
-5.87729752e-01 -6.10302746e-01 2.94679821e-01 1.76414579e-01
-8.33214819e-01 1.16568327e+00 -1.92499354e-01 -2.58967102e-01
-3.70618701e-01 -8.61078978e-01 -8.21131706e-01 -7.06673443e-01
-3.41433048e-01 2.59174764e-01 3.92007679e-01 -4.82986450... | [4.1002912521362305, 2.187426805496216] |
e6beb651-171c-4137-961e-66f992ff55e4 | multiple-imputation-using-chained-equations | null | null | https://doi.org/10.1002/sim.4067 | https://onlinelibrary.wiley.com/doi/epdf/10.1002/sim.4067 | Multiple imputation using chained equations: issues and guidance for practice | Multiple imputation by chained equations (MICE) is a flexible and practical approach to handling missing data. We describe the principles of the method and show how to impute categorical and quantitative variables, including skewed variables. We give guidance on how to specify the imputation model and how many imputati... | ['Ian R. White', 'Patrick Royston', 'Angela M. Wood'] | 2010-11-30 | null | null | null | statistics-in-medicine-304377399-2011-2010-11 | ['multivariate-time-series-imputation'] | ['time-series'] | [ 2.42353693e-01 -8.53450447e-02 -6.93105102e-01 -1.19870460e+00
-6.99065685e-01 -3.22278380e-01 -4.12581354e-01 3.07090551e-01
-4.04177338e-01 1.47757697e+00 6.66230559e-01 -6.69231355e-01
-5.23083091e-01 -6.26137614e-01 -5.92697144e-01 -3.98218870e-01
-1.67880598e-02 7.36963034e-01 -8.15479815e-01 1.13495782... | [7.835690975189209, 4.913211822509766] |
e509197d-3b8d-43c4-83e6-c3a310a7d12d | fighting-noise-and-imbalance-in-action-unit | 2303.02994 | null | https://arxiv.org/abs/2303.02994v1 | https://arxiv.org/pdf/2303.02994v1.pdf | Fighting noise and imbalance in Action Unit detection problems | Action Unit (AU) detection aims at automatically caracterizing facial expressions with the muscular activations they involve. Its main interest is to provide a low-level face representation that can be used to assist higher level affective computing tasks learning. Yet, it is a challenging task. Indeed, the available d... | ['Kevin Bailly', 'Arnaud Dapogny', 'Gauthier Tallec'] | 2023-03-06 | null | null | null | null | ['action-unit-detection'] | ['computer-vision'] | [ 1.99674413e-01 3.09549093e-01 -1.62386551e-01 -5.42604744e-01
-7.77684093e-01 -3.02071035e-01 2.67934293e-01 -4.60314751e-02
-2.73588598e-01 7.24895000e-01 3.07347775e-01 6.07656538e-01
2.52941310e-01 -3.63807023e-01 -3.87132853e-01 -9.82760429e-01
1.42313004e-01 -1.34242207e-01 -3.73726010e-01 -2.27851644... | [13.613018035888672, 1.6957881450653076] |
ef48d4f2-8336-4b54-8158-ab4aa65d5dec | harnessing-spatial-homogeneity-of | 2007.11899 | null | https://arxiv.org/abs/2007.11899v1 | https://arxiv.org/pdf/2007.11899v1.pdf | Harnessing spatial homogeneity of neuroimaging data: patch individual filter layers for CNNs | Neuroimaging data, e.g. obtained from magnetic resonance imaging (MRI), is comparably homogeneous due to (1) the uniform structure of the brain and (2) additional efforts to spatially normalize the data to a standard template using linear and non-linear transformations. Convolutional neural networks (CNNs), in contrast... | ['Jan Philipp Albrecht', 'Martin Weygandt', 'Kerstin Ritter', 'Friedemann Paul', 'Fabian Eitel'] | 2020-07-23 | null | null | null | null | ['alzheimer-s-disease-detection'] | ['medical'] | [ 3.81176680e-01 4.68559027e-01 1.40291050e-01 -7.96301126e-01
-4.70455945e-01 -2.03014284e-01 7.08375514e-01 2.20213234e-01
-9.73940611e-01 7.07987309e-01 4.30378318e-01 -7.48256743e-02
-1.31486997e-01 -5.90401590e-01 -8.90952706e-01 -4.59764212e-01
-6.43853724e-01 5.49775064e-01 2.69572645e-01 5.52242212... | [14.293983459472656, -2.0069308280944824] |
f0f75550-8bdc-4b27-b082-a5db643bd1c1 | ced-catalog-extraction-from-documents | 2304.14662 | null | https://arxiv.org/abs/2304.14662v1 | https://arxiv.org/pdf/2304.14662v1.pdf | CED: Catalog Extraction from Documents | Sentence-by-sentence information extraction from long documents is an exhausting and error-prone task. As the indicator of document skeleton, catalogs naturally chunk documents into segments and provide informative cascade semantics, which can help to reduce the search space. Despite their usefulness, catalogs are hard... | ['Wenliang Chen', 'Pingfu Chao', 'Baoxing Huai', 'Zhefeng Wang', 'Mengsong Wu', 'Junfei Ren', 'Zijian Yu', 'Zechang Li', 'Guoliang Zhang', 'Tong Zhu'] | 2023-04-28 | null | null | null | null | ['catalog-extraction'] | ['natural-language-processing'] | [-2.14897189e-03 -5.69568425e-02 -4.99782264e-01 -4.72500652e-01
-1.19604707e+00 -9.71367955e-01 5.88894844e-01 2.63491571e-01
-3.37450594e-01 7.23137259e-01 3.37551773e-01 -2.07653761e-01
1.14598431e-01 -6.50426209e-01 -5.46199024e-01 -3.51614356e-01
4.28973258e-01 6.50480270e-01 5.40452659e-01 -2.12060735... | [10.982760429382324, 8.669939994812012] |
cf74a701-789a-4b1f-b6b0-ffe719142de3 | unispeech-sat-universal-speech-representation | 2110.05752 | null | https://arxiv.org/abs/2110.05752v1 | https://arxiv.org/pdf/2110.05752v1.pdf | UniSpeech-SAT: Universal Speech Representation Learning with Speaker Aware Pre-Training | Self-supervised learning (SSL) is a long-standing goal for speech processing, since it utilizes large-scale unlabeled data and avoids extensive human labeling. Recent years witness great successes in applying self-supervised learning in speech recognition, while limited exploration was attempted in applying SSL for mod... | ['Xiangzhan Yu', 'Jinyu Li', 'Furu Wei', 'Yao Qian', 'Jian Wu', 'Shujie Liu', 'Zhuo Chen', 'Zhengyang Chen', 'Chengyi Wang', 'Yu Wu', 'Sanyuan Chen'] | 2021-10-12 | null | null | null | null | ['speaker-identification'] | ['speech'] | [ 5.04964054e-01 8.47612098e-02 -3.08091223e-01 -8.74867499e-01
-1.30692422e+00 -2.65431523e-01 5.31200945e-01 2.10087951e-02
-3.83297652e-01 6.00141406e-01 5.23729563e-01 -4.39610571e-01
2.64453422e-02 -7.00186566e-02 -4.86587107e-01 -7.86177993e-01
6.43215049e-03 2.18259394e-01 4.91697788e-02 -3.01693469... | [14.477108001708984, 6.301933288574219] |
36d5701a-82f1-441a-b0b7-a7d09f783dce | opal-occlusion-pattern-aware-loss-for | 2203.02231 | null | https://arxiv.org/abs/2203.02231v3 | https://arxiv.org/pdf/2203.02231v3.pdf | OPAL: Occlusion Pattern Aware Loss for Unsupervised Light Field Disparity Estimation | Light field disparity estimation is an essential task in computer vision with various applications. Although supervised learning-based methods have achieved both higher accuracy and efficiency than traditional optimization-based methods, the dependency on ground-truth disparity for training limits the overall generaliz... | ['Tao Yu', 'Haoqian Wang', 'Chao Deng', 'Jingyao Wu', 'Jiayin Zhao', 'Peng Li'] | 2022-03-04 | null | null | null | null | ['disparity-estimation'] | ['computer-vision'] | [ 2.89479494e-01 -2.87865400e-01 -1.65499225e-01 -6.02357328e-01
-6.73429012e-01 6.03913143e-02 2.00015366e-01 1.98034272e-02
-3.93268108e-01 8.74918103e-01 -2.03633443e-01 -1.74396664e-01
-1.37505665e-01 -9.50403035e-01 -5.15967429e-01 -9.26129580e-01
3.14515054e-01 3.55040818e-01 4.77371365e-01 2.64811546... | [9.307449340820312, -2.470959424972534] |
cd447d01-1019-430f-8615-2564e94ac66b | image-steganography-based-on-style-transfer | 2203.04500 | null | https://arxiv.org/abs/2203.04500v1 | https://arxiv.org/pdf/2203.04500v1.pdf | Image Steganography based on Style Transfer | Image steganography is the art and science of using images as cover for covert communications. With the development of neural networks, traditional image steganography is more likely to be detected by deep learning-based steganalysis. To improve upon this, we propose image steganography network based on style transfer,... | ['Yaofei Wang', 'Jian Wang', 'Cong Yu', 'Yu Zhang', 'Donghui Hu'] | 2022-03-09 | null | null | null | null | ['steganalysis', 'image-stylization', 'image-steganography'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.16384995e+00 6.00608528e-01 2.94993129e-02 5.86726293e-02
7.18988776e-02 -4.72580910e-01 6.72357559e-01 -1.00522995e+00
2.14215256e-02 4.10338789e-01 1.46255419e-01 -5.19763529e-01
8.06079447e-01 -1.01406741e+00 -1.18560994e+00 -8.69154155e-01
-7.80669749e-02 -6.43299073e-02 -1.95591524e-01 -3.43762904... | [4.306353569030762, 8.051715850830078] |
b367d379-6264-48b9-981e-e2510f230109 | direct-photometric-alignment-by-mesh | null | null | http://openaccess.thecvf.com/content_cvpr_2017/html/Lin_Direct_Photometric_Alignment_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Lin_Direct_Photometric_Alignment_CVPR_2017_paper.pdf | Direct Photometric Alignment by Mesh Deformation | The choice of motion models is vital in applications like image/video stitching and video stabilization. Conventional methods explored different approaches ranging from simple global parametric models to complex per-pixel optical flow. Mesh-based warping methods achieve a good balance between computational complexity a... | ['Loong-Fah Cheong', 'Shuaicheng Liu', 'Nianjuan Jiang', 'Kaimo Lin', 'Minh Do', 'Jiangbo Lu'] | 2017-07-01 | null | null | null | cvpr-2017-7 | ['image-stitching', 'video-stabilization'] | ['computer-vision', 'computer-vision'] | [ 3.93860161e-01 -6.00267589e-01 -2.23179594e-01 1.12712957e-01
-5.13102591e-01 -6.16701901e-01 5.79373002e-01 -3.76273036e-01
-1.89176366e-01 4.12325591e-01 -6.88243983e-03 4.42966633e-02
-1.37675991e-02 -4.39947873e-01 -6.47782743e-01 -9.66643810e-01
4.62107688e-01 1.53363317e-01 5.36264658e-01 -3.39824677... | [9.349893569946289, -2.336008071899414] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.