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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
99318390-0651-435a-a0c2-55192d7e4702 | dual-variational-generation-for-low-shot-1 | null | null | http://papers.nips.cc/paper/8535-dual-variational-generation-for-low-shot-heterogeneous-face-recognition | http://papers.nips.cc/paper/8535-dual-variational-generation-for-low-shot-heterogeneous-face-recognition.pdf | Dual Variational Generation for Low Shot Heterogeneous Face Recognition | Heterogeneous Face Recognition (HFR) is a challenging issue because of the large domain discrepancy and a lack of heterogeneous data. This paper considers HFR as a dual generation problem, and proposes a novel Dual Variational Generation (DVG) framework. It generates large-scale new paired heterogeneous images with the... | ['Huaibo Huang', 'Yibo Hu', 'Xiang Wu', 'Ran He', 'Chaoyou Fu'] | 2019-12-01 | null | null | null | neurips-2019-12 | ['heterogeneous-face-recognition'] | ['computer-vision'] | [ 1.07636392e-01 -1.35853386e-03 1.77403539e-02 -2.85318017e-01
-1.19780588e+00 -2.74830818e-01 4.24568981e-01 -8.27106416e-01
-3.12505066e-02 8.74890566e-01 1.26995638e-01 2.74405599e-01
-3.47352251e-02 -7.85960317e-01 -7.91536331e-01 -1.06920898e+00
4.98607159e-01 5.68790019e-01 -2.22940132e-01 -1.85206756... | [13.037351608276367, 0.25512441992759705] |
8557b5f1-306a-4597-986a-67e16c995318 | blind-acoustic-room-parameter-estimation | 2303.07449 | null | https://arxiv.org/abs/2303.07449v1 | https://arxiv.org/pdf/2303.07449v1.pdf | Blind Acoustic Room Parameter Estimation Using Phase Features | Modeling room acoustics in a field setting involves some degree of blind parameter estimation from noisy and reverberant audio. Modern approaches leverage convolutional neural networks (CNNs) in tandem with time-frequency representation. Using short-time Fourier transforms to develop these spectrogram-like features has... | ['Wenyu Jin', 'Adib Mehrabi', 'Christopher Ick'] | 2023-03-13 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 2.01898023e-01 -6.01586640e-01 7.57706821e-01 -4.40225393e-01
-1.40526867e+00 -6.90986276e-01 5.11638224e-01 1.36065319e-01
-4.44059879e-01 4.76001263e-01 8.42065215e-01 -1.91417947e-01
-4.38052446e-01 -4.76487130e-01 -5.85553586e-01 -8.33782375e-01
-4.98214006e-01 -5.01230657e-01 -3.45770985e-01 -4.51922148... | [15.172245025634766, 5.730571269989014] |
3a226431-ef1a-4e7c-98f4-5eb49990e6dc | compressed-sensing-constant-modulus | 2110.03385 | null | https://arxiv.org/abs/2110.03385v1 | https://arxiv.org/pdf/2110.03385v1.pdf | Compressed Sensing Constant Modulus Constrained Projection Matrix Design and High-Resolution DoA Estimation Methods | This paper proposes a compressed sensing-based high-resolution direction-of-arrival estimation method called gradient orthogonal matching pursuit (GOMP). It contains two main steps: a sparse coding approximation step using the well-known OMP method and a sequential iterative refinement step using a newly proposed gradi... | ['Martin Haardt', 'Khaled Ardah'] | 2021-10-07 | null | null | null | null | ['direction-of-arrival-estimation'] | ['audio'] | [ 5.54339945e-01 -4.12451714e-01 5.59618399e-02 -5.23212738e-02
-7.52190650e-01 1.30194113e-01 2.94048309e-01 -1.90482914e-01
-2.52492219e-01 5.33464193e-01 5.32281041e-01 -3.66553903e-01
-1.87354118e-01 -3.70942146e-01 -4.44620103e-01 -6.85525239e-01
-3.30266953e-01 -5.51311933e-02 2.64524043e-01 -9.84108821... | [6.512970924377441, 1.3813589811325073] |
36d6ccad-e823-435d-92ee-297deecb1dab | w-talc-weakly-supervised-temporal-activity | 1807.10418 | null | http://arxiv.org/abs/1807.10418v3 | http://arxiv.org/pdf/1807.10418v3.pdf | W-TALC: Weakly-supervised Temporal Activity Localization and Classification | Most activity localization methods in the literature suffer from the burden
of frame-wise annotation requirement. Learning from weak labels may be a
potential solution towards reducing such manual labeling effort. Recent years
have witnessed a substantial influx of tagged videos on the Internet, which can
serve as a ri... | ['Amit K. Roy-Chowdhury', 'Sujoy Paul', 'Sourya Roy'] | 2018-07-27 | w-talc-weakly-supervised-temporal-activity-1 | http://openaccess.thecvf.com/content_ECCV_2018/html/Sujoy_Paul_W-TALC_Weakly-supervised_Temporal_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Sujoy_Paul_W-TALC_Weakly-supervised_Temporal_ECCV_2018_paper.pdf | eccv-2018-9 | ['weakly-supervised-action-localization'] | ['computer-vision'] | [ 3.09300482e-01 -2.92565435e-01 -8.81738842e-01 -2.83047736e-01
-9.08140242e-01 -5.70657730e-01 7.12890148e-01 1.35276869e-01
-5.94200730e-01 7.62801766e-01 4.06486481e-01 1.87677085e-01
9.60827842e-02 -2.67037332e-01 -4.65702832e-01 -9.02284682e-01
-5.31479061e-01 -1.42194659e-01 5.97622752e-01 4.05806124... | [8.411432266235352, 0.6449281573295593] |
750cc919-4b94-4d59-9cf9-519ede077ee3 | render-for-cnn-viewpoint-estimation-in-images | 1505.05641 | null | http://arxiv.org/abs/1505.05641v1 | http://arxiv.org/pdf/1505.05641v1.pdf | Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views | Object viewpoint estimation from 2D images is an essential task in computer
vision. However, two issues hinder its progress: scarcity of training data with
viewpoint annotations, and a lack of powerful features. Inspired by the growing
availability of 3D models, we propose a framework to address both issues by
combinin... | ['Hao Su', 'Yangyan Li', 'Leonidas Guibas', 'Charles R. Qi'] | 2015-05-21 | render-for-cnn-viewpoint-estimation-in-images-1 | http://openaccess.thecvf.com/content_iccv_2015/html/Su_Render_for_CNN_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Su_Render_for_CNN_ICCV_2015_paper.pdf | iccv-2015-12 | ['viewpoint-estimation'] | ['computer-vision'] | [ 1.54117182e-01 4.35153022e-02 2.37380683e-01 -3.29291463e-01
-6.73873365e-01 -5.34019530e-01 8.85836244e-01 -4.91196930e-01
-8.15529823e-02 2.12788567e-01 5.53154498e-02 -1.23428740e-01
4.58219409e-01 -7.54717469e-01 -1.07982063e+00 -4.80834067e-01
4.64123577e-01 3.92040730e-01 4.79751527e-01 -2.34022111... | [8.384620666503906, -2.8108129501342773] |
b6c96444-c165-4fa7-b537-1bede4c2f910 | taiwanese-accented-mandarin-and-english-multi | null | null | https://aclanthology.org/2022.rocling-1.6 | https://aclanthology.org/2022.rocling-1.6.pdf | Taiwanese-Accented Mandarin and English Multi-Speaker Talking-Face Synthesis System | This paper proposes a multi-speaker talking-face synthesis system. The system incorporates voice cloning and lip-syncing technology to achieve text-to-talking-face generation by acquiring audio and video clips of any speaker and using zero-shot transfer learning. In addition, we used open-source corpora to train severa... | ['Chun-Hsin Wu', 'Kai-Chun Liao', 'Cho-Chun Hsieh', 'Jian-Peng Liao', 'Chia-Hsuan Lin'] | null | null | null | null | rocling-2022-11 | ['talking-face-generation', 'face-generation', 'voice-cloning'] | ['computer-vision', 'computer-vision', 'speech'] | [ 1.48728877e-01 1.75147921e-01 -1.31579846e-01 -4.75287616e-01
-1.07272816e+00 -2.68572569e-01 3.12768370e-01 -1.23356700e+00
1.61226526e-01 7.56626189e-01 5.87437451e-01 -2.98682034e-01
6.01679146e-01 -4.17327404e-01 -5.50624371e-01 -6.26213431e-01
5.01346946e-01 -4.43286598e-02 -6.82577640e-02 -3.62699240... | [14.812342643737793, 6.516161918640137] |
7e86cef6-90b4-4392-9593-b64d2a91fe84 | challenging-mitosis-detection-algorithms | 2211.16852 | null | https://arxiv.org/abs/2211.16852v1 | https://arxiv.org/pdf/2211.16852v1.pdf | Challenging mitosis detection algorithms: Global labels allow centroid localization | Mitotic activity is a crucial proliferation biomarker for the diagnosis and prognosis of different types of cancers. Nevertheless, mitosis counting is a cumbersome process for pathologists, prone to low reproducibility, due to the large size of augmented biopsy slides, the low density of mitotic cells, and pattern hete... | ['Valery Naranjo', 'Emiel Janssen', 'Sandra Morales', 'Julio Silva-Rodríguez', 'Umay Kiraz', 'Claudio Fernandez-Martín'] | 2022-11-30 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 3.09949249e-01 1.10730194e-01 -3.58629704e-01 3.94381806e-02
-9.24657822e-01 -5.71067572e-01 5.16307592e-01 6.84589863e-01
-8.22426260e-01 8.48125935e-01 -2.15696529e-01 -2.17160821e-01
1.07728645e-01 -6.46538019e-01 -4.47341681e-01 -1.29999268e+00
2.49101475e-01 5.11591256e-01 4.72849309e-01 2.97396064... | [14.960721015930176, -3.107600212097168] |
8ae1c731-6abb-456f-b164-b2e705ea0a64 | hierarchically-supervised-latent-dirichlet | null | null | http://papers.nips.cc/paper/4313-hierarchically-supervised-latent-dirichlet-allocation | http://papers.nips.cc/paper/4313-hierarchically-supervised-latent-dirichlet-allocation.pdf | Hierarchically Supervised Latent Dirichlet Allocation | We introduce hierarchically supervised latent Dirichlet allocation (HSLDA), a model for hierarchically and multiply labeled bag-of-word data. Examples of such data include web pages and their placement in directories, product descriptions and associated categories from product hierarchies, and free-text clinical record... | ['Adler J. Perotte', 'Frank Wood', 'Noemie Elhadad', 'Nicholas Bartlett'] | 2011-12-01 | null | null | null | neurips-2011-12 | ['product-categorization'] | ['miscellaneous'] | [-9.10821036e-02 2.19459206e-01 -8.34406257e-01 -8.55098724e-01
-9.47195351e-01 -5.28358102e-01 3.66161674e-01 8.96021366e-01
-1.03788495e-01 3.32916737e-01 7.99031734e-01 -2.47106835e-01
-2.69503981e-01 -6.96242034e-01 -8.18733796e-02 -7.23184824e-01
-2.92982697e-01 1.15326428e+00 -2.42945462e-01 3.27058643... | [9.52619743347168, 4.393645286560059] |
0028681e-51b0-4bd9-b838-7729aa03e471 | enforcing-reasoning-in-visual-commonsense | 1910.11124 | null | https://arxiv.org/abs/1910.11124v2 | https://arxiv.org/pdf/1910.11124v2.pdf | Enforcing Reasoning in Visual Commonsense Reasoning | The task of Visual Commonsense Reasoning is extremely challenging in the sense that the model has to not only be able to answer a question given an image, but also be able to learn to reason. The baselines introduced in this task are quite limiting because two networks are trained for predicting answers and rationales ... | ['Hammad A. Ayyubi', 'Md. Mehrab Tanjim', 'David J. Kriegman'] | 2019-10-21 | null | null | null | null | ['visual-commonsense-reasoning'] | ['reasoning'] | [ 3.68905395e-01 4.83622342e-01 1.32138461e-01 -5.97216487e-01
-9.31416392e-01 -7.47568369e-01 7.45998859e-01 4.49179821e-02
-4.57512885e-01 6.82909429e-01 2.20581442e-01 -5.45397699e-01
1.06297277e-01 -6.77054584e-01 -7.11165369e-01 -3.60085964e-01
5.55752754e-01 4.07671332e-01 2.44029179e-01 -1.17950805... | [10.77768611907959, 1.7963685989379883] |
1946372b-b1dc-46cf-8d86-f6c0a2889c4a | 3d-convolution-neural-network-based-person | 2106.03136 | null | https://arxiv.org/abs/2106.03136v1 | https://arxiv.org/pdf/2106.03136v1.pdf | 3D Convolution Neural Network based Person Identification using Gait cycles | Human identification plays a prominent role in terms of security. In modern times security is becoming the key term for an individual or a country, especially for countries which are facing internal or external threats. Gait analysis is interpreted as the systematic study of the locomotive in humans. It can be used to ... | ['Rijo Jackson Tom', 'Supraja P', 'Ravi Shekhar Tiwari'] | 2021-06-06 | null | null | null | null | ['person-identification'] | ['computer-vision'] | [ 1.38181940e-01 -4.91280019e-01 -9.72434804e-02 -1.64259989e-02
2.62266934e-01 -3.05653382e-02 1.78062782e-01 2.91203763e-02
-9.91293013e-01 6.27060175e-01 3.06839347e-02 2.72397012e-01
1.28268406e-01 -8.67562056e-01 -1.68852359e-01 -7.83011615e-01
-4.58887845e-01 1.72880486e-01 4.50879425e-01 -2.67154396... | [14.157527923583984, 1.460263729095459] |
ab0fca47-5c78-464c-a118-5e62955cd790 | exact-recovery-in-the-general-hypergraph | 2105.04770 | null | https://arxiv.org/abs/2105.04770v2 | https://arxiv.org/pdf/2105.04770v2.pdf | Exact Recovery in the General Hypergraph Stochastic Block Model | This paper investigates fundamental limits of exact recovery in the general d-uniform hypergraph stochastic block model (d-HSBM), wherein n nodes are partitioned into k disjoint communities with relative sizes (p1,..., pk). Each subset of nodes with cardinality d is generated independently as an order-d hyperedge with ... | ['Vincent Y. F. Tan', 'Qiaosheng Zhang'] | 2021-05-11 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 3.43008757e-01 6.02798700e-01 -5.42653263e-01 2.38802209e-01
-9.31263924e-01 -8.25264871e-01 3.07240874e-01 1.96439832e-01
-2.69845445e-02 5.78681886e-01 5.02060987e-02 -3.80736589e-01
-3.10972214e-01 -8.56283128e-01 -7.35122263e-01 -1.26350045e+00
-7.25748479e-01 8.61071289e-01 3.75126183e-01 3.02431025... | [6.895145893096924, 5.1207475662231445] |
3c2e9a52-31c8-4ac7-a760-95010ab4815c | on-practical-robust-reinforcement-learning | 2305.06657 | null | https://arxiv.org/abs/2305.06657v2 | https://arxiv.org/pdf/2305.06657v2.pdf | On Practical Robust Reinforcement Learning: Practical Uncertainty Set and Double-Agent Algorithm | We study a robust reinforcement learning (RL) with model uncertainty. Given nominal Markov decision process (N-MDP) that generate samples for training, an uncertainty set is defined, which contains some perturbed MDPs from N-MDP for the purpose of reflecting potential mismatched between training (i.e., N-MDP) and testi... | ['SongNam Hong', 'Ukjo Hwang'] | 2023-05-11 | null | null | null | null | ['q-learning'] | ['methodology'] | [-3.29479694e-01 2.98298031e-01 -3.29430461e-01 4.48689200e-02
-1.26501441e+00 -4.34759885e-01 2.41091624e-01 -2.64356993e-02
-4.71490353e-01 1.32451558e+00 -1.39739245e-01 -4.58721101e-01
-5.45555711e-01 -7.70840168e-01 -9.16446924e-01 -1.02322185e+00
-3.04874361e-01 5.99312127e-01 2.09928658e-02 -2.12510914... | [4.312176704406738, 2.366952657699585] |
3d530d18-0439-47a0-9249-7c08f21a6c03 | lamner-code-comment-generation-using | 2204.09654 | null | https://arxiv.org/abs/2204.09654v1 | https://arxiv.org/pdf/2204.09654v1.pdf | LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognition | Code comment generation is the task of generating a high-level natural language description for a given code method or function. Although researchers have been studying multiple ways to generate code comments automatically, previous work mainly considers representing a code token in its entirety semantics form only (e.... | ['Fatemeh Fard', 'Fuxiang Chen', 'Rishab Sharma'] | 2022-04-05 | null | null | null | null | ['code-comment-generation', 'comment-generation'] | ['computer-code', 'natural-language-processing'] | [ 9.36447158e-02 1.71943992e-01 -3.59158903e-01 -4.24879789e-01
-9.61690664e-01 -6.47276819e-01 5.45165122e-01 4.59004402e-01
-7.73243681e-02 4.84150618e-01 4.23050702e-01 -3.47268909e-01
7.34310091e-01 -7.89055347e-01 -7.45692611e-01 -1.00977384e-01
3.09931044e-03 -8.37909430e-02 1.97689429e-01 -1.05735406... | [7.678467750549316, 7.909031391143799] |
952c8673-c36d-497c-a66f-6b31e73b4c3e | financial-sentiment-analysis-using-finbert | 2306.02136 | null | https://arxiv.org/abs/2306.02136v1 | https://arxiv.org/pdf/2306.02136v1.pdf | Financial sentiment analysis using FinBERT with application in predicting stock movement | We apply sentiment analysis in financial context using FinBERT, and build a deep neural network model based on LSTM to predict the movement of financial market movement. We apply this model on stock news dataset, and compare its effectiveness to BERT, LSTM and classical ARIMA model. We find that sentiment is an effecti... | ['Andy Zeng', 'Tingsong Jiang'] | 2023-06-03 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-9.33077633e-01 -6.61782861e-01 -3.46761614e-01 -3.70378822e-01
1.62048429e-01 -5.86486340e-01 8.90054226e-01 -4.59373802e-01
-6.73615158e-01 7.35609412e-01 5.74749768e-01 -7.41842568e-01
1.39772296e-01 -1.34942794e+00 -5.88573813e-01 -1.41135484e-01
-3.72162133e-01 1.04270592e-01 3.38328481e-01 -8.28878999... | [4.448798656463623, 4.246963977813721] |
abd71044-e2a9-4f9d-a4aa-92c318640d4a | the-invertible-u-net-for-optical-flow-free | 2103.09576 | null | https://arxiv.org/abs/2103.09576v3 | https://arxiv.org/pdf/2103.09576v3.pdf | The U-Net based GLOW for Optical-Flow-free Video Interframe Generation | Video frame interpolation is the task of creating an interframe between two adjacent frames along the time axis. So, instead of simply averaging two adjacent frames to create an intermediate image, this operation should maintain semantic continuity with the adjacent frames. Most conventional methods use optical flow, a... | ['Donghoon Han', 'Nojun Kwak', 'Saem Park'] | 2021-03-17 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 1.47713795e-01 -6.74858093e-02 1.44246835e-02 -1.39121577e-01
1.38916567e-01 -1.99351966e-01 5.85324347e-01 -5.83557904e-01
-3.52948487e-01 1.08824646e+00 -2.86098979e-02 -1.59374654e-01
1.76736280e-01 -1.02700281e+00 -8.25420260e-01 -7.38118112e-01
4.12366800e-02 -7.72370547e-02 5.06786346e-01 -1.56887785... | [10.782602310180664, -1.490761160850525] |
7b1e7276-470c-4601-8cd0-9cf91baa544d | document-modeling-with-external-attention-for | null | null | https://aclanthology.org/P18-1188 | https://aclanthology.org/P18-1188.pdf | Document Modeling with External Attention for Sentence Extraction | Document modeling is essential to a variety of natural language understanding tasks. We propose to use external information to improve document modeling for problems that can be framed as sentence extraction. We develop a framework composed of a hierarchical document encoder and an attention-based extractor with attent... | ['Yi Chang', 'Jiangsheng Yu', 'Nikos Papasarantopoulos', 'Mirella Lapata', 'Shay B. Cohen', 'Shashi Narayan', 'Ronald Cardenas'] | 2018-07-01 | null | null | null | acl-2018-7 | ['extractive-document-summarization'] | ['natural-language-processing'] | [ 4.18832511e-01 4.69916672e-01 -1.45403966e-01 -5.21879733e-01
-1.32481539e+00 -6.62782550e-01 9.21724916e-01 3.59116167e-01
-6.57925308e-01 7.04544604e-01 1.09698081e+00 -1.29848659e-01
2.76714295e-01 -5.96627831e-01 -8.82292271e-01 -9.78629962e-02
4.84037519e-01 8.03169191e-01 -2.30291467e-02 -2.41684332... | [12.346600532531738, 9.352109909057617] |
bec05676-d6cc-47fd-8db1-31d5b2a5ac7e | active-visual-information-gathering-for | 2007.08037 | null | https://arxiv.org/abs/2007.08037v3 | https://arxiv.org/pdf/2007.08037v3.pdf | Active Visual Information Gathering for Vision-Language Navigation | Vision-language navigation (VLN) is the task of entailing an agent to carry out navigational instructions inside photo-realistic environments. One of the key challenges in VLN is how to conduct a robust navigation by mitigating the uncertainty caused by ambiguous instructions and insufficient observation of the environ... | ['Jianbing Shen', 'Wenguan Wang', 'Tianmin Shu', 'Hanqing Wang', 'Wei Liang'] | 2020-07-15 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4046_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123670307.pdf | eccv-2020-8 | ['vision-language-navigation'] | ['computer-vision'] | [ 1.52205423e-01 8.65658000e-02 -1.70882214e-02 -3.99941981e-01
-4.91088510e-01 -5.26998758e-01 6.19198143e-01 -9.98192877e-02
-9.95884001e-01 7.18686819e-01 1.48456007e-01 -5.65750420e-01
-1.96151912e-01 -6.79575801e-01 -6.20291114e-01 -8.14441919e-01
-4.42334637e-02 6.06741667e-01 2.48492718e-01 -5.46924233... | [4.52008056640625, 0.5736924409866333] |
f760fc43-cf9d-48c1-a73b-40b89e2ac2ac | streaming-belief-propagation-for-community | 2106.04805 | null | https://arxiv.org/abs/2106.04805v2 | https://arxiv.org/pdf/2106.04805v2.pdf | Streaming Belief Propagation for Community Detection | The community detection problem requires to cluster the nodes of a network into a small number of well-connected "communities". There has been substantial recent progress in characterizing the fundamental statistical limits of community detection under simple stochastic block models. However, in real-world applications... | ['Jakab Tardos', 'Ashkan Norouzi-Fard', 'Andrea Montanari', 'Filipe Miguel Goncalves de Almeida', 'Andre Linhares', 'Mohammadhossein Bateni', 'Yuchen Wu'] | 2021-06-09 | null | http://proceedings.neurips.cc/paper/2021/hash/e2a2dcc36a08a345332c751b2f2e476c-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/e2a2dcc36a08a345332c751b2f2e476c-Paper.pdf | neurips-2021-12 | ['stochastic-block-model'] | ['graphs'] | [ 3.49178761e-01 1.39587834e-01 -3.52138638e-01 -4.02441509e-02
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-6.51084840e-01 5.89213550e-01 7.64785409e-01 5.97043410... | [6.888237953186035, 5.121920585632324] |
62f2c76b-d470-4f79-bbc4-9032921a1526 | what-makes-us-laugh-investigations-into | null | null | https://aclanthology.org/W18-1101 | https://aclanthology.org/W18-1101.pdf | What makes us laugh? Investigations into Automatic Humor Classification | Most scholarly works in the field of computational detection of humour derive their inspiration from the incongruity theory. Incongruity is an indispensable facet in drawing a line between humorous and non-humorous occurrences but is immensely inadequate in shedding light on what actually made the particular occurrence... | ['Vikram Ahuja', 'Navjyoti Singh', 'Taradheesh Bali'] | 2018-06-01 | null | null | null | ws-2018-6 | ['humor-detection'] | ['natural-language-processing'] | [-2.07824960e-01 -3.03825647e-01 -6.77204579e-02 -2.89719477e-02
1.63807109e-01 -5.70737302e-01 9.44212496e-01 2.07537800e-01
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3.30268443e-01 3.08778942e-01 2.48560756e-01 -7.98986793... | [8.91905403137207, 10.96629810333252] |
14332219-6965-4bbf-b538-182ebe2e7e09 | cross-lingual-cross-modal-consolidation-for | null | null | https://aclanthology.org/2022.findings-naacl.142 | https://aclanthology.org/2022.findings-naacl.142.pdf | Cross-Lingual Cross-Modal Consolidation for Effective Multilingual Video Corpus Moment Retrieval | Existing multilingual video corpus moment retrieval (mVCMR) methods are mainly based on a two-stream structure. The visual stream utilizes the visual content in the video to estimate the query-visual similarity, and the subtitle stream exploits the query-subtitle similarity. The final query-video similarity ensembles s... | ['Ping Li', 'Mingming Sun', 'Hanyu Peng', 'Tan Yu', 'Jiaheng Liu'] | null | null | null | null | findings-naacl-2022-7 | ['moment-retrieval', 'video-similarity'] | ['computer-vision', 'computer-vision'] | [-2.28310928e-01 -8.85700941e-01 -4.20735240e-01 -8.25714394e-02
-1.22415483e+00 -6.39337420e-01 8.30938697e-01 8.96009579e-02
-4.32456136e-01 1.42491758e-01 4.00861770e-01 -1.13249190e-01
7.52164051e-02 -3.78098458e-01 -5.56961119e-01 -6.56308651e-01
5.10070145e-01 5.18745743e-02 5.14530957e-01 -2.37646848... | [10.300559997558594, 0.9188964366912842] |
9dd33e77-6192-42e1-92ab-471a1f16898b | a-brief-survey-on-person-recognition-at-a | 2212.08969 | null | https://arxiv.org/abs/2212.08969v1 | https://arxiv.org/pdf/2212.08969v1.pdf | A Brief Survey on Person Recognition at a Distance | Person recognition at a distance entails recognizing the identity of an individual appearing in images or videos collected by long-range imaging systems such as drones or surveillance cameras. Despite recent advances in deep convolutional neural networks (DCNNs), this remains challenging. Images or videos collected by ... | ['Rama Chellappa', 'Thirimachos Bourlai', 'Shuowen Hu', 'Carlos D. Castillo', 'Joshua Gleason', 'Neehar Peri', 'Chrisopher B. Nalty'] | 2022-12-17 | null | null | null | null | ['person-re-identification', 'person-recognition'] | ['computer-vision', 'computer-vision'] | [ 3.84396404e-01 -1.01694441e+00 3.75817060e-01 -5.46682358e-01
-3.21122140e-01 -6.74363494e-01 4.33096617e-01 -3.90644282e-01
-6.87266350e-01 8.03028762e-01 -5.18720224e-02 4.40456212e-01
-2.07479000e-01 -5.83481610e-01 -4.03626740e-01 -8.16145301e-01
-2.87272573e-01 8.55729878e-02 -7.04313219e-01 7.37982839... | [14.328944206237793, 1.036224126815796] |
1be7a59a-8331-413a-9448-4c8b78267b54 | elfis-expert-learning-for-fine-grained-image | 2303.09269 | null | https://arxiv.org/abs/2303.09269v1 | https://arxiv.org/pdf/2303.09269v1.pdf | ELFIS: Expert Learning for Fine-grained Image Recognition Using Subsets | Fine-Grained Visual Recognition (FGVR) tackles the problem of distinguishing highly similar categories. One of the main approaches to FGVR, namely subset learning, tries to leverage information from existing class taxonomies to improve the performance of deep neural networks. However, these methods rely on the existenc... | ['Petia Radeva', 'Bhalaji Nagarajan', 'Ignacio Sarasúa', 'Marc Bolaños', 'Jesús M. Rodríguez-de-Vera', 'Pablo Villacorta'] | 2023-03-16 | null | null | null | null | ['fine-grained-image-recognition', 'fine-grained-visual-recognition'] | ['computer-vision', 'computer-vision'] | [ 3.12696621e-02 1.98763292e-02 -3.33014280e-01 -6.16910934e-01
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2.15191156e-01 6.30155683e-01 4.17634338e-01 -1.38850152... | [9.584187507629395, 2.172175645828247] |
24014c27-eb55-4908-a37b-11c48ecad400 | systemic-risk-of-optioned-portfolios | 2209.04685 | null | https://arxiv.org/abs/2209.04685v1 | https://arxiv.org/pdf/2209.04685v1.pdf | Systemic Risk of Optioned Portfolios: Controllability and Optimization | We investigate the portfolio selection problem against the systemic risk which is measured by CoVaR. We first demonstrate that the systemic risk of pure stock portfolios is essentially uncontrollable due to the contagion effect and the seesaw effect. Next, we prove that it is necessary and sufficient to introduce optio... | ['Jiali Ma', 'Xueting Cui', 'Shushang Zhu', 'Xiaochuan Pang'] | 2022-09-10 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-5.15757442e-01 2.21247934e-02 -1.83396563e-01 3.41364145e-01
-2.55032390e-01 -1.03225231e+00 2.54196763e-01 -4.05312240e-01
-4.04424500e-03 7.26771832e-01 1.49363205e-01 -4.77319658e-01
-6.38327599e-01 -1.01736856e+00 -3.25007915e-01 -8.02081764e-01
-1.72877312e-01 -4.43850271e-02 -9.92631465e-02 -1.57431185... | [4.935173988342285, 3.954319953918457] |
bb3bc4aa-e04f-478b-9b69-bdb6b73cce97 | dynamic-decision-boundary-for-one-class | 2004.02273 | null | https://arxiv.org/abs/2004.02273v1 | https://arxiv.org/pdf/2004.02273v1.pdf | Dynamic Decision Boundary for One-class Classifiers applied to non-uniformly Sampled Data | A typical issue in Pattern Recognition is the non-uniformly sampled data, which modifies the general performance and capability of machine learning algorithms to make accurate predictions. Generally, the data is considered non-uniformly sampled when in a specific area of data space, they are not enough, leading us to m... | ['Riccardo La Grassa', 'Nicola Landro', 'Ignazio Gallo'] | 2020-04-05 | null | null | null | null | ['one-class-classifier'] | ['methodology'] | [ 3.97327214e-01 3.13361794e-01 -3.38349223e-01 -3.88572991e-01
-2.78087646e-01 -2.39141747e-01 3.71965528e-01 4.87654418e-01
-3.48005444e-01 1.04857731e+00 -5.94536960e-01 -3.25916857e-01
-4.45051730e-01 -1.08897114e+00 -5.76217532e-01 -8.83392334e-01
1.67618110e-03 7.40000665e-01 6.85102284e-01 9.62915421... | [8.499464988708496, 4.211613655090332] |
d7c32477-e2f5-4f2f-aa9b-687c35b6a18f | fusing-rgbd-tracking-and-segmentation-tree | 2104.00205 | null | https://arxiv.org/abs/2104.00205v1 | https://arxiv.org/pdf/2104.00205v1.pdf | Fusing RGBD Tracking and Segmentation Tree Sampling for Multi-Hypothesis Volumetric Segmentation | Despite rapid progress in scene segmentation in recent years, 3D segmentation methods are still limited when there is severe occlusion. The key challenge is estimating the segment boundaries of (partially) occluded objects, which are inherently ambiguous when considering only a single frame. In this work, we propose Mu... | ['Dmitry Berenson', 'Kun Huang', 'Andrew Price'] | 2021-04-01 | null | null | null | null | ['scene-segmentation'] | ['computer-vision'] | [ 5.22156417e-01 -1.77931786e-01 -8.44955519e-02 -2.58036792e-01
-5.78754663e-01 -9.24963355e-01 2.74780005e-01 1.44276381e-01
-3.38182747e-01 6.91212177e-01 -9.87458006e-02 -6.84923232e-02
1.79410547e-01 -3.97413343e-01 -6.60383523e-01 -3.14713836e-01
-2.86065377e-02 7.95729578e-01 1.13296533e+00 1.42730817... | [9.136979103088379, -0.48007291555404663] |
7f73422c-56e4-4615-b9e7-0aeae6782344 | metric-scale-truncation-robust-heatmaps-for | 2003.02953 | null | https://arxiv.org/abs/2003.02953v1 | https://arxiv.org/pdf/2003.02953v1.pdf | Metric-Scale Truncation-Robust Heatmaps for 3D Human Pose Estimation | Heatmap representations have formed the basis of 2D human pose estimation systems for many years, but their generalizations for 3D pose have only recently been considered. This includes 2.5D volumetric heatmaps, whose X and Y axes correspond to image space and the Z axis to metric depth around the subject. To obtain me... | ['Bastian Leibe', 'István Sárándi', 'Timm Linder', 'Kai O. Arras'] | 2020-03-05 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [-1.51537850e-01 2.74063230e-01 -1.99111439e-02 -5.83342552e-01
-7.00049460e-01 -5.59064806e-01 2.07289129e-01 -6.89847916e-02
-5.72137773e-01 4.53786284e-01 4.39636767e-01 1.57846063e-01
8.27616900e-02 -6.56345069e-01 -8.18618715e-01 -2.59773463e-01
-2.19368964e-01 9.43048537e-01 -1.17916465e-02 -1.59159452... | [6.991746425628662, -0.9270896315574646] |
eabb0e13-23ca-4591-9481-b5fcc898a2d6 | a-deep-dive-into-explainable-self-supervised | 2306.10798 | null | https://arxiv.org/abs/2306.10798v2 | https://arxiv.org/pdf/2306.10798v2.pdf | ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformers | In this paper we delve into the properties of transformers, attained through self-supervision, in the point cloud domain. Specifically, we evaluate the effectiveness of Masked Autoencoding as a pretraining scheme, and explore Momentum Contrast as an alternative. In our study we investigate the impact of data quantity o... | ['Adrian Munteanu', 'Konstantinos Moustakas', 'Vlassis Fotis', 'Ioannis Romanelis'] | 2023-06-19 | null | null | null | null | ['3d-point-cloud-classification', 'explainable-artificial-intelligence'] | ['computer-vision', 'computer-vision'] | [ 4.50880527e-02 1.92874387e-01 -3.45873795e-02 -1.74490452e-01
-6.77797735e-01 -7.77009130e-01 8.57980967e-01 1.84107572e-01
-2.23532394e-01 2.99688607e-01 4.07051116e-01 -2.51149207e-01
-1.47804156e-01 -8.04004610e-01 -9.83777583e-01 -7.19044864e-01
3.58498879e-02 4.91241187e-01 4.31224883e-01 -2.22383350... | [9.505839347839355, 1.7830580472946167] |
d86e3f4a-ffd5-4eb3-ae45-5ba2f9223e6d | fvor-robust-joint-shape-and-pose-optimization | 2205.07763 | null | https://arxiv.org/abs/2205.07763v1 | https://arxiv.org/pdf/2205.07763v1.pdf | FvOR: Robust Joint Shape and Pose Optimization for Few-view Object Reconstruction | Reconstructing an accurate 3D object model from a few image observations remains a challenging problem in computer vision. State-of-the-art approaches typically assume accurate camera poses as input, which could be difficult to obtain in realistic settings. In this paper, we present FvOR, a learning-based object recons... | ['QiXing Huang', 'Qi Shan', 'Zaiwei Zhang', 'Miguel Angel Bautista', 'Zhile Ren', 'Zhenpei Yang'] | 2022-05-16 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Yang_FvOR_Robust_Joint_Shape_and_Pose_Optimization_for_Few-View_Object_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Yang_FvOR_Robust_Joint_Shape_and_Pose_Optimization_for_Few-View_Object_CVPR_2022_paper.pdf | cvpr-2022-1 | ['object-reconstruction'] | ['computer-vision'] | [-1.94769144e-01 -5.35484590e-02 8.60079899e-02 -4.16064650e-01
-1.13246214e+00 -7.49678850e-01 4.07132506e-01 -5.15851676e-01
-6.63492605e-02 1.57591477e-01 1.32336663e-02 -3.85105647e-02
8.35425109e-02 -4.47335035e-01 -1.21245992e+00 -2.73540020e-01
4.63819712e-01 1.10139000e+00 2.27336988e-01 5.97827882... | [8.012419700622559, -2.655604362487793] |
7c306061-aa96-42f5-9282-7bb1f6fe5822 | real-time-human-detection-in-fire-scenarios | 2307.04223 | null | https://arxiv.org/abs/2307.04223v1 | https://arxiv.org/pdf/2307.04223v1.pdf | Real-time Human Detection in Fire Scenarios using Infrared and Thermal Imaging Fusion | Fire is considered one of the most serious threats to human lives which results in a high probability of fatalities. Those severe consequences stem from the heavy smoke emitted from a fire that mostly restricts the visibility of escaping victims and rescuing squad. In such hazardous circumstances, the use of a vision-b... | ['My-Ha Le', 'Nghe-Nhan Truong', 'Truong-Dong Do'] | 2023-07-09 | null | null | null | null | ['human-detection'] | ['computer-vision'] | [ 5.51709294e-01 -5.56160629e-01 5.18192232e-01 8.60159025e-02
-1.71714097e-01 -4.16533917e-01 4.65830892e-01 -1.23057932e-01
-8.88982892e-01 4.45838332e-01 -2.88713068e-01 -5.87242767e-02
1.22525014e-01 -9.21458781e-01 -2.28391141e-01 -1.04779375e+00
4.01390076e-01 -2.52767324e-01 5.33803582e-01 5.81338480... | [9.053510665893555, -1.0377546548843384] |
dd344407-e668-4c1f-b0c7-58fc805d9f71 | neural-architectures-for-nested-ner-through-1 | 1908.06926 | null | https://arxiv.org/abs/1908.06926v1 | https://arxiv.org/pdf/1908.06926v1.pdf | Neural Architectures for Nested NER through Linearization | We propose two neural network architectures for nested named entity recognition (NER), a setting in which named entities may overlap and also be labeled with more than one label. We encode the nested labels using a linearized scheme. In our first proposed approach, the nested labels are modeled as multilabels correspon... | ['Jan Hajič', 'Jana Straková', 'Milan Straka'] | 2019-08-19 | neural-architectures-for-nested-ner-through | https://aclanthology.org/P19-1527 | https://aclanthology.org/P19-1527.pdf | acl-2019-7 | ['hard-attention', 'nested-named-entity-recognition', 'nested-mention-recognition'] | ['methodology', 'natural-language-processing', 'natural-language-processing'] | [-1.17005862e-01 3.52254808e-01 -2.20061764e-02 -6.04158640e-01
-6.18948340e-01 -9.07762587e-01 5.92192590e-01 4.70755965e-01
-1.18876827e+00 9.46488857e-01 4.52681363e-01 -4.42369640e-01
2.93461949e-01 -6.21931791e-01 -6.02086306e-01 -4.64894831e-01
-2.52739966e-01 7.32214034e-01 -2.12611943e-01 1.33386970... | [9.809535026550293, 9.723176956176758] |
f3d60001-4530-4191-8f3c-c319fb9c8cd5 | a-unified-multi-view-multi-person-tracking | 2302.03820 | null | https://arxiv.org/abs/2302.03820v1 | https://arxiv.org/pdf/2302.03820v1.pdf | A Unified Multi-view Multi-person Tracking Framework | Although there is a significant development in 3D Multi-view Multi-person Tracking (3D MM-Tracking), current 3D MM-Tracking frameworks are designed separately for footprint and pose tracking. Specifically, frameworks designed for footprint tracking cannot be utilized in 3D pose tracking, because they directly obtain 3D... | ['Shan Jiang', 'Shoichi Masui', 'Hiroaki Fujimoto', 'Sosuke Yamao', 'Shigeyuki Odashima', 'Fan Yang'] | 2023-02-08 | null | null | null | null | ['multiple-people-tracking', 'pose-tracking', '3d-multi-person-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-3.59385580e-01 -4.93077278e-01 -2.59363085e-01 3.75197679e-02
-5.76672912e-01 -7.93798685e-01 3.32083523e-01 -2.42579356e-01
-2.42958590e-01 4.30735797e-01 1.48298085e-01 1.69128194e-01
3.11680771e-02 -7.43285120e-01 -5.17525136e-01 -3.95100296e-01
2.42900640e-01 5.45355916e-01 4.11609650e-01 -1.74504489... | [7.02747106552124, -1.0172698497772217] |
39498949-deb0-45a6-a8b9-743657cef03e | transformer-patcher-one-mistake-worth-one | 2301.09785 | null | https://arxiv.org/abs/2301.09785v1 | https://arxiv.org/pdf/2301.09785v1.pdf | Transformer-Patcher: One Mistake worth One Neuron | Large Transformer-based Pretrained Language Models (PLMs) dominate almost all Natural Language Processing (NLP) tasks. Nevertheless, they still make mistakes from time to time. For a model deployed in an industrial environment, fixing these mistakes quickly and robustly is vital to improve user experiences. Previous wo... | ['Zhang Xiong', 'Wenge Rong', 'Jie zhou', 'Xiaofeng Zhang', 'Yikang Shen', 'Zeyu Huang'] | 2023-01-24 | null | null | null | null | ['model-editing'] | ['natural-language-processing'] | [ 3.59444022e-01 1.78901628e-01 6.08713254e-02 -5.10852039e-01
-4.25576895e-01 -4.65510726e-01 2.91350573e-01 -1.63983151e-01
-3.94310713e-01 6.03259623e-01 -4.37717199e-01 -5.27599275e-01
2.36147959e-02 -7.64403343e-01 -1.00427055e+00 -3.09792340e-01
3.82048130e-01 5.22989511e-01 1.66131571e-01 -4.67934638... | [9.698269844055176, 7.477914810180664] |
07500b27-8dd3-4626-9869-c6b78693bfe4 | font-flow-guided-one-shot-talking-head | 2303.17789 | null | https://arxiv.org/abs/2303.17789v1 | https://arxiv.org/pdf/2303.17789v1.pdf | FONT: Flow-guided One-shot Talking Head Generation with Natural Head Motions | One-shot talking head generation has received growing attention in recent years, with various creative and practical applications. An ideal natural and vivid generated talking head video should contain natural head pose changes. However, it is challenging to map head pose sequences from driving audio since there exists... | ['Jizhong Han', 'Jiao Dai', 'Cai Yu', 'Yesheng Chai', 'Xiaomeng Fu', 'Xi Wang', 'Jin Liu'] | 2023-03-31 | null | null | null | null | ['talking-head-generation', 'pose-prediction'] | ['computer-vision', 'computer-vision'] | [ 1.71201080e-02 2.03759789e-01 1.32931709e-01 -5.49191952e-01
-1.02243853e+00 -1.50363669e-01 6.00037277e-01 -7.35047460e-01
4.03732568e-01 4.27234203e-01 8.56424809e-01 6.21706367e-01
2.27182031e-01 -1.16043538e-01 -6.72783911e-01 -8.01841319e-01
-3.54599543e-02 2.36247241e-01 -4.36457954e-02 -1.92303762... | [13.225131034851074, -0.42579248547554016] |
7fcd09e0-fd92-4be5-82a0-616d044ae05a | dformer-diffusion-guided-transformer-for | 2306.03437 | null | https://arxiv.org/abs/2306.03437v2 | https://arxiv.org/pdf/2306.03437v2.pdf | DFormer: Diffusion-guided Transformer for Universal Image Segmentation | This paper introduces an approach, named DFormer, for universal image segmentation. The proposed DFormer views universal image segmentation task as a denoising process using a diffusion model. DFormer first adds various levels of Gaussian noise to ground-truth masks, and then learns a model to predict denoising masks f... | ['Yanwei Pang', 'Fahad Shahbaz Khan', 'Jin Xie', 'Rao Muhammad Anwer', 'Jiale Cao', 'Hefeng Wang'] | 2023-06-06 | null | null | null | null | ['panoptic-segmentation'] | ['computer-vision'] | [ 4.95569289e-01 1.33570224e-01 -1.12185948e-01 -5.73233783e-01
-9.72185552e-01 -7.23282933e-01 4.80302483e-01 -4.50255185e-01
-4.78281111e-01 3.69240850e-01 8.95989761e-02 -2.51304835e-01
3.91111046e-01 -7.89544880e-01 -8.56372833e-01 -7.08365381e-01
2.93345183e-01 5.17761767e-01 3.44610482e-01 1.57602042... | [9.560737609863281, 0.28495633602142334] |
27fba2a0-698b-4a77-812d-f7a91452e4e1 | wman-weakly-supervised-moment-alignment | null | null | https://openreview.net/forum?id=BJx4rerFwB | https://openreview.net/pdf?id=BJx4rerFwB | wMAN: WEAKLY-SUPERVISED MOMENT ALIGNMENT NETWORK FOR TEXT-BASED VIDEO SEGMENT RETRIEVAL | Given a video and a sentence, the goal of weakly-supervised video moment retrieval is to locate the video segment which is described by the sentence without having access to temporal annotations during training. Instead, a model must learn how to identify the correct segment (i.e. moment) when only being provided with... | ['Bryan A. Plummer', 'Kate Saenko', 'Huijuan Xu', 'Reuben Tan'] | 2019-09-25 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [ 1.83601588e-01 -1.37332186e-01 -5.64734459e-01 -5.00543356e-01
-9.05218959e-01 -4.85461086e-01 8.48118722e-01 2.28005335e-01
-5.30862927e-01 3.17754924e-01 4.82066810e-01 -6.49892911e-02
2.65902787e-01 -4.62203264e-01 -9.68273044e-01 -4.62968379e-01
-1.86373711e-01 2.22424030e-01 1.87080309e-01 -6.20931499... | [10.107789039611816, 0.7890902757644653] |
35a26d98-14ce-44b4-b868-d26f0fa4d6a9 | label-penet-sequential-label-propagation-and | 1910.02624 | null | https://arxiv.org/abs/1910.02624v3 | https://arxiv.org/pdf/1910.02624v3.pdf | Label-PEnet: Sequential Label Propagation and Enhancement Networks for Weakly Supervised Instance Segmentation | Weakly-supervised instance segmentation aims to detect and segment object instances precisely, given imagelevel labels only. Unlike previous methods which are composed of multiple offline stages, we propose Sequential Label Propagation and Enhancement Networks (referred as Label-PEnet) that progressively transform imag... | ['Weilin Huang', 'Sheng Guo', 'Matthew R. Scott', 'Weifeng Ge'] | 2019-10-07 | label-penet-sequential-label-propagation-and-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Ge_Label-PEnet_Sequential_Label_Propagation_and_Enhancement_Networks_for_Weakly_Supervised_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Ge_Label-PEnet_Sequential_Label_Propagation_and_Enhancement_Networks_for_Weakly_Supervised_ICCV_2019_paper.pdf | iccv-2019-10 | ['weakly-supervised-instance-segmentation', 'image-level-supervised-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 7.51461267e-01 4.07421380e-01 -4.50840175e-01 -6.74472034e-01
-9.00008738e-01 -6.05223298e-01 4.29241300e-01 2.08938271e-01
-6.22665882e-01 5.03185749e-01 -6.14173710e-01 -9.96276736e-02
2.21809745e-01 -6.78752244e-01 -9.69685376e-01 -6.47575498e-01
1.41138017e-01 6.62189186e-01 6.92866623e-01 4.72383529... | [9.48993968963623, 0.5899838209152222] |
4e6bb683-72b9-42c1-833a-6ebceb3d6ad2 | towards-a-gold-standard-corpus-for-variable | null | null | https://aclanthology.org/L18-1084 | https://aclanthology.org/L18-1084.pdf | Towards a Gold Standard Corpus for Variable Detection and Linking in Social Science Publications | null | ['Peter Mutschke', 'Andrea Zielinski'] | 2018-05-01 | towards-a-gold-standard-corpus-for-variable-1 | https://aclanthology.org/L18-1084 | https://aclanthology.org/L18-1084.pdf | lrec-2018-5 | ['variable-detection'] | ['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... | [-7.373117446899414, 3.747725009918213] |
f2156713-cb06-452b-ade9-c210b840ce99 | ray-space-motion-compensation-for-lenslet | 2207.00522 | null | https://arxiv.org/abs/2207.00522v1 | https://arxiv.org/pdf/2207.00522v1.pdf | Ray-Space Motion Compensation for Lenslet Plenoptic Video Coding | Plenoptic images and videos bearing rich information demand a tremendous amount of data storage and high transmission cost. While there has been much study on plenoptic image coding, investigations into plenoptic video coding have been very limited. We investigate the motion compensation for plenoptic video coding from... | ['Byeungwoo Jeon', 'Jonghoon Yim', 'Vinh Van Duong', 'Thuc Nguyen Huu'] | 2022-07-01 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 5.29641092e-01 -1.32192761e-01 2.54999194e-02 -9.55853835e-02
-1.63912550e-01 -2.18620881e-01 3.12837899e-01 -4.08292383e-01
-2.02527866e-01 7.63441205e-01 2.20690057e-01 -1.80342972e-01
-1.82715833e-01 -7.15095460e-01 -3.89219463e-01 -1.05455542e+00
1.62580714e-01 2.08238419e-02 6.22355342e-01 5.84865473... | [11.105462074279785, -2.156357526779175] |
62552830-b9e9-4dea-9a8b-323be67e7040 | open-source-german-distant-speech-recognition | null | null | https://link.springer.com/chapter/10.1007/978-3-319-24033-6_54 | https://download.hrz.tu-darmstadt.de/pub/FB20/Dekanat/Publikationen/LangTech/Radeck-ArnethEtAl_TSD2015_SpeechCorpus.pdf | Open Source German Distant Speech Recognition: Corpus and Acoustic Model | We present a new freely available corpus for German distant speech recognition and report speaker-independent word error rate (WER) results for two open source speech recognizers trained on this corpus. The corpus has been recorded in a controlled environment with three different microphones at a distance of one meter.... | ['and Chris Biemann', 'Max Mühlhäuser', 'Stefan Radomski', 'Evandro Gouvea', 'Arvid Lange', 'Benjamin Milde', 'Stephan Radeck-Arneth'] | 2015-12-11 | null | null | null | international-conference-on-text-speech-and | ['distant-speech-recognition'] | ['speech'] | [-1.21274009e-01 8.10818449e-02 3.80642205e-01 -6.15763307e-01
-1.47756982e+00 -6.67441547e-01 5.98430753e-01 -2.60281771e-01
-5.81284404e-01 3.37079167e-01 5.42995334e-01 -6.14725947e-01
2.84155697e-01 -1.39487118e-01 -1.33409619e-01 -7.16698527e-01
-1.97615623e-01 5.31249404e-01 1.80971697e-01 -2.25817055... | [14.52806282043457, 6.548745632171631] |
925ff465-90c7-442c-a683-908d210421f9 | cross-domain-few-shot-learning-via-meta | 2202.05713 | null | https://arxiv.org/abs/2202.05713v3 | https://arxiv.org/pdf/2202.05713v3.pdf | Cross Domain Few-Shot Learning via Meta Adversarial Training | Few-shot relation classification (RC) is one of the critical problems in machine learning. Current research merely focuses on the set-ups that both training and testing are from the same domain. However, in practice, this assumption is not always guaranteed. In this study, we present a novel model that takes into consi... | ['Yongyi Mao', 'Chune Li', 'Richong Zhang', 'Jirui Qi'] | 2022-02-11 | null | null | null | null | ['cross-domain-few-shot', 'cross-domain-few-shot-learning', 'few-shot-relation-classification', 'relation-classification', 'few-shot-relation-classification'] | ['computer-vision', 'computer-vision', 'methodology', 'natural-language-processing', 'natural-language-processing'] | [ 3.70690405e-01 1.93509549e-01 -2.22833529e-01 -3.47377062e-01
-2.83767372e-01 -3.26166034e-01 7.15700686e-01 -1.10839084e-01
-3.71552020e-01 1.04297280e+00 -2.59873033e-01 -1.89457491e-01
-1.03570364e-01 -1.12751114e+00 -5.21685183e-01 -5.61727643e-01
3.39323968e-01 4.40761000e-01 5.67236483e-01 -4.82782960... | [10.216848373413086, 3.1290273666381836] |
4343b255-ddb8-48ae-9091-7344f0043d8b | contextual-reasoning-for-scene-generation | 2305.02255 | null | https://arxiv.org/abs/2305.02255v1 | https://arxiv.org/pdf/2305.02255v1.pdf | Contextual Reasoning for Scene Generation (Technical Report) | We present a continuation to our previous work, in which we developed the MR-CKR framework to reason with knowledge overriding across contexts organized in multi-relational hierarchies. Reasoning is realized via ASP with algebraic measures, allowing for flexible definitions of preferences. In this paper, we show how to... | ['Daria Stepanova', 'Rafael Kiesel', 'Thomas Eiter', 'Loris Bozzato'] | 2023-05-03 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 9.13592929e-04 5.28353751e-01 8.76650885e-02 -7.06240594e-01
-2.45391473e-01 -6.01658165e-01 9.77545142e-01 4.58668262e-01
-2.10552320e-01 6.62046194e-01 8.15608278e-02 -3.08030277e-01
-6.45791709e-01 -1.27061164e+00 -8.16772103e-01 -2.42575690e-01
-1.27482563e-01 5.92714548e-01 8.72451782e-01 -4.50381070... | [8.768560409545898, 6.7373881340026855] |
3bfa6cdb-5171-4bb3-950b-7f0540395a72 | arsc-net-adventitious-respiratory-sound | null | null | https://ieeexplore.ieee.org/document/9669787/ | https://ieeexplore.ieee.org/document/9669787/ | ARSC-Net: Adventitious Respiratory Sound Classification Network Using Parallel Paths with Channel-Spatial Attention | Automatic identification of adventitious respiratory sound has still been a challenging problem in recent years. To address this challenge, we propose an adventitious respiratory sound classification network (ARSC-Net), which combines residual block with channel-spatial attention for accurate classification. Specifical... | ['Jianxin Wang', 'Fan Wu', 'Hulin Kuang', 'Jin Liu', 'Jianhong Cheng', 'Lei Xu'] | 2022-01-14 | null | null | null | ieee-international-conference-on-3 | ['sound-classification'] | ['audio'] | [ 1.33786062e-02 -5.62647164e-01 1.82971731e-01 1.34455889e-01
-9.74741161e-01 -2.25763902e-01 -3.54480296e-02 1.06472924e-01
-3.10723782e-01 2.48053864e-01 3.25897813e-01 -2.45745227e-01
-2.95703381e-01 -4.27322596e-01 -2.17795536e-01 -7.58915722e-01
-1.09584682e-01 -3.05477351e-01 4.77150291e-01 5.83108477... | [15.041692733764648, 4.889824390411377] |
c829eca7-1fa5-46c5-a8a0-e369c39d0806 | continuous-time-q-learning-for-mckean-vlasov | 2306.16208 | null | https://arxiv.org/abs/2306.16208v2 | https://arxiv.org/pdf/2306.16208v2.pdf | Continuous Time q-learning for McKean-Vlasov Control Problems | This paper studies the q-learning, recently coined as the continuous time counterpart of Q-learning by Jia and Zhou (2023), for continuous time Mckean-Vlasov control problems in the setting of entropy-regularized reinforcement learning. In contrast to the single agent's control problem in Jia and Zhou (2023), the mean-... | ['Xiang Yu', 'Xiaoli Wei'] | 2023-06-28 | null | null | null | null | ['q-learning'] | ['methodology'] | [-2.83282399e-01 3.59740824e-01 -3.94216746e-01 2.65842885e-01
-9.04244661e-01 -4.42169130e-01 2.10485771e-01 2.96739340e-01
-8.72668207e-01 1.63866389e+00 -1.90096125e-01 -3.17731827e-01
-8.18406522e-01 -6.73593760e-01 -8.95615101e-01 -1.08594549e+00
-5.51723897e-01 1.77297890e-01 -9.27459672e-02 -2.40866318... | [4.195976734161377, 2.5038814544677734] |
eaf6d1b3-4a89-4fae-a7f2-6322ca35bb46 | using-gaussian-processes-for-rumour-stance | 1609.01962 | null | http://arxiv.org/abs/1609.01962v1 | http://arxiv.org/pdf/1609.01962v1.pdf | Using Gaussian Processes for Rumour Stance Classification in Social Media | Social media tend to be rife with rumours while new reports are released
piecemeal during breaking news. Interestingly, one can mine multiple reactions
expressed by social media users in those situations, exploring their stance
towards rumours, ultimately enabling the flagging of highly disputed rumours as
being potent... | ['Michal Lukasik', 'Kalina Bontcheva', 'Trevor Cohn', 'Rob Procter', 'Maria Liakata', 'Arkaitz Zubiaga'] | 2016-09-07 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [-9.45324749e-02 3.75568718e-01 -3.40638012e-01 -3.00316662e-01
-7.68000364e-01 -5.96707106e-01 1.30299556e+00 6.63882792e-01
-1.43401980e-01 7.42102265e-01 5.67302942e-01 -4.44271624e-01
2.49721631e-01 -6.95137262e-01 -3.50047261e-01 -5.77246726e-01
9.32828337e-02 7.70305932e-01 3.52249593e-01 -5.79416990... | [8.231082916259766, 10.106389045715332] |
a1b159d8-1e3a-4838-af11-a3701e50b328 | boosting-video-text-retrieval-with-explicit | 2208.04215 | null | https://arxiv.org/abs/2208.04215v2 | https://arxiv.org/pdf/2208.04215v2.pdf | Boosting Video-Text Retrieval with Explicit High-Level Semantics | Video-text retrieval (VTR) is an attractive yet challenging task for multi-modal understanding, which aims to search for relevant video (text) given a query (video). Existing methods typically employ completely heterogeneous visual-textual information to align video and text, whilst lacking the awareness of homogeneous... | ['Errui Ding', 'Jungong Han', 'Zhong Ji', 'Fu Li', 'Dongliang He', 'Di Xu', 'Haoran Wang'] | 2022-08-08 | null | null | null | null | ['video-text-retrieval'] | ['computer-vision'] | [ 1.70055926e-01 -2.19361767e-01 -5.23426890e-01 -1.37018457e-01
-7.63024986e-01 -4.40524578e-01 7.90287077e-01 3.04870158e-01
-1.91247895e-01 9.98021215e-02 6.22021377e-01 -3.85028832e-02
6.15580231e-02 -6.05006516e-01 -6.03008151e-01 -3.72726053e-01
2.77458489e-01 2.24031001e-01 4.40015286e-01 -1.72164172... | [10.304486274719238, 1.041745662689209] |
4b195700-2470-4a34-8ce5-5e7ba5e1a34c | self-supervised-learning-of-object | 2304.04325 | null | https://arxiv.org/abs/2304.04325v1 | https://arxiv.org/pdf/2304.04325v1.pdf | Self-Supervised Learning of Object Segmentation from Unlabeled RGB-D Videos | This work proposes a self-supervised learning system for segmenting rigid objects in RGB images. The proposed pipeline is trained on unlabeled RGB-D videos of static objects, which can be captured with a camera carried by a mobile robot. A key feature of the self-supervised training process is a graph-matching algorith... | ['Kostas Bekris', 'Abdeslam Boularias', 'Yunfu Deng', 'Shiyang Lu'] | 2023-04-09 | null | null | null | null | ['point-cloud-registration', 'graph-matching'] | ['computer-vision', 'graphs'] | [ 7.42425501e-01 8.48974660e-02 -3.41475070e-01 -4.97895688e-01
-5.53223491e-01 -7.19741881e-01 3.93054157e-01 -1.61267594e-02
-3.08044106e-01 3.03520001e-02 -5.13217807e-01 1.38342157e-01
-3.29965819e-03 -6.58576965e-01 -1.05791879e+00 -6.87092364e-01
-7.64266402e-02 8.14003646e-01 7.53678977e-01 3.20272267... | [7.732198715209961, -2.677171230316162] |
b9025547-bfad-4db5-aa20-c7d3f7b429ff | self-supervised-generalisation-with-meta | 1901.08933 | null | https://arxiv.org/abs/1901.08933v3 | https://arxiv.org/pdf/1901.08933v3.pdf | Self-Supervised Generalisation with Meta Auxiliary Learning | Learning with auxiliary tasks can improve the ability of a primary task to generalise. However, this comes at the cost of manually labelling auxiliary data. We propose a new method which automatically learns appropriate labels for an auxiliary task, such that any supervised learning task can be improved without requiri... | ['Shikun Liu', 'Edward Johns', 'Andrew J. Davison'] | 2019-01-25 | self-supervised-generalisation-with-meta-1 | http://papers.nips.cc/paper/8445-self-supervised-generalisation-with-meta-auxiliary-learning | http://papers.nips.cc/paper/8445-self-supervised-generalisation-with-meta-auxiliary-learning.pdf | neurips-2019-12 | ['auxiliary-learning'] | ['methodology'] | [ 7.10358441e-01 6.43976748e-01 -8.49305093e-02 -6.11674309e-01
-1.35419559e+00 -7.51109004e-01 8.53240013e-01 1.50291324e-01
-7.67973185e-01 8.10288429e-01 1.46395832e-01 -2.83565909e-01
1.57405198e-01 -2.91341364e-01 -7.73595333e-01 -9.59862649e-01
3.13158333e-01 7.37490177e-01 1.47542253e-01 -1.83371052... | [9.473678588867188, 3.8527956008911133] |
8b612000-dae7-4158-834f-5f5fc9192644 | a-data-bootstrapping-recipe-for-low-resource-1 | null | null | https://aclanthology.org/2021.conll-1.45 | https://aclanthology.org/2021.conll-1.45.pdf | A Data Bootstrapping Recipe for Low-Resource Multilingual Relation Classification | Relation classification (sometimes called ‘extraction’) requires trustworthy datasets for fine-tuning large language models, as well as for evaluation. Data collection is challenging for Indian languages, because they are syntactically and morphologically diverse, as well as different from resource-rich languages like ... | ['Soumen Chakrabarti', 'Niloy Ganguly', 'Animesh Mukherjee', 'Bidisha Samanta', 'Arijit Nag'] | null | null | null | null | conll-emnlp-2021-11 | ['relation-classification'] | ['natural-language-processing'] | [-2.13697568e-01 1.83024257e-01 -4.97147799e-01 -4.39126700e-01
-1.43213093e+00 -9.63700175e-01 5.19214988e-01 4.02215213e-01
-5.99243224e-01 1.23317051e+00 3.96223515e-01 -6.11851454e-01
-1.18811410e-02 -9.08654153e-01 -6.11850560e-01 -3.03592175e-01
1.57745443e-02 1.27755928e+00 -5.30588403e-02 -4.14349824... | [9.965147018432617, 9.282919883728027] |
83677a2d-c5e6-439d-bb99-c5832063f430 | adnet-leveraging-error-bias-towards-normal | 2109.05721 | null | https://arxiv.org/abs/2109.05721v2 | https://arxiv.org/pdf/2109.05721v2.pdf | ADNet: Leveraging Error-Bias Towards Normal Direction in Face Alignment | The recent progress of CNN has dramatically improved face alignment performance. However, few works have paid attention to the error-bias with respect to error distribution of facial landmarks. In this paper, we investigate the error-bias issue in face alignment, where the distributions of landmark errors tend to sprea... | ['Fangyun Wei', 'Jongyoo Kim', 'Chong Li', 'Hao Yang', 'Yangyu Huang'] | 2021-09-13 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Huang_ADNet_Leveraging_Error-Bias_Towards_Normal_Direction_in_Face_Alignment_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Huang_ADNet_Leveraging_Error-Bias_Towards_Normal_Direction_in_Face_Alignment_ICCV_2021_paper.pdf | iccv-2021-1 | ['face-alignment'] | ['computer-vision'] | [-2.88867116e-01 2.72642933e-02 -9.55213830e-02 -7.20358372e-01
-2.31004983e-01 1.13618053e-01 4.46372509e-01 -3.63404751e-01
-3.30248922e-01 3.73576373e-01 4.59705174e-01 2.12923095e-01
-6.48421096e-03 -7.18195677e-01 -6.28167987e-01 -7.58494556e-01
2.72757828e-01 3.57744604e-01 5.94457276e-02 -3.54842484... | [13.44289493560791, 0.499544233083725] |
34ca9a92-5c4f-4552-acae-7f6a7f1fb2f4 | pointgrid-a-deep-network-for-3d-shape | null | null | http://openaccess.thecvf.com/content_cvpr_2018/html/Le_PointGrid_A_Deep_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Le_PointGrid_A_Deep_CVPR_2018_paper.pdf | PointGrid: A Deep Network for 3D Shape Understanding | This paper presents a new deep learning architecture called PointGrid that is designed for 3D model recognition from unorganized point clouds. The new architecture embeds the input point cloud into a 3D grid by a simple, yet effective, sampling strategy and directly learns transformations and features from their raw co... | ['Truc Le', 'Ye Duan'] | 2018-06-01 | null | null | null | cvpr-2018-6 | ['3d-part-segmentation'] | ['computer-vision'] | [-4.88473833e-01 -5.01356982e-02 -1.53412640e-01 -3.90853345e-01
-8.14873815e-01 -3.66927773e-01 6.65154159e-01 1.39351368e-01
-1.49169415e-01 3.83005857e-01 -3.87605488e-01 -3.21940988e-01
5.16972356e-02 -1.21235025e+00 -1.16735280e+00 -4.71974134e-01
-1.15925185e-01 1.11787617e+00 2.44680673e-01 8.20591599... | [7.981328010559082, -3.651167154312134] |
6eb41f3a-dc27-416e-ba89-a6342939af80 | towards-trustworthy-explanation-on-causal | 2306.14115 | null | https://arxiv.org/abs/2306.14115v1 | https://arxiv.org/pdf/2306.14115v1.pdf | Towards Trustworthy Explanation: On Causal Rationalization | With recent advances in natural language processing, rationalization becomes an essential self-explaining diagram to disentangle the black box by selecting a subset of input texts to account for the major variation in prediction. Yet, existing association-based approaches on rationalization cannot identify true rationa... | ['Hengrui Cai', 'Yong Cai', 'Yunlong Wang', 'Tong Wu', 'Wenbo Zhang'] | 2023-06-25 | null | null | null | null | ['causal-inference', 'causal-inference'] | ['knowledge-base', 'miscellaneous'] | [ 5.59152305e-01 4.76743579e-01 -1.02160871e+00 -4.63385969e-01
-4.04869199e-01 -2.06508636e-01 7.03425467e-01 5.68466306e-01
-4.53503877e-02 9.05968964e-01 5.78008711e-01 -7.19324589e-01
-6.73305929e-01 -6.56603813e-01 -6.47649109e-01 -3.60415578e-01
-7.30615780e-02 4.07210857e-01 -1.70802802e-01 1.24000795... | [8.283187866210938, 5.6162519454956055] |
8ce66774-464e-4b82-828f-684161afcba3 | multi-resolution-location-based-training-for | 2301.06458 | null | https://arxiv.org/abs/2301.06458v1 | https://arxiv.org/pdf/2301.06458v1.pdf | Multi-resolution location-based training for multi-channel continuous speech separation | The performance of automatic speech recognition (ASR) systems severely degrades when multi-talker speech overlap occurs. In meeting environments, speech separation is typically performed to improve the robustness of ASR systems. Recently, location-based training (LBT) was proposed as a new training criterion for multi-... | ['DeLiang Wang', 'Hassan Taherian'] | 2023-01-16 | null | null | null | null | ['speech-separation', 'speaker-separation'] | ['speech', 'speech'] | [ 8.45182016e-02 -6.99341238e-01 3.50947291e-01 -4.15880620e-01
-1.66540742e+00 -7.12707758e-01 3.07161570e-01 -2.83350050e-01
-3.26181263e-01 4.66156721e-01 4.72554475e-01 -3.35533261e-01
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-3.20461541e-01 2.00642511e-01 -2.06313565e-01 -5.55315353... | [14.866063117980957, 5.9532084465026855] |
a8b8c7ad-7157-4a63-9786-dcb6e6c6e7ba | temp-taxonomy-expansion-with-dynamic-margin | null | null | https://aclanthology.org/2021.emnlp-main.313 | https://aclanthology.org/2021.emnlp-main.313.pdf | TEMP: Taxonomy Expansion with Dynamic Margin Loss through Taxonomy-Paths | As an essential form of knowledge representation, taxonomies are widely used in various downstream natural language processing tasks. However, with the continuously rising of new concepts, many existing taxonomies are unable to maintain coverage by manual expansion. In this paper, we propose TEMP, a self-supervised tax... | ['Xiaojie Yuan', 'Haiying Wu', 'Ning Jiang', 'Yanlong Wen', 'Hongyuan Xu', 'Zichen Liu'] | null | null | null | null | emnlp-2021-11 | ['taxonomy-expansion'] | ['natural-language-processing'] | [ 2.55224198e-01 2.32877418e-01 -4.52440500e-01 -3.42747360e-01
-1.53949112e-01 -6.89696133e-01 6.39370799e-01 7.46380210e-01
-6.67307794e-01 6.90576971e-01 4.51667964e-01 -3.27988684e-01
-2.95836002e-01 -1.10587156e+00 -3.40027183e-01 -2.15833440e-01
-1.76118910e-01 9.01978016e-01 2.31277913e-01 -4.85629261... | [9.250040054321289, 8.062782287597656] |
0c845c82-588c-452c-8bce-e3911a99ebae | beyond-convolutions-a-novel-deep-learning | 2102.13631 | null | https://arxiv.org/abs/2102.13631v1 | https://arxiv.org/pdf/2102.13631v1.pdf | Beyond Convolutions: A Novel Deep Learning Approach for Raw Seismic Data Ingestion | Traditional seismic processing workflows (SPW) are expensive, requiring over a year of human and computational effort. Deep learning (DL) based data-driven seismic workflows (DSPW) hold the potential to reduce these timelines to a few minutes. Raw seismic data (terabytes) and required subsurface prediction (gigabytes) ... | ['Anshumali Shrivastava', 'Antoine Vial-Aussavy', 'Anu Chandran', 'Menal Gupta', 'Aditya Desai', 'Zhaozhuo Xu'] | 2021-02-26 | null | null | null | null | ['irregular-time-series'] | ['time-series'] | [ 1.60633013e-01 7.81979263e-02 6.54911518e-01 -2.39837706e-01
-9.76681232e-01 -2.72769868e-01 6.73638880e-01 8.80907774e-02
-8.36709082e-01 3.50704074e-01 6.36354089e-01 -4.81814295e-01
-3.05781841e-01 -1.22111738e+00 -9.57765222e-01 -7.78279185e-01
-9.18584943e-01 7.53236353e-01 6.71821117e-01 -5.91823161... | [6.855991363525391, 2.5121665000915527] |
5779444a-8d0c-4ee7-9ec7-8262f82affe9 | meta-reinforced-synthetic-data-for-one-shot-1 | 1911.07164 | null | https://arxiv.org/abs/1911.07164v1 | https://arxiv.org/pdf/1911.07164v1.pdf | Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition | One-shot fine-grained visual recognition often suffers from the problem of training data scarcity for new fine-grained classes. To alleviate this problem, an off-the-shelf image generator can be applied to synthesize additional training images, but these synthesized images are often not helpful for actually improving t... | ['Yanwei Fu', 'Satoshi Tsutsui', 'David Crandall'] | 2019-11-17 | meta-reinforced-synthetic-data-for-one-shot | http://papers.nips.cc/paper/8570-meta-reinforced-synthetic-data-for-one-shot-fine-grained-visual-recognition | http://papers.nips.cc/paper/8570-meta-reinforced-synthetic-data-for-one-shot-fine-grained-visual-recognition.pdf | neurips-2019-12 | ['fine-grained-visual-recognition'] | ['computer-vision'] | [ 5.02107203e-01 1.97825104e-01 -4.41280454e-01 -6.35413408e-01
-1.03314269e+00 -4.43202615e-01 7.55010128e-01 -4.69353825e-01
-2.58943528e-01 7.44281888e-01 2.13420928e-01 1.11166582e-01
1.50489658e-01 -8.49121928e-01 -1.02848816e+00 -6.73569679e-01
5.85627079e-01 4.19311166e-01 1.91733446e-02 -2.55315136... | [9.962484359741211, 2.368959426879883] |
250fba80-3f93-452c-b561-1d6324b17351 | integrative-semantic-dependency-parsing-via | 1401.6050 | null | http://arxiv.org/abs/1401.6050v1 | http://arxiv.org/pdf/1401.6050v1.pdf | Integrative Semantic Dependency Parsing via Efficient Large-scale Feature Selection | Semantic parsing, i.e., the automatic derivation of meaning representation
such as an instantiated predicate-argument structure for a sentence, plays a
critical role in deep processing of natural language. Unlike all other top
systems of semantic dependency parsing that have to rely on a pipeline
framework to chain up ... | ['Chunyu Kit', 'Xiaotian Zhang', 'Hai Zhao'] | 2014-01-23 | null | null | null | null | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [ 5.37832141e-01 6.71081603e-01 -1.12389266e-01 -6.50719404e-01
-1.09454107e+00 -7.60398507e-01 5.15555799e-01 5.67125797e-01
-6.37642264e-01 5.40693343e-01 2.68620610e-01 -6.58379853e-01
-1.60828844e-01 -7.27248251e-01 -5.09398043e-01 -3.91414016e-01
9.16801542e-02 5.68268478e-01 5.43670654e-01 -3.56473863... | [10.292357444763184, 9.440902709960938] |
2504ceb5-8069-4ad8-8355-6f4cc39ca7c1 | automatic-code-summarization-via-multi | 2006.05405 | null | https://arxiv.org/abs/2006.05405v5 | https://arxiv.org/pdf/2006.05405v5.pdf | Retrieval-Augmented Generation for Code Summarization via Hybrid GNN | Source code summarization aims to generate natural language summaries from structured code snippets for better understanding code functionalities. However, automatic code summarization is challenging due to the complexity of the source code and the language gap between the source code and natural language summaries. Mo... | ['Yang Liu', 'JingKai Siow', 'Xiaofei Xie', 'Yu Chen', 'Shangqing Liu'] | 2020-06-09 | retrieval-augmented-generation-for-code | https://openreview.net/forum?id=zv-typ1gPxA | https://openreview.net/pdf?id=zv-typ1gPxA | iclr-2021-1 | ['code-summarization'] | ['computer-code'] | [-4.00641002e-02 -3.38389054e-02 -2.92637378e-01 -1.34390462e-02
-1.09706032e+00 -5.30822039e-01 2.94121325e-01 7.31279612e-01
9.70541537e-02 4.55419332e-01 6.44838333e-01 -2.41617456e-01
-1.18848823e-01 -7.35580146e-01 -5.87733448e-01 -2.37395659e-01
-2.90645510e-01 -1.37669191e-01 5.06261170e-01 -3.33865911... | [7.554312229156494, 7.96572208404541] |
289377a6-da7e-4539-be2b-119514dd688e | j-net-randomly-weighted-u-net-for-audio | 1911.12926 | null | https://arxiv.org/abs/1911.12926v1 | https://arxiv.org/pdf/1911.12926v1.pdf | J-Net: Randomly weighted U-Net for audio source separation | Several results in the computer vision literature have shown the potential of randomly weighted neural networks. While they perform fairly well as feature extractors for discriminative tasks, a positive correlation exists between their performance and their fully trained counterparts. According to these discoveries, we... | ['Hung-Yi Lee', 'Yen-Min Hsu', 'Bo-Wen Chen'] | 2019-11-29 | null | null | null | null | ['audio-source-separation'] | ['audio'] | [ 4.65316266e-01 2.70137787e-01 -1.18168011e-01 -1.82098895e-01
-7.24270821e-01 -4.19614255e-01 4.10568625e-01 -3.41435254e-01
-4.40769196e-01 5.25246143e-01 3.76908451e-01 -2.60965884e-01
-4.75989670e-01 -6.43960357e-01 -6.29239917e-01 -9.62572753e-01
-3.37721556e-01 3.79082203e-01 3.42317194e-01 -2.95162201... | [15.376206398010254, 5.405327796936035] |
974e32c3-ff6f-47d6-b7da-ae66d6753465 | subdimensional-expansion-for-multi-objective | 2102.01353 | null | https://arxiv.org/abs/2102.01353v2 | https://arxiv.org/pdf/2102.01353v2.pdf | Subdimensional Expansion for Multi-objective Multi-agent Path Finding | Conventional multi-agent path planners typically determine a path that optimizes a single objective, such as path length. Many applications, however, may require multiple objectives, say time-to-completion and fuel use, to be simultaneously optimized in the planning process. Often, these criteria may not be readily com... | ['Howie Choset', 'Sivakumar Rathinam', 'Zhongqiang Ren'] | 2021-02-02 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-8.16070214e-02 1.23174943e-01 -2.35145718e-01 2.44115561e-01
-6.93956614e-01 -9.84658122e-01 2.76577830e-01 4.63122785e-01
-5.73802948e-01 1.22746146e+00 -9.85668674e-02 -3.33234012e-01
-9.63475704e-01 -1.06233752e+00 -2.66828328e-01 -6.73126817e-01
-4.67477292e-01 1.17852879e+00 2.30713069e-01 -4.02117610... | [4.957076549530029, 1.9174559116363525] |
4ef05f8e-1c5b-4377-90b8-41e494d21398 | visual-semantic-information-pursuit-a-survey | 1903.05434 | null | http://arxiv.org/abs/1903.05434v1 | http://arxiv.org/pdf/1903.05434v1.pdf | Visual Semantic Information Pursuit: A Survey | Visual semantic information comprises two important parts: the meaning of
each visual semantic unit and the coherent visual semantic relation conveyed by
these visual semantic units. Essentially, the former one is a visual perception
task while the latter one corresponds to visual context reasoning. Remarkable
advances... | ['Daqi Liu', 'Josef Kittler', 'Miroslaw Bober'] | 2019-03-13 | null | null | null | null | ['visual-relationship-detection'] | ['computer-vision'] | [ 5.77900171e-01 2.51277909e-02 -2.46356130e-01 -3.69373411e-01
-6.36715665e-02 -4.90302891e-01 6.18064225e-01 4.13788438e-01
-1.15619905e-01 3.65576476e-01 5.65067232e-02 -8.43879357e-02
-1.15253188e-01 -5.09725988e-01 -5.58504045e-01 -7.82430649e-01
4.68967795e-01 2.12402031e-01 4.39740717e-01 -2.31352486... | [10.268915176391602, 1.4763625860214233] |
34576ea7-4a92-48f4-9c41-8557a02f0eb8 | avoiding-reasoning-shortcuts-adversarial | 1906.07132 | null | https://arxiv.org/abs/1906.07132v1 | https://arxiv.org/pdf/1906.07132v1.pdf | Avoiding Reasoning Shortcuts: Adversarial Evaluation, Training, and Model Development for Multi-Hop QA | Multi-hop question answering requires a model to connect multiple pieces of evidence scattered in a long context to answer the question. In this paper, we show that in the multi-hop HotpotQA (Yang et al., 2018) dataset, the examples often contain reasoning shortcuts through which models can directly locate the answer b... | ['Yichen Jiang', 'Mohit Bansal'] | 2019-06-17 | avoiding-reasoning-shortcuts-adversarial-1 | https://aclanthology.org/P19-1262 | https://aclanthology.org/P19-1262.pdf | acl-2019-7 | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 2.03505471e-01 5.48584640e-01 9.38753858e-02 -3.49963903e-01
-1.60160601e+00 -1.33222806e+00 5.67281544e-01 1.25797898e-01
-3.68918002e-01 5.85210502e-01 4.19737011e-01 -7.91803598e-01
8.53744000e-02 -1.00175011e+00 -1.11062634e+00 -2.31973335e-01
4.48113948e-01 5.97631991e-01 8.60638916e-01 -6.57782257... | [11.09365463256836, 7.993206977844238] |
7bdaca2e-511c-4f47-ac83-d3442f6a3ce3 | learning-rich-representation-of-keyphrases | null | null | https://openreview.net/forum?id=mSw7ck7b7L | https://openreview.net/pdf?id=mSw7ck7b7L | Learning Rich Representation of Keyphrases from Text | In this work, we explore how to learn task-specific language models aimed towards learning rich representation of keyphrases from text documents. We experiment with different masking strategies for training transformer language models (LMs) in discriminative as well as generative settings. In the discriminative setting... | ['Anonymous'] | 2021-10-16 | null | null | null | acl-arr-october-2021-10 | ['keyphrase-generation', 'keyphrase-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 3.74776840e-01 5.04552424e-01 2.56674495e-02 1.71278164e-01
-1.65040994e+00 -7.42043197e-01 9.79574203e-01 5.58660090e-01
-7.25561619e-01 1.07023275e+00 7.61641979e-01 -2.41605490e-01
-2.39838362e-01 -7.97259271e-01 -9.63195562e-01 -5.17035723e-01
-3.70182768e-02 4.98810321e-01 1.35345072e-01 -5.68201840... | [12.312111854553223, 9.020480155944824] |
6d2a8932-e6bd-4fd4-ae5e-f012811c46d9 | robustloc-robust-camera-pose-regression-in | 2211.11238 | null | https://arxiv.org/abs/2211.11238v4 | https://arxiv.org/pdf/2211.11238v4.pdf | RobustLoc: Robust Camera Pose Regression in Challenging Driving Environments | Camera relocalization has various applications in autonomous driving. Previous camera pose regression models consider only ideal scenarios where there is little environmental perturbation. To deal with challenging driving environments that may have changing seasons, weather, illumination, and the presence of unstable o... | ['Diego Navarro Navarro', 'Andreas Hartmannsgruber', 'Wee Peng Tay', 'Rui She', 'Qiyu Kang', 'Sijie Wang'] | 2022-11-21 | null | null | null | null | ['camera-absolute-pose-regression', 'camera-localization', 'camera-relocalization', 'visual-localization'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-4.33415622e-01 -1.51931882e-01 -1.21026933e-01 -3.79559845e-01
-6.30583167e-01 -7.99167931e-01 5.21261573e-01 -7.04383969e-01
-3.33114505e-01 4.95904565e-01 -6.93360195e-02 -1.63180411e-01
3.76788855e-01 -3.97729486e-01 -1.20000196e+00 -7.25983560e-01
2.23305896e-01 2.83795744e-01 2.19097167e-01 -3.54588598... | [8.02891731262207, -2.0426535606384277] |
0a703d1c-cc10-4c98-abe9-63161ac9d0cb | disambiguating-prepositional-phrase | null | null | https://aclanthology.org/P14-3010 | https://aclanthology.org/P14-3010.pdf | Disambiguating prepositional phrase attachment sites with sense information captured in contextualized distributional data | null | ['Clayton Greenberg'] | 2014-06-01 | null | null | null | acl-2014-6 | ['prepositional-phrase-attachment'] | ['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... | [-7.21804141998291, 3.846081018447876] |
cd17f394-00f4-41c0-979c-93e7d9c55521 | margin-preserving-self-paced-contrastive | 2103.08454 | null | https://arxiv.org/abs/2103.08454v2 | https://arxiv.org/pdf/2103.08454v2.pdf | Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation | To bridge the gap between the source and target domains in unsupervised domain adaptation (UDA), the most common strategy puts focus on matching the marginal distributions in the feature space through adversarial learning. However, such category-agnostic global alignment lacks of exploiting the class-level joint distri... | ['Yao Zhao', 'Jiayu Zhou', 'Yang Liu', 'Shuai Zheng', 'Zhenfeng Zhu', 'Zhizhe Liu'] | 2021-03-15 | null | null | null | null | ['cardiac-segmentation'] | ['medical'] | [ 4.72427458e-01 7.30258003e-02 -3.25452685e-01 -5.80321491e-01
-9.11638856e-01 -5.47813654e-01 4.80943501e-01 7.38794357e-03
-4.08573925e-01 6.30415201e-01 1.60376169e-03 1.32855743e-01
-1.44916207e-01 -7.76707947e-01 -5.59636712e-01 -1.08691978e+00
3.59704494e-01 3.50211561e-01 2.27228731e-01 -6.44278228... | [14.473763465881348, -1.871621012687683] |
06dd4ef2-1a87-45d4-9899-e620ba46c4cc | on-the-descriptive-power-of-lidar-intensity | 2108.01383 | null | https://arxiv.org/abs/2108.01383v1 | https://arxiv.org/pdf/2108.01383v1.pdf | On the descriptive power of LiDAR intensity images for segment-based loop closing in 3-D SLAM | We propose an extension to the segment-based global localization method for LiDAR SLAM using descriptors learned considering the visual context of the segments. A new architecture of the deep neural network is presented that learns the visual context acquired from synthetic LiDAR intensity images. This approach allows ... | ['Piotr Skrzypczyński', 'Jan Wietrzykowski'] | 2021-08-03 | null | null | null | null | ['loop-closure-detection'] | ['computer-vision'] | [ 1.40943721e-01 -1.90708101e-01 -3.12166601e-01 -9.33548450e-01
-6.98716164e-01 -4.42449838e-01 7.78213263e-01 2.06276193e-01
-6.97661161e-01 7.52902687e-01 1.80107266e-01 -3.77040878e-02
-4.07713115e-01 -7.27801621e-01 -8.06537747e-01 -3.60217452e-01
-1.27103239e-01 6.91836059e-01 6.44228831e-02 -2.31283084... | [7.58914041519165, -2.142848491668701] |
9bde79e2-28af-48ab-bae0-1c34564257e1 | a-prompt-independent-and-interpretable | null | null | https://aclanthology.org/2021.ccl-1.107 | https://aclanthology.org/2021.ccl-1.107.pdf | A Prompt-independent and Interpretable Automated Essay Scoring Method for Chinese Second Language Writing | “With the increasing popularity of learning Chinese as a second language (L2) the development of an automatic essay scoring (AES) method specially for Chinese L2 essays has become animportant task. To build a robust model that could easily adapt to prompt changes we propose 90linguistic features with consideration of b... | ['Hu Renfen', 'Wang Yupei'] | null | null | null | null | ccl-2021-8 | ['automated-essay-scoring'] | ['natural-language-processing'] | [-4.44835782e-01 1.59828231e-01 -5.08352160e-01 -4.23866451e-01
-1.14095628e+00 -6.89329445e-01 5.65371513e-01 4.52062041e-01
-5.88621736e-01 8.90144944e-01 4.91758823e-01 -5.23187280e-01
-2.25973979e-01 -3.58293563e-01 -3.16998839e-01 8.82182196e-02
3.40409935e-01 4.91119772e-02 -1.23448491e-01 -3.82729739... | [11.251654624938965, 9.443991661071777] |
689c5ccc-5a92-4460-ad7a-244cf81d9ae6 | guiding-interaction-behaviors-for-multi-modal | null | null | https://aclanthology.org/W17-2803 | https://aclanthology.org/W17-2803.pdf | Guiding Interaction Behaviors for Multi-modal Grounded Language Learning | Multi-modal grounded language learning connects language predicates to physical properties of objects in the world. Sensing with multiple modalities, such as audio, haptics, and visual colors and shapes while performing interaction behaviors like lifting, dropping, and looking on objects enables a robot to ground non-v... | ['Raymond Mooney', 'Jivko Sinapov', 'Jesse Thomason'] | 2017-08-01 | null | null | null | ws-2017-8 | ['grounded-language-learning'] | ['natural-language-processing'] | [ 3.75716358e-01 2.66912133e-01 1.10827163e-01 -3.01305085e-01
-6.54563487e-01 -7.72644937e-01 3.58247429e-01 7.79862583e-01
-4.57165629e-01 5.76910853e-01 1.58392996e-01 -2.57196337e-01
-3.20595026e-01 -7.67812729e-01 -8.54732990e-01 -3.76879156e-01
-3.47478062e-01 3.67048889e-01 5.56578159e-01 -4.21935797... | [10.43257999420166, 1.5936479568481445] |
29bf9328-3f2e-49bf-a72a-fdd159d4807b | representation-biases-in-sentence | 2301.13039 | null | https://arxiv.org/abs/2301.13039v1 | https://arxiv.org/pdf/2301.13039v1.pdf | Representation biases in sentence transformers | Variants of the BERT architecture specialised for producing full-sentence representations often achieve better performance on downstream tasks than sentence embeddings extracted from vanilla BERT. However, there is still little understanding of what properties of inputs determine the properties of such representations.... | ['Sebastian Padó', 'Dmitry Nikolaev'] | 2023-01-30 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [ 8.37060809e-02 2.51508236e-01 -2.89023906e-01 -7.81594217e-01
-3.92292649e-01 -8.22343767e-01 8.23670924e-01 7.48844922e-01
-7.85146058e-01 5.78131676e-01 1.02751172e+00 -5.70861816e-01
-1.93581551e-01 -8.96890759e-01 -5.84053159e-01 -3.37333620e-01
-1.34745613e-01 4.69482869e-01 9.49145630e-02 -7.12217748... | [10.487841606140137, 8.974483489990234] |
1a52c672-c45f-4d62-9213-a9aa9bb6d310 | rusentne-2023-evaluating-entity-oriented | 2305.17679 | null | https://arxiv.org/abs/2305.17679v1 | https://arxiv.org/pdf/2305.17679v1.pdf | RuSentNE-2023: Evaluating Entity-Oriented Sentiment Analysis on Russian News Texts | The paper describes the RuSentNE-2023 evaluation devoted to targeted sentiment analysis in Russian news texts. The task is to predict sentiment towards a named entity in a single sentence. The dataset for RuSentNE-2023 evaluation is based on the Russian news corpus RuSentNE having rich sentiment-related annotation. The... | ['Natalia Loukachevitch', 'Nicolay Rusnachenko', 'Anton Golubev'] | 2023-05-28 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-4.89377677e-01 5.43494344e-01 -1.83034688e-01 -6.35255396e-01
-9.36356664e-01 -7.13831663e-01 7.52559245e-01 4.98492748e-01
-7.23014593e-01 1.01106191e+00 6.19349241e-01 1.47196829e-01
2.62373656e-01 -4.93372917e-01 -2.42964864e-01 -4.97135639e-01
2.30860695e-01 5.86852849e-01 -1.94475539e-02 -1.00152326... | [11.234968185424805, 6.933368682861328] |
6f6a0b89-e518-47b3-9827-7c52e959b4c2 | single-image-cloud-detection-via-multi-image | 2007.15144 | null | https://arxiv.org/abs/2007.15144v1 | https://arxiv.org/pdf/2007.15144v1.pdf | Single Image Cloud Detection via Multi-Image Fusion | Artifacts in imagery captured by remote sensing, such as clouds, snow, and shadows, present challenges for various tasks, including semantic segmentation and object detection. A primary challenge in developing algorithms for identifying such artifacts is the cost of collecting annotated training data. In this work, we ... | ['M. Usman Rafique', 'Hunter Blanton', 'Connor Greenwell', 'Scott Workman', 'Nathan Jacobs'] | 2020-07-29 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [ 7.83585250e-01 -2.83335775e-01 1.05436236e-01 -6.45141661e-01
-1.20034015e+00 -9.26899254e-01 8.09562802e-02 3.20056081e-01
-2.89437771e-01 4.79604781e-01 -4.64407742e-01 -4.70378160e-01
1.42994910e-01 -7.30530620e-01 -8.68067443e-01 -4.76240814e-01
-1.71610370e-01 1.30305335e-01 2.82132357e-01 2.14464083... | [9.655633926391602, -1.6225709915161133] |
f740c675-cba2-418a-9e00-25b8229a2087 | self-contrastive-learning-for-session-based | 2306.01266 | null | https://arxiv.org/abs/2306.01266v1 | https://arxiv.org/pdf/2306.01266v1.pdf | Self Contrastive Learning for Session-based Recommendation | Session-based recommendation, which aims to predict the next item of users' interest as per an existing sequence interaction of items, has attracted growing applications of Contrastive Learning (CL) with improved user and item representations. However, these contrastive objectives: (1) serve a similar role as the cross... | ['Aldo Lipani', 'Xi Wang', 'Zhengxiang Shi'] | 2023-06-02 | null | null | null | null | ['session-based-recommendations'] | ['miscellaneous'] | [ 2.49765486e-01 -2.95224518e-01 -4.00174260e-01 -3.08175862e-01
-6.11111104e-01 -6.24135673e-01 6.99102998e-01 3.44282269e-01
-3.85695815e-01 5.94131529e-01 2.06527025e-01 -2.69273251e-01
-4.78502333e-01 -6.82696640e-01 -8.11886787e-01 -6.22671306e-01
-2.50683010e-01 2.28960142e-01 6.01943433e-02 -3.77477199... | [10.031098365783691, 5.555354118347168] |
75afb196-9f01-4b37-8557-81b2afa48507 | improving-non-native-word-level-pronunciation | 2203.01826 | null | https://arxiv.org/abs/2203.01826v1 | https://arxiv.org/pdf/2203.01826v1.pdf | Improving Non-native Word-level Pronunciation Scoring with Phone-level Mixup Data Augmentation and Multi-source Information | Deep learning-based pronunciation scoring models highly rely on the availability of the annotated non-native data, which is costly and has scalability issues. To deal with the data scarcity problem, data augmentation is commonly used for model pretraining. In this paper, we propose a phone-level mixup, a simple yet eff... | ['Zejun Ma', 'Xiaohai Tian', 'Wei Li', 'Kai Wang', 'Shaojun Gao', 'Kaiqi Fu'] | 2022-03-01 | null | null | null | null | ['word-level-pronunciation-scoring'] | ['speech'] | [ 6.60412312e-02 -2.22921386e-01 -3.98724601e-02 -3.00593913e-01
-1.07164431e+00 -3.81094635e-01 2.60130793e-01 -5.89917041e-02
-5.86725593e-01 7.72657812e-01 4.21415597e-01 -2.02415273e-01
3.41268212e-01 -4.51220572e-01 -3.74923766e-01 -8.06613088e-01
2.64231503e-01 1.94545150e-01 -1.12464003e-01 -2.46565625... | [14.53555679321289, 6.732218265533447] |
1ce2130d-952d-495f-94bd-657c105a482d | a-maximum-entropy-feature-descriptor-for-age | null | null | http://openaccess.thecvf.com/content_cvpr_2015/html/Gong_A_Maximum_Entropy_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Gong_A_Maximum_Entropy_2015_CVPR_paper.pdf | A Maximum Entropy Feature Descriptor for Age Invariant Face Recognition | In this paper, we propose a new approach to overcome the representation and matching problems in age invariant face recognition. First, a new maximum entropy feature descriptor (MEFD) is developed that encodes the microstructure of facial images into a set of discrete codes in terms of maximum entropy. By densely sampl... | ['Xuelong. Li', 'DaCheng Tao', 'Zhifeng Li', 'Jianzhuang Liu', 'Dihong Gong'] | 2015-06-01 | null | null | null | cvpr-2015-6 | ['age-invariant-face-recognition'] | ['computer-vision'] | [ 2.73508728e-01 8.27143192e-02 -3.63398254e-01 -7.24678278e-01
-4.03885186e-01 -2.05213502e-01 6.55913234e-01 -4.49567974e-01
-1.88897923e-01 7.09046364e-01 1.97682142e-01 2.86663920e-01
-8.24091807e-02 -7.19114661e-01 -2.99706936e-01 -6.18138015e-01
-4.08570617e-01 4.42613587e-02 -4.11494732e-01 1.22590736... | [13.31588077545166, 0.6800175309181213] |
8aef60d9-09e7-4b44-b29b-0eb04c928326 | behancepr-a-punctuation-restoration-dataset | null | null | https://aclanthology.org/2022.findings-naacl.149 | https://aclanthology.org/2022.findings-naacl.149.pdf | BehancePR: A Punctuation Restoration Dataset for Livestreaming Video Transcript | Given the increasing number of livestreaming videos, automatic speech recognition and post-processing for livestreaming video transcripts are crucial for efficient data management as well as knowledge mining. A key step in this process is punctuation restoration which restores fundamental text structures such as phrase... | ['Thien Nguyen', 'Franck Dernoncourt', 'Amir Pouran Ben Veyseh', 'Viet Lai'] | null | null | null | null | findings-naacl-2022-7 | ['punctuation-restoration'] | ['natural-language-processing'] | [ 4.34381336e-01 -2.48917595e-01 -1.62611231e-01 -3.15149784e-01
-1.11054182e+00 -7.07294941e-01 3.05790126e-01 -1.01657212e-01
-3.84326100e-01 7.19620705e-01 9.06048477e-01 -3.11629891e-01
3.66608202e-01 -5.30396178e-02 -6.18018270e-01 -3.74974698e-01
-2.16676772e-01 -2.02850074e-01 2.21931830e-01 -1.89284891... | [10.457326889038086, 0.7553386688232422] |
426cec60-0795-4be0-9d46-2d72f8969c2d | improving-human-sperm-head-morphology | 2202.07191 | null | https://arxiv.org/abs/2202.07191v3 | https://arxiv.org/pdf/2202.07191v3.pdf | Improving Human Sperm Head Morphology Classification with Unsupervised Anatomical Feature Distillation | With rising male infertility, sperm head morphology classification becomes critical for accurate and timely clinical diagnosis. Recent deep learning (DL) morphology analysis methods achieve promising benchmark results, but leave performance and robustness on the table by relying on limited and possibly noisy class labe... | ['Danny Z. Chen', 'Yunxia Cao', 'Yiru Zhou', 'Xiaomin Zha', 'Jingjing Zhang', 'Yejia Zhang'] | 2022-02-15 | null | null | null | null | ['sperm-morphology-classification', 'morphology-classification'] | ['computer-vision', 'computer-vision'] | [ 2.67870992e-01 4.09502029e-01 8.97000358e-02 -6.53762102e-01
-9.19445395e-01 -8.64269733e-01 5.33049762e-01 5.49364269e-01
-5.21210313e-01 8.72144103e-01 -9.79957879e-02 -1.73923865e-01
9.13214833e-02 -7.32069910e-01 -6.07615650e-01 -9.21093285e-01
6.00509420e-02 7.87299395e-01 1.81342512e-01 4.76638705... | [14.762602806091309, -3.1106832027435303] |
b56c3141-6eee-4273-bd82-93fdb1514619 | one-ring-to-bring-them-all-towards-open-set | 2206.03600 | null | https://arxiv.org/abs/2206.03600v2 | https://arxiv.org/pdf/2206.03600v2.pdf | OneRing: A Simple Method for Source-free Open-partial Domain Adaptation | In this paper, we investigate Source-free Open-partial Domain Adaptation (SF-OPDA), which addresses the situation where there exist both domain and category shifts between source and target domains. Under the SF-OPDA setting, which aims to address data privacy concerns, the model cannot access source data anymore durin... | ['Joost Van de Weijer', 'Shangling Jui', 'Kai Wang', 'Yaxing Wang', 'Shiqi Yang'] | 2022-06-07 | null | null | null | null | ['universal-domain-adaptation', 'partial-domain-adaptation', 'open-set-learning'] | ['computer-vision', 'methodology', 'miscellaneous'] | [ 3.60053420e-01 2.68974036e-01 -5.91798186e-01 -5.94908953e-01
-9.96852100e-01 -6.76927567e-01 4.41805065e-01 1.05410457e-01
-5.95264256e-01 1.24934947e+00 1.42874476e-02 -2.44460940e-01
1.31961927e-01 -7.03265190e-01 -5.70956767e-01 -5.90672016e-01
3.21380138e-01 6.60579205e-01 2.94965565e-01 6.67189956... | [10.376599311828613, 3.190476894378662] |
ffd0e4bd-b22d-493c-a0d9-0e7b60ca0a94 | nested-scale-editing-for-conditional-image | 2006.02038 | null | https://arxiv.org/abs/2006.02038v1 | https://arxiv.org/pdf/2006.02038v1.pdf | Nested Scale Editing for Conditional Image Synthesis | We propose an image synthesis approach that provides stratified navigation in the latent code space. With a tiny amount of partial or very low-resolution image, our approach can consistently out-perform state-of-the-art counterparts in terms of generating the closest sampled image to the ground truth. We achieve this t... | ['Jie Min', 'Lingzhi Zhang', 'James C. Gee', 'Yinshuang Xu', 'Tarmily Wen', 'Jiancong Wang', 'Jianbo Shi'] | 2020-06-03 | null | null | null | null | ['image-outpainting'] | ['computer-vision'] | [ 6.81358755e-01 -3.12553160e-02 -3.09823304e-01 -1.93498850e-01
-1.12017739e+00 -7.45608270e-01 8.29543293e-01 -2.17301250e-01
-9.62252691e-02 6.31070673e-01 4.73014832e-01 2.81368732e-01
-8.35859962e-03 -6.69234991e-01 -8.60567331e-01 -6.01106226e-01
3.11638445e-01 4.46075983e-02 2.57330924e-01 -3.70022267... | [11.536538124084473, -0.5822892189025879] |
c16cbe6e-3b88-48e4-9bc0-8306fc5004d4 | groundnet-segmentation-aware-monocular-ground | 1811.07222 | null | https://arxiv.org/abs/1811.07222v4 | https://arxiv.org/pdf/1811.07222v4.pdf | GroundNet: Monocular Ground Plane Normal Estimation with Geometric Consistency | We focus on estimating the 3D orientation of the ground plane from a single image. We formulate the problem as an inter-mingled multi-task prediction problem by jointly optimizing for pixel-wise surface normal direction, ground plane segmentation, and depth estimates. Specifically, our proposed model, GroundNet, first ... | ['Xi Li', 'Kris Kitani', 'Xinshuo Weng', 'Yunze Man'] | 2018-11-17 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 3.18810403e-01 1.34770334e-01 9.78595540e-02 -3.03348631e-01
-1.08272660e+00 -8.31877649e-01 3.35746109e-01 3.35765123e-01
-4.06137794e-01 3.82862777e-01 -1.34941682e-01 -1.47693425e-01
2.33392477e-01 -1.01381481e+00 -8.93543839e-01 -6.83465719e-01
-3.05366904e-01 3.92169595e-01 5.81905365e-01 -1.16851358... | [8.372243881225586, -2.5666520595550537] |
45fd2aca-8a3d-417e-8a3f-f1f79b8ccb40 | evaluating-the-effectiveness-of-pre-trained | 2302.10199 | null | https://arxiv.org/abs/2302.10199v1 | https://arxiv.org/pdf/2302.10199v1.pdf | Evaluating the Effectiveness of Pre-trained Language Models in Predicting the Helpfulness of Online Product Reviews | Businesses and customers can gain valuable information from product reviews. The sheer number of reviews often necessitates ranking them based on their potential helpfulness. However, only a few reviews ever receive any helpfulness votes on online marketplaces. Sorting all reviews based on the few existing votes can ca... | ['Dimitar Shterionov', 'Javad PourMostafa Roshan Sharami', 'Ali Boluki'] | 2023-02-19 | null | null | null | null | ['feature-engineering', 'xlm-r'] | ['methodology', 'natural-language-processing'] | [-4.47470784e-01 4.81878668e-02 -6.78878307e-01 -5.06146073e-01
-9.32700276e-01 -8.05668533e-01 9.63683963e-01 5.90416431e-01
-6.80505276e-01 6.06388867e-01 3.33925873e-01 -6.45687222e-01
4.58144955e-02 -6.51914001e-01 -3.00006956e-01 -1.28122613e-01
3.79657418e-01 4.03040886e-01 -2.50192821e-01 -6.71344817... | [11.137591361999512, 6.926249027252197] |
5e82cd64-0ad0-4990-856b-70bc6cf61e65 | videocapsulenet-a-simplified-network-for | 1805.08162 | null | http://arxiv.org/abs/1805.08162v1 | http://arxiv.org/pdf/1805.08162v1.pdf | VideoCapsuleNet: A Simplified Network for Action Detection | The recent advances in Deep Convolutional Neural Networks (DCNNs) have shown
extremely good results for video human action classification, however, action
detection is still a challenging problem. The current action detection
approaches follow a complex pipeline which involves multiple tasks such as tube
proposals, opt... | ['Yogesh S Rawat', 'Mubarak Shah', 'Kevin Duarte'] | 2018-05-21 | videocapsulenet-a-simplified-network-for-1 | http://papers.nips.cc/paper/7988-videocapsulenet-a-simplified-network-for-action-detection | http://papers.nips.cc/paper/7988-videocapsulenet-a-simplified-network-for-action-detection.pdf | neurips-2018-12 | ['multiple-action-detection'] | ['computer-vision'] | [ 5.96000366e-02 1.68639511e-01 -4.13894922e-01 -1.49936318e-01
-5.97413898e-01 -6.09224617e-01 3.49201173e-01 -2.82259285e-01
-4.88347977e-01 1.71662793e-01 5.74888647e-01 2.26621002e-01
1.62114769e-01 -4.18099344e-01 -8.42561245e-01 -7.40810335e-01
-5.11806130e-01 -7.94422030e-02 6.69690311e-01 3.30381960... | [9.158905982971191, 0.017797349020838737] |
22e5a61a-6e05-4314-83e6-3e89f8f19456 | a-semi-autoregressive-graph-generative-model | 2306.12018 | null | https://arxiv.org/abs/2306.12018v1 | https://arxiv.org/pdf/2306.12018v1.pdf | A Semi-Autoregressive Graph Generative Model for Dependency Graph Parsing | Recent years have witnessed the impressive progress in Neural Dependency Parsing. According to the different factorization approaches to the graph joint probabilities, existing parsers can be roughly divided into autoregressive and non-autoregressive patterns. The former means that the graph should be factorized into m... | ['Ping Li', 'Mingming Sun', 'Ye Ma'] | 2023-06-21 | null | null | null | null | ['dependency-parsing'] | ['natural-language-processing'] | [-1.44049689e-01 6.17397726e-01 -1.44423060e-02 -5.03084183e-01
-4.37349826e-01 -6.08226120e-01 4.04407084e-01 -8.40389132e-02
-3.44179221e-03 5.39422989e-01 5.03547370e-01 -5.58370411e-01
7.44478256e-02 -9.26820815e-01 -1.00256145e+00 -7.33519554e-01
-3.23558927e-01 6.71034873e-01 1.40773162e-01 -2.68254369... | [10.33139705657959, 9.58723258972168] |
ebdcff51-b747-46b8-92f9-5e895542a54b | dp-2-nilm-a-distributed-and-privacy | 2207.00041 | null | https://arxiv.org/abs/2207.00041v1 | https://arxiv.org/pdf/2207.00041v1.pdf | DP$^2$-NILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring | Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household-level into appliance-level consumption, can help analyze electricity consumption behaviours of users and enable practical smart energy and smart grid applicati... | ['Xizhong Chen', 'Qian Wang', 'Fanlin Meng', 'Shuang Dai'] | 2022-06-30 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'time-series'] | [-2.03797951e-01 -2.90912718e-01 -1.21898070e-01 -7.14489162e-01
-8.50584507e-01 -5.36343396e-01 5.11518180e-01 1.19813316e-01
-1.49428397e-01 9.59588349e-01 2.27621153e-01 -3.59162211e-01
-2.59654880e-01 -9.95686710e-01 -2.33617589e-01 -1.25535965e+00
-2.80698150e-01 1.03327490e-01 -4.70156103e-01 2.77089506... | [5.888927459716797, 2.797316789627075] |
bc8da434-17b1-47f7-b805-b44ec3aafa2c | writing-style-aware-document-level-event | 2201.03188 | null | https://arxiv.org/abs/2201.03188v1 | https://arxiv.org/pdf/2201.03188v1.pdf | Writing Style Aware Document-level Event Extraction | Event extraction, the technology that aims to automatically get the structural information from documents, has attracted more and more attention in many fields. Most existing works discuss this issue with the token-level multi-label classification framework by distinguishing the tokens as different roles while ignoring... | ['Lixin Cui', 'Lu Bai', 'Yue Wang', 'Zhuo Xu'] | 2022-01-10 | null | null | null | null | ['document-level-event-extraction'] | ['natural-language-processing'] | [ 1.38007358e-01 2.40701139e-02 -6.14803255e-01 -5.39540529e-01
-3.15215230e-01 -4.91799533e-01 9.48904455e-01 6.00558937e-01
-4.86542881e-01 7.23098040e-01 5.65805793e-01 -8.83086026e-02
-2.70739287e-01 -7.84520447e-01 -4.00100827e-01 -6.91413462e-01
4.85463172e-01 3.99376541e-01 5.97668588e-01 -1.65858120... | [9.169719696044922, 9.187223434448242] |
4e7eab4b-49c3-461b-a556-237d628242cf | korean-specific-dataset-for-table-question | 2201.06223 | null | https://arxiv.org/abs/2201.06223v2 | https://arxiv.org/pdf/2201.06223v2.pdf | Korean-Specific Dataset for Table Question Answering | Existing question answering systems mainly focus on dealing with text data. However, much of the data produced daily is stored in the form of tables that can be found in documents and relational databases, or on the web. To solve the task of question answering over tables, there exist many datasets for table question a... | ['Kyungkoo Min', 'Hansol Jang', 'Hyun Kim', 'Myoseop Sim', 'Jooyoung Choi', 'Changwook Jun'] | 2022-01-17 | null | https://aclanthology.org/2022.lrec-1.657 | https://aclanthology.org/2022.lrec-1.657.pdf | lrec-2022-6 | ['unsupervised-pre-training'] | ['methodology'] | [-1.36748657e-01 1.71664089e-01 -1.74956769e-01 -4.87170488e-01
-1.67360008e+00 -1.02659523e+00 1.96649522e-01 5.93110740e-01
-2.00948477e-01 8.35834920e-01 6.06661916e-01 -5.56025565e-01
-3.94355878e-02 -1.26634574e+00 -7.77065277e-01 2.53886133e-01
3.56578976e-01 9.55744386e-01 6.42227888e-01 -7.11805701... | [10.076653480529785, 7.889129161834717] |
d8f3122e-7813-446f-88ca-be9cb7c1b6b9 | acrofod-an-adaptive-method-for-cross-domain | 2209.10904 | null | https://arxiv.org/abs/2209.10904v1 | https://arxiv.org/pdf/2209.10904v1.pdf | AcroFOD: An Adaptive Method for Cross-domain Few-shot Object Detection | Under the domain shift, cross-domain few-shot object detection aims to adapt object detectors in the target domain with a few annotated target data. There exists two significant challenges: (1) Highly insufficient target domain data; (2) Potential over-adaptation and misleading caused by inappropriately amplified targe... | ['Wei-Shi Zheng', 'Shiyong Li', 'Song Xie', 'Yunmu Huang', 'Lingxiao Yang', 'Yipeng Gao'] | 2022-09-22 | null | null | null | null | ['cross-domain-few-shot'] | ['computer-vision'] | [ 4.97134864e-01 -3.82988483e-01 -9.62238088e-02 -1.80350974e-01
-8.41566503e-01 -1.94537073e-01 4.95668471e-01 -2.29252890e-01
-3.92493308e-01 7.85547078e-01 2.96899471e-02 2.88844526e-01
6.82736337e-02 -5.31211138e-01 -4.48618293e-01 -8.60699415e-01
5.17492831e-01 3.73906821e-01 1.13932288e+00 -1.65213943... | [9.38917064666748, 1.4963964223861694] |
e5449212-db5f-4bb0-84d1-44d5e6cb91b6 | segmentation-is-all-you-need | 1904.13300 | null | https://arxiv.org/abs/1904.13300v3 | https://arxiv.org/pdf/1904.13300v3.pdf | Segmentation is All You Need | Region proposal mechanisms are essential for existing deep learning approaches to object detection in images. Although they can generally achieve a good detection performance under normal circumstances, their recall in a scene with extreme cases is unacceptably low. This is mainly because bounding box annotations conta... | ['Thomas Lukasiewicz', 'Zhenghua Xu', 'Weiyang Wang', 'Zehua Cheng', 'Yuxiang Wu'] | 2019-04-30 | null | null | null | null | ['head-detection', 'robust-object-detection'] | ['computer-vision', 'computer-vision'] | [ 2.82719493e-01 -6.65540621e-02 -3.01692192e-03 -5.43172359e-01
-1.02271569e+00 -3.71248513e-01 4.12166476e-01 3.27222735e-01
-6.42711520e-01 2.02692226e-01 -3.83212328e-01 2.37691216e-02
3.68744940e-01 -7.79112875e-01 -7.07320273e-01 -8.08618188e-01
3.27468663e-01 2.05189481e-01 1.40096962e+00 -9.23550036... | [9.324237823486328, 0.7451703548431396] |
8776e8b4-6464-4eaf-8013-ab0746863f09 | failure-detection-for-motion-prediction-of | 2301.04421 | null | https://arxiv.org/abs/2301.04421v2 | https://arxiv.org/pdf/2301.04421v2.pdf | Failure Detection for Motion Prediction of Autonomous Driving: An Uncertainty Perspective | Motion prediction is essential for safe and efficient autonomous driving. However, the inexplicability and uncertainty of complex artificial intelligence models may lead to unpredictable failures of the motion prediction module, which may mislead the system to make unsafe decisions. Therefore, it is necessary to develo... | ['Hong Wang', 'Jun Li', 'Liang Peng', 'Yanchao Xu', 'Wenbo Shao'] | 2023-01-11 | null | null | null | null | ['motion-prediction'] | ['computer-vision'] | [-1.44128531e-01 1.33691728e-01 -2.56818384e-01 -4.23930109e-01
-3.01267087e-01 -2.71729797e-01 7.72260666e-01 2.22066641e-01
-2.96462059e-01 9.55614448e-01 -1.19188003e-01 -5.41433811e-01
-4.74066079e-01 -8.51821899e-01 -4.31484967e-01 -7.39352882e-01
-9.83394589e-03 3.11566442e-01 6.20646954e-01 -2.67386828... | [5.54959774017334, 1.409559965133667] |
eb2d940d-9718-4fb8-adea-8c8037875e18 | stagnet-an-attentive-semantic-rnn-for-group | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Mengshi_Qi_stagNet_An_Attentive_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Mengshi_Qi_stagNet_An_Attentive_ECCV_2018_paper.pdf | stagNet: An Attentive Semantic RNN for Group Activity Recognition | Group activity recognition plays a fundamental role in a variety of applications, e.g. sports video analysis and intelligent surveillance. How to model the spatio-temporal contextual information in a scene still remains a crucial yet challenging issue. We propose a novel attentive semantic recurrent neural network (RNN... | ['Luc van Gool', 'Jiebo Luo', 'Jie Qin', 'Yunhong Wang', 'Mengshi Qi', 'Annan Li'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['group-activity-recognition'] | ['computer-vision'] | [ 1.99451163e-01 -4.99483824e-01 -2.13392481e-01 -3.63724768e-01
-1.58128470e-01 -7.40443170e-02 5.09523749e-01 5.74139096e-02
-4.09366131e-01 2.18597084e-01 7.85448194e-01 2.14141305e-03
-5.11187613e-01 -6.01903915e-01 -6.78642809e-01 -7.14151561e-01
-2.95985907e-01 -2.35374168e-01 4.44764704e-01 -2.02864796... | [8.385503768920898, 0.6594128012657166] |
c7bd3e15-acb5-4a49-b092-f79f67666f66 | visual-object-tracking-in-first-person-vision | 2209.13502 | null | https://arxiv.org/abs/2209.13502v1 | https://arxiv.org/pdf/2209.13502v1.pdf | Visual Object Tracking in First Person Vision | The understanding of human-object interactions is fundamental in First Person Vision (FPV). Visual tracking algorithms which follow the objects manipulated by the camera wearer can provide useful information to effectively model such interactions. In the last years, the computer vision community has significantly impro... | ['Christian Micheloni', 'Giovanni Maria Farinella', 'Antonino Furnari', 'Matteo Dunnhofer'] | 2022-09-27 | null | null | null | null | ['visual-tracking', 'human-object-interaction-detection', 'visual-object-tracking'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.51712699e-02 -3.47166151e-01 -4.11993116e-01 5.45201898e-02
-1.76328689e-01 -7.62127638e-01 6.71563745e-01 -2.25114107e-01
-6.05495393e-01 4.89432842e-01 -6.16072603e-02 -1.09933019e-01
-7.93801174e-02 -7.76266083e-02 -7.31629372e-01 -5.60033858e-01
-1.94406211e-01 4.10175264e-01 7.55048454e-01 3.62936668... | [6.35005521774292, -1.9932475090026855] |
a394f753-5951-4569-9df0-20c7264c235b | building-competitive-direct-acoustics-to-word | 1712.03133 | null | http://arxiv.org/abs/1712.03133v1 | http://arxiv.org/pdf/1712.03133v1.pdf | Building competitive direct acoustics-to-word models for English conversational speech recognition | Direct acoustics-to-word (A2W) models in the end-to-end paradigm have
received increasing attention compared to conventional sub-word based automatic
speech recognition models using phones, characters, or context-dependent hidden
Markov model states. This is because A2W models recognize words from speech
without any de... | ['Michael Picheny', 'George Saon', 'Bhuvana Ramabhadran', 'Brian Kingsbury', 'Kartik Audhkhasi'] | 2017-12-08 | null | null | null | null | ['english-conversational-speech-recognition'] | ['speech'] | [ 2.31162041e-01 1.82527915e-01 -1.07474610e-01 -4.47119176e-01
-1.34345484e+00 -5.26156068e-01 5.21937430e-01 -1.27790794e-01
-7.63501287e-01 4.02045101e-01 3.00052047e-01 -1.11027610e+00
3.79356086e-01 -2.21117169e-01 -4.25735742e-01 -5.08296728e-01
9.99346673e-02 6.36218429e-01 4.59945887e-01 -3.02120328... | [14.362090110778809, 6.833063125610352] |
3b83c04b-7901-452f-8d75-3e924db1c722 | a-comprehensive-survey-on-deep-learning-for | 2306.02051 | null | https://arxiv.org/abs/2306.02051v2 | https://arxiv.org/pdf/2306.02051v2.pdf | A Comprehensive Survey on Deep Learning for Relation Extraction: Recent Advances and New Frontiers | Relation extraction (RE) involves identifying the relations between entities from unstructured texts. RE serves as the foundation for many natural language processing (NLP) applications, such as knowledge graph completion, question answering, and information retrieval. In recent years, deep neural networks have dominat... | ['Ruifeng Xu', 'Ying Shen', 'Wai Lam', 'Hong Cheng', 'Rui Zhang', 'Lingzhi Wang', 'Min Yang', 'Yang Deng', 'Xiaoyan Zhao'] | 2023-06-03 | null | null | null | null | ['knowledge-graph-completion', 'relation-extraction', 'information-retrieval'] | ['knowledge-base', 'natural-language-processing', 'natural-language-processing'] | [ 1.41807303e-01 3.34174484e-01 -4.93458182e-01 -2.72124976e-01
-5.95851541e-01 -3.43993604e-01 6.38857365e-01 5.91629088e-01
-4.34750050e-01 7.92090654e-01 3.24822336e-01 -3.03402454e-01
-1.82393208e-01 -1.17208040e+00 -4.79995996e-01 -2.45482311e-01
-3.69917661e-01 7.80747592e-01 -2.29582503e-01 -3.64612788... | [9.268342018127441, 8.635517120361328] |
31072593-89fe-4b64-b969-f4b04ccad19c | semantics-as-a-foreign-language | null | null | https://aclanthology.org/D18-1263 | https://aclanthology.org/D18-1263.pdf | Semantics as a Foreign Language | We propose a novel approach to semantic dependency parsing (SDP) by casting the task as an instance of multi-lingual machine translation, where each semantic representation is a different foreign dialect. To that end, we first generalize syntactic linearization techniques to account for the richer semantic dependency g... | ['Ido Dagan', 'Gabriel Stanovsky'] | 2018-10-01 | null | null | null | emnlp-2018-10 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [ 2.20304593e-01 6.70171797e-01 -5.99411488e-01 -8.04841459e-01
-1.21920133e+00 -9.64270532e-01 5.85299730e-01 2.11054951e-01
-1.95287317e-01 7.61140764e-01 5.74183166e-01 -8.10798049e-01
4.61778730e-01 -7.67032266e-01 -1.07695925e+00 -1.81546062e-01
1.39809385e-01 6.55101418e-01 7.55825266e-02 -4.91763324... | [10.45916748046875, 9.445958137512207] |
9f19357a-53c2-40fc-a1d6-72ae1fdaa037 | channel-estimation-for-reconfigurable-4 | 1912.03619 | null | https://arxiv.org/abs/1912.03619v2 | https://arxiv.org/pdf/1912.03619v2.pdf | Channel Estimation for Reconfigurable Intelligent Surface Aided Multi-User mmWave MIMO Systems | Channel acquisition is one of the main challenges for the deployment of reconfigurable intelligent surface (RIS) aided communication systems. This is because an RIS has a large number of reflective elements, which are passive devices with no active transmitting/receiving abilities. In this paper, we study the channel e... | ['Wei Yu', 'Hei Victor Cheng', 'Ying-Chang Liang', 'Jie Chen'] | 2019-12-08 | null | null | null | null | ['compressive-sensing'] | ['computer-vision'] | [ 4.85081971e-01 2.23334804e-01 1.96121112e-01 3.49235564e-01
-3.96317601e-01 -3.13026547e-01 -5.79085052e-02 -6.29707992e-01
2.25378945e-01 6.47804081e-01 2.56011099e-01 -2.86968678e-01
-3.16264510e-01 -8.27886224e-01 -6.82891428e-01 -1.28916454e+00
-1.69616655e-01 -1.02844670e-01 -2.86060810e-01 -1.83663413... | [6.318902492523193, 1.2848496437072754] |
f1e6b9fc-beb8-45dd-8b29-30d7105c00ba | green-runner-a-tool-for-efficient-model | 2305.16849 | null | https://arxiv.org/abs/2305.16849v1 | https://arxiv.org/pdf/2305.16849v1.pdf | Green Runner: A tool for efficient model selection from model repositories | Deep learning models have become essential in software engineering, enabling intelligent features like image captioning and document generation. However, their popularity raises concerns about environmental impact and inefficient model selection. This paper introduces GreenRunnerGPT, a novel tool for efficiently select... | ['Luis Cruz', 'Taylan Selvi', 'Anj Simmons', 'Scott Barnett', 'Jai Kannan'] | 2023-05-26 | null | null | null | null | ['image-captioning'] | ['computer-vision'] | [ 1.17597673e-02 -2.27585132e-03 -7.43088186e-01 -2.12256551e-01
-1.19443238e+00 -5.30871272e-01 4.37583208e-01 -1.67670935e-01
-3.61955464e-01 6.33230805e-01 -9.61097181e-02 -7.51435637e-01
-5.25615454e-01 -7.32773185e-01 -7.62897789e-01 -3.97742689e-01
1.39372736e-01 7.04600692e-01 -5.19573689e-01 2.19173685... | [8.339669227600098, 3.721388578414917] |
ddf0b2e3-28ff-4ac1-9338-ac6479b175f0 | event-causality-extraction-with-event-1 | 2301.11621 | null | https://arxiv.org/abs/2301.11621v1 | https://arxiv.org/pdf/2301.11621v1.pdf | Event Causality Extraction with Event Argument Correlations | Event Causality Identification (ECI), which aims to detect whether a causality relation exists between two given textual events, is an important task for event causality understanding. However, the ECI task ignores crucial event structure and cause-effect causality component information, making it struggle for downstre... | ['Jinqiao Shi', 'Tingwen Liu', 'Quangang Li', 'Xin Cong', 'Jiawei Sheng', 'Shiyao Cui'] | 2023-01-27 | event-causality-extraction-with-event | https://aclanthology.org/2022.coling-1.201 | https://aclanthology.org/2022.coling-1.201.pdf | coling-2022-10 | ['event-causality-identification'] | ['natural-language-processing'] | [ 2.55650461e-01 -1.01645283e-01 -1.77329347e-01 -3.37880105e-01
-5.77633262e-01 -7.82970726e-01 8.75079393e-01 6.25146568e-01
-2.62436599e-01 7.20859587e-01 7.03551769e-01 -6.08436584e-01
-3.44550729e-01 -9.25307930e-01 -5.24183035e-01 -3.58304381e-01
-3.97681147e-01 7.01098219e-02 6.11599803e-01 2.16737032... | [9.062359809875488, 9.134879112243652] |
01d13095-86af-4495-ab3a-b525e65ac9e1 | a-new-finsler-minimal-path-model-with | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Chen_A_New_Finsler_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Chen_A_New_Finsler_CVPR_2016_paper.pdf | A New Finsler Minimal Path Model With Curvature Penalization for Image Segmentation and Closed Contour Detection | In this paper, we propose a new curvature penalized minimal path model for image segmentation via closed contour detection based on the weighted Euler elastica curves, firstly introduced to the field of computer vision in [22]. Our image segmentation method extracts a collection of curvature penalized minimal geodesics... | ['Jean-Marie Mirebeau', 'Laurent D. Cohen', 'Da Chen'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['contour-detection'] | ['computer-vision'] | [ 2.23762378e-01 5.26799858e-01 1.17531307e-01 -3.42907578e-01
-4.94878858e-01 -7.14691579e-01 5.49357295e-01 3.49458516e-01
-8.39464188e-01 1.79941788e-01 -3.31684202e-01 -1.73628032e-01
-4.52422172e-01 -6.28208876e-01 -5.83621681e-01 -6.78243279e-01
-2.31347620e-01 2.91175932e-01 5.85895479e-01 -3.57537001... | [7.3459038734436035, 3.938511610031128] |
8aa82778-1871-4707-8e41-3a0d66105877 | why-topological-data-analysis-detects | 2304.06877 | null | https://arxiv.org/abs/2304.06877v1 | https://arxiv.org/pdf/2304.06877v1.pdf | Why Topological Data Analysis Detects Financial Bubbles? | We present a heuristic argument for the propensity of Topological Data Analysis (TDA) to detect early warning signals of critical transitions in financial time series. Our argument is based on the Log-Periodic Power Law Singularity (LPPLS) model, which characterizes financial bubbles as super-exponential growth (or dec... | ['Vahid Nateghi', 'Matteo Manzi', 'Marian Gidea', 'Samuel W. Akingbade'] | 2023-04-14 | null | null | null | null | ['topological-data-analysis'] | ['graphs'] | [-4.36747164e-01 4.56120148e-02 -1.69361606e-01 4.73163754e-01
-1.79270983e-01 -1.04804051e+00 9.25068855e-01 4.61289465e-01
2.88918942e-01 5.00674367e-01 1.50536031e-01 -1.07251990e+00
-2.33110949e-01 -8.33991766e-01 -5.52146196e-01 -5.70940673e-01
-1.36391509e+00 2.16753468e-01 6.63376510e-01 -2.58292764... | [4.77943229675293, 4.124654293060303] |
38b35eee-aac6-45dc-bc86-7b9109d0983c | knowledge-augmented-deep-neural-networks-for | null | null | http://proceedings.neurips.cc/paper/2020/hash/a51fb975227d6640e4fe47854476d133-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/a51fb975227d6640e4fe47854476d133-Paper.pdf | Knowledge Augmented Deep Neural Networks for Joint Facial Expression and Action Unit Recognition | Facial expression and action units (AUs) represent two levels of descriptions of the facial behavior. Due to the underlying facial anatomy and the need to form a meaningful coherent expression, they are strongly correlated. This paper proposes to systematically capture their dependencies and incorporate them into a dee... | ['Qiang Ji', 'Yuru Wang', 'Tengfei Song', 'Zijun Cui'] | 2020-12-01 | null | null | null | neurips-2020-12 | ['action-unit-detection'] | ['computer-vision'] | [ 2.94647038e-01 3.02726984e-01 -3.27957660e-01 -8.91017437e-01
-8.16474319e-01 -2.64408495e-02 5.42757034e-01 -3.64861459e-01
-1.97893813e-01 3.91198128e-01 3.27669159e-02 4.88024205e-01
1.34745374e-01 -3.85495871e-01 -6.48733497e-01 -7.94292450e-01
-3.85732204e-02 9.66369584e-02 -3.59377712e-02 6.64620399... | [13.611403465270996, 1.6252211332321167] |
3499edc2-5bd4-4b29-bc8f-023149fc21c0 | entity-aware-syntax-tree-based-data | 2209.02267 | null | https://arxiv.org/abs/2209.02267v1 | https://arxiv.org/pdf/2209.02267v1.pdf | Entity Aware Syntax Tree Based Data Augmentation for Natural Language Understanding | Understanding the intention of the users and recognizing the semantic entities from their sentences, aka natural language understanding (NLU), is the upstream task of many natural language processing tasks. One of the main challenges is to collect a sufficient amount of annotated data to train a model. Existing researc... | ['Noboru Matsuda', 'Wenge Rong', 'Jiangneng Li', 'Jianbin Cui', 'Jiaxing Xu'] | 2022-09-06 | null | null | null | null | ['text-augmentation', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.71787006e-01 5.14914334e-01 -4.49454963e-01 -4.78515118e-01
-1.94328517e-01 -1.59499332e-01 5.65993965e-01 4.34456319e-01
-5.12599170e-01 8.51233602e-01 6.91429257e-01 -3.70083839e-01
5.12312472e-01 -8.18982720e-01 -4.43527460e-01 -1.20040044e-01
2.54051507e-01 5.70320606e-01 7.68205374e-02 -3.10423851... | [9.823588371276855, 9.151368141174316] |
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