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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
57df171a-3ec6-41a2-9a22-1a031a1d6053 | complex-query-answering-on-eventuality | 2305.19068 | null | https://arxiv.org/abs/2305.19068v1 | https://arxiv.org/pdf/2305.19068v1.pdf | Complex Query Answering on Eventuality Knowledge Graph with Implicit Logical Constraints | Querying incomplete knowledge graphs (KGs) using deep learning approaches can naturally leverage the reasoning and generalization ability to learn to infer better answers. Traditional neural complex query answering (CQA) approaches mostly work on entity-centric KGs. However, in the real world, we also need to make logi... | ['Yangqiu Song', 'Chen Luo', 'Weiqi Wang', 'Xin Liu', 'Jiaxin Bai'] | 2023-05-30 | null | null | null | null | ['complex-query-answering', 'knowledge-graphs'] | ['knowledge-base', 'knowledge-base'] | [-1.03564188e-01 3.04280072e-01 -4.55631673e-01 -5.88738441e-01
-5.10128796e-01 -6.80859804e-01 4.49417293e-01 5.42043865e-01
-1.22586600e-01 7.74522185e-01 1.29052550e-01 -7.25533962e-01
-3.23545456e-01 -1.65602946e+00 -1.34859693e+00 -8.86997301e-03
-3.42442214e-01 4.83059615e-01 3.50710630e-01 -3.61981750... | [9.209332466125488, 7.655979156494141] |
50834c1f-d826-482a-8457-2bae91c6144d | interbert-vision-and-language-interaction-for | 2003.13198 | null | https://arxiv.org/abs/2003.13198v4 | https://arxiv.org/pdf/2003.13198v4.pdf | InterBERT: Vision-and-Language Interaction for Multi-modal Pretraining | Multi-modal pretraining for learning high-level multi-modal representation is a further step towards deep learning and artificial intelligence. In this work, we propose a novel model, namely InterBERT (BERT for Interaction), which is the first model of our series of multimodal pretraining methods M6 (MultiModality-to-M... | ['Jie Liu', 'An Yang', 'Junyang Lin', 'Hongxia Yang', 'Yichang Zhang', 'Jingren Zhou'] | 2020-03-30 | null | null | null | null | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 1.68164968e-01 -1.87159494e-01 -3.43607187e-01 -3.19762409e-01
-1.45276427e+00 -5.34750879e-01 9.74669337e-01 -4.76411730e-01
-7.17754066e-01 1.06263436e-01 2.85728782e-01 -4.44814861e-01
1.64146096e-01 -4.48218912e-01 -1.15835607e+00 -7.16422975e-01
4.55973744e-01 9.67764139e-01 2.30997205e-01 -4.06651556... | [10.920677185058594, 1.5175108909606934] |
d76412a3-103e-4fce-85dd-8f04e126bdcf | defext-a-semi-supervised-definition | 1606.02514 | null | http://arxiv.org/abs/1606.02514v1 | http://arxiv.org/pdf/1606.02514v1.pdf | DefExt: A Semi Supervised Definition Extraction Tool | We present DefExt, an easy to use semi supervised Definition Extraction Tool.
DefExt is designed to extract from a target corpus those textual fragments
where a term is explicitly mentioned together with its core features, i.e. its
definition. It works on the back of a Conditional Random Fields based
sequential labelin... | ['Luis Espinosa-Anke', 'Roberto Carlini', 'Francesco Ronzano', 'Horacio Saggion'] | 2016-06-08 | null | null | null | null | ['definition-extraction'] | ['natural-language-processing'] | [ 3.96091133e-01 2.51561165e-01 -4.65598911e-01 -4.48817194e-01
-9.64195192e-01 -1.21278679e+00 8.95501971e-01 3.12171876e-01
-3.98502141e-01 1.04483104e+00 1.07816875e-01 -7.99840629e-01
-2.14279294e-02 -5.80796301e-01 -3.27922523e-01 -4.16604519e-01
9.05994400e-02 6.81082904e-01 3.18545550e-01 -3.14216256... | [9.915082931518555, 9.339882850646973] |
7712f27c-4a1e-4827-97ae-21dc419929a8 | retrieving-signals-in-the-frequency-domain | null | null | https://openreview.net/forum?id=BylB4kBtwB | https://openreview.net/pdf?id=BylB4kBtwB | Retrieving Signals in the Frequency Domain with Deep Complex Extractors | Recent advances have made it possible to create deep complex-valued neural networks. Despite this progress, the potential power of fully complex intermediate computations and representations has not yet been explored for many challenging learning problems. Building on recent advances, we propose a novel mechanism for e... | ['Christopher J Pal', 'Negar Rostamzadeh', 'Jonathan Binas', 'Mirco Ravanelli', 'Ying Zhang', 'Ousmane Dia', 'Olexa Bilaniuk', 'Chiheb Trabelsi'] | 2019-09-25 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 5.43214679e-01 4.30316925e-02 2.19917312e-01 -3.65666181e-01
-9.15070057e-01 -5.62803507e-01 6.88942671e-01 2.89726764e-01
-6.47505820e-01 6.64374828e-01 1.67212337e-02 -3.44751286e-03
-5.64218342e-01 -5.97423375e-01 -6.91778541e-01 -6.92794025e-01
-7.60141075e-01 -3.28156084e-01 9.47427824e-02 -4.37035650... | [15.357429504394531, 5.563119411468506] |
feea9126-f9bd-4cc2-a76d-7f5ded4016d9 | modeling-human-like-concept-learning-with | 2306.02797 | null | https://arxiv.org/abs/2306.02797v1 | https://arxiv.org/pdf/2306.02797v1.pdf | Modeling Human-like Concept Learning with Bayesian Inference over Natural Language | We model learning of abstract symbolic concepts by performing Bayesian inference over utterances in natural language. For efficient inference, we use a large language model as a proposal distribution. We fit a prior to human data to better model human learners, and evaluate on both generative and logical concepts. | ['Kevin Ellis'] | 2023-06-05 | null | null | null | null | ['bayesian-inference'] | ['methodology'] | [-1.14649840e-01 5.02451479e-01 -2.37105951e-01 -9.59574699e-01
-5.56533098e-01 -5.05864084e-01 1.03550422e+00 3.65333110e-01
-7.15198934e-01 8.20496976e-01 2.87894309e-01 -7.63371587e-01
9.48256329e-02 -1.01891768e+00 -1.04028392e+00 -4.00162749e-02
2.24851351e-02 1.17248380e+00 3.84931415e-01 2.18835622... | [8.870331764221191, 7.037091255187988] |
afd5be5b-b647-48d3-aeed-b25aaf105f3a | relational-graph-learning-on-visual-and | 2011.01619 | null | https://arxiv.org/abs/2011.01619v2 | https://arxiv.org/pdf/2011.01619v2.pdf | Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery | Automatic surgical gesture recognition is fundamentally important to enable intelligent cognitive assistance in robotic surgery. With recent advancement in robot-assisted minimally invasive surgery, rich information including surgical videos and robotic kinematics can be recorded, which provide complementary knowledge ... | ['Pheng Ann Heng', 'Yueming Jin', 'Jie Ying Wu', 'Yonghao Long', 'Qi Dou', 'Yun-hui Liu', 'Mathias Unberath', 'Bo Lu'] | 2020-11-03 | null | null | null | null | ['surgical-gesture-recognition'] | ['medical'] | [-1.59306869e-01 8.09853226e-02 -7.02840865e-01 -9.05168876e-02
-7.36480057e-01 -6.22959375e-01 3.40254575e-01 -1.63481101e-01
-5.60423732e-01 2.44792879e-01 7.74709582e-01 -4.16899472e-01
-5.10698080e-01 -2.72496074e-01 -6.59542382e-01 -6.64628863e-01
-4.27034527e-01 1.00293815e-01 -2.55770504e-01 -1.77955672... | [14.04504680633545, -3.3665788173675537] |
71fcff22-5522-48db-bc58-875293cca5cf | pre-training-intent-aware-encoders-for-zero | 2305.14827 | null | https://arxiv.org/abs/2305.14827v1 | https://arxiv.org/pdf/2305.14827v1.pdf | Pre-training Intent-Aware Encoders for Zero- and Few-Shot Intent Classification | Intent classification (IC) plays an important role in task-oriented dialogue systems as it identifies user intents from given utterances. However, models trained on limited annotations for IC often suffer from a lack of generalization to unseen intent classes. We propose a novel pre-training method for text encoders th... | ['Vittorio Castelli', 'Yi Zhang', 'Salvatore Romeo', 'Raphael Shu', 'Nikolaos Pappas', 'Elman Mansimov', 'James Gung', 'Mujeen Sung'] | 2023-05-24 | null | null | null | null | ['intent-classification', 'task-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing'] | [ 5.28951347e-01 3.45824152e-01 -1.64398491e-01 -7.92366147e-01
-6.97680235e-01 -4.03037906e-01 8.55235934e-01 2.55842656e-01
-7.01957285e-01 4.78386641e-01 7.46688187e-01 -3.22777867e-01
4.88244265e-01 -4.52097148e-01 -3.80829513e-01 -1.70533359e-01
5.39752245e-02 6.63921356e-01 -7.44035840e-02 -2.33146459... | [12.439800262451172, 7.611617088317871] |
620ef3c3-15f1-4ec2-85b2-b22345a70072 | on-arrhythmia-detection-by-deep-learning-and | 1904.00138 | null | http://arxiv.org/abs/1904.00138v4 | http://arxiv.org/pdf/1904.00138v4.pdf | On Arrhythmia Detection by Deep Learning and Multidimensional Representation | An electrocardiogram (ECG) is a time-series signal that is represented by
one-dimensional (1-D) data. Higher dimensional representation contains more
information that is accessible for feature extraction. Hidden variables such as
frequency relation and morphology of segment is not directly accessible in the
time domain... | ['S. Wibowo', 'C. Hao', 'M. Majmudar', 'K. S. Rajput'] | 2019-03-30 | null | null | null | null | ['arrhythmia-detection', 'electrocardiography-ecg'] | ['medical', 'methodology'] | [ 3.18363190e-01 -2.46201959e-02 1.32254988e-01 -4.03489739e-01
-5.79815209e-01 -3.25028569e-01 -1.19120695e-01 2.05370396e-01
-2.76147604e-01 9.84418809e-01 -1.52309567e-01 -4.75915611e-01
-3.45829546e-01 -6.10704482e-01 -1.82939231e-01 -5.78358591e-01
-8.24202538e-01 1.01487100e-01 -3.36063087e-01 8.67958292... | [14.3155517578125, 3.2941038608551025] |
f4141c4a-f5c9-43d6-8bdb-f53124f8e1d5 | challenges-and-considerations-with-code-mixed | 2106.07823 | null | https://arxiv.org/abs/2106.07823v1 | https://arxiv.org/pdf/2106.07823v1.pdf | Challenges and Considerations with Code-Mixed NLP for Multilingual Societies | Multilingualism refers to the high degree of proficiency in two or more languages in the written and oral communication modes. It often results in language mixing, a.k.a. code-mixing, when a multilingual speaker switches between multiple languages in a single utterance of a text or speech. This paper discusses the curr... | ['Mayank Singh', 'Vivek Srivastava'] | 2021-06-15 | null | null | null | null | ['multilingual-nlp'] | ['natural-language-processing'] | [-3.17113817e-01 3.49605381e-01 -3.64089549e-01 -6.62273727e-04
-9.84641850e-01 -9.18844461e-01 9.41214383e-01 2.01802045e-01
-2.89366752e-01 1.00157225e+00 8.14534843e-01 -9.08054471e-01
2.49878973e-01 -3.46453786e-01 -5.14105737e-01 -4.32621479e-01
2.67468959e-01 5.26776969e-01 -6.65127158e-01 -7.15446353... | [9.095099449157715, 10.447872161865234] |
728b44d3-46d8-4fe8-96e1-18d985dd8a8e | learning-to-perceive-in-deep-model-free | 2301.03730 | null | https://arxiv.org/abs/2301.03730v2 | https://arxiv.org/pdf/2301.03730v2.pdf | Learning to Perceive in Deep Model-Free Reinforcement Learning | This work proposes a novel model-free Reinforcement Learning (RL) agent that is able to learn how to complete an unknown task having access to only a part of the input observation. We take inspiration from the concepts of visual attention and active perception that are characteristic of humans and tried to apply them t... | ['Francisco S. Melo', 'Alberto Sardinha', 'Gonçalo Querido'] | 2023-01-10 | null | null | null | null | ['hard-attention', 'atari-games'] | ['methodology', 'playing-games'] | [ 1.23021556e-02 4.64236826e-01 9.59751457e-02 2.22600043e-01
-6.08095154e-02 -3.99900228e-01 8.96478176e-01 -1.68231770e-01
-1.08056664e+00 6.96096957e-01 9.89686102e-02 -3.37224573e-01
-1.98165208e-01 -8.46231699e-01 -6.63136542e-01 -7.68501103e-01
-6.99791461e-02 6.31821215e-01 5.75358272e-01 -7.03854382... | [4.060299396514893, 1.344606637954712] |
ba7e5649-2a01-4561-9539-f8d025529c5d | structure-aware-generation-network-for-recipe | 2009.00944 | null | https://arxiv.org/abs/2009.00944v1 | https://arxiv.org/pdf/2009.00944v1.pdf | Structure-Aware Generation Network for Recipe Generation from Images | Sharing food has become very popular with the development of social media. For many real-world applications, people are keen to know the underlying recipes of a food item. In this paper, we are interested in automatically generating cooking instructions for food. We investigate an open research task of generating cooki... | ['Steven C. H. Hoi', 'Chunyan Miao', 'Hao Wang', 'Guosheng Lin'] | 2020-09-02 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/5757_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123720358.pdf | eccv-2020-8 | ['recipe-generation'] | ['miscellaneous'] | [ 6.55268788e-01 2.29520723e-01 -5.51504642e-02 -4.48306799e-01
-5.50855100e-01 -6.06859028e-01 4.16814804e-01 2.91598409e-01
-2.21581906e-02 4.76501077e-01 6.80369020e-01 -9.49195102e-02
5.02651274e-01 -1.25800645e+00 -1.15691102e+00 -7.15245605e-01
3.80210727e-01 1.13284141e-01 -5.79141155e-02 -2.33628094... | [11.521129608154297, 4.449831485748291] |
b3ae3550-5905-4ba7-83ac-acd9d5fc9829 | comparison-of-binaural-rtf-vector-based | 2104.05079 | null | https://arxiv.org/abs/2104.05079v2 | https://arxiv.org/pdf/2104.05079v2.pdf | Comparison of Binaural RTF-Vector-Based Direction of Arrival Estimation Methods Exploiting an External Microphone | In this paper we consider a binaural hearing aid setup, where in addition to the head-mounted microphones an external microphone is available. For this setup, we investigate the performance of several relative transfer function (RTF) vector estimation methods to estimate the direction of arrival (DOA) of the target spe... | ['Simon Doclo', 'Daniel Fejgin'] | 2021-04-11 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 2.01294407e-01 -3.98540914e-01 1.09714890e+00 4.60492680e-03
-1.15554368e+00 -3.45734745e-01 4.99574661e-01 -1.08135501e-02
-5.04573524e-01 4.33574170e-01 6.54046237e-01 -2.95908958e-01
-2.05721542e-01 -2.35004723e-01 -4.48924452e-01 -1.20482683e+00
-2.55229436e-02 -8.61952901e-02 1.30352661e-01 2.16363976... | [15.12881851196289, 5.768864631652832] |
0590429a-c70a-4809-91cd-ade1c328834d | lightning-fast-video-anomaly-detection-via | 2211.15597 | null | https://arxiv.org/abs/2211.15597v1 | https://arxiv.org/pdf/2211.15597v1.pdf | Lightning Fast Video Anomaly Detection via Adversarial Knowledge Distillation | We propose a very fast frame-level model for anomaly detection in video, which learns to detect anomalies by distilling knowledge from multiple highly accurate object-level teacher models. To improve the fidelity of our student, we distill the low-resolution anomaly maps of the teachers by jointly applying standard and... | ['Mubarak Shah', 'Fahad Shahbaz Khan', 'Radu Tudor Ionescu', 'Dana Dascalescu', 'Florinel-Alin Croitoru', 'Nicolae-Catalin Ristea'] | 2022-11-28 | null | null | null | null | ['video-anomaly-detection'] | ['computer-vision'] | [-3.06116082e-02 -3.80913764e-02 1.60036474e-01 -1.62107795e-01
-9.30274546e-01 -4.00305748e-01 6.58879876e-01 1.31176010e-01
-5.68817854e-01 3.07914853e-01 -7.76831955e-02 -3.67057145e-01
4.15958285e-01 -3.88094723e-01 -8.89414430e-01 -3.73841166e-01
-4.00892168e-01 3.64249468e-01 9.57174361e-01 2.58815363... | [7.862059593200684, 1.5878725051879883] |
84243748-d6fe-44d2-9177-1bc4b3ff3575 | object-pose-estimation-with-statistical | 2303.12246 | null | https://arxiv.org/abs/2303.12246v1 | https://arxiv.org/pdf/2303.12246v1.pdf | Object Pose Estimation with Statistical Guarantees: Conformal Keypoint Detection and Geometric Uncertainty Propagation | The two-stage object pose estimation paradigm first detects semantic keypoints on the image and then estimates the 6D pose by minimizing reprojection errors. Despite performing well on standard benchmarks, existing techniques offer no provable guarantees on the quality and uncertainty of the estimation. In this paper, ... | ['Marco Pavone', 'Heng Yang'] | 2023-03-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_Object_Pose_Estimation_With_Statistical_Guarantees_Conformal_Keypoint_Detection_and_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Object_Pose_Estimation_With_Statistical_Guarantees_Conformal_Keypoint_Detection_and_CVPR_2023_paper.pdf | cvpr-2023-1 | ['keypoint-detection'] | ['computer-vision'] | [-6.28095195e-02 5.52398145e-01 -1.55838445e-01 -4.68887165e-02
-1.36821806e+00 -9.66100991e-01 3.68780315e-01 2.34081283e-01
-7.05207735e-02 5.19729257e-01 -2.54404038e-01 5.44106402e-02
-5.52465737e-01 -6.31933749e-01 -1.34481502e+00 -7.11660743e-01
-9.63575467e-02 9.41719353e-01 2.48080745e-01 3.01317215... | [7.5230231285095215, -2.661745309829712] |
a515f1c7-752e-4cf3-ad2f-1725a1a333ee | progressive-attention-memory-network-for | 1904.08607 | null | http://arxiv.org/abs/1904.08607v1 | http://arxiv.org/pdf/1904.08607v1.pdf | Progressive Attention Memory Network for Movie Story Question Answering | This paper proposes the progressive attention memory network (PAMN) for movie
story question answering (QA). Movie story QA is challenging compared to VQA in
two aspects: (1) pinpointing the temporal parts relevant to answer the question
is difficult as the movies are typically longer than an hour, (2) it has both
vide... | ['Kyung-Su Kim', 'Sungjin Kim', 'Junyeong Kim', 'Minuk Ma', 'Chang D. Yoo'] | 2019-04-18 | progressive-attention-memory-network-for-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Kim_Progressive_Attention_Memory_Network_for_Movie_Story_Question_Answering_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Kim_Progressive_Attention_Memory_Network_for_Movie_Story_Question_Answering_CVPR_2019_paper.pdf | cvpr-2019-6 | ['video-story-qa'] | ['computer-vision'] | [ 2.27082804e-01 -6.89617470e-02 -1.07211478e-01 -2.72177786e-01
-1.14106750e+00 -6.59983754e-01 5.15778005e-01 3.15766871e-01
-2.54665613e-01 6.06642008e-01 7.66845942e-01 -9.44551229e-02
-2.92267889e-01 -5.67165315e-01 -6.76372409e-01 -2.96714723e-01
2.84189850e-01 5.21095574e-01 8.82404208e-01 -4.02450919... | [10.45472240447998, 1.0627299547195435] |
4bc8b7c5-958b-466a-a733-50820f02dd3e | maximum-entropy-regularized-multi-goal | 1905.08786 | null | https://arxiv.org/abs/1905.08786v3 | https://arxiv.org/pdf/1905.08786v3.pdf | Maximum Entropy-Regularized Multi-Goal Reinforcement Learning | In Multi-Goal Reinforcement Learning, an agent learns to achieve multiple goals with a goal-conditioned policy. During learning, the agent first collects the trajectories into a replay buffer, and later these trajectories are selected randomly for replay. However, the achieved goals in the replay buffer are often biase... | ['Volker Tresp', 'Rui Zhao', 'Xudong Sun'] | 2019-05-21 | null | null | null | null | ['multi-goal-reinforcement-learning'] | ['methodology'] | [-1.90975770e-01 4.08059448e-01 -2.10363969e-01 -2.77250916e-01
-1.15857100e+00 -4.80743438e-01 4.80930358e-01 2.09746197e-01
-9.45355177e-01 1.09143686e+00 5.40933073e-01 1.85334235e-01
-4.97509688e-01 -7.30979979e-01 -7.29201972e-01 -8.16443503e-01
-2.59911716e-01 6.90558374e-01 8.45894292e-02 -2.94291973... | [4.049983978271484, 1.8802367448806763] |
8f2268e9-90df-43e3-b4cf-bf355a523ba3 | plgan-generative-adversarial-networks-for | 2204.07243 | null | https://arxiv.org/abs/2204.07243v1 | https://arxiv.org/pdf/2204.07243v1.pdf | PLGAN: Generative Adversarial Networks for Power-Line Segmentation in Aerial Images | Accurate segmentation of power lines in various aerial images is very important for UAV flight safety. The complex background and very thin structures of power lines, however, make it an inherently difficult task in computer vision. This paper presents PLGAN, a simple yet effective method based on generative adversaria... | ['Song Wang', 'XiaoFeng Wang', 'Rabab Abdelfattah'] | 2022-04-14 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 4.63409752e-01 -1.61317140e-01 2.20538482e-01 -3.67962271e-02
-2.73151785e-01 -1.34381199e+00 6.46151900e-02 -5.54228485e-01
1.07877508e-01 5.82755208e-01 -6.83639884e-01 -4.06342566e-01
-4.92985034e-03 -1.35583651e+00 -6.31892025e-01 -7.88081050e-01
1.61628798e-02 -1.86426565e-02 3.32944304e-01 -2.91162312... | [8.866540908813477, -0.9774723052978516] |
5532c112-6270-4c1a-abb8-afcb0dcbb0dd | cuni-submission-to-mt4all-shared-task | null | null | https://aclanthology.org/2022.sigul-1.10 | https://aclanthology.org/2022.sigul-1.10.pdf | CUNI Submission to MT4All Shared Task | This paper describes our submission to the MT4All Shared Task in unsupervised machine translation from English to Ukrainian, Kazakh and Georgian in the legal domain. In addition to the standard pipeline for unsupervised training (pretraining followed by denoising and back-translation), we used supervised training on a ... | ['Ondrej Bojar', 'Ivana Kvapilíková'] | null | null | null | null | sigul-lrec-2022-6 | ['unsupervised-machine-translation'] | ['natural-language-processing'] | [ 3.38812590e-01 3.29725534e-01 -3.78703266e-01 -6.24473691e-01
-1.49241376e+00 -8.68847966e-01 1.02162933e+00 -1.38640150e-01
-7.47340679e-01 1.22097516e+00 4.01513100e-01 -9.20907438e-01
3.45712155e-01 -2.33955085e-01 -5.18831611e-01 -3.70287955e-01
3.19551468e-01 1.38423622e+00 -2.35297769e-01 -6.17896676... | [11.505001068115234, 10.414359092712402] |
73ceab22-a7db-46a4-b482-e3076055d6f6 | oakink-a-large-scale-knowledge-repository-for | 2203.15709 | null | https://arxiv.org/abs/2203.15709v1 | https://arxiv.org/pdf/2203.15709v1.pdf | OakInk: A Large-scale Knowledge Repository for Understanding Hand-Object Interaction | Learning how humans manipulate objects requires machines to acquire knowledge from two perspectives: one for understanding object affordances and the other for learning human's interactions based on the affordances. Even though these two knowledge bases are crucial, we find that current databases lack a comprehensive a... | ['Cewu Lu', 'Liu Liu', 'Anran Xu', 'Fei Wu', 'Xinyu Zhan', 'Kailin Li', 'Lixin Yang'] | 2022-03-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yang_OakInk_A_Large-Scale_Knowledge_Repository_for_Understanding_Hand-Object_Interaction_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yang_OakInk_A_Large-Scale_Knowledge_Repository_for_Understanding_Hand-Object_Interaction_CVPR_2022_paper.pdf | cvpr-2022-1 | ['grasp-generation'] | ['computer-vision'] | [-8.73998478e-02 -1.65346369e-01 -2.05885723e-01 -3.15659285e-01
-2.16666162e-01 -8.71745527e-01 2.27700770e-01 -3.79704274e-02
-2.74024978e-02 6.02350950e-01 3.52196783e-01 1.15024306e-01
-3.15694302e-01 -6.98430002e-01 -8.95229042e-01 -2.04213738e-01
-8.81826803e-02 7.34203517e-01 3.02959412e-01 -2.02394858... | [5.193013668060303, -0.11599580198526382] |
2d0e5ea9-bfdb-4ed7-a670-51bc4429badf | 2305-14550 | 2305.14550 | null | https://arxiv.org/abs/2305.14550v2 | https://arxiv.org/pdf/2305.14550v2.pdf | Sequence Modeling is a Robust Contender for Offline Reinforcement Learning | Offline reinforcement learning (RL) allows agents to learn effective, return-maximizing policies from a static dataset. Three major paradigms for offline RL are Q-Learning, Imitation Learning, and Sequence Modeling. A key open question is: which paradigm is preferred under what conditions? We study this question empiri... | ['Amy Zhang', 'Shagun Sodhani', 'Alborz Geramifard', 'Rohan Chitnis', 'Prajjwal Bhargava'] | 2023-05-23 | null | null | null | null | ['q-learning', 'open-question', 'offline-rl', 'd4rl'] | ['methodology', 'natural-language-processing', 'playing-games', 'robots'] | [-2.23346099e-01 -2.18770832e-01 -6.54088438e-01 -1.01821057e-01
-9.52686429e-01 -7.36432970e-01 5.31442225e-01 -1.32778183e-01
-8.99651766e-01 9.41085160e-01 3.98934871e-01 -5.97702920e-01
-3.01464766e-01 -9.90383327e-02 -7.78201461e-01 -4.99825954e-01
-4.57875222e-01 4.63474870e-01 7.59770870e-02 -4.04701531... | [4.008408069610596, 1.8415672779083252] |
764170a9-753d-433b-8f81-7e101cd87d9e | image-classification-using-sequence-of-pixels | 2209.11495 | null | https://arxiv.org/abs/2209.11495v1 | https://arxiv.org/pdf/2209.11495v1.pdf | Image Classification using Sequence of Pixels | This study compares sequential image classification methods based on recurrent neural networks. We describe methods based on recurrent neural networks such as Long-Short-Term memory(LSTM), bidirectional Long-Short-Term memory(BiLSTM) architectures, etc. We also review the state-of-the-art sequential image classificatio... | ['Gajraj Kuldeep'] | 2022-09-23 | null | null | null | null | ['sequential-image-classification'] | ['computer-vision'] | [ 5.03906190e-01 -1.62608296e-01 -6.83963299e-02 -2.95945317e-01
-2.44378954e-01 -5.56858927e-02 5.15091479e-01 -3.49181175e-01
-7.62724578e-01 6.95677936e-01 1.19063947e-02 -7.34402239e-01
2.40937471e-01 -6.52955532e-01 -6.18408322e-01 -8.49226773e-01
-2.16685031e-02 -1.62339211e-01 4.18782920e-01 -2.20752627... | [10.837899208068848, 6.245568752288818] |
44efb963-3ca9-4399-9174-cac3c99b59fd | situated-incremental-natural-language | null | null | https://aclanthology.org/C14-1170 | https://aclanthology.org/C14-1170.pdf | Situated Incremental Natural Language Understanding using a Multimodal, Linguistically-driven Update Model | null | ['Spyros Kousidis', 'David Schlangen', 'Casey Kennington'] | 2014-08-01 | situated-incremental-natural-language-1 | https://aclanthology.org/C14-1170 | https://aclanthology.org/C14-1170.pdf | coling-2014-8 | ['dialogue-management'] | ['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... | [-7.383481502532959, 3.5878829956054688] |
f5a9e98e-b33b-4cbb-be3c-a5c8736aef18 | morphological-development-at-the-evolutionary | 2010.14894 | null | https://arxiv.org/abs/2010.14894v2 | https://arxiv.org/pdf/2010.14894v2.pdf | Morphological Development at the Evolutionary Timescale: Robotic Developmental Evolution | Evolution and development operate at different timescales; generations for the one, a lifetime for the other. These two processes, the basis of much of life on earth, interact in many non-trivial ways, but their temporal hierarchy -- evolution overarching development -- is observed for most multicellular lifeforms. Whe... | ['Jun Tani', 'Fabien C. Y. Benureau'] | 2020-10-28 | null | null | null | null | ['developmental-learning'] | ['robots'] | [-2.33700544e-01 3.41230899e-01 1.43859208e-01 4.01284605e-01
5.48873782e-01 -6.54903352e-01 4.03459191e-01 -4.16943170e-02
-4.93805587e-01 8.64777744e-01 -4.62584645e-02 -1.14512660e-01
-3.49491626e-01 -7.95982957e-01 -6.63541615e-01 -9.77736890e-01
-5.49421728e-01 8.37117910e-01 4.08814520e-01 -6.84424460... | [5.646261215209961, 4.045917510986328] |
6b241911-a054-470f-8e07-12bb0935fb43 | learning-to-find-proofs-and-theorems-by | 2205.14229 | null | https://arxiv.org/abs/2205.14229v3 | https://arxiv.org/pdf/2205.14229v3.pdf | Learning to Find Proofs and Theorems by Learning to Refine Search Strategies: The Case of Loop Invariant Synthesis | We propose a new approach to automated theorem proving where an AlphaZero-style agent is self-training to refine a generic high-level expert strategy expressed as a nondeterministic program. An analogous teacher agent is self-training to generate tasks of suitable relevance and difficulty for the learner. This allows l... | ['André Platzer', 'Jonathan Laurent'] | 2022-05-27 | null | null | null | null | ['program-synthesis', 'automated-theorem-proving', 'automated-theorem-proving'] | ['computer-code', 'miscellaneous', 'reasoning'] | [ 4.72710937e-01 1.09578073e+00 -2.95757085e-01 -2.28240222e-01
-7.84098029e-01 -7.33675122e-01 6.84761107e-01 -4.02141958e-02
-1.38062788e-02 1.03850222e+00 -3.48731428e-01 -1.01082754e+00
2.12382004e-01 -1.04995263e+00 -1.02685833e+00 -9.71611664e-02
-4.04314548e-02 5.64292848e-01 2.46422902e-01 -4.11356241... | [8.492393493652344, 7.242042064666748] |
f61c5901-683f-4514-91bc-1c2e418e5f2a | weakly-supervised-3d-classification-of-chest | 2011.00149 | null | https://arxiv.org/abs/2011.00149v1 | https://arxiv.org/pdf/2011.00149v1.pdf | Weakly Supervised 3D Classification of Chest CT using Aggregated Multi-Resolution Deep Segmentation Features | Weakly supervised disease classification of CT imaging suffers from poor localization owing to case-level annotations, where even a positive scan can hold hundreds to thousands of negative slices along multiple planes. Furthermore, although deep learning segmentation and classification models extract distinctly unique ... | ['Joseph Y. Lo', 'Geoffrey D. Rubin', 'Maciej A. Mazurowski', 'Rui Hou', "Vincent M. D'Anniballe", 'Khrystyna Faryna', 'Fakrul I. Tushar', 'Anindo Saha'] | 2020-10-31 | null | null | null | null | ['3d-classification'] | ['computer-vision'] | [ 4.78730023e-01 4.66011256e-01 -4.21277463e-01 -5.48997164e-01
-9.62960541e-01 -7.02092648e-01 4.70074385e-01 5.12031019e-01
-1.77419603e-01 4.02889371e-01 2.19768062e-01 -6.81500435e-01
-3.81096184e-01 -6.48081839e-01 -2.85704255e-01 -7.47315526e-01
-2.09120527e-01 7.86081910e-01 4.21884924e-01 3.24799955... | [15.252848625183105, -2.2360198497772217] |
cbd33f01-f5ae-46f5-b179-5a59604db4f7 | discontinuous-constituency-and-bert-a-case-1 | 2203.01063 | null | https://arxiv.org/abs/2203.01063v2 | https://arxiv.org/pdf/2203.01063v2.pdf | Discontinuous Constituency and BERT: A Case Study of Dutch | In this paper, we set out to quantify the syntactic capacity of BERT in the evaluation regime of non-context free patterns, as occurring in Dutch. We devise a test suite based on a mildly context-sensitive formalism, from which we derive grammars that capture the linguistic phenomena of control verb nesting and verb ra... | ['Gijs Wijnholds', 'Konstantinos Kogkalidis'] | 2022-03-02 | null | https://aclanthology.org/2022.findings-acl.298 | https://aclanthology.org/2022.findings-acl.298.pdf | findings-acl-2022-5 | ['probing-language-models'] | ['natural-language-processing'] | [ 1.50905266e-01 2.81380147e-01 1.52466998e-01 -5.00007510e-01
-5.21405697e-01 -6.67539418e-01 5.43287933e-01 2.90110707e-01
-4.75005180e-01 8.12895954e-01 4.07270998e-01 -4.51240927e-01
-2.56069779e-01 -6.46266818e-01 -2.81932503e-01 -3.53216410e-01
-3.40218723e-01 7.19749153e-01 6.59057498e-01 -8.08844984... | [10.372519493103027, 9.389172554016113] |
0dbce5f7-781d-4314-b298-c420e939c4a2 | association-of-genomic-subtypes-of-lower | 1906.03720 | null | https://arxiv.org/abs/1906.03720v1 | https://arxiv.org/pdf/1906.03720v1.pdf | Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm | Recent analysis identified distinct genomic subtypes of lower-grade glioma tumors which are associated with shape features. In this study, we propose a fully automatic way to quantify tumor imaging characteristics using deep learning-based segmentation and test whether these characteristics are predictive of tumor geno... | ['Maciej A. Mazurowski', 'Mateusz Buda', 'Ashirbani Saha'] | 2019-06-09 | null | null | null | null | ['3d-medical-imaging-segmentation'] | ['medical'] | [-5.08019552e-02 3.37766737e-01 -1.03530072e-01 -2.34595954e-01
-1.10441208e+00 -7.76934862e-01 3.26920867e-01 7.93429255e-01
-6.01067841e-01 5.97806811e-01 2.90266424e-01 -5.40237129e-01
-4.80470389e-01 -8.79586816e-01 -4.90951031e-01 -1.27716851e+00
-5.50463915e-01 5.43490767e-01 -9.13270414e-02 6.10807016... | [14.861413955688477, -2.7600343227386475] |
c49c1e48-7204-41f0-89fd-5bf3c01e6f63 | swincross-cross-modal-swin-transformer-for | 2302.03861 | null | https://arxiv.org/abs/2302.03861v1 | https://arxiv.org/pdf/2302.03861v1.pdf | SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images | Radiotherapy (RT) combined with cetuximab is the standard treatment for patients with inoperable head and neck cancers. Segmentation of head and neck (H&N) tumors is a prerequisite for radiotherapy planning but a time-consuming process. In recent years, deep convolutional neural networks have become the de facto standa... | ['Quanzheng Li', 'Kuang Gong', 'Se-In Jang', 'Junyu Chen', 'Gary Y. Li'] | 2023-02-08 | null | null | null | null | ['tumor-segmentation'] | ['computer-vision'] | [ 2.14693516e-01 6.01489209e-02 -5.12778223e-01 -2.21287653e-01
-1.18501544e+00 -3.80024880e-01 5.79614937e-01 -1.38405010e-01
-6.95243299e-01 6.39296949e-01 2.61371762e-01 -4.37984198e-01
-2.97769874e-01 -7.48595297e-01 -4.79796380e-01 -1.09073329e+00
2.37652659e-01 7.34700620e-01 3.56591791e-01 -3.25658530... | [14.63736629486084, -2.423926591873169] |
95e2b55d-af5e-448e-af9b-1b2ded506bdb | evaluating-the-stability-of-semantic-concept | 2304.14864 | null | https://arxiv.org/abs/2304.14864v1 | https://arxiv.org/pdf/2304.14864v1.pdf | Evaluating the Stability of Semantic Concept Representations in CNNs for Robust Explainability | Analysis of how semantic concepts are represented within Convolutional Neural Networks (CNNs) is a widely used approach in Explainable Artificial Intelligence (XAI) for interpreting CNNs. A motivation is the need for transparency in safety-critical AI-based systems, as mandated in various domains like automated driving... | ['Korinna Bade', 'Christian Hellert', 'Gesina Schwalbe', 'Georgii Mikriukov'] | 2023-04-28 | null | null | null | null | ['dimensionality-reduction'] | ['methodology'] | [ 3.58227581e-01 3.45042318e-01 -5.33180647e-02 -4.94576871e-01
-1.79813877e-02 -5.03331423e-01 8.41045856e-01 5.42044044e-01
-4.55263555e-01 2.95359790e-01 1.46586329e-01 -5.39100826e-01
-8.18417192e-01 -7.84720838e-01 -4.42412108e-01 -4.58058327e-01
-1.48499280e-01 1.38568565e-01 2.26518363e-02 -3.22179735... | [8.911687850952148, 5.571674823760986] |
c6c8c4d4-b297-4a25-bb1e-28f5dbb65128 | one-class-classifiers-based-on-entropic | 1604.02477 | null | http://arxiv.org/abs/1604.02477v4 | http://arxiv.org/pdf/1604.02477v4.pdf | One-class classifiers based on entropic spanning graphs | One-class classifiers offer valuable tools to assess the presence of outliers
in data. In this paper, we propose a design methodology for one-class
classifiers based on entropic spanning graphs. Our approach takes into account
the possibility to process also non-numeric data by means of an embedding
procedure. The span... | ['Cesare Alippi', 'Lorenzo Livi'] | 2016-04-08 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [ 2.16885224e-01 2.74260193e-01 8.36086199e-02 -5.19732833e-01
-2.75996387e-01 -3.13371271e-01 5.59323013e-01 9.43618953e-01
-5.91663420e-01 6.32024229e-01 -5.80935955e-01 -9.10259560e-02
-6.77568793e-01 -9.64283884e-01 -8.16943467e-01 -8.54385316e-01
-2.78179556e-01 6.07915282e-01 1.87546797e-02 -1.63467288... | [7.961429119110107, 4.013911724090576] |
b1ab4ac6-51a1-4811-b3e8-faee5eadee4a | connotation-in-translation | null | null | https://aclanthology.org/W15-2903 | https://aclanthology.org/W15-2903.pdf | Connotation in Translation | null | ['Marine Carpuat'] | 2015-09-01 | null | null | null | ws-2015-9 | ['subjectivity-analysis'] | ['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... | [-7.176114082336426, 3.766986131668091] |
356a35d2-658f-4f1f-866e-94c4049a20b5 | k-means-clustering-and-ensemble-of | 1702.07333 | null | http://arxiv.org/abs/1702.07333v1 | http://arxiv.org/pdf/1702.07333v1.pdf | k-Means Clustering and Ensemble of Regressions: An Algorithm for the ISIC 2017 Skin Lesion Segmentation Challenge | This abstract briefly describes a segmentation algorithm developed for the
ISIC 2017 Skin Lesion Detection Competition hosted at [ref]. The objective of
the competition is to perform a segmentation (in the form of a binary mask
image) of skin lesions in dermoscopic images as close as possible to a
segmentation performe... | ['Monica Iglesias', 'David Alvarez'] | 2017-02-23 | null | null | null | null | ['skin-lesion-segmentation'] | ['medical'] | [ 8.20591688e-01 1.15110435e-01 -7.72752762e-02 -3.19997966e-01
-7.99807787e-01 -5.65172613e-01 4.40983534e-01 7.26745486e-01
-6.07609153e-01 3.06289345e-01 -1.25919953e-01 -3.57994407e-01
-6.77519143e-02 -6.08283818e-01 -2.78995335e-01 -8.91726196e-01
1.94863722e-01 4.65862334e-01 8.31941128e-01 4.02677834... | [15.558588981628418, -3.0003750324249268] |
7ef6231e-c8ca-408e-af76-278153bd1263 | an-efficient-and-layout-independent-automatic | 1909.01754 | null | https://arxiv.org/abs/1909.01754v4 | https://arxiv.org/pdf/1909.01754v4.pdf | An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector | This paper presents an efficient and layout-independent Automatic License Plate Recognition (ALPR) system based on the state-of-the-art YOLO object detector that contains a unified approach for license plate (LP) detection and layout classification to improve the recognition results using post-processing rules. The sys... | ['David Menotti', 'Gabriel R. Gonçalves', 'Rayson Laroca', 'William Robson Schwartz', 'Luiz A. Zanlorensi', 'Eduardo Todt'] | 2019-09-04 | null | null | null | null | ['license-plate-recognition', 'license-plate-detection'] | ['computer-vision', 'computer-vision'] | [-1.51997030e-01 -7.66757846e-01 5.56362830e-02 -2.70064652e-01
-1.20916784e+00 -7.23076522e-01 5.27334929e-01 -3.96992087e-01
-6.33906305e-01 2.91535646e-01 -2.75681108e-01 -1.74851611e-01
3.61209810e-01 -5.11630893e-01 -9.92902637e-01 -5.71951628e-01
2.52618611e-01 6.54728711e-01 8.29551995e-01 -1.58381268... | [9.840736389160156, -4.918113708496094] |
3de76c6f-4bdd-42e9-8caf-53e17005e72b | learn-how-to-prune-pixels-for-multi-view | 2305.03572 | null | https://arxiv.org/abs/2305.03572v1 | https://arxiv.org/pdf/2305.03572v1.pdf | Learn how to Prune Pixels for Multi-view Neural Image-based Synthesis | Image-based rendering techniques stand at the core of an immersive experience for the user, as they generate novel views given a set of multiple input images. Since they have shown good performance in terms of objective and subjective quality, the research community devotes great effort to their improvement. However, t... | ['Félix Henry', 'Marco Cagnazzo', 'Enzo Tartaglione', 'Marta Milovanović'] | 2023-05-05 | null | null | null | null | ['neural-rendering'] | ['computer-vision'] | [ 6.74266875e-01 1.07475422e-01 2.17605308e-01 -3.03941131e-01
-7.05508888e-01 -5.25773823e-01 4.09188479e-01 -6.70011640e-02
-2.88355827e-01 5.11062205e-01 7.68144354e-02 -5.09060919e-01
2.31937334e-01 -9.66674626e-01 -7.37878084e-01 -4.42974061e-01
-1.17045984e-01 -8.07185918e-02 4.12359267e-01 -1.57990709... | [10.489952087402344, -2.0536062717437744] |
ec9b86d8-1c2f-49f7-9184-bd03af8d60d7 | animating-face-using-disentangled-audio | 1910.00726 | null | https://arxiv.org/abs/1910.00726v1 | https://arxiv.org/pdf/1910.00726v1.pdf | Animating Face using Disentangled Audio Representations | All previous methods for audio-driven talking head generation assume the input audio to be clean with a neutral tone. As we show empirically, one can easily break these systems by simply adding certain background noise to the utterance or changing its emotional tone (to such as sad). To make talking head generation rob... | ['Gaurav Mittal', 'Baoyuan Wang'] | 2019-10-02 | null | null | null | null | ['talking-head-generation'] | ['computer-vision'] | [ 3.67614567e-01 2.85975724e-01 -1.64994210e-01 -3.68204325e-01
-1.21073091e+00 -6.33127809e-01 5.85492373e-01 -4.98157084e-01
1.41710982e-01 7.00323582e-01 7.58086920e-01 9.04807001e-02
2.42344081e-01 -5.51037908e-01 -5.82215965e-01 -9.14858341e-01
-5.84400445e-02 3.18309069e-01 -1.06602311e-01 -4.87918615... | [14.90186595916748, 6.418883800506592] |
fbbc9840-094c-4a59-9458-edb0e3db760a | compositional-generalization-in-grounded | 2207.02518 | null | https://arxiv.org/abs/2207.02518v1 | https://arxiv.org/pdf/2207.02518v1.pdf | Compositional Generalization in Grounded Language Learning via Induced Model Sparsity | We provide a study of how induced model sparsity can help achieve compositional generalization and better sample efficiency in grounded language learning problems. We consider simple language-conditioned navigation problems in a grid world environment with disentangled observations. We show that standard neural archite... | ['Alexander Ilin', 'Sam Spilsbury'] | 2022-07-06 | null | https://aclanthology.org/2022.naacl-srw.19 | https://aclanthology.org/2022.naacl-srw.19.pdf | naacl-acl-2022-7 | ['grounded-language-learning'] | ['natural-language-processing'] | [ 1.65814713e-01 3.14245522e-01 -1.57724947e-01 -1.45636082e-01
-5.40843248e-01 -6.79692268e-01 5.65216243e-01 1.31812274e-01
-5.68542957e-01 7.83875942e-01 5.49909115e-01 -2.03674927e-01
-2.01418489e-01 -8.46201122e-01 -7.08244503e-01 -9.04572070e-01
-5.03880084e-01 8.57075453e-01 -2.02244699e-01 -3.74162883... | [4.288275241851807, 1.1287130117416382] |
c719d964-bbd4-4492-80ec-3e9190a51bae | underwater-object-detection-using-invert | 2005.11552 | null | https://arxiv.org/abs/2005.11552v1 | https://arxiv.org/pdf/2005.11552v1.pdf | Underwater object detection using Invert Multi-Class Adaboost with deep learning | In recent years, deep learning based methods have achieved promising performance in standard object detection. However, these methods lack sufficient capabilities to handle underwater object detection due to these challenges: (1) Objects in real applications are usually small and their images are blurry, and (2) images... | ['Huiyu Zhou', 'ShengKe Wang', 'Lei Tong', 'Long Chen', 'Junyu Dong', 'Zheheng Jiang', 'Zhihua Liu'] | 2020-05-23 | null | null | null | null | ['small-object-detection'] | ['computer-vision'] | [ 7.06709996e-02 -3.75228822e-01 6.56622946e-01 -3.64990145e-01
-4.25535172e-01 -1.19349673e-01 2.42429629e-01 5.70664443e-02
-1.01616764e+00 4.21351463e-01 -6.41167834e-02 2.94235080e-01
-3.31161231e-01 -9.66165721e-01 -8.02157521e-01 -1.00947022e+00
-2.02426180e-01 1.76387310e-01 8.68833780e-01 -4.09294009... | [10.63297176361084, -3.4907915592193604] |
753810fa-7a35-4a54-81b5-12c2ffd959af | 30m-resolution-global-annual-burned-area | 1805.02579 | null | http://arxiv.org/abs/1805.02579v1 | http://arxiv.org/pdf/1805.02579v1.pdf | 30m resolution Global Annual Burned Area Mapping based on Landsat images and Google Earth Engine | Heretofore, global burned area (BA) products are only available at coarse
spatial resolution, since most of the current global BA products are produced
with the help of active fire detection or dense time-series change analysis,
which requires very high temporal resolution. In this study, however, we focus
on automated... | ['Xiaomei Zhang', 'Weili Jiao', 'Zhaoming Zhang', 'Tengfei Long', 'Bingfang Wu', 'Guizhou Wang', 'Ranyu Yin', 'Guojin He', 'Chao Tang'] | 2018-05-07 | null | null | null | null | ['fire-detection'] | ['time-series'] | [ 1.36455372e-01 -4.22292769e-01 -1.13174438e-01 1.13988109e-01
-5.31572282e-01 -5.06737709e-01 7.94028342e-01 2.60188013e-01
-6.03339314e-01 1.03614724e+00 1.17674664e-01 -8.19669008e-01
-2.59782016e-01 -1.57557893e+00 -4.24911916e-01 -6.45050883e-01
-5.81632018e-01 -4.64464948e-02 2.07614824e-01 -5.73609948... | [9.409017562866211, -1.5031814575195312] |
a4046e4d-c074-4bb8-ab26-9333c8f965c9 | countingmot-joint-counting-detection-and-re | 2212.05861 | null | https://arxiv.org/abs/2212.05861v2 | https://arxiv.org/pdf/2212.05861v2.pdf | CountingMOT: Joint Counting, Detection and Re-Identification for Multiple Object Tracking | The recent trend in multiple object tracking (MOT) is jointly solving detection and tracking, where object detection and appearance feature (or motion) are learned simultaneously. Despite competitive performance, in crowded scenes, joint detection and tracking usually fail to find accurate object associations due to mi... | ['Yuhang Shi', 'Bowen Chen', 'Hui Cao', 'Denglu Wu', 'Honghai Liu', 'Weibo Jiang', 'Weihong Ren'] | 2022-12-12 | null | null | null | null | ['multiple-object-tracking'] | ['computer-vision'] | [-9.74039212e-02 -5.16707659e-01 1.62398905e-01 3.43894437e-02
-5.38511515e-01 -3.85839969e-01 5.62634051e-01 3.15073609e-01
-7.92535126e-01 7.39292383e-01 -2.62445986e-01 2.03510180e-01
1.77439407e-01 -4.94270861e-01 -7.66759336e-01 -6.52890801e-01
-2.70293534e-01 8.41569662e-01 9.79752600e-01 3.90856326... | [6.442792892456055, -2.0785903930664062] |
f5d45739-1bc8-4233-85fa-8b7cac233e26 | weakly-supervised-few-shot-object | 2001.09540 | null | https://arxiv.org/abs/2001.09540v3 | https://arxiv.org/pdf/2001.09540v3.pdf | Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings | Significant progress has been made recently in developing few-shot object segmentation methods. Learning is shown to be successful in few-shot segmentation settings, using pixel-level, scribbles and bounding box supervision. This paper takes another approach, i.e., only requiring image-level label for few-shot object s... | ['Boris N. Oreshkin', 'Martin Jagersand', 'Mennatullah Siam', 'Hengshuai Yao', 'Naren Doraiswamy'] | 2020-01-26 | null | null | null | null | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [ 1.86025113e-01 -6.12292811e-03 -5.35102963e-01 -6.11696720e-01
-1.16160953e+00 -3.92861634e-01 4.96566862e-01 1.11257277e-01
-6.90024197e-01 1.56248957e-01 -1.17285110e-01 7.20720142e-02
2.71960735e-01 -4.68223035e-01 -1.14981925e+00 -4.23677772e-01
1.05534345e-01 4.49294239e-01 1.00578952e+00 1.15329004... | [9.583985328674316, 0.7910906076431274] |
159b66db-9c6c-498a-892a-d9da76bf2429 | scarp-3d-shape-completion-in-arbitrary-poses | 2301.07213 | null | https://arxiv.org/abs/2301.07213v1 | https://arxiv.org/pdf/2301.07213v1.pdf | SCARP: 3D Shape Completion in ARbitrary Poses for Improved Grasping | Recovering full 3D shapes from partial observations is a challenging task that has been extensively addressed in the computer vision community. Many deep learning methods tackle this problem by training 3D shape generation networks to learn a prior over the full 3D shapes. In this training regime, the methods expect th... | ['Madhava Krishna', 'Srinath Sridhar', 'Brojeshwar B.', 'Gaurav Singh', 'Aditya Agarwal', 'Bipasha Sen'] | 2023-01-17 | null | null | null | null | ['3d-shape-generation'] | ['computer-vision'] | [-1.37181468e-02 2.68633157e-01 -1.59212813e-01 -4.08141166e-01
-9.25425112e-01 -9.67603385e-01 5.45634091e-01 -3.40960920e-01
-1.91571325e-01 2.15415046e-01 9.85024199e-02 5.91882206e-02
1.16533339e-01 -5.86370945e-01 -1.27603281e+00 -6.29297376e-01
2.61232793e-01 1.23773563e+00 -1.20861873e-01 -7.22398013... | [8.136815071105957, -3.1883723735809326] |
1b4fdcb1-954b-4adf-968a-a2203909b144 | towards-creating-a-deployable-grasp-type | 2101.05357 | null | https://arxiv.org/abs/2101.05357v1 | https://arxiv.org/pdf/2101.05357v1.pdf | Towards Creating a Deployable Grasp Type Probability Estimator for a Prosthetic Hand | For lower arm amputees, prosthetic hands promise to restore most of physical interaction capabilities. This requires to accurately predict hand gestures capable of grabbing varying objects and execute them timely as intended by the user. Current approaches often rely on physiological signal inputs such as Electromyogra... | ['Gunar Schirner', 'Deniz Erdogmus', 'Mo Han', 'Mehrshad Zandigohar'] | 2021-01-13 | null | null | null | null | ['electromyography-emg'] | ['medical'] | [ 2.66780943e-01 6.74993694e-02 -4.72379327e-01 1.27397040e-02
-4.65584099e-01 -5.23159683e-01 1.85828760e-01 -8.49676728e-01
-3.91868204e-01 8.43652368e-01 -2.03333329e-02 -2.88708746e-01
-3.60511780e-01 -3.17565084e-01 -8.89040768e-01 -6.77979887e-01
-1.29931495e-01 5.65552175e-01 6.85914010e-02 1.98675841... | [6.818605422973633, 0.1438596248626709] |
68e91553-b8e2-4ec1-8b02-22ef755f74e2 | on-the-limitations-of-elo-real-world-games | 2206.12301 | null | https://arxiv.org/abs/2206.12301v3 | https://arxiv.org/pdf/2206.12301v3.pdf | On the Limitations of Elo: Real-World Games, are Transitive, not Additive | Real-world competitive games, such as chess, go, or StarCraft II, rely on Elo models to measure the strength of their players. Since these games are not fully transitive, using Elo implicitly assumes they have a strong transitive component that can correctly be identified and extracted. In this study, we investigate th... | ['Gauthier Gidel', 'Wojciech Marian Czarnecki', 'Quentin Bertrand'] | 2022-06-21 | null | null | null | null | ['starcraft-ii'] | ['playing-games'] | [-1.19557030e-01 1.40904993e-01 4.05375957e-02 3.34076166e-01
-1.63408816e-01 -1.04682565e+00 6.23819590e-01 3.39307636e-02
-3.99376512e-01 7.75713205e-01 1.21000623e-02 -2.86942303e-01
-8.88106465e-01 -9.89567876e-01 -3.94616246e-01 -2.31080353e-01
-2.56259680e-01 8.32363605e-01 7.46487141e-01 -6.42662764... | [3.4473302364349365, 1.4436495304107666] |
17e4511e-5bd5-4f76-84b6-55a075e63c96 | anomaly-segmentation-model-for-defects | 2208.05994 | null | https://arxiv.org/abs/2208.05994v3 | https://arxiv.org/pdf/2208.05994v3.pdf | Anomaly segmentation model for defects detection in electroluminescence images of heterojunction solar cells | Efficient defect detection in solar cell manufacturing is crucial for stable green energy technology manufacturing. This paper presents a deep-learning-based automatic detection model SeMaCNN for classification and semantic segmentation of electroluminescent images for solar cell quality evaluation and anomalies detect... | ['Semen Budennyy', 'Leonid Zhukov', 'Igor Shakhray', 'Evgeny Terukov', 'Dmitry Saykin', 'Fedor Egorov', 'Artem Vasilyev', 'Alexey Korovin'] | 2022-08-11 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 1.53421208e-01 -1.27000913e-01 2.06572667e-01 -1.49911582e-01
-6.82689548e-01 -3.22962105e-01 6.80686347e-03 4.93694454e-01
3.00695971e-02 7.56252944e-01 -9.02016878e-01 -3.57358567e-02
-1.45856529e-01 -1.18593597e+00 -6.61801338e-01 -1.00692725e+00
4.75138158e-01 5.52120626e-01 -1.15459725e-01 1.16180055... | [7.287578582763672, 1.879257082939148] |
812def89-6893-42ea-a9da-4b43abdbd272 | learning-to-interact-an-adaptive-interaction | null | null | https://aclanthology.org/2020.icon-main.8 | https://aclanthology.org/2020.icon-main.8.pdf | Learning to Interact: An Adaptive Interaction Framework for Knowledge Graph Embeddings | Knowledge Graph (KG) Embedding methods have been widely studied in the past few years and many methods have been proposed. These methods represent entities and relations in the KG as vectors in a vector space, trained to distinguish correct edges from the incorrect ones. For this distinction, simple functions of vector... | ['Partha Talukdar', 'Nilesh Agrawal', '. Chandrahas'] | null | null | null | null | icon-2020-12 | ['knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'methodology'] | [-4.99669388e-02 2.41870537e-01 -6.88049078e-01 -3.60973597e-01
2.09507883e-01 -4.08374339e-01 5.75434268e-01 5.90786755e-01
-3.63143474e-01 7.17309892e-01 2.20827937e-01 -5.00533618e-02
-5.32070458e-01 -1.08451617e+00 -5.69291532e-01 -6.32724285e-01
-3.46237510e-01 5.11968613e-01 4.91037697e-01 -9.69626009... | [8.771641731262207, 7.886181354522705] |
a7322ea0-def7-4446-9586-bb06742e50c6 | image-to-image-translation-with-conditional | 1611.07004 | null | http://arxiv.org/abs/1611.07004v3 | http://arxiv.org/pdf/1611.07004v3.pdf | Image-to-Image Translation with Conditional Adversarial Networks | We investigate conditional adversarial networks as a general-purpose solution
to image-to-image translation problems. These networks not only learn the
mapping from input image to output image, but also learn a loss function to
train this mapping. This makes it possible to apply the same generic approach
to problems th... | ['Jun-Yan Zhu', 'Tinghui Zhou', 'Alexei A. Efros', 'Phillip Isola'] | 2016-11-21 | image-to-image-translation-with-conditional-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Isola_Image-To-Image_Translation_With_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Isola_Image-To-Image_Translation_With_CVPR_2017_paper.pdf | cvpr-2017-7 | ['fundus-to-angiography-generation', 'cross-view-image-to-image-translation', 'nuclear-segmentation'] | ['computer-vision', 'computer-vision', 'medical'] | [ 4.82142985e-01 1.38289943e-01 3.02360952e-01 -5.02346158e-01
-9.55786705e-01 -1.07539642e+00 4.85431373e-01 -4.57893521e-01
-4.38781053e-01 7.58170366e-01 -2.60184109e-01 -4.63621646e-01
2.87394851e-01 -7.72371769e-01 -9.93599951e-01 -5.94240129e-01
2.10299119e-01 2.42637143e-01 1.28397435e-01 -1.10737169... | [11.51661491394043, -0.5753437280654907] |
d4047c58-5b18-4354-aa6d-f6d7058018c2 | transfer-cross-modality-knowledge-transfer | 2306.15114 | null | https://arxiv.org/abs/2306.15114v1 | https://arxiv.org/pdf/2306.15114v1.pdf | Transfer: Cross Modality Knowledge Transfer using Adversarial Networks -- A Study on Gesture Recognition | Knowledge transfer across sensing technology is a novel concept that has been recently explored in many application domains, including gesture-based human computer interaction. The main aim is to gather semantic or data driven information from a source technology to classify / recognize instances of unseen classes in t... | ['Sandeep K. S. Gupta', 'Ayan Banerjee', 'Payal Kamboj'] | 2023-06-26 | null | null | null | null | ['gesture-recognition', 'transfer-learning'] | ['computer-vision', 'miscellaneous'] | [ 7.12641180e-01 -4.11793590e-01 -3.81419212e-01 -4.35515493e-01
-6.73495829e-01 -8.33181262e-01 5.79792559e-01 -2.07715660e-01
-4.10955876e-01 2.23794028e-01 2.95003325e-01 1.15484647e-01
-3.56017917e-01 -7.21665025e-01 -7.93446124e-01 -4.88782734e-01
-1.96946040e-02 1.97379947e-01 2.79487967e-01 2.17531323... | [6.666743755340576, -0.1654644012451172] |
c108a3c2-6f4d-424a-8c54-8e47c270d6d5 | byt5-towards-a-token-free-future-with-pre | 2105.13626 | null | https://arxiv.org/abs/2105.13626v3 | https://arxiv.org/pdf/2105.13626v3.pdf | ByT5: Towards a token-free future with pre-trained byte-to-byte models | Most widely-used pre-trained language models operate on sequences of tokens corresponding to word or subword units. By comparison, token-free models that operate directly on raw text (bytes or characters) have many benefits: they can process text in any language out of the box, they are more robust to noise, and they m... | ['Colin Raffel', 'Adam Roberts', 'Mihir Kale', 'Sharan Narang', 'Rami Al-Rfou', 'Noah Constant', 'Aditya Barua', 'Linting Xue'] | 2021-05-28 | null | null | null | null | ['cross-lingual-natural-language-inference', 'cross-lingual-question-answering', 'extreme-summarization', 'cross-lingual-ner'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [ 2.71930486e-01 -3.64915490e-01 -2.21152872e-01 -3.08871448e-01
-1.04516947e+00 -7.82593071e-01 7.66593695e-01 4.84202772e-01
-8.56158912e-01 4.24436271e-01 2.67501026e-01 -1.03367949e+00
6.57194436e-01 -9.78639901e-01 -8.75488400e-01 -3.42929751e-01
4.60494645e-02 2.56786108e-01 4.50036347e-01 -3.22642326... | [10.716962814331055, 8.582703590393066] |
08bc5cb4-cd78-452f-96c9-a3e8d70f2529 | osre-object-to-spot-rotation-estimation-for | 2303.00725 | null | https://arxiv.org/abs/2303.00725v1 | https://arxiv.org/pdf/2303.00725v1.pdf | OSRE: Object-to-Spot Rotation Estimation for Bike Parking Assessment | Current deep models provide remarkable object detection in terms of object classification and localization. However, estimating object rotation with respect to other visual objects in the visual context of an input image still lacks deep studies due to the unavailability of object datasets with rotation annotations. Th... | ['Chen Xu', 'Jian Lu', 'Ahmed Elazab', 'Saifullah Bello', 'Zaid Al-huda', 'Saghir Alfasly'] | 2023-03-01 | null | null | null | null | ['image-smoothing'] | ['computer-vision'] | [-3.69159013e-01 -1.92377567e-01 -1.44327134e-01 -3.17290813e-01
-4.66541111e-01 -2.72791415e-01 6.03154838e-01 -5.58830261e-01
-2.97900319e-01 1.88015074e-01 -1.43873036e-01 -3.48402590e-01
2.80943751e-01 -5.30657053e-01 -8.96071374e-01 -3.68257701e-01
2.16249213e-01 4.16403830e-01 3.71287763e-01 -3.99364591... | [8.00202465057373, -2.1021475791931152] |
4ed6e4c4-7de3-4af0-8b19-3b2bf687592a | defending-model-inversion-and-membership | 2005.03915 | null | https://arxiv.org/abs/2005.03915v2 | https://arxiv.org/pdf/2005.03915v2.pdf | Defending Model Inversion and Membership Inference Attacks via Prediction Purification | Neural networks are susceptible to data inference attacks such as the model inversion attack and the membership inference attack, where the attacker could infer the reconstruction and the membership of a data sample from the confidence scores predicted by the target classifier. In this paper, we propose a unified appro... | ['Ee-Chien Chang', 'Ziqi Yang', 'Bin Shao', 'Fan Zhang', 'Bohan Xuan'] | 2020-05-08 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 5.03736496e-01 3.74291360e-01 1.17965274e-01 -3.09128929e-02
-6.50856376e-01 -1.21518338e+00 5.78625083e-01 4.46410060e-01
-4.48559225e-01 8.67568910e-01 -5.29409468e-01 -6.06422782e-01
5.99708781e-02 -1.16927361e+00 -1.10177159e+00 -1.12470186e+00
-5.59942145e-03 4.08066541e-01 5.34645915e-01 2.33909935... | [5.837861061096191, 7.398087501525879] |
ba7050a2-ada1-4a0a-9f48-8de406c0c266 | multitask-identity-aware-image-steganography | 2107.05819 | null | https://arxiv.org/abs/2107.05819v1 | https://arxiv.org/pdf/2107.05819v1.pdf | Multitask Identity-Aware Image Steganography via Minimax Optimization | High-capacity image steganography, aimed at concealing a secret image in a cover image, is a technique to preserve sensitive data, e.g., faces and fingerprints. Previous methods focus on the security during transmission and subsequently run a risk of privacy leakage after the restoration of secret images at the receivi... | ['Xi Li', 'Jupeng Xia', 'Cuizhu Bao', 'Liangli Zheng', 'Songyuan Li', 'Pengyi Zhang', 'Jiabao Cui'] | 2021-07-13 | null | null | null | null | ['image-steganography'] | ['computer-vision'] | [ 9.84298527e-01 3.70086581e-02 4.68723476e-02 -1.63694650e-01
-4.59396333e-01 -3.88253301e-01 3.98347020e-01 -6.41969383e-01
-3.39862019e-01 5.38808286e-01 1.48119584e-01 -2.70342976e-01
-3.85205224e-02 -8.31305385e-01 -8.51379871e-01 -1.27546751e+00
4.82452810e-02 -3.67888331e-01 -1.18716573e-02 -1.16319850... | [4.356055736541748, 8.032719612121582] |
ec3b0c30-902c-4d9d-8aa6-106eeaa32e4e | temporally-consistent-online-depth-estimation | 2111.09337 | null | https://arxiv.org/abs/2111.09337v3 | https://arxiv.org/pdf/2111.09337v3.pdf | Temporally Consistent Online Depth Estimation in Dynamic Scenes | Temporally consistent depth estimation is crucial for online applications such as augmented reality. While stereo depth estimation has received substantial attention as a promising way to generate 3D information, there is relatively little work focused on maintaining temporal stability. Indeed, based on our analysis, c... | ['Mathias Unberath', 'Ganesh Venkatesh', 'Russell H. Taylor', 'Francis X. Creighton', 'Dilin Wang', 'Wei Ye', 'Zhaoshuo Li'] | 2021-11-17 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [ 2.77718514e-01 -2.88761884e-01 -1.27866790e-01 -3.68972927e-01
-5.97148716e-01 -4.70135868e-01 6.85639620e-01 -1.01759277e-01
-3.84484261e-01 7.17360914e-01 4.58021402e-01 -4.81961556e-02
1.05532669e-01 -5.25173962e-01 -6.20422482e-01 -5.78985155e-01
-8.41507018e-02 -6.42691776e-02 5.29305220e-01 -1.28478985... | [8.730781555175781, -2.2897043228149414] |
aeb6b0ae-49a9-47a5-8aef-886ac18ba591 | nowcasting-of-covid-19-confirmed-cases | 2010.05079 | null | http://arxiv.org/abs/2010.05079v1 | http://arxiv.org/pdf/2010.05079v1.pdf | Nowcasting of COVID-19 confirmed cases: Foundations, trends, and challenges | The coronavirus disease 2019 (COVID-19) has become a public health emergency
of international concern affecting more than 200 countries and territories
worldwide. As of September 30, 2020, it has caused a pandemic outbreak with
more than 33 million confirmed infections and more than 1 million reported
deaths worldwide.... | [] | 2020-10-10 | null | null | null | null | ['univariate-time-series-forecasting'] | ['time-series'] | [-5.21630608e-02 -3.79730284e-01 -2.80202538e-01 -5.00902124e-02
-3.65908623e-01 -5.59241354e-01 6.00109279e-01 4.83715326e-01
-3.89523119e-01 9.24632907e-01 3.02940667e-01 -8.33019018e-01
-2.84531176e-01 -5.23902535e-01 -1.98482558e-01 -5.40966332e-01
-3.91936302e-01 6.84553504e-01 -1.98304564e-01 -1.41034156... | [5.998527526855469, 4.386691570281982] |
b821d8a7-6d2f-4eba-9fe4-9bf60fe40f8d | expeditious-saliency-guided-mix-up-through | 2212.04875 | null | https://arxiv.org/abs/2212.04875v2 | https://arxiv.org/pdf/2212.04875v2.pdf | Expeditious Saliency-guided Mix-up through Random Gradient Thresholding | Mix-up training approaches have proven to be effective in improving the generalization ability of Deep Neural Networks. Over the years, the research community expands mix-up methods into two directions, with extensive efforts to improve saliency-guided procedures but minimal focus on the arbitrary path, leaving the ran... | ['Haohan Wang', 'Yong Jae Lee', 'Eric P. Xing', 'Zeyi Huang', 'Minh-Long Luu'] | 2022-12-09 | null | null | null | null | ['classifier-calibration', 'weakly-supervised-object-localization', 'classifier-calibration'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 1.25020608e-01 -1.58820301e-02 -3.14871758e-01 -1.98033169e-01
-6.75211966e-01 -6.62476778e-01 6.56298101e-01 -1.12266708e-02
-4.65977669e-01 5.61439753e-01 1.06871966e-02 -5.11863708e-01
-2.29294062e-01 -6.76319182e-01 -8.00410330e-01 -8.05635452e-01
7.75948837e-02 2.21961737e-01 4.29118216e-01 -2.52478659... | [9.134758949279785, 2.969470262527466] |
78fe6682-0e0e-4c32-94c1-c1dc7b1a8ed1 | exploring-content-based-image-retrieval-for | 2110.06331 | null | https://arxiv.org/abs/2110.06331v1 | https://arxiv.org/pdf/2110.06331v1.pdf | Exploring Content Based Image Retrieval for Highly Imbalanced Melanoma Data using Style Transfer, Semantic Image Segmentation and Ensemble Learning | Lesion images are frequently taken in open-set settings. Because of this, the image data generated is extremely varied in nature.It is difficult for a convolutional neural network to find proper features and generalise well, as a result content based image retrieval (CBIR) system for lesion images are difficult to buil... | ['Priyam Mehta'] | 2021-10-12 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [ 1.55804932e-01 -2.75470048e-01 -1.66451577e-02 -3.12285364e-01
-8.50124180e-01 -4.78531212e-01 5.98008454e-01 6.57747030e-01
-7.42356420e-01 8.08743596e-01 1.95195928e-01 -4.02786247e-02
-8.59425187e-01 -7.29573846e-01 -8.67147222e-02 -7.88744330e-01
-5.83646968e-02 7.58290514e-02 2.76042491e-01 -3.82535994... | [14.341096878051758, -1.6938635110855103] |
b63496d1-d0ea-410d-85fd-48959424dbfe | differential-diffusion-giving-each-pixel-its | 2306.00950 | null | https://arxiv.org/abs/2306.00950v1 | https://arxiv.org/pdf/2306.00950v1.pdf | Differential Diffusion: Giving Each Pixel Its Strength | Text-based image editing has advanced significantly in recent years. With the rise of diffusion models, image editing via textual instructions has become ubiquitous. Unfortunately, current models lack the ability to customize the quantity of the change per pixel or per image fragment, resorting to changing the entire i... | ['Ohad Fried', 'Eran Levin'] | 2023-06-01 | null | null | null | null | ['text-based-image-editing'] | ['computer-vision'] | [ 3.06056261e-01 7.15996921e-02 -1.14231072e-01 -7.08523393e-02
-1.69515997e-01 -9.13625717e-01 8.28629673e-01 2.65711062e-02
-5.61201572e-01 5.73232770e-01 -1.58453584e-02 -5.26672184e-01
1.94803521e-01 -7.98948526e-01 -6.16103113e-01 -6.28462315e-01
1.59620389e-01 5.92189506e-02 3.36907804e-01 -1.15063138... | [11.403704643249512, -0.31007468700408936] |
e814c447-c04b-4fad-a5d8-63a90635d547 | boundary-corrected-multi-scale-fusion-network | 2203.00436 | null | https://arxiv.org/abs/2203.00436v1 | https://arxiv.org/pdf/2203.00436v1.pdf | Boundary Corrected Multi-scale Fusion Network for Real-time Semantic Segmentation | Image semantic segmentation aims at the pixel-level classification of images, which has requirements for both accuracy and speed in practical application. Existing semantic segmentation methods mainly rely on the high-resolution input to achieve high accuracy and do not meet the requirements of inference time. Although... | ['Yidong Li', 'Xu Wang', 'Tengfei Liang', 'Yi Jin', 'Tianjiao Jiang'] | 2022-03-01 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [ 4.67928499e-01 -2.05736995e-01 -5.15165459e-03 -7.10607171e-01
-9.00724828e-01 -1.19674364e-02 1.62652969e-01 -1.14700850e-02
-5.98440349e-01 2.43914112e-01 -4.34414148e-01 -1.36641294e-01
-7.41729289e-02 -1.28217518e+00 -6.99250102e-01 -4.34577435e-01
5.46985030e-01 2.52918929e-01 1.06490409e+00 -2.90005412... | [9.400670051574707, -0.4473358988761902] |
5e5340da-e08c-4ae0-a61a-c966a32b2856 | pavel-decorative-patterns-with-packed | 2102.01029 | null | https://arxiv.org/abs/2102.01029v1 | https://arxiv.org/pdf/2102.01029v1.pdf | PAVEL: Decorative Patterns with Packed Volumetric Elements | Many real-world hand-crafted objects are decorated with elements that are packed onto the object's surface and deformed to cover it as much as possible. Examples are artisanal ceramics and metal jewelry. Inspired by these objects, we present a method to enrich surfaces with packed volumetric decorations. Our algorithm ... | ['Andrea Giachetti', 'Riccardo Scateni', 'Fabio Pellacini', 'Filippo Andrea Fanni'] | 2021-02-01 | null | null | null | null | ['physical-simulations'] | ['miscellaneous'] | [ 3.90593737e-01 4.18022633e-01 5.86149156e-01 1.11991741e-01
3.82126272e-02 -6.94923580e-01 2.64419854e-01 9.90679041e-02
1.33044407e-01 8.27146649e-01 2.26091570e-03 3.88407297e-02
-2.61546671e-01 -1.32822168e+00 -8.12736809e-01 -6.15671456e-01
5.31089492e-03 7.22812593e-01 6.98796153e-01 -3.59032929... | [9.207664489746094, -3.443728446960449] |
451f7235-a5a9-453a-a5f1-888b188c7eaf | rsi-cb-a-large-scale-remote-sensing-image | 1705.10450 | null | https://arxiv.org/abs/1705.10450v3 | https://arxiv.org/pdf/1705.10450v3.pdf | RSI-CB: A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data | In recent years, deep convolutional neural network (DCNN) has seen a breakthrough progress in natural image recognition because of three points: universal approximation ability via DCNN, large-scale database (such as ImageNet), and supercomputing ability powered by GPU. The remote sensing field is still lacking a large... | ['Xin Dou', 'Jie Chen', 'Haifeng Li', 'Chao Tao', 'Zhixiang Hou', 'Min Deng', 'Ling Zhao', 'Jian Peng'] | 2017-05-30 | null | null | null | null | ['remote-sensing-image-classification'] | ['miscellaneous'] | [-3.24394345e-01 -5.14428437e-01 6.98565841e-02 -5.77822864e-01
-1.58028156e-01 -4.94560540e-01 4.37922001e-01 2.13450670e-01
-7.71975160e-01 8.21129024e-01 5.66738360e-02 -4.51808840e-01
-8.12056810e-02 -1.77859318e+00 -5.49918115e-01 -5.98676920e-01
-2.32831597e-01 2.36624718e-01 3.31046849e-01 -5.35034537... | [9.723048210144043, -1.3860746622085571] |
4edca1b1-1bdf-47dc-b4d8-f2a317795ff6 | oneshotstl-one-shot-seasonal-trend | 2304.01506 | null | https://arxiv.org/abs/2304.01506v1 | https://arxiv.org/pdf/2304.01506v1.pdf | OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And Forecasting | Seasonal-trend decomposition is one of the most fundamental concepts in time series analysis that supports various downstream tasks, including time series anomaly detection and forecasting. However, existing decomposition methods rely on batch processing with a time complexity of O(W), where W is the number of data poi... | ['Feifei Li', 'Bin Wu', 'Jian Tan', 'Ye Li', 'Xiao He'] | 2023-04-04 | null | null | null | null | ['time-series-anomaly-detection'] | ['time-series'] | [-1.94721580e-01 -7.03459978e-01 3.88179161e-02 -1.92943737e-01
-5.00184655e-01 -7.49457538e-01 3.22112262e-01 7.39561081e-01
-2.31848568e-01 6.13645278e-02 -9.60893258e-02 -8.27158213e-01
7.33260531e-03 -7.19355404e-01 -2.40709618e-01 -7.12404370e-01
-6.16789162e-01 1.82342842e-01 3.41818660e-01 -3.47674191... | [7.2464518547058105, 2.8638250827789307] |
3c95bfb6-5dee-47ef-a5f5-3e62de9129f3 | accerl-policy-acceleration-framework-for-deep | 2211.15023 | null | https://arxiv.org/abs/2211.15023v1 | https://arxiv.org/pdf/2211.15023v1.pdf | AcceRL: Policy Acceleration Framework for Deep Reinforcement Learning | Deep reinforcement learning has achieved great success in various fields with its super decision-making ability. However, the policy learning process requires a large amount of training time, causing energy consumption. Inspired by the redundancy of neural networks, we propose a lightweight parallel training framework ... | ['Hongjie Zhang'] | 2022-11-28 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [-6.72582462e-02 -1.60217449e-01 -5.65740228e-01 -1.73244804e-01
-1.29022244e-02 -2.29426816e-01 1.32453039e-01 2.30263174e-01
-9.00493324e-01 7.70637512e-01 -9.77429375e-02 -3.76357168e-01
-1.61687836e-01 -9.23268855e-01 -8.42073441e-01 -7.30647862e-01
3.41416597e-02 1.65433645e-01 3.04944277e-01 -1.34815872... | [4.06593132019043, 2.1564180850982666] |
0b9bff16-d246-413b-a22c-fd10c8bdec0f | semantic-search-in-documents-enriched-by-lod | null | null | https://aclanthology.org/L14-1040 | https://aclanthology.org/L14-1040.pdf | Semantic Search in Documents Enriched by LOD-based Annotations | This paper deals with information retrieval on semantically enriched web-scale document collections. It particularly focuses on web-crawled content in which mentions of entities appearing in Freebase, DBpedia and other Linked Open Data resources have been identified. A special attention is paid to indexing structures a... | ['Jan Kouril', 'Pavel Smrz'] | 2014-05-01 | null | null | null | lrec-2014-5 | ['semantic-retrieval'] | ['natural-language-processing'] | [-2.60269701e-01 3.84891540e-01 7.40839988e-02 -6.21911604e-03
-8.10141683e-01 -5.05671620e-01 9.62945461e-01 1.00183690e+00
-8.90451312e-01 1.29903185e+00 3.70739460e-01 2.60387212e-01
-9.40942645e-01 -1.18405104e+00 -9.51656550e-02 -2.75884569e-01
-4.30293679e-01 8.23115528e-01 7.24338233e-01 -8.31172705... | [9.252182006835938, 8.106517791748047] |
92c60cda-1d97-4081-a1eb-8623390c3164 | speechformer-a-hierarchical-efficient-1 | 2302.14638 | null | https://arxiv.org/abs/2302.14638v1 | https://arxiv.org/pdf/2302.14638v1.pdf | SpeechFormer++: A Hierarchical Efficient Framework for Paralinguistic Speech Processing | Paralinguistic speech processing is important in addressing many issues, such as sentiment and neurocognitive disorder analyses. Recently, Transformer has achieved remarkable success in the natural language processing field and has demonstrated its adaptation to speech. However, previous works on Transformer in the spe... | ['Lan Du', 'Jianxin Pang', 'Xiangmin Xu', 'Xiaofen Xing', 'Weidong Chen'] | 2023-02-27 | null | null | null | null | ['alzheimer-s-disease-detection', 'speech-emotion-recognition'] | ['medical', 'speech'] | [ 1.04144499e-01 -8.62654373e-02 1.21968821e-01 -5.41440308e-01
-5.46080589e-01 -9.49078575e-02 3.52880210e-01 2.33815491e-01
-3.39580148e-01 3.08610737e-01 6.57359481e-01 -1.56256706e-01
3.50148305e-02 -6.16868615e-01 -1.18114009e-01 -6.28688633e-01
9.78450626e-02 -9.43164900e-02 -4.92538549e-02 -3.16806823... | [13.775372505187988, 5.867352485656738] |
21ad340d-ee18-43a4-bbf1-5e61ab980171 | text-compression-for-sentiment-analysis-via | 1709.06990 | null | http://arxiv.org/abs/1709.06990v1 | http://arxiv.org/pdf/1709.06990v1.pdf | Text Compression for Sentiment Analysis via Evolutionary Algorithms | Can textual data be compressed intelligently without losing accuracy in
evaluating sentiment? In this study, we propose a novel evolutionary
compression algorithm, PARSEC (PARts-of-Speech for sEntiment Compression),
which makes use of Parts-of-Speech tags to compress text in a way that
sacrifices minimal classification... | ['Emmanuel Dufourq', 'Bruce A. Bassett'] | 2017-09-20 | null | null | null | null | ['text-compression'] | ['natural-language-processing'] | [ 5.72681785e-01 -1.06098890e-01 -9.43731517e-02 -6.83626056e-01
-4.76713926e-01 -8.69534016e-01 2.77674764e-01 8.25867176e-01
-8.26791584e-01 3.94294620e-01 3.19309384e-01 -4.47512120e-01
-6.77481592e-02 -7.55380690e-01 -2.25308701e-01 -5.35098016e-01
2.67216057e-01 6.80526733e-01 -1.91114962e-01 -6.94738984... | [11.070464134216309, 6.9448699951171875] |
4dcbab14-54af-4a3b-8269-eabaf840d9f8 | marking-anything-application-of-point-cloud | 2306.07559 | null | https://arxiv.org/abs/2306.07559v1 | https://arxiv.org/pdf/2306.07559v1.pdf | Marking anything: application of point cloud in extracting video target features | Extracting retrievable features from video is of great significance for structured video database construction, video copyright protection and fake video rumor refutation. Inspired by point cloud data processing, this paper proposes a method for marking anything (MA) in the video, which can extract the contour features... | ['Xiangchun Xu'] | 2023-06-13 | null | null | null | null | ['object-tracking', 'multi-object-tracking'] | ['computer-vision', 'computer-vision'] | [-2.20489398e-01 -9.06444848e-01 -2.19518110e-01 3.58672023e-01
-4.48013365e-01 -6.26858711e-01 2.05339402e-01 3.17725502e-02
-3.40151489e-01 4.45046306e-01 -1.21458225e-01 -1.28048882e-01
-1.03372984e-01 -7.32993066e-01 -6.74076378e-01 -5.64482331e-01
-3.09998989e-01 1.06781900e-01 9.20303643e-01 -1.95589676... | [12.217761039733887, 0.842216968536377] |
f563c584-be89-4180-8375-3d74a49759df | structbert-incorporating-language-structures | 1908.04577 | null | https://arxiv.org/abs/1908.04577v3 | https://arxiv.org/pdf/1908.04577v3.pdf | StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding | Recently, the pre-trained language model, BERT (and its robustly optimized version RoBERTa), has attracted a lot of attention in natural language understanding (NLU), and achieved state-of-the-art accuracy in various NLU tasks, such as sentiment classification, natural language inference, semantic textual similarity an... | ['Zuyi Bao', 'Ming Yan', 'Jiangnan Xia', 'Bin Bi', 'Luo Si', 'Liwei Peng', 'Chen Wu', 'Wei Wang'] | 2019-08-13 | null | https://openreview.net/forum?id=BJgQ4lSFPH | https://openreview.net/pdf?id=BJgQ4lSFPH | iclr-2020-1 | ['linguistic-acceptability'] | ['natural-language-processing'] | [-8.23363289e-02 2.15054020e-01 -3.12488347e-01 -6.20701432e-01
-9.11973655e-01 -7.52895296e-01 5.72506666e-01 4.70875978e-01
-4.58075315e-01 5.16821444e-01 4.61820304e-01 -6.41906619e-01
-3.40301031e-03 -8.07135522e-01 -8.08081985e-01 -1.45569459e-01
1.36969864e-01 5.29434204e-01 2.36153185e-01 -6.04725420... | [11.066021919250488, 8.454672813415527] |
223463f8-744d-4f0c-be66-c2008dba89ea | transmef-a-transformer-based-multi-exposure | 2112.01030 | null | https://arxiv.org/abs/2112.01030v2 | https://arxiv.org/pdf/2112.01030v2.pdf | TransMEF: A Transformer-Based Multi-Exposure Image Fusion Framework using Self-Supervised Multi-Task Learning | In this paper, we propose TransMEF, a transformer-based multi-exposure image fusion framework that uses self-supervised multi-task learning. The framework is based on an encoder-decoder network, which can be trained on large natural image datasets and does not require ground truth fusion images. We design three self-su... | ['Zhijian Song', 'Manning Wang', 'Shaolei Liu', 'Linhao Qu'] | 2021-12-02 | null | null | null | null | ['multi-exposure-image-fusion'] | ['computer-vision'] | [ 2.79846668e-01 -3.47470254e-01 4.42131907e-02 -5.99876881e-01
-1.31493616e+00 -3.28015462e-02 4.68646973e-01 -1.71719164e-01
-2.92943388e-01 5.04200697e-01 4.15167123e-01 1.83896452e-01
-6.94466904e-02 -7.60079801e-01 -7.45246053e-01 -7.11923242e-01
4.01263833e-01 7.75691494e-02 2.76698381e-01 -4.47139055... | [10.566774368286133, -1.855233907699585] |
fbb66dc4-99f7-439e-8cad-8a2839874abf | mumic-multimodal-embedding-for-multi-label | 2211.05232 | null | https://arxiv.org/abs/2211.05232v1 | https://arxiv.org/pdf/2211.05232v1.pdf | MuMIC -- Multimodal Embedding for Multi-label Image Classification with Tempered Sigmoid | Multi-label image classification is a foundational topic in various domains. Multimodal learning approaches have recently achieved outstanding results in image representation and single-label image classification. For instance, Contrastive Language-Image Pretraining (CLIP) demonstrates impressive image-text representat... | ['Hadas Harush Boker', 'Karen Lastmann Assaraf', 'Gil Amsalem', 'Guy Nadav', 'Moran Beladev', 'Sarai Mizrachi', 'Fengjun Wang'] | 2022-11-02 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 5.13845980e-01 -3.52890104e-01 -5.52221298e-01 -4.37814385e-01
-1.27746916e+00 -5.99100411e-01 5.39494932e-01 3.56783450e-01
-6.61352396e-01 4.03558701e-01 -1.33131430e-01 1.99071616e-02
1.45475380e-02 -2.06738785e-01 -5.93864679e-01 -7.90726364e-01
3.03047746e-01 4.88396108e-01 -2.05120206e-01 -3.73698138... | [9.80264663696289, 3.9655349254608154] |
126f5499-537e-46e4-85d4-efb65f20141d | instant-teaching-an-end-to-end-semi | 2103.11402 | null | https://arxiv.org/abs/2103.11402v1 | https://arxiv.org/pdf/2103.11402v1.pdf | Instant-Teaching: An End-to-End Semi-Supervised Object Detection Framework | Supervised learning based object detection frameworks demand plenty of laborious manual annotations, which may not be practical in real applications. Semi-supervised object detection (SSOD) can effectively leverage unlabeled data to improve the model performance, which is of great significance for the application of ob... | ['Hao Li', 'Qi Qian', 'Zhibin Wang', 'Chaohui Yu', 'Qiang Zhou'] | 2021-03-21 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhou_Instant-Teaching_An_End-to-End_Semi-Supervised_Object_Detection_Framework_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhou_Instant-Teaching_An_End-to-End_Semi-Supervised_Object_Detection_Framework_CVPR_2021_paper.pdf | cvpr-2021-1 | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 6.08854182e-02 1.05001293e-01 -1.87817752e-01 -5.62185824e-01
-7.65445650e-01 -4.35580552e-01 4.84546661e-01 -4.72937934e-02
-7.40028322e-01 6.19806945e-01 -4.96047974e-01 -3.54944110e-01
2.54142314e-01 -5.48811316e-01 -9.31589305e-01 -6.24253988e-01
4.45028931e-01 1.88120350e-01 7.04222441e-01 -8.23910311... | [9.218522071838379, 1.2837787866592407] |
e8726c8a-f902-4beb-94ac-0d215ed7510b | self-supervised-contrastive-video-speech | 2008.06607 | null | https://arxiv.org/abs/2008.06607v1 | https://arxiv.org/pdf/2008.06607v1.pdf | Self-supervised Contrastive Video-Speech Representation Learning for Ultrasound | In medical imaging, manual annotations can be expensive to acquire and sometimes infeasible to access, making conventional deep learning-based models difficult to scale. As a result, it would be beneficial if useful representations could be derived from raw data without the need for manual annotations. In this paper, w... | ['Mohammad Alsharid', 'Jianbo Jiao', 'Aris T. Papageorghiou', 'Yifan Cai', 'Lior Drukker', 'J. Alison Noble'] | 2020-08-14 | null | null | null | null | ['eye-tracking'] | ['computer-vision'] | [ 3.96199465e-01 7.15805829e-01 1.09377146e-01 -5.22648573e-01
-8.43666255e-01 -2.61037529e-01 2.89186507e-01 4.93127972e-01
-1.84189051e-01 3.56977552e-01 3.00353587e-01 -1.54857352e-01
-4.02489811e-01 -4.18041497e-01 -8.81287873e-01 -7.29550362e-01
-3.79940778e-01 4.21872765e-01 9.67720523e-02 -2.45104581... | [14.263283729553223, -2.4240689277648926] |
a9989ccf-1080-492b-b93f-4a59cf30b71f | skipconvgan-monaural-speech-dereverberation | 2211.12623 | null | https://arxiv.org/abs/2211.12623v1 | https://arxiv.org/pdf/2211.12623v1.pdf | SkipConvGAN: Monaural Speech Dereverberation using Generative Adversarial Networks via Complex Time-Frequency Masking | With the advancements in deep learning approaches, the performance of speech enhancing systems in the presence of background noise have shown significant improvements. However, improving the system's robustness against reverberation is still a work in progress, as reverberation tends to cause loss of formant structure ... | ['J. H. L. Hansen', 'Vinay Kothapally'] | 2022-11-22 | null | null | null | null | ['speech-dereverberation'] | ['speech'] | [ 1.01166643e-01 -3.44071686e-01 7.37222433e-01 -1.57178402e-01
-1.16954446e+00 -5.97377837e-01 4.12701041e-01 -2.92903244e-01
-1.16259560e-01 8.17246258e-01 5.96888363e-01 -4.83377844e-01
1.08829036e-01 -6.03886068e-01 -5.81261337e-01 -9.95781839e-01
-1.46620974e-01 -3.65001202e-01 -8.35146606e-02 -5.75251400... | [15.124850273132324, 5.984768390655518] |
238bdeab-cf7c-48f0-9909-4ff3e8ee35c6 | learning-from-a-biased-sample | 2209.01754 | null | https://arxiv.org/abs/2209.01754v2 | https://arxiv.org/pdf/2209.01754v2.pdf | Learning from a Biased Sample | The empirical risk minimization approach to data-driven decision making assumes that we can learn a decision rule from training data drawn under the same conditions as the ones we want to deploy it in. However, in a number of settings, we may be concerned that our training sample is biased, and that some groups (charac... | ['Stefan Wager', 'Lihua Lei', 'Roshni Sahoo'] | 2022-09-05 | null | null | null | null | ['length-of-stay-prediction'] | ['medical'] | [ 3.79366517e-01 6.66117609e-01 -5.33854485e-01 -6.30632699e-01
-1.19902658e+00 -1.72923118e-01 -1.06810458e-01 3.67545664e-01
-6.34182870e-01 1.15307879e+00 -5.90949059e-02 -6.44150615e-01
-6.76551163e-01 -9.84823763e-01 -1.06086099e+00 -9.51406360e-01
-2.93139547e-01 7.44243085e-01 -5.07090807e-01 2.94559985... | [8.206835746765137, 5.133822917938232] |
1bebb5a6-ee50-413a-a790-8f4e024ed3a6 | scatter-based-common-spatial-patterns-a | 2303.06019 | null | https://arxiv.org/abs/2303.06019v1 | https://arxiv.org/pdf/2303.06019v1.pdf | Scatter-based common spatial patterns -- a unified spatial filtering framework | The common spatial pattern (CSP) approach is known as one of the most popular spatial filtering techniques for EEG classification in motor imagery (MI) based brain-computer interfaces (BCIs). However, it still suffers some drawbacks such as sensitivity to noise, non-stationarity, and limitation to binary classification... | ['Jens Haueisen', 'Daniel Baumgarten', 'Johannes Vorwerk', 'Milana Komosar', 'Jinlong Dong'] | 2023-03-07 | null | null | null | null | ['eeg', 'eeg'] | ['methodology', 'time-series'] | [ 4.62290019e-01 -7.91650116e-01 1.88289791e-01 1.74462900e-03
-4.72327322e-01 -4.31786001e-01 6.88916624e-01 -5.86424321e-02
-6.90891206e-01 1.08446193e+00 1.56338796e-01 -2.03561500e-01
-1.14411128e+00 -3.48386735e-01 -3.89403880e-01 -1.19716644e+00
-1.45869702e-01 2.01595336e-01 5.62579572e-01 -2.15908304... | [12.888236999511719, 3.4349608421325684] |
4c3b5521-894f-4f12-86ff-eb2854952534 | how-will-your-tweet-be-received-predicting-1 | 2104.10513 | null | https://arxiv.org/abs/2104.10513v1 | https://arxiv.org/pdf/2104.10513v1.pdf | How Will Your Tweet Be Received? Predicting the Sentiment Polarity of Tweet Replies | Twitter sentiment analysis, which often focuses on predicting the polarity of tweets, has attracted increasing attention over the last years, in particular with the rise of deep learning (DL). In this paper, we propose a new task: predicting the predominant sentiment among (first-order) replies to a given tweet. Theref... | ['Stefan Evert', 'Mahshad Lotfinia', 'Hamidreza Naderi Boldaji', 'Anguelos Nicolaou', 'Philipp Heinrich', 'Vincent Christlein', 'Mehrpad Monajem', 'Soroosh Tayebi Arasteh'] | 2021-04-21 | how-will-your-tweet-be-received-predicting | https://ieeexplore.ieee.org/document/9364527 | https://ieeexplore.ieee.org/document/9364527 | null | ['twitter-sentiment-analysis'] | ['natural-language-processing'] | [ 1.83630422e-01 2.01233089e-01 -2.19412386e-01 -8.18705857e-01
-6.41229808e-01 -4.76148278e-01 8.41352701e-01 6.13359451e-01
-5.76531768e-01 8.84478927e-01 5.55346489e-01 -1.23026565e-01
4.96248156e-01 -7.56169438e-01 -6.26495838e-01 -3.77942801e-01
3.01835716e-01 5.95309138e-01 2.24974513e-01 -5.89480042... | [11.07568645477295, 7.033869743347168] |
894ac673-d31b-42de-9b0f-4163a6d96b60 | speaker-extraction-with-co-speech-gestures | 2203.16840 | null | https://arxiv.org/abs/2203.16840v2 | https://arxiv.org/pdf/2203.16840v2.pdf | Speaker Extraction with Co-Speech Gestures Cue | Speaker extraction seeks to extract the clean speech of a target speaker from a multi-talker mixture speech. There have been studies to use a pre-recorded speech sample or face image of the target speaker as the speaker cue. In human communication, co-speech gestures that are naturally timed with speech also contribute... | ['Haizhou Li', 'Xinyuan Qian', 'Zexu Pan'] | 2022-03-31 | null | null | null | null | ['speech-separation'] | ['speech'] | [ 3.74032944e-01 2.31574133e-01 -1.04544282e-01 -5.21366298e-01
-7.76964426e-01 -3.26362640e-01 8.12093019e-01 -4.20922816e-01
-3.07356805e-01 2.64238834e-01 5.04865110e-01 7.21193105e-02
4.27003391e-02 -3.46206725e-02 -4.21620876e-01 -1.09382689e+00
7.46882111e-02 3.09999913e-01 1.58652768e-01 1.72033533... | [14.621295928955078, 5.429705619812012] |
e13d3500-2cc1-42cb-87b9-36194c83f990 | training-protocol-matters-towards-accurate | 2203.06696 | null | https://arxiv.org/abs/2203.06696v2 | https://arxiv.org/pdf/2203.06696v2.pdf | Training Protocol Matters: Towards Accurate Scene Text Recognition via Training Protocol Searching | The development of scene text recognition (STR) in the era of deep learning has been mainly focused on novel architectures of STR models. However, training protocol (i.e., settings of the hyper-parameters involved in the training of STR models), which plays an equally important role in successfully training a good STR ... | ['Wei Chu', 'Jingdong Chen', 'Chunhua Shen', 'Yongtao Wang', 'Xiaojie Chu'] | 2022-03-13 | null | null | null | null | ['scene-text-recognition'] | ['computer-vision'] | [ 5.07808737e-02 -6.65279686e-01 -1.06522277e-01 -2.60364473e-01
-4.73953366e-01 -5.40076271e-02 2.02066481e-01 -3.69172454e-01
-3.94523740e-01 4.47803587e-01 -2.75209725e-01 -1.65219903e-01
-3.04185271e-01 -8.72314572e-01 -7.26770639e-01 -8.74622941e-01
4.21583265e-01 3.62643510e-01 3.19527835e-01 -9.44445133... | [11.893820762634277, 2.199610710144043] |
199be981-55b3-46c5-887e-8bef66e6dd3b | finding-regions-of-heterogeneity-in-decision | 2110.14508 | null | https://arxiv.org/abs/2110.14508v1 | https://arxiv.org/pdf/2110.14508v1.pdf | Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance | Individuals often make different decisions when faced with the same context, due to personal preferences and background. For instance, judges may vary in their leniency towards certain drug-related offenses, and doctors may vary in their preference for how to start treatment for certain types of patients. With these ex... | ['David Sontag', 'Leora Horwitz', 'Saul Blecker', 'Michael Oberst', 'Christina X Ji', 'Justin Lim'] | 2021-10-27 | null | http://proceedings.neurips.cc/paper/2021/hash/81930c54e08b6d26d9638dd2e4656dc1-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/81930c54e08b6d26d9638dd2e4656dc1-Paper.pdf | neurips-2021-12 | ['clinical-knowledge'] | ['miscellaneous'] | [ 4.60382491e-01 2.84015805e-01 -9.04438853e-01 -7.03112662e-01
-9.22451615e-01 -6.45432889e-01 3.73941362e-01 5.92668235e-01
-5.66665888e-01 9.01726604e-01 7.52093196e-01 -4.55121279e-01
-3.55854899e-01 -4.60338444e-01 -4.63376999e-01 -5.98278046e-01
3.84091549e-02 9.61831450e-01 -3.23012024e-01 3.49104047... | [8.196854591369629, 5.405247211456299] |
34f684ec-9501-452c-875e-1c3ff1737b4d | griddehazenet-attention-based-multi-scale | 1908.03245 | null | https://arxiv.org/abs/1908.03245v1 | https://arxiv.org/pdf/1908.03245v1.pdf | GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing | We propose an end-to-end trainable Convolutional Neural Network (CNN), named GridDehazeNet, for single image dehazing. The GridDehazeNet consists of three modules: pre-processing, backbone, and post-processing. The trainable pre-processing module can generate learned inputs with better diversity and more pertinent feat... | ['Jun Chen', 'Zhihao Shi', 'Yongrui Ma', 'Xiaohong Liu'] | 2019-08-08 | griddehazenet-attention-based-multi-scale-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Liu_GridDehazeNet_Attention-Based_Multi-Scale_Network_for_Image_Dehazing_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Liu_GridDehazeNet_Attention-Based_Multi-Scale_Network_for_Image_Dehazing_ICCV_2019_paper.pdf | iccv-2019-10 | ['image-relighting'] | ['computer-vision'] | [ 3.84433568e-01 1.63112998e-01 7.99171329e-01 -3.47883373e-01
-6.98856115e-01 -2.56777145e-02 5.56190789e-01 -9.46576595e-02
-4.92175251e-01 6.01784110e-01 1.92077905e-01 -7.64902234e-02
-2.47906566e-01 -1.21928847e+00 -9.05750811e-01 -1.20610976e+00
1.26427069e-01 1.39579892e-01 2.75815398e-01 -5.04957855... | [10.928959846496582, -3.096630096435547] |
c28dcb02-69fb-4afb-96c7-96282b695fa6 | realistic-speech-driven-facial-animation-with | 1906.06337 | null | https://arxiv.org/abs/1906.06337v1 | https://arxiv.org/pdf/1906.06337v1.pdf | Realistic Speech-Driven Facial Animation with GANs | Speech-driven facial animation is the process that automatically synthesizes talking characters based on speech signals. The majority of work in this domain creates a mapping from audio features to visual features. This approach often requires post-processing using computer graphics techniques to produce realistic albe... | ['Maja Pantic', 'Konstantinos Vougioukas', 'Stavros Petridis'] | 2019-06-14 | null | null | null | null | ['audio-visual-synchronization', 'audio-visual-synchronization'] | ['audio', 'computer-vision'] | [ 4.69928980e-01 4.12040830e-01 2.60608971e-01 -4.82424706e-01
-1.12343943e+00 -4.93428826e-01 7.94152498e-01 -5.06738067e-01
2.70489126e-01 6.16591454e-01 6.98954523e-01 3.41711253e-01
5.16299367e-01 -1.18545704e-01 -7.03843236e-01 -7.47685730e-01
7.33432397e-02 -5.41554345e-03 -1.90720364e-01 -7.65378699... | [13.235795974731445, -0.43998846411705017] |
1179f064-1a15-4d9a-8cd9-a2b46a4080ef | auxiliary-tasks-to-boost-biaffine-semantic | null | null | https://aclanthology.org/2022.findings-acl.190 | https://aclanthology.org/2022.findings-acl.190.pdf | Auxiliary tasks to boost Biaffine Semantic Dependency Parsing | The biaffine parser of (CITATION) was successfully extended to semantic dependency parsing (SDP) (CITATION). Its performance on graphs is surprisingly high given that, without the constraint of producing a tree, all arcs for a given sentence are predicted independently from each other (modulo a shared representation of... | ['Marie Candito'] | null | null | null | null | findings-acl-2022-5 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [ 3.67748857e-01 9.74338770e-01 -2.45943457e-01 -7.03126073e-01
-1.14412677e+00 -9.96144772e-01 7.49562740e-01 2.95372039e-01
-1.58550635e-01 7.52384245e-01 6.13414109e-01 -9.70394373e-01
1.60477236e-01 -7.76229501e-01 -1.04134238e+00 -4.88336325e-01
-4.05828446e-01 6.19334936e-01 4.62986350e-01 -1.43539235... | [10.34415340423584, 9.462157249450684] |
6eaedfcb-d7ff-47fa-8bd2-efa4116a9254 | logsmooth-gradient-concentration-and-tighter | 2002.04121 | null | https://arxiv.org/abs/2002.04121v3 | https://arxiv.org/pdf/2002.04121v3.pdf | Logsmooth Gradient Concentration and Tighter Runtimes for Metropolized Hamiltonian Monte Carlo | We show that the gradient norm $\|\nabla f(x)\|$ for $x \sim \exp(-f(x))$, where $f$ is strongly convex and smooth, concentrates tightly around its mean. This removes a barrier in the prior state-of-the-art analysis for the well-studied Metropolized Hamiltonian Monte Carlo (HMC) algorithm for sampling from a strongly l... | ['Kevin Tian', 'Ruoqi Shen', 'Yin Tat Lee'] | 2020-02-10 | null | null | null | null | ['art-analysis'] | ['computer-vision'] | [ 1.89645067e-01 5.38328104e-02 -9.25596803e-02 -2.03326494e-01
-1.52453411e+00 -5.14947772e-01 2.26740479e-01 2.91719615e-01
-7.67498732e-01 1.20105314e+00 -4.46882367e-01 -4.71137911e-01
-2.99730361e-01 -9.21709836e-01 -1.07372665e+00 -1.34882092e+00
-4.41233903e-01 7.73168147e-01 1.88454881e-01 -2.37879217... | [6.311785697937012, 4.561042308807373] |
6050a882-30be-48d2-a7a8-db7e606ba12b | local-advantage-networks-for-cooperative | 2112.12458 | null | https://arxiv.org/abs/2112.12458v2 | https://arxiv.org/pdf/2112.12458v2.pdf | Local Advantage Networks for Cooperative Multi-Agent Reinforcement Learning | Multi-agent reinforcement learning (MARL) enables us to create adaptive agents in challenging environments, even when the agents have limited observation. Modern MARL methods have focused on finding factorized value functions. While successful, the resulting methods have convoluted network structures. We take a radical... | ['Diederik M. Roijers', 'Ann Nowé', 'Mathieu Reymond', 'Raphaël Avalos'] | 2021-12-23 | null | null | null | null | ['smac-1', 'smac'] | ['playing-games', 'playing-games'] | [-5.23882806e-01 2.81891197e-01 -2.92746276e-01 3.42830308e-02
-9.16299403e-01 -8.65279436e-01 6.06573105e-01 -3.97982895e-02
-8.27957094e-01 1.23964739e+00 -6.80132732e-02 -2.45748058e-01
-3.01693648e-01 -6.04342937e-01 -8.73513818e-01 -1.01617956e+00
-4.50584859e-01 9.19615567e-01 2.51348823e-01 -6.86722517... | [3.8148250579833984, 2.0114328861236572] |
2fb14e1f-bac2-47b1-8b89-8ac8c26fc96d | amodal-intra-class-instance-segmentation-new | 2303.06596 | null | https://arxiv.org/abs/2303.06596v1 | https://arxiv.org/pdf/2303.06596v1.pdf | Amodal Intra-class Instance Segmentation: New Dataset and Benchmark | Images of realistic scenes often contain intra-class objects that are heavily occluded from each other, making the amodal perception task that requires parsing the occluded parts of the objects challenging. Although important for downstream tasks such as robotic grasping systems, the lack of large-scale amodal datasets... | ['Krista A. Ehinger', 'Qiuhong Ke', 'Jiayang Ao'] | 2023-03-12 | null | null | null | null | ['amodal-instance-segmentation', 'robotic-grasping'] | ['computer-vision', 'robots'] | [ 4.43279207e-01 7.27202237e-01 -2.21295625e-01 -6.76195920e-01
-6.35162711e-01 -6.74430966e-01 7.56773114e-01 -6.40510116e-03
-1.55614495e-01 5.40810585e-01 -8.04397166e-02 -1.55894905e-01
-1.97936166e-02 -3.25144738e-01 -1.21528697e+00 -7.31170177e-01
6.71795309e-02 1.02457905e+00 2.68032253e-01 2.09660128... | [9.550297737121582, 0.35405102372169495] |
92fc5f2f-8409-4da6-9b07-b38d53fb3880 | recurrent-neural-circuits-for-contour-1 | 2010.15314 | null | https://arxiv.org/abs/2010.15314v1 | https://arxiv.org/pdf/2010.15314v1.pdf | Recurrent neural circuits for contour detection | We introduce a deep recurrent neural network architecture that approximates visual cortical circuits. We show that this architecture, which we refer to as the gamma-net, learns to solve contour detection tasks with better sample efficiency than state-of-the-art feedforward networks, while also exhibiting a classic perc... | ['Thomas Serre', 'Alekh Ashok', 'Junkyung Kim', 'Drew Linsley'] | 2020-10-29 | recurrent-neural-circuits-for-contour | https://openreview.net/forum?id=H1gB4RVKvB | https://openreview.net/pdf?id=H1gB4RVKvB | iclr-2020-1 | ['contour-detection'] | ['computer-vision'] | [ 4.15341973e-01 1.60836026e-01 1.27260908e-01 -2.47891054e-01
4.48387265e-02 -7.53625572e-01 3.94071221e-01 -1.66485667e-01
-4.19194400e-01 2.39372492e-01 2.00512663e-01 -5.46441495e-01
3.23219121e-01 -9.30532753e-01 -9.10256624e-01 -6.96569502e-01
4.06885073e-02 -1.03227962e-02 4.05050337e-01 -2.73753613... | [9.619902610778809, 2.436069965362549] |
9727faa4-6607-4b84-b8ea-83ad31876c2d | dap3d-net-where-what-and-how-actions-occur-in | 1602.03346 | null | http://arxiv.org/abs/1602.03346v1 | http://arxiv.org/pdf/1602.03346v1.pdf | DAP3D-Net: Where, What and How Actions Occur in Videos? | Action parsing in videos with complex scenes is an interesting but
challenging task in computer vision. In this paper, we propose a generic 3D
convolutional neural network in a multi-task learning manner for effective Deep
Action Parsing (DAP3D-Net) in videos. Particularly, in the training phase,
action localization, c... | ['Ling Shao', 'Li Liu', 'Yi Zhou'] | 2016-02-10 | null | null | null | null | ['action-understanding', 'action-parsing'] | ['computer-vision', 'natural-language-processing'] | [ 2.38462314e-01 -1.15728810e-01 -2.98086733e-01 -4.88010049e-01
-6.04786634e-01 -4.09893632e-01 3.81668895e-01 -2.49948800e-01
-2.31380537e-01 3.99431109e-01 6.22138739e-01 2.78571010e-01
1.24978177e-01 -4.25652295e-01 -1.03314793e+00 -5.95014393e-01
-2.36243114e-01 3.50674301e-01 3.63464773e-01 1.77892774... | [8.20669174194336, 0.5205084085464478] |
03fc5634-e08c-4c92-bcc4-1961431522f6 | visual-speech-recognition | 1409.1411 | null | http://arxiv.org/abs/1409.1411v1 | http://arxiv.org/pdf/1409.1411v1.pdf | Visual Speech Recognition | Lip reading is used to understand or interpret speech without hearing it, a
technique especially mastered by people with hearing difficulties. The ability
to lip read enables a person with a hearing impairment to communicate with
others and to engage in social activities, which otherwise would be difficult.
Recent adva... | ['Ahmad B. A. Hassanat'] | 2014-09-03 | null | null | null | null | ['audio-visual-speech-recognition'] | ['speech'] | [ 7.61526287e-01 1.25324965e-01 -1.21890783e-01 -1.54203102e-01
-3.87902856e-01 -3.28965217e-01 8.21470916e-01 -9.00918804e-03
-4.30697143e-01 4.64419007e-01 2.67632633e-01 -4.74594116e-01
6.28209636e-02 -2.38681883e-01 -1.44140884e-01 -7.08657384e-01
3.59799236e-01 -1.29684299e-01 1.43473119e-01 7.53888562... | [14.326709747314453, 5.0243306159973145] |
6a9e4b85-6ea5-47ac-b083-8ffa17c33411 | deep-learning-denoising-for-eog-artifacts | 2009.08809 | null | https://arxiv.org/abs/2009.08809v1 | https://arxiv.org/pdf/2009.08809v1.pdf | Deep learning denoising for EOG artifacts removal from EEG signals | There are many sources of interference encountered in the electroencephalogram (EEG) recordings, specifically ocular, muscular, and cardiac artifacts. Rejection of EEG artifacts is an essential process in EEG analysis since such artifacts cause many problems in EEG signals analysis. One of the most challenging issues i... | ['Donya Khaledyan', 'Abolfazl Zargari Khuzani', 'Najmeh Mashhadi', 'Morteza Heidari'] | 2020-09-12 | null | null | null | null | ['eeg-denoising'] | ['methodology'] | [ 4.29094076e-01 -2.51037359e-01 6.52435184e-01 -3.73000950e-01
-5.05713761e-01 -2.14045003e-01 -1.36145167e-02 -3.10554564e-01
-4.16164726e-01 1.10684836e+00 -8.87444541e-02 -3.76774482e-02
-2.55760580e-01 -3.30610633e-01 -7.51048505e-01 -8.25294495e-01
9.71048325e-02 -1.48855045e-01 -1.35507658e-01 1.49871334... | [13.155966758728027, 3.416534900665283] |
d54634ee-618e-40f5-92b1-681b3f7bef3b | scientific-document-summarization-via | 1706.03449 | null | http://arxiv.org/abs/1706.03449v1 | http://arxiv.org/pdf/1706.03449v1.pdf | Scientific document summarization via citation contextualization and scientific discourse | The rapid growth of scientific literature has made it difficult for the
researchers to quickly learn about the developments in their respective fields.
Scientific document summarization addresses this challenge by providing
summaries of the important contributions of scientific papers. We present a
framework for scient... | ['Nazli Goharian', 'Arman Cohan'] | 2017-06-12 | null | null | null | null | ['scientific-article-summarization'] | ['natural-language-processing'] | [ 3.18439007e-01 3.57883543e-01 -5.86945355e-01 1.96014196e-02
-1.32857740e+00 -6.04356349e-01 8.92242730e-01 9.40770745e-01
-4.39456642e-01 1.07762802e+00 1.12324536e+00 -2.38392740e-01
-3.37691844e-01 -4.08539265e-01 -6.84330642e-01 -5.19297123e-01
5.00374019e-01 3.85363251e-01 7.86413327e-02 2.14985982... | [12.226410865783691, 9.483297348022461] |
e44240b9-11de-4630-87a8-a246d0dd346f | depth-from-monocular-images-using-a-semi | 1703.03867 | null | http://arxiv.org/abs/1703.03867v3 | http://arxiv.org/pdf/1703.03867v3.pdf | Depth from Monocular Images using a Semi-Parallel Deep Neural Network (SPDNN) Hybrid Architecture | Deep neural networks are applied to a wide range of problems in recent years.
In this work, Convolutional Neural Network (CNN) is applied to the problem of
determining the depth from a single camera image (monocular depth). Eight
different networks are designed to perform depth estimation, each of them
suitable for a f... | ['H. Javidnia', 'S. Bazrafkan', 'P. Corcoran', 'J. Lemley'] | 2017-03-10 | null | null | null | null | ['stereo-depth-estimation'] | ['computer-vision'] | [ 4.06733155e-01 2.07021490e-01 1.59970596e-01 -5.16122699e-01
4.58722562e-02 -1.50316045e-01 5.46188176e-01 -1.57169923e-01
-8.93241107e-01 5.54980516e-01 2.52504032e-02 1.08221263e-01
-1.21379167e-01 -1.14825678e+00 -6.49733782e-01 -6.63222551e-01
1.30233854e-01 5.88651240e-01 5.88638902e-01 -1.25258014... | [8.724571228027344, -2.3032894134521484] |
42660256-3494-42d5-988c-4cc3a2e9c73d | wasserstein-distances-for-stereo-disparity | 2007.03085 | null | https://arxiv.org/abs/2007.03085v2 | https://arxiv.org/pdf/2007.03085v2.pdf | Wasserstein Distances for Stereo Disparity Estimation | Existing approaches to depth or disparity estimation output a distribution over a set of pre-defined discrete values. This leads to inaccurate results when the true depth or disparity does not match any of these values. The fact that this distribution is usually learned indirectly through a regression loss causes furth... | ['Wei-Lun Chao', 'Divyansh Garg', 'Kilian Q. Weinberger', 'Yan Wang', 'Mark Campbell', 'Bharath Hariharan'] | 2020-07-06 | null | http://proceedings.neurips.cc/paper/2020/hash/fe7ecc4de28b2c83c016b5c6c2acd826-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/fe7ecc4de28b2c83c016b5c6c2acd826-Paper.pdf | neurips-2020-12 | ['stereo-depth-estimation', '3d-object-detection-from-stereo-images'] | ['computer-vision', 'computer-vision'] | [ 1.40081942e-01 -6.06004987e-03 5.93227036e-02 -7.01337397e-01
-8.66119921e-01 -5.66924751e-01 4.60798621e-01 1.06135838e-01
-5.19464433e-01 6.64597511e-01 -2.84937590e-01 -3.06801021e-01
3.18111390e-01 -9.30650413e-01 -8.88511300e-01 -6.56087399e-01
2.15257276e-02 6.32061779e-01 6.42272890e-01 -9.93844643... | [7.95172119140625, -2.5523877143859863] |
fc9606c7-e85f-4fdc-861b-65b5fa233ee8 | few-shot-emotion-recognition-in-conversation | 2109.09366 | null | https://arxiv.org/abs/2109.09366v1 | https://arxiv.org/pdf/2109.09366v1.pdf | Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks | Several recent studies on dyadic human-human interactions have been done on conversations without specific business objectives. However, many companies might benefit from studies dedicated to more precise environments such as after sales services or customer satisfaction surveys. In this work, we place ourselves in the... | ['Chloé Clavel', 'Luce Lefeuvre', 'Hélène Flamein', 'Matthieu Labeau', 'Gaël Guibon'] | 2021-09-20 | null | https://aclanthology.org/2021.emnlp-main.549 | https://aclanthology.org/2021.emnlp-main.549.pdf | emnlp-2021-11 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [ 1.38658419e-01 6.52470961e-02 5.23089990e-02 -6.94373608e-01
-3.83951932e-01 -5.24080753e-01 8.70955825e-01 5.37050015e-04
-3.95912707e-01 8.95614386e-01 3.41395140e-01 1.02506883e-01
-3.42505723e-02 -4.13938940e-01 1.05888046e-01 -5.90022981e-01
-1.12286270e-01 8.67821693e-01 1.23517001e-02 -6.18774891... | [12.982938766479492, 6.278955459594727] |
311738bb-4577-417b-8102-2414b87e4696 | the-impact-of-lexical-and-grammatical-1 | 2202.13972 | null | https://arxiv.org/abs/2202.13972v2 | https://arxiv.org/pdf/2202.13972v2.pdf | The impact of lexical and grammatical processing on generating code from natural language | Considering the seq2seq architecture of TranX for natural language to code translation, we identify four key components of importance: grammatical constraints, lexical preprocessing, input representations, and copy mechanisms. To study the impact of these components, we use a state-of-the-art architecture that relies o... | ['Benoît Crabbé', 'Nathanaël Beau'] | 2022-02-28 | null | https://aclanthology.org/2022.findings-acl.173 | https://aclanthology.org/2022.findings-acl.173.pdf | findings-acl-2022-5 | ['code-translation'] | ['computer-code'] | [ 3.39542627e-01 4.24884647e-01 -2.84982771e-01 -1.37435406e-01
-7.49777734e-01 -6.52839482e-01 5.38985074e-01 1.99668705e-01
-1.85613826e-01 5.95151603e-01 5.15432179e-01 -1.01118577e+00
2.46676430e-01 -6.04336977e-01 -1.04419422e+00 2.16927156e-01
-1.67359531e-01 1.34136856e-01 2.01386973e-01 -5.99693120... | [7.79381799697876, 7.880321502685547] |
4e6a5f41-5f6d-4f0d-8ba6-77976770b72c | vox-e-text-guided-voxel-editing-of-3d-objects | 2303.12048 | null | https://arxiv.org/abs/2303.12048v1 | https://arxiv.org/pdf/2303.12048v1.pdf | Vox-E: Text-guided Voxel Editing of 3D Objects | Large scale text-guided diffusion models have garnered significant attention due to their ability to synthesize diverse images that convey complex visual concepts. This generative power has more recently been leveraged to perform text-to-3D synthesis. In this work, we present a technique that harnesses the power of lat... | ['Hadar Averbuch-Elor', 'Peter Hedman', 'Gal Fiebelman', 'Etai Sella'] | 2023-03-21 | null | null | null | null | ['text-to-3d'] | ['computer-vision'] | [ 4.31292713e-01 2.16660202e-01 1.72547758e-01 -3.28422725e-01
-5.93962312e-01 -6.70399249e-01 8.78581405e-01 -1.10294469e-01
1.22117335e-02 4.05195445e-01 5.10370791e-01 -5.80205731e-02
1.38677865e-01 -6.78383708e-01 -7.60191798e-01 -6.15098953e-01
3.35736990e-01 4.44649428e-01 1.05930097e-01 -9.62721854... | [9.377267837524414, -3.2369260787963867] |
1684cb28-7a3b-4872-bad9-eaced3106e5f | segformer-simple-and-efficient-design-for | 2105.15203 | null | https://arxiv.org/abs/2105.15203v3 | https://arxiv.org/pdf/2105.15203v3.pdf | SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers | We present SegFormer, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perception (MLP) decoders. SegFormer has two appealing features: 1) SegFormer comprises a novel hierarchically structured Transformer encoder which outputs multiscale features. I... | ['Ping Luo', 'Jose M. Alvarez', 'Anima Anandkumar', 'Zhiding Yu', 'Wenhai Wang', 'Enze Xie'] | 2021-05-31 | null | http://proceedings.neurips.cc/paper/2021/hash/64f1f27bf1b4ec22924fd0acb550c235-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/64f1f27bf1b4ec22924fd0acb550c235-Paper.pdf | neurips-2021-12 | ['thermal-image-segmentation'] | ['computer-vision'] | [ 1.24018155e-01 2.91392207e-01 1.15223778e-02 -1.32472172e-01
-1.16163337e+00 -4.39557850e-01 2.51599282e-01 -1.05198540e-01
-2.08494604e-01 4.97682393e-01 1.97962329e-01 -4.78989542e-01
1.72769904e-01 -8.60136211e-01 -8.44663918e-01 -6.17521107e-01
1.43589175e-04 2.76704520e-01 7.13315189e-01 -1.65363938... | [9.496602058410645, 0.06744525581598282] |
6bdcee42-3127-491e-8cef-4f58c89c2978 | anchor-diffusion-for-unsupervised-video-1 | 1910.10895 | null | https://arxiv.org/abs/1910.10895v1 | https://arxiv.org/pdf/1910.10895v1.pdf | Anchor Diffusion for Unsupervised Video Object Segmentation | Unsupervised video object segmentation has often been tackled by methods based on recurrent neural networks and optical flow. Despite their complexity, these kinds of approaches tend to favour short-term temporal dependencies and are thus prone to accumulating inaccuracies, which cause drift over time. Moreover, simple... | ['Philip H. S. Torr', 'Zhao Yang', 'Weiming Hu', 'Song Bai', 'Qiang Wang', 'Luca Bertinetto'] | 2019-10-24 | anchor-diffusion-for-unsupervised-video | http://openaccess.thecvf.com/content_ICCV_2019/html/Yang_Anchor_Diffusion_for_Unsupervised_Video_Object_Segmentation_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Yang_Anchor_Diffusion_for_Unsupervised_Video_Object_Segmentation_ICCV_2019_paper.pdf | iccv-2019-10 | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 4.47090238e-01 4.48896065e-02 -3.42236787e-01 -1.73815474e-01
-5.18067241e-01 -3.76256198e-01 6.49223745e-01 1.18720099e-01
-8.14836740e-01 6.37489498e-01 2.40057129e-02 -1.50228247e-01
4.13198695e-02 -4.06692386e-01 -9.42754805e-01 -7.59315848e-01
-1.16423063e-01 2.75578052e-01 8.84086847e-01 -5.28334565... | [9.066657066345215, -0.097753144800663] |
7d86d25d-9963-4a98-9ac8-e1ebf0adbcac | maxim-multi-axis-mlp-for-image-processing | 2201.02973 | null | https://arxiv.org/abs/2201.02973v2 | https://arxiv.org/pdf/2201.02973v2.pdf | MAXIM: Multi-Axis MLP for Image Processing | Recent progress on Transformers and multi-layer perceptron (MLP) models provide new network architectural designs for computer vision tasks. Although these models proved to be effective in many vision tasks such as image recognition, there remain challenges in adapting them for low-level vision. The inflexibility to su... | ['Yinxiao Li', 'Alan Bovik', 'Peyman Milanfar', 'Feng Yang', 'Han Zhang', 'Hossein Talebi', 'Zhengzhong Tu'] | 2022-01-09 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Tu_MAXIM_Multi-Axis_MLP_for_Image_Processing_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Tu_MAXIM_Multi-Axis_MLP_for_Image_Processing_CVPR_2022_paper.pdf | cvpr-2022-1 | ['image-dehazing', 'photo-retouching', 'single-image-deraining'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.88453048e-01 -2.65318990e-01 -2.22724508e-02 -3.68500829e-01
-5.10675907e-01 -1.36534229e-01 6.17979944e-01 -1.25899166e-01
-6.30422294e-01 3.35148335e-01 1.99042186e-01 -1.97362602e-01
6.59151226e-02 -4.76685405e-01 -7.68501043e-01 -1.12390375e+00
-5.97459823e-02 -3.79778326e-01 5.63857257e-01 -1.16570517... | [10.711359977722168, -1.8456138372421265] |
ec93eee0-6625-4aa8-a5a1-762de8e9db1f | self-supervised-feature-learning-by-cross | 2004.05749 | null | https://arxiv.org/abs/2004.05749v1 | https://arxiv.org/pdf/2004.05749v1.pdf | Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences | The success of supervised learning requires large-scale ground truth labels which are very expensive, time-consuming, or may need special skills to annotate. To address this issue, many self- or un-supervised methods are developed. Unlike most existing self-supervised methods to learn only 2D image features or only 3D ... | ['Yu-cheng Chen', 'YingLi Tian', 'Mingyi He', 'Longlong Jing', 'Ling Zhang'] | 2020-04-13 | null | null | null | null | ['3d-shape-retrieval', '3d-shape-recognition', '3d-part-segmentation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 6.06332608e-02 6.14980869e-02 -1.93115070e-01 -6.91775441e-01
-1.06318259e+00 -6.91662431e-01 4.90639448e-01 2.63164248e-02
-1.20948493e-01 7.76686147e-02 -2.50808537e-01 1.04485855e-01
-1.54092029e-01 -8.76270056e-01 -9.36459601e-01 -5.69417536e-01
-6.74282089e-02 7.72384465e-01 2.58096993e-01 2.17285037... | [8.108726501464844, -3.4492099285125732] |
cbff6161-e8a7-4b3d-8c10-f0aeea0f387c | graph-aware-language-model-pre-training-on-a | 2306.02592 | null | https://arxiv.org/abs/2306.02592v1 | https://arxiv.org/pdf/2306.02592v1.pdf | Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications | Model pre-training on large text corpora has been demonstrated effective for various downstream applications in the NLP domain. In the graph mining domain, a similar analogy can be drawn for pre-training graph models on large graphs in the hope of benefiting downstream graph applications, which has also been explored b... | ['Trishul Chilimbi', 'Belinda Zeng', 'Yi Xu', 'Carl Yang', 'Sheng Wang', 'Qing Ping', 'Xiang Song', 'Vassilis N. Ioannidis', 'Houyu Zhang', 'Jun Ma', 'Da Zheng', 'Han Xie'] | 2023-06-05 | null | null | null | null | ['graph-mining'] | ['graphs'] | [ 1.79874286e-01 6.58058107e-01 -4.82638538e-01 -3.41074169e-01
-4.40445989e-01 -6.55025959e-01 6.04294002e-01 4.03970242e-01
-6.41691452e-03 3.31200838e-01 3.17442328e-01 -8.37941349e-01
-8.54022056e-02 -1.23352730e+00 -4.38163757e-01 -2.12555438e-01
-2.04826742e-01 6.77507579e-01 3.99390101e-01 -3.78998518... | [8.670293807983398, 7.50308084487915] |
0508ef62-b3ee-4ba7-9fce-082dab9e9102 | network-aware-5g-edge-computing-for-object | 2112.13194 | null | https://arxiv.org/abs/2112.13194v2 | https://arxiv.org/pdf/2112.13194v2.pdf | Network-Aware 5G Edge Computing for Object Detection: Augmenting Wearables to "See" More, Farther and Faster | Advanced wearable devices are increasingly incorporating high-resolution multi-camera systems. As state-of-the-art neural networks for processing the resulting image data are computationally demanding, there has been growing interest in leveraging fifth generation (5G) wireless connectivity and mobile edge computing fo... | ['J. R. Rizzo', 'Yao Wang', 'Sundeep Rangan', 'Yi Fang', 'William Seiple', 'Todd Hudson', 'Maurizio Porfiri', 'Mahya Beheshti', 'Marco Mezzavilla', 'Alain Boldini', 'Haoyang Pei', 'Yixuan Lyu', 'Yu Hao', 'Tommy Azzino', 'Zhongzheng Yuan'] | 2021-12-25 | null | null | null | null | ['real-time-object-detection'] | ['computer-vision'] | [ 9.45712551e-02 -4.01366234e-01 1.27299845e-01 1.82529032e-01
-5.62038660e-01 -4.89108771e-01 7.85739254e-03 -5.24322629e-01
-4.49173033e-01 3.79065007e-01 2.48929754e-01 -7.64912844e-01
-3.25807631e-01 -8.16082299e-01 -5.14596105e-01 -3.78813893e-01
-6.36603594e-01 3.88255566e-02 1.41467646e-01 2.29540259... | [10.263348579406738, -1.7814526557922363] |
0216c49e-73a0-47be-a4aa-eb954dbe362a | knowledge-enhanced-model-for-live-video | 2304.14657 | null | https://arxiv.org/abs/2304.14657v1 | https://arxiv.org/pdf/2304.14657v1.pdf | Knowledge Enhanced Model for Live Video Comment Generation | Live video commenting is popular on video media platforms, as it can create a chatting atmosphere and provide supplementary information for users while watching videos. Automatically generating live video comments can improve user experience and enable human-like generation for bot chatting. Existing works mostly focus... | ['Qin Jin', 'Wenping Chen', 'Junkai Ding', 'Jieting Chen'] | 2023-04-28 | null | null | null | null | ['comment-generation'] | ['natural-language-processing'] | [ 8.44836235e-02 -5.66304885e-02 -3.92746806e-01 -3.50049883e-01
-6.14398777e-01 -5.70567250e-01 6.65113926e-01 -3.35003018e-01
-1.30318075e-01 7.46764898e-01 8.00326347e-01 -5.59201390e-02
6.34171844e-01 -1.17752194e-01 -4.70135003e-01 -4.01863337e-01
2.97466922e-03 -3.84271473e-01 3.94919872e-01 -6.37869611... | [10.670393943786621, 0.6611711382865906] |
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