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18f1e3f3-fe23-4c04-95a7-e18f3077393c | a-high-precision-self-supervised-monocular | 2203.04812 | null | https://arxiv.org/abs/2203.04812v1 | https://arxiv.org/pdf/2203.04812v1.pdf | A high-precision self-supervised monocular visual odometry in foggy weather based on robust cycled generative adversarial networks and multi-task learning aided depth estimation | This paper proposes a high-precision self-supervised monocular VO, which is specifically designed for navigation in foggy weather. A cycled generative adversarial network is designed to obtain high-quality self-supervised loss via forcing the forward and backward half-cycle to output consistent estimation. Moreover, gr... | ['Guowen An', 'Fengchao Li', 'Jiangang Yu', 'Xiuyuan Li'] | 2022-03-09 | null | null | null | null | ['monocular-visual-odometry'] | ['robots'] | [-2.29594931e-01 -2.42867344e-03 4.82736409e-01 -5.00453115e-01
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-5.05640507e-02 1.92237273e-01 1.51793748e-01 -4.47493315... | [10.884488105773926, -3.2460615634918213] |
f6d279ae-e0f6-423d-9bc6-0c02b28f36ea | uncertainty-estimation-with-normalized-logits | 2302.07608 | null | https://arxiv.org/abs/2302.07608v1 | https://arxiv.org/pdf/2302.07608v1.pdf | Uncertainty-Estimation with Normalized Logits for Out-of-Distribution Detection | Out-of-distribution (OOD) detection is critical for preventing deep learning models from making incorrect predictions to ensure the safety of artificial intelligence systems. Especially in safety-critical applications such as medical diagnosis and autonomous driving, the cost of incorrect decisions is usually unbearabl... | ['Yu Qiao', 'Mouxiao Huang'] | 2023-02-15 | null | null | null | null | ['medical-diagnosis'] | ['medical'] | [-4.92020734e-02 4.67492968e-01 -2.64728129e-01 -7.17184663e-01
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9.13218185e-02 3.51843745e-01 3.32944483e-01 2.49097899... | [7.78695821762085, 3.705505132675171] |
5459497f-aeeb-445a-9c8e-168669b64e3c | egodistill-egocentric-head-motion | 2301.02217 | null | https://arxiv.org/abs/2301.02217v1 | https://arxiv.org/pdf/2301.02217v1.pdf | EgoDistill: Egocentric Head Motion Distillation for Efficient Video Understanding | Recent advances in egocentric video understanding models are promising, but their heavy computational expense is a barrier for many real-world applications. To address this challenge, we propose EgoDistill, a distillation-based approach that learns to reconstruct heavy egocentric video clip features by combining the se... | ['Kristen Grauman', 'Tushar Nagarajan', 'Shuhan Tan'] | 2023-01-05 | null | null | null | null | ['video-understanding'] | ['computer-vision'] | [-1.38597354e-01 -1.49506956e-01 -5.73933005e-01 -4.33112621e-01
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3d0539c0-d4e1-4842-9821-5aa2521fff79 | semi-supervised-deep-regression-with | 2302.07579 | null | https://arxiv.org/abs/2302.07579v1 | https://arxiv.org/pdf/2302.07579v1.pdf | Semi-Supervised Deep Regression with Uncertainty Consistency and Variational Model Ensembling via Bayesian Neural Networks | Deep regression is an important problem with numerous applications. These range from computer vision tasks such as age estimation from photographs, to medical tasks such as ejection fraction estimation from echocardiograms for disease tracking. Semi-supervised approaches for deep regression are notably under-explored c... | ['Kwang-Ting Cheng', 'Xiaomeng Li', 'Weihang Dai'] | 2023-02-15 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [ 1.87249258e-02 3.78792018e-01 -3.97644818e-01 -7.85720706e-01
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a0e35e95-2772-421a-b7b0-21f15aeaf6d3 | cascade-graph-neural-networks-for-rgb-d | 2008.03087 | null | https://arxiv.org/abs/2008.03087v1 | https://arxiv.org/pdf/2008.03087v1.pdf | Cascade Graph Neural Networks for RGB-D Salient Object Detection | In this paper, we study the problem of salient object detection (SOD) for RGB-D images using both color and depth information.A major technical challenge in performing salient object detection fromRGB-D images is how to fully leverage the two complementary data sources. Current works either simply distill prior knowled... | ['Siwei Lyu', 'Hong Cheng', 'Fan Yang', 'Ao Luo', 'Zhicheng Jiao', 'Xin Li'] | 2020-08-07 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1571_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123570341.pdf | eccv-2020-8 | ['rgb-d-salient-object-detection'] | ['computer-vision'] | [ 3.27148736e-02 3.15924942e-01 -1.93607569e-01 -2.76574880e-01
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83f8156c-2c5a-44e9-911b-7279114b74f1 | a-convolutional-attention-network-for-extreme | 1602.03001 | null | http://arxiv.org/abs/1602.03001v2 | http://arxiv.org/pdf/1602.03001v2.pdf | A Convolutional Attention Network for Extreme Summarization of Source Code | Attention mechanisms in neural networks have proved useful for problems in
which the input and output do not have fixed dimension. Often there exist
features that are locally translation invariant and would be valuable for
directing the model's attention, but previous attentional architectures are not
constructed to le... | ['Miltiadis Allamanis', 'Charles Sutton', 'Hao Peng'] | 2016-02-09 | null | null | null | null | ['extreme-summarization'] | ['natural-language-processing'] | [ 2.58192182e-01 1.26892567e-01 -1.68015182e-01 -3.32623720e-01
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-2.51900971e-01 3.17607448e-02 2.91200131e-01 -5.91905825... | [7.638814449310303, 7.9031572341918945] |
c9e7b881-45df-422c-9438-f1560884349a | modeling-human-cognition-with-a-hybrid-deep | 2301.06216 | null | https://arxiv.org/abs/2301.06216v2 | https://arxiv.org/pdf/2301.06216v2.pdf | Modeling Human Cognition with a Hybrid Deep Reinforcement Learning Agent | Human cognition model could help us gain insights in how human cognition behaviors work under external stimuli, pave the way for synthetic data generation, and assist in adaptive intervention design for cognition regulation. When the external stimuli is highly dynamic, it becomes hard to model the effect that how the s... | ['Xinyu Zhang', 'Songlin Xu'] | 2023-01-15 | null | null | null | null | ['synthetic-data-generation', 'synthetic-data-generation'] | ['medical', 'miscellaneous'] | [-3.89159620e-01 -2.31018275e-01 5.52096367e-02 2.07751766e-02
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-9.82188582e-02 1.14278615e-01 2.12061614e-01 -4.86825138... | [4.1854753494262695, 2.135960340499878] |
6a28d737-16a4-4060-a7ab-b9fbf415c414 | contextualized-knowledge-aware-attentive | 2104.05216 | null | https://arxiv.org/abs/2104.05216v1 | https://arxiv.org/pdf/2104.05216v1.pdf | Contextualized Knowledge-aware Attentive Neural Network: Enhancing Answer Selection with Knowledge | Answer selection, which is involved in many natural language processing applications such as dialog systems and question answering (QA), is an important yet challenging task in practice, since conventional methods typically suffer from the issues of ignoring diverse real-world background knowledge. In this paper, we ex... | ['Ying Shen', 'Wai Lam', 'Min Yang', 'Yaliang Li', 'Yuexiang Xie', 'Yang Deng'] | 2021-04-12 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [-4.82007191e-02 7.05226138e-02 -3.31796333e-02 -5.99134147e-01
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2.77829677e-01 6.25398874e-01 5.53517520e-01 -7.78465748... | [10.69620418548584, 7.923274517059326] |
2881f046-527d-45dc-aa74-959c1dfcba3b | a-span-based-dynamic-local-attention-model | null | null | https://aclanthology.org/2021.acl-short.26 | https://aclanthology.org/2021.acl-short.26.pdf | A Span-based Dynamic Local Attention Model for Sequential Sentence Classification | Sequential sentence classification aims to classify each sentence in the document based on the context in which sentences appear. Most existing work addresses this problem using a hierarchical sequence labeling network. However, they ignore considering the latent segment structure of the document, in which contiguous s... | ['Zipeng Chen', 'Jiangyue Yan', 'Zhenxi Lin', 'Qianli Ma', 'Xichen Shang'] | 2021-08-01 | null | null | null | acl-2021-5 | ['sentence-classification'] | ['natural-language-processing'] | [ 2.71594673e-01 -2.17528313e-01 -5.96875250e-01 -8.35120618e-01
-6.79452240e-01 -4.66357678e-01 4.23112780e-01 6.01629734e-01
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-5.84935918e-02 -1.02647841e-01 4.23020005e-01 -1.83085412... | [11.105637550354004, 8.765676498413086] |
5e5815a2-4af7-4431-a2cc-e5f15b4c0ed2 | vsvc-backdoor-attack-against-keyword-spotting | 2212.10103 | null | https://arxiv.org/abs/2212.10103v1 | https://arxiv.org/pdf/2212.10103v1.pdf | VSVC: Backdoor attack against Keyword Spotting based on Voiceprint Selection and Voice Conversion | Keyword spotting (KWS) based on deep neural networks (DNNs) has achieved massive success in voice control scenarios. However, training of such DNN-based KWS systems often requires significant data and hardware resources. Manufacturers often entrust this process to a third-party platform. This makes the training process... | ['Shunhui Ji', 'Yan Xiao', 'Hai Dong', 'Pengcheng Zhang', 'Hanbo Cai'] | 2022-12-20 | null | null | null | null | ['voice-conversion', 'voice-conversion', 'keyword-spotting'] | ['audio', 'speech', 'speech'] | [-1.06760055e-01 -1.85859486e-01 -2.02043742e-01 1.23181229e-03
-2.99318761e-01 -1.08323658e+00 1.76721171e-01 -5.38306475e-01
-5.25284410e-01 3.07172865e-01 -4.30843771e-01 -1.15914702e+00
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2.19864577e-01 -2.16082871e-01 1.61046997e-01 3.69953662... | [13.956228256225586, 5.817488193511963] |
18f64a60-4480-4304-9eb1-63aa4479e0e7 | the-hanabi-challenge-a-new-frontier-for-ai | 1902.00506 | null | https://arxiv.org/abs/1902.00506v2 | https://arxiv.org/pdf/1902.00506v2.pdf | The Hanabi Challenge: A New Frontier for AI Research | From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making. In recent years, machine learning has made dramatic advances with artificial agents reaching superhuman performance in challenge domains like Go, Atari, and some variants of poker. A... | ['Marc G. Bellemare', 'Shibl Mourad', 'Neil Burch', 'Subhodeep Moitra', 'Michael Bowling', 'Marc Lanctot', 'Hugo Larochelle', 'Jakob N. Foerster', 'Emilio Parisotto', 'Nolan Bard', 'Iain Dunning', 'H. Francis Song', 'Vincent Dumoulin', 'Sarath Chandar', 'Edward Hughes'] | 2019-02-01 | null | null | null | null | ['game-of-hanabi'] | ['playing-games'] | [-2.88035542e-01 1.37978628e-01 1.14125438e-01 -1.81947742e-02
-1.97400808e-01 -4.88279879e-01 6.98488176e-01 3.33153233e-02
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-1.05134599e-01 -7.88623631e-01 -1.11938186e-01 -4.61065620e-01
-3.28257471e-01 1.08356583e+00 5.44775963e-01 -9.68655348... | [3.554410457611084, 1.5000791549682617] |
a5603dba-80a3-4c88-ae5c-052028146fc8 | visual-information-guided-zero-shot | 2201.09107 | null | https://arxiv.org/abs/2201.09107v2 | https://arxiv.org/pdf/2201.09107v2.pdf | Visual Information Guided Zero-Shot Paraphrase Generation | Zero-shot paraphrase generation has drawn much attention as the large-scale high-quality paraphrase corpus is limited. Back-translation, also known as the pivot-based method, is typical to this end. Several works leverage different information as "pivot" such as language, semantic representation and so on. In this pape... | ['Xiaojun Wan', 'Zhe Lin'] | 2022-01-22 | null | https://aclanthology.org/2022.coling-1.568 | https://aclanthology.org/2022.coling-1.568.pdf | coling-2022-10 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 5.93856946e-02 -7.22596720e-02 -4.49808031e-01 -2.45686755e-01
-7.38174796e-01 -5.90282559e-01 9.38837647e-01 -1.81125596e-01
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4.95827198e-01 -7.57093012e-01 -1.10014927e+00 -2.60517299e-01
1.14277256e+00 3.70638818e-01 1.24948464e-01 -4.34100449... | [11.222764015197754, 1.0104187726974487] |
43bcdaa5-02dc-4f63-96b2-4d9513b9b827 | recent-advances-in-artificial-intelligence | 2301.05864 | null | https://arxiv.org/abs/2301.05864v1 | https://arxiv.org/pdf/2301.05864v1.pdf | Recent advances in artificial intelligence for retrosynthesis | Retrosynthesis is the cornerstone of organic chemistry, providing chemists in material and drug manufacturing access to poorly available and brand-new molecules. Conventional rule-based or expert-based computer-aided synthesis has obvious limitations, such as high labor costs and limited search space. In recent years, ... | ['Mingli Song', 'Tingjun Hou', 'Shaolun Yao', 'Lingxiang Jia', 'Tiantao Liu', 'Zunlei Feng', 'Jie Song', 'Zipeng Zhong'] | 2023-01-14 | null | null | null | null | ['retrosynthesis'] | ['medical'] | [ 3.84341270e-01 -2.32281506e-01 -8.63019049e-01 6.97951987e-02
-3.67979825e-01 -1.14974630e+00 4.83904928e-01 8.22431207e-01
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-1.01797529e-01 -6.26879752e-01 -3.90823036e-01 -9.59831059e-01
-2.11403314e-02 4.97057050e-01 1.67596601e-02 -3.97130102... | [4.491466045379639, 6.109657287597656] |
25c6baf6-d303-4545-86d4-86dae823cf3d | attention-based-spatial-temporal-graph-neural | 2305.00985 | null | https://arxiv.org/abs/2305.00985v1 | https://arxiv.org/pdf/2305.00985v1.pdf | Attention-based Spatial-Temporal Graph Neural ODE for Traffic Prediction | Traffic forecasting is an important issue in intelligent traffic systems (ITS). Graph neural networks (GNNs) are effective deep learning models to capture the complex spatio-temporal dependency of traffic data, achieving ideal prediction performance. In this paper, we propose attention-based graph neural ODE (ASTGODE) ... | ['Jane Macfarlane', 'Hadi Meidani', 'Weiheng Zhong'] | 2023-05-01 | null | null | null | null | ['traffic-prediction'] | ['time-series'] | [-5.58997393e-01 -2.30502442e-01 -3.93597573e-01 -1.69742703e-01
3.12611997e-01 2.44320542e-01 4.47539270e-01 -5.37599623e-01
3.05321246e-01 6.09114468e-01 5.57933860e-02 -1.00794601e+00
-2.86386281e-01 -1.01056933e+00 -4.80301231e-01 -2.80413687e-01
-2.66105533e-01 7.09559619e-01 8.06619465e-01 -7.67227411... | [6.457385063171387, 2.072253465652466] |
0ac0098b-b1d3-4004-b64d-67957c464744 | sequence-classification-with-human-attention | null | null | https://aclanthology.org/K18-1030 | https://aclanthology.org/K18-1030.pdf | Sequence Classification with Human Attention | Learning attention functions requires large volumes of data, but many NLP tasks simulate human behavior, and in this paper, we show that human attention really does provide a good inductive bias on many attention functions in NLP. Specifically, we use estimated human attention derived from eye-tracking corpora to regul... | ['Anders S{\\o}gaard', 'Joachim Bingel', 'Maria Barrett', 'Nora Hollenstein', 'Marek Rei'] | 2018-10-01 | null | null | null | conll-2018-10 | ['grammatical-error-detection'] | ['natural-language-processing'] | [-4.03708458e-01 3.88189167e-01 -1.71156302e-01 -5.37437916e-01
-4.53408599e-01 -4.55056787e-01 1.80638954e-01 1.70040801e-01
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3.40824127e-01 -3.71392041e-01 -5.34444094e-01 -1.73068017e-01
3.29589874e-01 3.91930252e-01 -3.88526618e-01 -4.30729330... | [10.657845497131348, 8.53451919555664] |
ff8333ac-6f96-414f-bf49-14fb5eb39f3e | nonnegative-dictionary-learning-in-the | null | null | http://papers.nips.cc/paper/4273-nonnegative-dictionary-learning-in-the-exponential-noise-model-for-adaptive-music-signal-representation | http://papers.nips.cc/paper/4273-nonnegative-dictionary-learning-in-the-exponential-noise-model-for-adaptive-music-signal-representation.pdf | Nonnegative dictionary learning in the exponential noise model for adaptive music signal representation | In this paper we describe a maximum likelihood likelihood approach for dictionary learning in the multiplicative exponential noise model. This model is prevalent in audio signal processing where it underlies a generative composite model of the power spectrogram. Maximum joint likelihood estimation of the dictionary and... | ['Cédric Févotte', 'Onur Dikmen'] | 2011-12-01 | null | null | null | neurips-2011-12 | ['audio-signal-processing'] | ['audio'] | [ 3.93950582e-01 -7.58576617e-02 2.10210219e-01 -1.02878951e-01
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5.22745512e-02 4.34218496e-01 -5.42633124e-02 -1.64539680... | [15.441733360290527, 5.584949493408203] |
8a519d65-6d72-4980-91f6-2d03cd3f96b1 | 3d-multi-object-tracking-with-differentiable | 2206.13785 | null | https://arxiv.org/abs/2206.13785v1 | https://arxiv.org/pdf/2206.13785v1.pdf | 3D Multi-Object Tracking with Differentiable Pose Estimation | We propose a novel approach for joint 3D multi-object tracking and reconstruction from RGB-D sequences in indoor environments. To this end, we detect and reconstruct objects in each frame while predicting dense correspondences mappings into a normalized object space. We leverage those correspondences to inform a graph ... | ['Matthias Nießner', 'Norman Müller', 'Zeju Qiu', 'Dominik Schmauser'] | 2022-06-28 | null | null | null | null | ['3d-multi-object-tracking'] | ['computer-vision'] | [ 4.34858315e-02 -3.29106987e-01 1.48119375e-01 -2.43752360e-01
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-2.67402321e-01 6.49811327e-01 5.10106504e-01 1.96108297... | [6.9193034172058105, -2.311037302017212] |
bdbdca73-f43c-481c-9ac9-c8f486c0aebf | lightweight-image-inpainting-by-stripe-window | 2301.00553 | null | https://arxiv.org/abs/2301.00553v1 | https://arxiv.org/pdf/2301.00553v1.pdf | Lightweight Image Inpainting by Stripe Window Transformer with Joint Attention to CNN | Image inpainting is an important task in computer vision. As admirable methods are presented, the inpainted image is getting closer to reality. However, the result is still not good enough in the reconstructed texture and structure based on human vision. Although more and more larger models have been proposed recently ... | ['Kuan-Hsien Liu', 'Po-Wei Chen', 'Tsung-Jung Liu'] | 2023-01-02 | null | null | null | null | ['image-inpainting'] | ['computer-vision'] | [ 4.87296320e-02 -2.36921072e-01 3.50559428e-02 -2.19243601e-01
-2.45172337e-01 1.04190625e-01 1.32655859e-01 -3.44683588e-01
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3.63265693e-01 -1.86870530e-01 3.96200657e-01 -3.96277130... | [11.244551658630371, -1.6545051336288452] |
858120dc-bbec-4c0d-9956-28fd1f5f5657 | an-empirical-study-on-multi-task-learning-for | null | null | https://aclanthology.org/2020.coling-industry.6 | https://aclanthology.org/2020.coling-industry.6.pdf | An Empirical Study on Multi-Task Learning for Text Style Transfer and Paraphrase Generation | The topic of this paper is neural multi-task training for text style transfer. We present an efficient method for neutral-to-style transformation using the transformer framework. We demonstrate how to prepare a robust model utilizing large paraphrases corpora together with a small parallel style transfer corpus. We stu... | ['Katarzyna Beksa', 'Tymoteusz Krumholc', 'Jaroslaw Piersa', 'Katarzyna Witkowska', 'Hyungtak Choi', 'Kseniia Ryzhova', 'Pawel Bujnowski'] | 2020-12-01 | null | null | null | coling-2020-8 | ['paraphrase-generation', 'paraphrase-generation'] | ['computer-code', 'natural-language-processing'] | [ 5.59231997e-01 7.04387724e-02 -9.35779139e-02 -6.98003352e-01
-9.85000908e-01 -8.31489444e-01 9.56163049e-01 -4.82097805e-01
-6.12429559e-01 1.19592762e+00 3.81818384e-01 -3.86810720e-01
3.70566219e-01 -7.36121058e-01 -8.39295447e-01 -3.46357226e-01
7.91159153e-01 9.11722481e-01 3.78388762e-02 -8.35118949... | [11.553722381591797, 9.677913665771484] |
0ff370f5-2972-4781-90ba-71ae1853ad3a | flickr1024-a-dataset-for-stereo-image-super | 1903.06332 | null | https://arxiv.org/abs/1903.06332v2 | https://arxiv.org/pdf/1903.06332v2.pdf | Flickr1024: A Large-Scale Dataset for Stereo Image Super-Resolution | With the popularity of dual cameras in recently released smart phones, a growing number of super-resolution (SR) methods have been proposed to enhance the resolution of stereo image pairs. However, the lack of high-quality stereo datasets has limited the research in this area. To facilitate the training and evaluation ... | ['Longguang Wang', 'Wei An', 'Jungang Yang', 'Yulan Guo', 'Yingqian Wang'] | 2019-03-15 | null | null | null | null | ['stereo-image-super-resolution'] | ['computer-vision'] | [ 4.06688720e-01 -5.83905935e-01 5.85866943e-02 -3.46167356e-01
-1.08325958e+00 -5.08689344e-01 5.77191114e-01 -4.97877896e-01
-1.69554636e-01 6.45724058e-01 5.62011182e-01 1.01151720e-01
1.84091628e-02 -5.49635470e-01 -4.36800510e-01 -5.25865674e-01
3.42301577e-01 -1.55590594e-01 5.45304716e-01 -7.51803070... | [10.67517375946045, -2.2087149620056152] |
642ac01b-7625-4c36-bc30-4432d4fb5d97 | single-node-injection-label-specificity | 2305.02901 | null | https://arxiv.org/abs/2305.02901v1 | https://arxiv.org/pdf/2305.02901v1.pdf | Single Node Injection Label Specificity Attack on Graph Neural Networks via Reinforcement Learning | Graph neural networks (GNNs) have achieved remarkable success in various real-world applications. However, recent studies highlight the vulnerability of GNNs to malicious perturbations. Previous adversaries primarily focus on graph modifications or node injections to existing graphs, yielding promising results but with... | ['Qi Xuan', 'Zhen Wang', 'Shanqing Yu', 'Hongjie Ni', 'Jinhuan Wang', 'Yuqian Lv', 'Jian Zhang', 'Dayuan Chen'] | 2023-05-04 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 2.98131227e-01 3.78176719e-01 -2.45363176e-01 2.63071179e-01
-5.46086431e-01 -9.93914068e-01 5.43210208e-01 -9.41632167e-02
-3.38574976e-01 6.39162540e-01 -3.87274683e-01 -7.23156929e-01
-7.51221925e-02 -1.09521520e+00 -1.04511344e+00 -7.17881322e-01
-6.42790973e-01 4.08218563e-01 1.86347678e-01 -6.65609479... | [6.140402793884277, 7.349571704864502] |
6070364d-b3e6-47f6-9198-5c79def1643e | position-guided-point-cloud-panoptic | 2303.13509 | null | https://arxiv.org/abs/2303.13509v1 | https://arxiv.org/pdf/2303.13509v1.pdf | Position-Guided Point Cloud Panoptic Segmentation Transformer | DEtection TRansformer (DETR) started a trend that uses a group of learnable queries for unified visual perception. This work begins by applying this appealing paradigm to LiDAR-based point cloud segmentation and obtains a simple yet effective baseline. Although the naive adaptation obtains fair results, the instance se... | ['Jiangmiao Pang', 'Dahua Lin', 'Chen Change Loy', 'Tai Wang', 'Wenwei Zhang', 'Zeqi Xiao'] | 2023-03-23 | null | null | null | null | ['panoptic-segmentation', 'point-cloud-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.34198391e-01 -2.76953951e-02 -1.01226911e-01 -3.71806562e-01
-8.97369623e-01 -7.71364510e-01 5.57795763e-01 -1.44421101e-01
-1.34824872e-01 2.27164283e-01 -2.15765476e-01 -7.27546886e-02
-5.55644222e-02 -7.61431694e-01 -8.98274958e-01 -8.38997543e-01
1.39401928e-01 8.28919530e-01 8.04696679e-01 -1.74611613... | [7.984828948974609, -3.0314862728118896] |
4b482d9c-e772-419c-a7ba-e8f937e29b10 | double-deck-multi-agent-pickup-and-delivery | 2304.14309 | null | https://arxiv.org/abs/2304.14309v1 | https://arxiv.org/pdf/2304.14309v1.pdf | Double-Deck Multi-Agent Pickup and Delivery: Multi-Robot Rearrangement in Large-Scale Warehouses | We introduce a new problem formulation, Double-Deck Multi-Agent Pickup and Delivery (DD-MAPD), which models the multi-robot shelf rearrangement problem in automated warehouses. DD-MAPD extends both Multi-Agent Pickup and Delivery (MAPD) and Multi-Agent Path Finding (MAPF) by allowing agents to move beneath shelves or l... | ['Hang Ma', 'Baiyu Li'] | 2023-04-27 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [-4.93040323e-01 2.80128896e-01 1.01668254e-01 -2.29416624e-01
-7.35161006e-01 -1.32332134e+00 5.94705790e-02 6.86156809e-01
-9.50683355e-02 9.09297466e-01 -1.71798214e-01 -4.50031191e-01
-9.97144938e-01 -1.16803980e+00 -1.14624846e+00 -3.88731629e-01
-6.80298865e-01 1.61302853e+00 4.75381374e-01 -6.28875613... | [4.960483074188232, 1.7095260620117188] |
2e7a0a74-553f-455c-bea7-bd22197cfc95 | self-supervised-learning-based-depth | 2304.06966 | null | https://arxiv.org/abs/2304.06966v1 | https://arxiv.org/pdf/2304.06966v1.pdf | Self-Supervised Learning based Depth Estimation from Monocular Images | Depth Estimation has wide reaching applications in the field of Computer vision such as target tracking, augmented reality, and self-driving cars. The goal of Monocular Depth Estimation is to predict the depth map, given a 2D monocular RGB image as input. The traditional depth estimation methods are based on depth cues... | ['Haoyang Pei', 'Mohit Kewlani', 'Akash Mishra', 'Mayank Poddar'] | 2023-04-14 | null | null | null | null | ['self-driving-cars', 'monocular-depth-estimation'] | ['computer-vision', 'computer-vision'] | [ 1.75205678e-01 8.43557492e-02 -4.04304788e-02 -7.58692026e-01
-1.89289227e-01 -4.42825556e-01 8.41999412e-01 -1.47272646e-01
-4.53071117e-01 4.83193964e-01 8.55920557e-03 -2.94318557e-01
4.09984589e-01 -1.04273474e+00 -5.17017424e-01 -2.57899642e-01
-1.67515278e-02 4.32642877e-01 4.76425886e-01 -1.94151029... | [8.585369110107422, -2.3394546508789062] |
9196d56a-ee65-4e29-8cdd-d97041f1f8e0 | spending-thinking-time-wisely-accelerating-1 | 2210.12628 | null | https://arxiv.org/abs/2210.12628v1 | https://arxiv.org/pdf/2210.12628v1.pdf | Spending Thinking Time Wisely: Accelerating MCTS with Virtual Expansions | One of the most important AI research questions is to trade off computation versus performance since ``perfect rationality" exists in theory but is impossible to achieve in practice. Recently, Monte-Carlo tree search (MCTS) has attracted considerable attention due to the significant performance improvement in various c... | ['Yang Gao', 'Pieter Abbeel', 'Weirui Ye'] | 2022-10-23 | spending-thinking-time-wisely-accelerating | https://openreview.net/forum?id=33nhOe3cTd | https://openreview.net/pdf?id=33nhOe3cTd | null | ['board-games', 'atari-games'] | ['playing-games', 'playing-games'] | [ 7.30641559e-02 -1.64038077e-01 -2.91904986e-01 -9.12747085e-02
-1.04105699e+00 -5.80178678e-01 2.46168777e-01 -2.82898933e-01
-7.99087763e-01 9.85250473e-01 -4.62599695e-01 -5.85530400e-01
-2.19886646e-01 -8.40652764e-01 -3.62908959e-01 -7.08813488e-01
7.06971660e-02 6.89363718e-01 5.57597816e-01 -1.84490025... | [3.763401985168457, 1.730046272277832] |
8acc39ee-58a7-4290-821e-0a626651e5fe | efficient-hdr-reconstruction-from-real-world | 2306.10311 | null | https://arxiv.org/abs/2306.10311v2 | https://arxiv.org/pdf/2306.10311v2.pdf | Efficient HDR Reconstruction from Real-World Raw Images | High dynamic range (HDR) imaging is still a significant yet challenging problem due to the limited dynamic range of generic image sensors. Most existing learning-based HDR reconstruction methods take a set of bracketed-exposure sRGB images to extend the dynamic range, and thus are computational- and memory-inefficient ... | ['Jingyu Yang', 'Yihao Liu', 'Qirui Yang'] | 2023-06-17 | null | null | null | null | ['hdr-reconstruction'] | ['computer-vision'] | [ 5.05524516e-01 -4.40556616e-01 1.52163312e-01 -3.84097427e-01
-8.58171761e-01 -4.08148199e-01 3.39774460e-01 -5.62383950e-01
-4.69144136e-01 3.18017483e-01 1.21953167e-01 -3.14764023e-01
-8.13119188e-02 -6.54598713e-01 -8.31046820e-01 -7.78075755e-01
-5.47577143e-02 -7.19964784e-03 6.69843912e-01 -1.53836235... | [10.812997817993164, -2.227518081665039] |
72b2b171-8d42-415c-85d3-2f1a98e02733 | feature-mixing-for-writer-retrieval-and | 2306.12939 | null | https://arxiv.org/abs/2306.12939v1 | https://arxiv.org/pdf/2306.12939v1.pdf | Feature Mixing for Writer Retrieval and Identification on Papyri Fragments | This paper proposes a deep-learning-based approach to writer retrieval and identification for papyri, with a focus on identifying fragments associated with a specific writer and those corresponding to the same image. We present a novel neural network architecture that combines a residual backbone with a feature mixing ... | ['Robert Sablatnig', 'Marco Peer'] | 2023-06-22 | null | null | null | null | ['retrieval'] | ['methodology'] | [ 1.39140449e-02 -5.24804235e-01 -2.93067694e-01 -1.00801162e-01
-1.16594946e+00 -6.63889468e-01 1.04250884e+00 -2.23352194e-01
-3.08693975e-01 3.18731219e-01 1.02405161e-01 1.74908340e-01
-1.58203796e-01 -5.84046781e-01 -5.15298963e-01 -7.57392049e-01
6.53897300e-02 5.24270058e-01 -2.56974827e-02 -1.87279761... | [11.870752334594727, 2.4622952938079834] |
0083077c-f3b9-413b-a921-daaf7f964b78 | single-image-reflection-removal-through | 1911.06634 | null | https://arxiv.org/abs/1911.06634v2 | https://arxiv.org/pdf/1911.06634v2.pdf | Single Image Reflection Removal through Cascaded Refinement | We address the problem of removing undesirable reflections from a single image captured through a glass surface, which is an ill-posed, challenging but practically important problem for photo enhancement. Inspired by iterative structure reduction for hidden community detection in social networks, we propose an Iterativ... | ['Yixiao Yang', 'Stephen Lin', 'Kun He', 'John E. Hopcroft', 'Chao Li'] | 2019-11-15 | single-image-reflection-removal-through-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Li_Single_Image_Reflection_Removal_Through_Cascaded_Refinement_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Single_Image_Reflection_Removal_Through_Cascaded_Refinement_CVPR_2020_paper.pdf | cvpr-2020-6 | ['reflection-removal'] | ['computer-vision'] | [ 8.40468884e-01 1.67216152e-01 4.86640781e-01 -2.01571826e-02
-4.74134892e-01 1.13142356e-01 2.55408347e-01 -4.13174987e-01
-5.25223389e-02 4.72486556e-01 5.77992737e-01 -8.82288143e-02
2.75428081e-03 -9.96677935e-01 -8.62058938e-01 -1.12314832e+00
2.76666045e-01 -2.10253328e-01 1.03658728e-01 -3.19519341... | [10.669526100158691, -2.8090732097625732] |
0c36c834-c69b-4cf7-803e-745ac33ca90b | can-shuffling-video-benefit-temporal-bias | 2207.14698 | null | https://arxiv.org/abs/2207.14698v2 | https://arxiv.org/pdf/2207.14698v2.pdf | Can Shuffling Video Benefit Temporal Bias Problem: A Novel Training Framework for Temporal Grounding | Temporal grounding aims to locate a target video moment that semantically corresponds to the given sentence query in an untrimmed video. However, recent works find that existing methods suffer a severe temporal bias problem. These methods do not reason the target moment locations based on the visual-textual semantic al... | ['Jianxin Liao', 'Qi Qi', 'Jingyu Wang', 'Pengfei Ren', 'Haifeng Sun', 'Jiachang Hao'] | 2022-07-29 | null | null | null | null | ['language-based-temporal-localization'] | ['computer-vision'] | [ 2.90082805e-02 -2.20145598e-01 -6.72565639e-01 -4.50388581e-01
-6.92939162e-01 -7.45961845e-01 6.19209051e-01 -4.48610261e-02
-2.14392081e-01 2.52078384e-01 4.92110342e-01 -1.52772903e-01
6.08474808e-03 -4.43956375e-01 -8.17366183e-01 -4.18859184e-01
-1.52256802e-01 -3.08558792e-01 5.34485221e-01 -1.85365617... | [9.994281768798828, 0.7427493333816528] |
13af72d2-a543-40e3-a41a-64488db3e94f | lazy-modeling-of-variants-of-token-swapping | 1809.05959 | null | http://arxiv.org/abs/1809.05959v1 | http://arxiv.org/pdf/1809.05959v1.pdf | Lazy Modeling of Variants of Token Swapping Problem and Multi-agent Path Finding through Combination of Satisfiability Modulo Theories and Conflict-based Search | We address item relocation problems in graphs in this paper. We assume items
placed in vertices of an undirected graph with at most one item per vertex.
Items can be moved across edges while various constraints depending on the type
of relocation problem must be satisfied. We introduce a general problem
formulation tha... | ['Pavel Surynek'] | 2018-09-16 | null | null | null | null | ['multi-agent-path-finding'] | ['playing-games'] | [ 2.49379098e-01 1.93335801e-01 -2.45862126e-01 -1.10329194e-02
-3.28462362e-01 -7.80409992e-01 1.76519454e-01 5.51958084e-01
-2.83620089e-01 1.38781726e+00 -2.04244927e-01 -4.63385433e-01
-7.50255883e-01 -1.05584681e+00 -5.64583957e-01 -4.40950990e-01
-3.93601745e-01 1.06436121e+00 9.01283979e-01 -6.08754396... | [4.984126091003418, 1.8358161449432373] |
bc128722-46df-4f4a-ab72-c48d4b8dd645 | segmenting-transparent-objects-in-the-wild | 2003.13948 | null | https://arxiv.org/abs/2003.13948v3 | https://arxiv.org/pdf/2003.13948v3.pdf | Segmenting Transparent Objects in the Wild | Transparent objects such as windows and bottles made by glass widely exist in the real world. Segmenting transparent objects is challenging because these objects have diverse appearance inherited from the image background, making them had similar appearance with their surroundings. Besides the technical difficulty of t... | ['Wenhai Wang', 'Mingyu Ding', 'Chunhua Shen', 'Ping Luo', 'Wenjia Wang', 'Enze Xie'] | 2020-03-31 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2016_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580681.pdf | eccv-2020-8 | ['transparent-objects'] | ['computer-vision'] | [ 1.38939902e-01 3.26673277e-02 2.46473521e-01 -3.32583368e-01
-1.68443218e-01 -5.70153296e-01 1.56745791e-01 -5.08890212e-01
-1.52573764e-01 4.72267538e-01 -4.12546128e-01 -2.09706813e-01
2.47805968e-01 -7.10774899e-01 -7.57899404e-01 -7.28579640e-01
1.36868641e-01 2.86792219e-01 8.42187524e-01 -2.65636891... | [10.054588317871094, -0.7690457105636597] |
80777f68-fb3d-4501-8d70-e1b2c20fa92c | learning-to-navigate-in-turbulent-flows-with | 2306.04781 | null | https://arxiv.org/abs/2306.04781v1 | https://arxiv.org/pdf/2306.04781v1.pdf | Learning to Navigate in Turbulent Flows with Aerial Robot Swarms: A Cooperative Deep Reinforcement Learning Approach | Aerial operation in turbulent environments is a challenging problem due to the chaotic behavior of the flow. This problem is made even more complex when a team of aerial robots is trying to achieve coordinated motion in turbulent wind conditions. In this paper, we present a novel multi-robot controller to navigate in t... | ['David Saldaña', 'Kostas Daniilidis', 'Juan Calderon', 'Siddharth Mayya', 'Diego Patiño'] | 2023-06-07 | null | null | null | null | ['navigate'] | ['reasoning'] | [-1.45944342e-01 -1.06074505e-01 4.17935848e-01 5.08672148e-02
3.17816764e-01 -7.66846836e-01 1.44718692e-01 1.45198360e-01
-3.21919918e-01 6.35045111e-01 6.10569827e-02 -6.13372214e-03
-6.53233469e-01 -9.08388376e-01 -5.84154069e-01 -7.26195157e-01
-5.25003016e-01 6.48747981e-01 3.24320704e-01 -9.96081948... | [4.536502838134766, 1.4710510969161987] |
5fb977fe-52ba-488d-9e97-15792669d126 | convergence-of-gradient-descent-with-linearly | 2302.01463 | null | https://arxiv.org/abs/2302.01463v2 | https://arxiv.org/pdf/2302.01463v2.pdf | Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy | We study gradient descent under linearly correlated noise. Our work is motivated by recent practical methods for optimization with differential privacy (DP), such as DP-FTRL, which achieve strong performance in settings where privacy amplification techniques are infeasible (such as in federated learning). These methods... | ['Brendan Mcmahan', 'Keith Rush', 'Zachary Charles', 'Ryan McKenna', 'Anastasia Koloskova'] | 2023-02-02 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-1.83801521e-02 -7.02637881e-02 -2.68635958e-01 -1.82809904e-01
-8.81005526e-01 -1.31135929e+00 1.79310262e-01 2.62253154e-02
-4.26179886e-01 7.34979153e-01 6.48625851e-01 -7.72022009e-01
-2.02875242e-01 -6.05384886e-01 -9.16713715e-01 -8.28554690e-01
-4.46520030e-01 -5.85976504e-02 -6.15670681e-01 -3.28455389... | [5.848475933074951, 6.556402683258057] |
8c6548f2-ccd4-40b2-8913-cf71b972fde7 | jigsaw-vit-learning-jigsaw-puzzles-in-vision | 2207.11971 | null | https://arxiv.org/abs/2207.11971v2 | https://arxiv.org/pdf/2207.11971v2.pdf | Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer | The success of Vision Transformer (ViT) in various computer vision tasks has promoted the ever-increasing prevalence of this convolution-free network. The fact that ViT works on image patches makes it potentially relevant to the problem of jigsaw puzzle solving, which is a classical self-supervised task aiming at reord... | ['Johan A. K. Suykens', 'Qinghua Tao', 'Yahui Liu', 'Xi Shen', 'Yingyi Chen'] | 2022-07-25 | null | null | null | null | ['learning-with-noisy-labels', 'learning-with-noisy-labels'] | ['computer-vision', 'natural-language-processing'] | [ 2.41644233e-01 -2.94679636e-03 1.94932632e-02 -1.84481010e-01
-4.07520592e-01 -7.60349214e-01 3.82025540e-01 -1.96443662e-01
-4.96476352e-01 6.01431310e-01 -9.85659808e-02 -3.68325889e-01
-2.09395781e-01 -8.32704246e-01 -9.85122144e-01 -7.49412358e-01
-1.41953276e-02 1.43001303e-01 3.01728815e-01 -4.00647074... | [9.556662559509277, 1.959618091583252] |
71b40280-114f-4875-88ea-0bca004ff483 | graph-guided-deformation-for-point-cloud | 2112.01840 | null | https://arxiv.org/abs/2112.01840v1 | https://arxiv.org/pdf/2112.01840v1.pdf | Graph-Guided Deformation for Point Cloud Completion | For a long time, the point cloud completion task has been regarded as a pure generation task. After obtaining the global shape code through the encoder, a complete point cloud is generated using the shape priorly learnt by the networks. However, such models are undesirably biased towards prior average objects and inher... | ['Shaojie Shen', 'Liang Heng', 'Lingyun Xu', 'Jieqi Shi'] | 2021-11-11 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [-2.22768933e-02 2.74466634e-01 4.01739597e-01 -2.68824250e-01
-4.92580414e-01 -6.32613063e-01 5.67153096e-01 -1.89341187e-01
-1.09835327e-01 3.50614637e-01 -2.82896101e-01 -2.47614831e-01
1.08541034e-01 -1.11128747e+00 -1.30247808e+00 -4.49816763e-01
1.50521062e-02 7.81142890e-01 5.78735247e-02 -3.30525339... | [8.457903861999512, -3.5563912391662598] |
49831644-9d5e-46c6-a7c8-9813b909a49d | on-the-development-of-a-bayesian-optimisation | 2207.09154 | null | https://arxiv.org/abs/2207.09154v2 | https://arxiv.org/pdf/2207.09154v2.pdf | Investigating Bayesian optimization for expensive-to-evaluate black box functions: Application in fluid dynamics | Bayesian optimization provides an effective method to optimize expensive-to-evaluate black box functions. It has been widely applied to problems in many fields, including notably in computer science, e.g. in machine learning to optimize hyperparameters of neural networks, and in engineering, e.g. in fluid dynamics to o... | ['Kevin Wilson', 'Sylvain Laizet', 'Andrew Wynn', "Joseph O'Connor", 'Richard D. Whalley', 'Yu Guan', 'Mike Diessner'] | 2022-07-19 | null | null | null | null | ['bayesian-optimisation'] | ['methodology'] | [ 4.88465205e-02 -3.27693522e-01 1.84193641e-01 -2.04287902e-01
-4.54396427e-01 -3.46264660e-01 4.09831434e-01 3.02184105e-01
-6.34933114e-01 1.03042829e+00 -3.44962507e-01 -4.54482615e-01
-6.39681101e-01 -6.24398291e-01 -5.13484478e-01 -1.10908663e+00
-2.28202850e-01 8.12892020e-01 2.82708794e-01 -1.02377981... | [6.238903999328613, 3.679579257965088] |
b362a91c-1caf-4661-9389-bc87fb330373 | quantifying-the-scanner-induced-domain-gap-in | 2103.16515 | null | https://arxiv.org/abs/2103.16515v1 | https://arxiv.org/pdf/2103.16515v1.pdf | Quantifying the Scanner-Induced Domain Gap in Mitosis Detection | Automated detection of mitotic figures in histopathology images has seen vast improvements, thanks to modern deep learning-based pipelines. Application of these methods, however, is in practice limited by strong variability of images between labs. This results in a domain shift of the images, which causes a performance... | ['Andreas Maier', 'Francesco Ciompi', 'Natalie ter Hoeve', 'Katharina Breininger', 'Nikolas Stathonikos', 'Robert Klopfleisch', 'Mitko Veta', 'Christof Bertram', 'Marc Aubreville'] | 2021-03-30 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 2.59685993e-01 -1.74015183e-02 1.61126480e-01 -2.24750102e-01
-1.20289469e+00 -8.28101695e-01 6.90720856e-01 4.68724042e-01
-7.48323739e-01 8.00795496e-01 -1.32041857e-01 -3.49451244e-01
2.65845517e-03 -4.62192595e-01 -8.66789877e-01 -1.27012980e+00
2.87507147e-01 7.53491938e-01 5.19518733e-01 2.64258802... | [15.134425163269043, -3.151902914047241] |
4aa43c8e-ecd6-4d57-916b-7305ba5a7d9c | provably-efficient-safe-exploration-via | 2003.00534 | null | https://arxiv.org/abs/2003.00534v2 | https://arxiv.org/pdf/2003.00534v2.pdf | Provably Efficient Safe Exploration via Primal-Dual Policy Optimization | We study the Safe Reinforcement Learning (SRL) problem using the Constrained Markov Decision Process (CMDP) formulation in which an agent aims to maximize the expected total reward subject to a safety constraint on the expected total value of a utility function. We focus on an episodic setting with the function approxi... | ['Mihailo R. Jovanović', 'Xiaohan Wei', 'Zhaoran Wang', 'Zhuoran Yang', 'Dongsheng Ding'] | 2020-03-01 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-1.14321634e-02 5.50981462e-01 -4.29400116e-01 1.24926761e-01
-8.19369137e-01 -6.06250525e-01 1.60155505e-01 2.65059143e-01
-9.14863050e-01 1.01784348e+00 -2.93277293e-01 -5.88123679e-01
-7.39715874e-01 -7.50610590e-01 -8.70075762e-01 -1.00409544e+00
-5.39416254e-01 4.58373040e-01 -1.34992257e-01 3.97510082... | [4.334485054016113, 2.866194248199463] |
a20402ba-05ec-4dff-975d-336134848fff | learning-to-drive-using-sparse-imitation | 2205.12128 | null | https://arxiv.org/abs/2205.12128v1 | https://arxiv.org/pdf/2205.12128v1.pdf | Learning to Drive Using Sparse Imitation Reinforcement Learning | In this paper, we propose Sparse Imitation Reinforcement Learning (SIRL), a hybrid end-to-end control policy that combines the sparse expert driving knowledge with reinforcement learning (RL) policy for autonomous driving (AD) task in CARLA simulation environment. The sparse expert is designed based on hand-crafted rul... | ['Alper Yilmaz', 'Yuci Han'] | 2022-05-24 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-3.37048203e-01 3.76869917e-01 -2.70700250e-02 2.37176940e-01
-4.44215238e-01 -4.56385165e-01 5.16700029e-01 -7.04848394e-02
-6.77365363e-01 1.01591575e+00 -2.58139998e-01 -5.59928060e-01
-1.84917465e-01 -7.89858878e-01 -9.24145639e-01 -8.58165205e-01
-3.17227840e-01 3.37908596e-01 3.51696610e-01 -5.64528644... | [4.972261428833008, 1.4598803520202637] |
8edc6ec1-7cf0-467e-9247-3af41b428aa7 | counterfactual-diagnosis | 1910.06772 | null | https://arxiv.org/abs/1910.06772v3 | https://arxiv.org/pdf/1910.06772v3.pdf | Counterfactual diagnosis | Machine learning promises to revolutionize clinical decision making and diagnosis. In medical diagnosis a doctor aims to explain a patient's symptoms by determining the diseases \emph{causing} them. However, existing diagnostic algorithms are purely associative, identifying diseases that are strongly correlated with a ... | ['Saurabh Johri', 'Jonathan G. Richens', 'Ciaran M. Lee'] | 2019-10-15 | null | null | null | null | ['counterfactual-inference'] | ['miscellaneous'] | [ 4.36711282e-01 1.01741278e+00 -5.44996619e-01 -5.04606724e-01
-7.38726795e-01 -3.77111554e-01 6.38215125e-01 4.15911108e-01
-2.66228735e-01 1.13282967e+00 3.94267827e-01 -7.45646596e-01
-7.78868079e-01 -6.23492539e-01 -4.34685588e-01 -6.92241967e-01
-1.77515209e-01 1.31879604e+00 -3.74793172e-01 3.42436880... | [8.38121223449707, 5.543274402618408] |
8068bc49-9897-4483-a83d-cef72dd7f569 | going-the-extra-mile-in-face-image-quality | 2207.04904 | null | https://arxiv.org/abs/2207.04904v1 | https://arxiv.org/pdf/2207.04904v1.pdf | Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model | Computer vision models for image quality assessment (IQA) predict the subjective effect of generic image degradation, such as artefacts, blurs, bad exposure, or colors. The scarcity of face images in existing IQA datasets (below 10\%) is limiting the precision of IQA required for accurately filtering low-quality face i... | ['Dietmar Saupe', 'Yanning Zhang', 'Hantao Liu', 'Yu Zhu', 'Jinqiu Sun', 'Oliver Wiedemann', 'Vlad Hosu', 'Hanhe Lin', 'Shaolin Su'] | 2022-07-11 | null | null | null | null | ['face-image-quality', 'face-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 3.46852005e-01 -1.86459839e-01 2.36667201e-01 -5.83170533e-01
-7.25775719e-01 -2.88811922e-01 4.56608027e-01 -7.03119516e-01
1.26213029e-01 5.40302396e-01 5.24349034e-01 1.89081714e-01
-2.44374365e-01 -6.30369067e-01 -4.70648825e-01 -6.57853484e-01
1.73321635e-01 -3.10091749e-02 -5.31027615e-01 7.85273612... | [12.852072715759277, 0.028658581897616386] |
644bb7b7-ce52-422e-8533-0105135afb00 | referring-expression-generation-in-time | null | null | https://aclanthology.org/L18-1476 | https://aclanthology.org/L18-1476.pdf | Referring Expression Generation in time-constrained communication | null | ["r{\\'e}", "Andr{\\'e} Mariotti", 'Iv Paraboni'] | 2018-05-01 | referring-expression-generation-in-time-1 | https://aclanthology.org/L18-1476 | https://aclanthology.org/L18-1476.pdf | lrec-2018-5 | ['referring-expression-generation'] | ['computer-vision'] | [-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... | [-7.206428527832031, 3.620995283126831] |
a3979f27-8db2-44e3-93c3-74a2ce4b94e0 | bigvgan-a-universal-neural-vocoder-with-large | 2206.04658 | null | https://arxiv.org/abs/2206.04658v2 | https://arxiv.org/pdf/2206.04658v2.pdf | BigVGAN: A Universal Neural Vocoder with Large-Scale Training | Despite recent progress in generative adversarial network (GAN)-based vocoders, where the model generates raw waveform conditioned on acoustic features, it is challenging to synthesize high-fidelity audio for numerous speakers across various recording environments. In this work, we present BigVGAN, a universal vocoder ... | ['Sungroh Yoon', 'Bryan Catanzaro', 'Boris Ginsburg', 'Wei Ping', 'Sang-gil Lee'] | 2022-06-09 | null | null | null | null | ['audio-generation', 'music-generation', 'music-generation'] | ['audio', 'audio', 'music'] | [ 3.07397600e-02 7.13907108e-02 3.60628366e-01 -1.40194759e-01
-1.59870386e+00 -7.82853067e-01 3.22489887e-01 -7.78506279e-01
2.78535575e-01 7.03134418e-01 5.16346574e-01 -1.96150333e-01
3.91027272e-01 -6.16290569e-01 -8.75482678e-01 -7.63339877e-01
1.14117644e-03 2.82714665e-01 -2.10836932e-01 -2.54828066... | [15.41445255279541, 6.152064800262451] |
b72295d1-ac76-41e4-8c8c-d73cab83be62 | mathematical-imaging-methods-for-mitosis | 1609.04649 | null | http://arxiv.org/abs/1609.04649v3 | http://arxiv.org/pdf/1609.04649v3.pdf | Mathematical Imaging Methods for Mitosis Analysis in Live-Cell Phase Contrast Microscopy | In this paper we propose a workflow to detect and track mitotic cells in
time-lapse microscopy image sequences. In order to avoid the requirement for
cell lines expressing fluorescent markers and the associated phototoxicity,
phase contrast microscopy is often preferred over fluorescence microscopy in
live-cell imaging... | [] | 2017-02-10 | null | null | null | null | ['mitosis-detection'] | ['medical'] | [ 4.59599227e-01 -4.40645039e-01 4.04953241e-01 8.43788981e-02
-3.99269253e-01 -8.30580115e-01 3.69652987e-01 6.93396986e-01
-8.87440979e-01 9.53361928e-01 -7.18842506e-01 -2.25016266e-01
3.94663699e-02 -6.60838842e-01 -1.00663371e-01 -1.37617636e+00
2.08701655e-01 5.92080653e-01 3.70037973e-01 4.87121820... | [14.35417366027832, -3.0614616870880127] |
49c9b665-234f-43cc-b302-b8d5df5164ab | lvm-med-learning-large-scale-self-supervised | 2306.11925 | null | https://arxiv.org/abs/2306.11925v2 | https://arxiv.org/pdf/2306.11925v2.pdf | LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching | Obtaining large pre-trained models that can be fine-tuned to new tasks with limited annotated samples has remained an open challenge for medical imaging data. While pre-trained deep networks on ImageNet and vision-language foundation models trained on web-scale data are prevailing approaches, their effectiveness on med... | ['Mathias Niepert', 'Daniel Sonntag', 'Pengtao Xie', 'Shadi Albarqouni', 'Nhat Ho', 'Paul Swoboda', 'Binh T. Nguyen', 'Tri Cao', 'Tan N. Pham', 'Nghiem T. Diep', 'Hoang Nguyen', 'Duy M. H. Nguyen'] | 2023-06-20 | null | null | null | null | ['contrastive-learning', 'self-supervised-learning', 'graph-matching', 'medical-image-segmentation', 'lesion-segmentation', 'diabetic-retinopathy-grading', 'contrastive-learning'] | ['computer-vision', 'computer-vision', 'graphs', 'medical', 'medical', 'medical', 'methodology'] | [ 4.61020976e-01 3.63948315e-01 -3.93413931e-01 -5.12160063e-01
-9.70758438e-01 -5.98702133e-02 3.99520844e-01 4.87973452e-01
-7.20852375e-01 4.50679630e-01 1.59824550e-01 -3.35362017e-01
-1.86680570e-01 -5.04268289e-01 -7.10874379e-01 -5.26027739e-01
-2.58042663e-01 8.18626344e-01 2.77136594e-01 -2.02849694... | [14.73281478881836, -2.3029839992523193] |
430751e4-fea3-474c-832b-6886f2101751 | improving-lexical-embeddings-with-semantic | null | null | https://aclanthology.org/P14-2089 | https://aclanthology.org/P14-2089.pdf | Improving Lexical Embeddings with Semantic Knowledge | null | ['Mark Dredze', 'Mo Yu'] | 2014-06-01 | null | null | null | acl-2014-6 | ['learning-word-embeddings'] | ['methodology'] | [-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... | [-7.188699722290039, 3.784574508666992] |
5ad46faf-695c-45ce-88af-4f9e3ccc1da8 | tracking-by-associating-clips | 2212.10149 | null | https://arxiv.org/abs/2212.10149v1 | https://arxiv.org/pdf/2212.10149v1.pdf | Tracking by Associating Clips | The tracking-by-detection paradigm today has become the dominant method for multi-object tracking and works by detecting objects in each frame and then performing data association across frames. However, its sequential frame-wise matching property fundamentally suffers from the intermediate interruptions in a video, su... | ['Joon-Young Lee', 'In So Kweon', 'Seoung Wug Oh', 'KwanYong Park', 'Sanghyun Woo'] | 2022-12-20 | null | null | null | null | ['chunking'] | ['natural-language-processing'] | [-2.85327546e-02 -6.96724832e-01 -3.68146062e-01 1.32370263e-01
-7.09885180e-01 -6.74784720e-01 4.44356203e-01 2.82042414e-01
-4.86670882e-01 6.06809080e-01 -1.27733409e-01 8.74126926e-02
-3.42888795e-02 -4.51652080e-01 -8.46317828e-01 -7.52438307e-01
-1.93333119e-01 3.36465597e-01 1.23911238e+00 1.50150761... | [6.441262245178223, -2.0286006927490234] |
da38c0ad-1fe4-49fc-a235-90639a03b616 | color-constancy-with-derivative-colors | 1611.08389 | null | http://arxiv.org/abs/1611.08389v1 | http://arxiv.org/pdf/1611.08389v1.pdf | Color Constancy with Derivative Colors | Information about the illuminant color is well contained in both achromatic
regions and the specular components of highlight regions. In this paper, we
propose a novel way to achieve color constancy by exploiting such clues. The
key to our approach lies in the use of suitably extracted derivative colors,
which are able... | ['Long Quan', 'Huan Lei', 'Guang Jiang'] | 2016-11-25 | null | null | null | null | ['color-constancy'] | ['computer-vision'] | [-4.53624576e-02 -6.03589594e-01 1.64703816e-01 -1.72753319e-01
-4.02621478e-01 -6.87223613e-01 5.77745497e-01 -1.24518089e-01
-3.49134922e-01 6.63204193e-01 -2.46937335e-01 6.10144623e-02
8.20402503e-02 -7.08140075e-01 -4.75195110e-01 -9.63558972e-01
1.63662001e-01 1.25466943e-01 3.37827593e-01 -7.05465004... | [10.374496459960938, -2.6914613246917725] |
5aa3d686-681a-4dcd-835c-81345d677d0e | mixdehazenet-mix-structure-block-for-image | 2305.17654 | null | https://arxiv.org/abs/2305.17654v1 | https://arxiv.org/pdf/2305.17654v1.pdf | MixDehazeNet : Mix Structure Block For Image Dehazing Network | Image dehazing is a typical task in the low-level vision field. Previous studies verified the effectiveness of the large convolutional kernel and attention mechanism in dehazing. However, there are two drawbacks: the multi-scale properties of an image are readily ignored when a large convolutional kernel is introduced,... | ['Bingrong Xu', 'DuanFeng Chu', 'Qian Xiong', 'LiPing Lu'] | 2023-05-28 | null | null | null | null | ['image-dehazing'] | ['computer-vision'] | [ 2.68519316e-02 -2.81784952e-01 3.38754982e-01 -2.57922895e-02
-2.42559165e-01 1.51508600e-01 3.14910620e-01 -1.85616881e-01
-5.38481951e-01 3.14264417e-01 1.76313624e-01 -9.06446278e-02
-9.61840674e-02 -1.01070511e+00 -8.35802317e-01 -1.13727427e+00
1.44253850e-01 -4.18759555e-01 8.41433764e-01 -4.56979901... | [10.954547882080078, -3.018432378768921] |
638649c8-f448-497c-9275-775e78b237b6 | dynamic-anchor-learning-for-arbitrary | 2012.04150 | null | https://arxiv.org/abs/2012.04150v2 | https://arxiv.org/pdf/2012.04150v2.pdf | Dynamic Anchor Learning for Arbitrary-Oriented Object Detection | Arbitrary-oriented objects widely appear in natural scenes, aerial photographs, remote sensing images, etc., thus arbitrary-oriented object detection has received considerable attention. Many current rotation detectors use plenty of anchors with different orientations to achieve spatial alignment with ground truth boxe... | ['Linhao Li', 'Hongwei Zhang', 'Lingjuan Miao', 'Zhiqiang Zhou', 'Qi Ming'] | 2020-12-08 | null | null | null | null | ['multi-oriented-scene-text-detection', 'object-detection-in-aerial-images'] | ['computer-vision', 'computer-vision'] | [-3.49318199e-02 -4.44829464e-01 -5.14778435e-01 -3.12502623e-01
-7.93817103e-01 -5.47493398e-01 4.44334000e-01 2.34362945e-01
-4.11551505e-01 4.65703309e-01 -2.15285867e-01 -2.34076232e-01
-1.21016093e-01 -9.78573382e-01 -4.07129288e-01 -1.00215495e+00
-7.87094235e-02 2.71119416e-01 6.46758914e-01 6.38671517... | [8.805208206176758, -0.7994343042373657] |
92878183-45e1-435e-b2be-ddf404fe73a2 | unsupervised-salience-learning-for-person-re | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Zhao_Unsupervised_Salience_Learning_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Zhao_Unsupervised_Salience_Learning_2013_CVPR_paper.pdf | Unsupervised Salience Learning for Person Re-identification | Human eyes can recognize person identities based on some small salient regions. However, such valuable salient information is often hidden when computing similarities of images with existing approaches. Moreover, many existing approaches learn discriminative features and handle drastic viewpoint change in a supervised ... | ['Rui Zhao', 'Wanli Ouyang', 'Xiaogang Wang'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['patch-matching'] | ['computer-vision'] | [ 2.93261886e-01 -3.82755637e-01 3.11441887e-02 -4.07029003e-01
-2.85083562e-01 -5.80927253e-01 5.02400935e-01 3.47779661e-01
-5.76219738e-01 5.48305690e-01 4.16353643e-01 7.80055285e-01
-3.61056104e-02 -5.99700272e-01 -4.53463823e-01 -6.54147506e-01
2.00922772e-01 2.61933595e-01 1.64502189e-01 -6.31072521... | [14.756058692932129, 1.0083731412887573] |
1ced3f32-6c60-45f5-b056-95cd1f691ebe | text-diae-degradation-invariant-autoencoders | 2203.04814 | null | https://arxiv.org/abs/2203.04814v4 | https://arxiv.org/pdf/2203.04814v4.pdf | Text-DIAE: A Self-Supervised Degradation Invariant Autoencoders for Text Recognition and Document Enhancement | In this paper, we propose a Text-Degradation Invariant Auto Encoder (Text-DIAE), a self-supervised model designed to tackle two tasks, text recognition (handwritten or scene-text) and document image enhancement. We start by employing a transformer-based architecture that incorporates three pretext tasks as learning obj... | ['Dimosthenis Karatzas', 'Lluis Gomez', 'Josep Lladós', 'Yousri Kessentini', 'Alicia Fornés', 'Ali Furkan Biten', 'Andres Mafla', 'Sanket Biswas', 'Mohamed Ali Souibgui'] | 2022-03-09 | null | null | null | null | ['document-enhancement', 'scene-text-recognition'] | ['computer-vision', 'computer-vision'] | [ 9.00468230e-01 -8.64551365e-02 1.00058913e-01 -5.71986496e-01
-9.83003914e-01 -4.58723903e-01 8.80331814e-01 -2.28707656e-01
-4.39400434e-01 4.05524671e-01 5.95333613e-02 -2.55672693e-01
-1.69651378e-02 -1.81606904e-01 -6.39329493e-01 -7.89155841e-01
3.25103551e-01 2.55725116e-01 -1.01601988e-01 -1.49111301... | [11.846346855163574, 2.2009079456329346] |
fb1f8925-6271-41c5-be5c-110c34e34546 | antman-sparse-low-rank-compression-to-1 | 1910.01740 | null | https://arxiv.org/abs/1910.01740v1 | https://arxiv.org/pdf/1910.01740v1.pdf | AntMan: Sparse Low-Rank Compression to Accelerate RNN inference | Wide adoption of complex RNN based models is hindered by their inference performance, cost and memory requirements. To address this issue, we develop AntMan, combining structured sparsity with low-rank decomposition synergistically, to reduce model computation, size and execution time of RNNs while attaining desired ac... | ['Samyam Rajbhandari', 'Harsh Shrivastava', 'Yuxiong He'] | 2019-10-02 | antman-sparse-low-rank-compression-to | https://openreview.net/forum?id=BJgsN3R9Km | https://openreview.net/pdf?id=BJgsN3R9Km | iclr-2019-5 | ['low-rank-compression'] | ['computer-code'] | [ 2.23371565e-01 1.84886858e-01 -5.77338576e-01 -3.59824359e-01
-9.62558925e-01 -4.40393627e-01 2.33633712e-01 -7.88220465e-02
-5.41326165e-01 5.40174663e-01 4.58558887e-01 -6.55614316e-01
-2.66787201e-01 -5.16588211e-01 -8.58275950e-01 -1.09760672e-01
3.00779164e-01 5.95403492e-01 -3.19458008e-01 1.25505462... | [8.77525806427002, 3.6551153659820557] |
3c938875-5d9c-49b6-a5a1-cd3589a55570 | a-zero-few-shot-anomaly-classification-and | 2305.17382 | null | https://arxiv.org/abs/2305.17382v2 | https://arxiv.org/pdf/2305.17382v2.pdf | A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD | In this technical report, we briefly introduce our solution for the Zero/Few-shot Track of the Visual Anomaly and Novelty Detection (VAND) 2023 Challenge. For industrial visual inspection, building a single model that can be rapidly adapted to numerous categories without or with only a few normal reference images is a ... | ['Jiangning Zhang', 'Yue Han', 'Xuhai Chen'] | 2023-05-27 | null | null | null | null | ['anomaly-classification'] | ['computer-vision'] | [ 2.91334484e-02 -9.50814709e-02 1.34601876e-01 -1.63345784e-01
-6.75702035e-01 -3.36509705e-01 3.94690692e-01 2.39619061e-01
-2.57805824e-01 -1.20307561e-02 -4.58739460e-01 -2.88752392e-02
-2.69622896e-02 -3.80032331e-01 -7.58116007e-01 -4.92735922e-01
3.92090045e-02 5.52442968e-02 4.74803239e-01 -3.02833647... | [7.684839725494385, 1.9455512762069702] |
cc306e11-e9dc-4163-8d56-80bd1ed889f8 | combo-a-complete-benchmark-for-open-kg | 2302.03905 | null | https://arxiv.org/abs/2302.03905v1 | https://arxiv.org/pdf/2302.03905v1.pdf | COMBO: A Complete Benchmark for Open KG Canonicalization | Open knowledge graph (KG) consists of (subject, relation, object) triples extracted from millions of raw text. The subject and object noun phrases and the relation in open KG have severe redundancy and ambiguity and need to be canonicalized. Existing datasets for open KG canonicalization only provide gold entity-level ... | ['Kewei Tu', 'Pengjun Xie', 'Yuting Zheng', 'Weiqi Wu', 'Yong Jiang', 'Chengyue Jiang'] | 2023-02-08 | null | null | null | null | ['open-knowledge-graph-canonicalization'] | ['knowledge-base'] | [-3.41479331e-01 4.98648047e-01 -5.24128020e-01 -3.90473247e-01
-8.93830419e-01 -8.90093625e-01 5.25321186e-01 5.64654827e-01
-2.78082758e-01 8.37448359e-01 6.53361678e-01 -2.73776025e-01
-1.42803162e-01 -1.07084537e+00 -9.83074129e-01 -1.38428450e-01
-1.64119586e-01 8.52913916e-01 8.19206089e-02 -2.99676806... | [9.153640747070312, 8.287605285644531] |
e84dfb0c-999b-4235-98de-601e58e48c1c | prepositional-phrase-attachment-problem | null | null | https://aclanthology.org/W15-0102 | https://aclanthology.org/W15-0102.pdf | Prepositional Phrase Attachment Problem Revisited: how Verbnet can Help | null | ['Yuliya Lierler', 'Daniel Bailey', 'Benjamin Susman'] | 2015-04-01 | null | null | null | ws-2015-4 | ['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... | [-7.403817176818848, 3.673025369644165] |
b4442f0c-e7d0-44ad-a208-f0126b380db3 | point-cloud-registration-for-lidar-and | 2302.07184 | null | https://arxiv.org/abs/2302.07184v1 | https://arxiv.org/pdf/2302.07184v1.pdf | Point Cloud Registration for LiDAR and Photogrammetric Data: a Critical Synthesis and Performance Analysis on Classic and Deep Learning Algorithms | Recent advances in computer vision and deep learning have shown promising performance in estimating rigid/similarity transformation between unregistered point clouds of complex objects and scenes. However, their performances are mostly evaluated using a limited number of datasets from a single sensor (e.g. Kinect or Re... | ['Shuang Song', 'Rongjun Qin', 'Ningli Xu'] | 2023-02-14 | null | null | null | null | ['point-cloud-registration'] | ['computer-vision'] | [-1.48712136e-02 -4.57579851e-01 5.69383539e-02 -2.61364788e-01
-9.06868100e-01 -5.85031033e-01 9.65133429e-01 1.38661772e-01
-4.73924965e-01 2.94014245e-01 -2.30876908e-01 -1.06474645e-01
-3.85896146e-01 -7.61510551e-01 -7.38663733e-01 -6.79326117e-01
-1.75319642e-01 1.01106703e+00 1.00651711e-01 -4.73890573... | [7.747100830078125, -2.835925340652466] |
b4cb3425-f0f8-4ba1-97bb-bb6c8bb5aedd | lighttag-text-annotation-platform | 2109.02320 | null | https://arxiv.org/abs/2109.02320v1 | https://arxiv.org/pdf/2109.02320v1.pdf | LightTag: Text Annotation Platform | Text annotation tools assume that their user's goal is to create a labeled corpus. However, users view annotation as a necessary evil on the way to deliver business value through NLP. Thus an annotation tool should optimize for the throughput of the global NLP process, not only the productivity of individual annotators... | ['Tal Perry'] | 2021-09-06 | null | https://aclanthology.org/2021.emnlp-demo.3 | https://aclanthology.org/2021.emnlp-demo.3.pdf | emnlp-acl-2021-11 | ['text-annotation'] | ['natural-language-processing'] | [ 7.78104439e-02 9.13326561e-01 -5.30554056e-01 -3.52604300e-01
-6.56309903e-01 -1.15180254e+00 5.16849160e-01 5.39618850e-01
-4.51918274e-01 5.53386807e-01 6.25777364e-01 -4.52524602e-01
6.64032623e-02 -4.54439551e-01 1.42100938e-02 -1.02319822e-01
7.13560700e-01 9.88099873e-01 1.47004068e-01 -6.49317056... | [9.411933898925781, 8.786335945129395] |
b5551a82-5839-4ba5-b763-cfb432b0c2eb | a-tangled-web-the-faint-signals-of-deception | null | null | https://aclanthology.org/L16-1558 | https://aclanthology.org/L16-1558.pdf | A Tangled Web: The Faint Signals of Deception in Text - Boulder Lies and Truth Corpus (BLT-C) | We present an approach to creating corpora for use in detecting deception in text, including a discussion of the challenges peculiar to this task. Our approach is based on soliciting several types of reviews from writers and was implemented using Amazon Mechanical Turk. We describe the multi-dimensional corpus of revie... | ['Franco Salvetti', 'James H. Martin', 'John B. Lowe'] | 2016-05-01 | a-tangled-web-the-faint-signals-of-deception-1 | https://aclanthology.org/L16-1558 | https://aclanthology.org/L16-1558.pdf | lrec-2016-5 | ['deception-detection'] | ['miscellaneous'] | [-4.18866239e-02 3.94005366e-02 1.53258577e-01 -5.92261136e-01
-7.39332020e-01 -8.22132289e-01 9.22505021e-01 -2.16911500e-03
-4.66624260e-01 6.33502543e-01 3.36076170e-01 -2.88117796e-01
1.98772848e-01 -1.01569884e-01 -3.77617776e-01 -2.71612078e-01
5.20902753e-01 4.86419171e-01 -2.33260319e-01 -4.35150295... | [8.236237525939941, 10.409440040588379] |
bfe0adae-a570-4629-b125-040a8a039aae | a-multi-source-graph-representation-of-the | null | null | https://aclanthology.org/2022.lrec-1.138 | https://aclanthology.org/2022.lrec-1.138.pdf | A Multi-source Graph Representation of the Movie Domain for Recommendation Dialogues Analysis | In dialogue analysis, characterising named entities in the domain of interest is relevant in order to understand how people are making use of them for argumentation purposes. The movie recommendation domain is a frequently considered case study for many applications and by linguistic studies and, since many different r... | ['Sabrina Mennella', 'Maria Di Maro', 'Martina Di Bratto', 'Antonio Origlia'] | null | null | null | null | lrec-2022-6 | ['movie-recommendation'] | ['miscellaneous'] | [-2.38182545e-01 4.16000605e-01 -2.66778022e-01 -2.00470120e-01
-1.46618575e-01 -8.14336538e-01 1.04084742e+00 1.05215228e+00
-4.39210594e-01 7.48474240e-01 7.02063918e-01 -3.78877938e-01
-5.56571543e-01 -7.90445745e-01 -6.58216849e-02 -2.00586826e-01
1.11744307e-01 6.74533129e-01 5.06619275e-01 -6.93507016... | [9.647112846374512, 9.417040824890137] |
b480780d-2352-4581-822e-b51428bc0362 | 1st-place-solution-for-eccv-2022-ood-cv | 2301.04796 | null | https://arxiv.org/abs/2301.04796v1 | https://arxiv.org/pdf/2301.04796v1.pdf | 1st Place Solution for ECCV 2022 OOD-CV Challenge Object Detection Track | OOD-CV challenge is an out-of-distribution generalization task. To solve this problem in object detection track, we propose a simple yet effective Generalize-then-Adapt (G&A) framework, which is composed of a two-stage domain generalization part and a one-stage domain adaptation part. The domain generalization part is ... | ['Yueting Zhuang', 'ShiLiang Pu', 'Di Xie', 'Shicai Yang', 'WeiJie Chen', 'Binbin Chen', 'Wei Zhao'] | 2023-01-12 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 8.11449364e-02 1.26003191e-01 -4.14435565e-01 -7.34881520e-01
-8.26044679e-01 -5.23822129e-01 7.02246606e-01 5.89090995e-02
-3.79918754e-01 3.10992897e-01 -2.34197155e-01 -2.61348695e-01
7.50787914e-01 -5.61843872e-01 -7.07729220e-01 -5.87196469e-01
1.31425887e-01 8.21705759e-01 5.52440524e-01 -3.29329111... | [9.40744400024414, 1.4724887609481812] |
635efa7b-702a-4081-82c5-d7e0cb14a9e5 | stvgformer-spatio-temporal-video-grounding | 2207.02756 | null | https://arxiv.org/abs/2207.02756v1 | https://arxiv.org/pdf/2207.02756v1.pdf | STVGFormer: Spatio-Temporal Video Grounding with Static-Dynamic Cross-Modal Understanding | In this technical report, we introduce our solution to human-centric spatio-temporal video grounding task. We propose a concise and effective framework named STVGFormer, which models spatiotemporal visual-linguistic dependencies with a static branch and a dynamic branch. The static branch performs cross-modal understan... | ['Wei-Shi Zheng', 'Tiancai Ye', 'Zhi Jin', 'Jian-Fang Hu', 'Chaolei Tan', 'Zihang Lin'] | 2022-07-06 | null | null | null | null | ['video-grounding', 'spatio-temporal-video-grounding'] | ['computer-vision', 'computer-vision'] | [-1.31147400e-01 2.04313453e-02 -3.85475427e-01 -5.37064731e-01
-5.76855719e-01 -4.67187881e-01 5.75342059e-01 -8.62289220e-02
-4.58293110e-01 4.58441556e-01 5.59308827e-01 -6.18004352e-02
1.73365325e-01 -2.78614312e-01 -8.77203345e-01 -4.50333595e-01
-3.89042735e-01 2.76352257e-01 7.69437492e-01 -2.63073504... | [9.455328941345215, 0.6582515239715576] |
2d02c72a-eccf-440e-b77e-e7741d2c7ef0 | improving-speaker-independent-lipreading-with | 1708.01565 | null | http://arxiv.org/abs/1708.01565v1 | http://arxiv.org/pdf/1708.01565v1.pdf | Improving Speaker-Independent Lipreading with Domain-Adversarial Training | We present a Lipreading system, i.e. a speech recognition system using only
visual features, which uses domain-adversarial training for speaker
independence. Domain-adversarial training is integrated into the optimization
of a lipreader based on a stack of feedforward and LSTM (Long Short-Term
Memory) recurrent neural ... | ['Michael Wand', 'Juergen Schmidhuber'] | 2017-08-04 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 3.54628235e-01 4.62752312e-01 -3.31804156e-01 -3.59580040e-01
-1.37817824e+00 -5.05799949e-01 6.61467612e-01 -5.51570535e-01
-4.76522267e-01 5.91031134e-01 2.78779715e-01 -4.66141433e-01
7.09521890e-01 1.06441274e-01 -6.93885386e-01 -4.95745659e-01
3.06398809e-01 1.37333691e-01 2.12613940e-02 8.90011266... | [14.343911170959473, 5.018189430236816] |
4f1f9457-4524-469b-a8f3-84a1892a3a8d | adversarial-attack-and-defense-for-dehazing | 2303.17255 | null | https://arxiv.org/abs/2303.17255v1 | https://arxiv.org/pdf/2303.17255v1.pdf | Adversarial Attack and Defense for Dehazing Networks | The research on single image dehazing task has been widely explored. However, as far as we know, no comprehensive study has been conducted on the robustness of the well-trained dehazing models. Therefore, there is no evidence that the dehazing networks can resist malicious attacks. In this paper, we focus on designing ... | ['James Tin-Yau Kwok', 'Yuan Yan Tang', 'Chengwei Peng', 'Xiaofeng Cong', 'Jie Gui'] | 2023-03-30 | null | null | null | null | ['image-dehazing', 'single-image-dehazing'] | ['computer-vision', 'computer-vision'] | [ 3.13554019e-01 -3.02059114e-01 1.22337915e-01 2.83209290e-02
-2.57822156e-01 -5.31604886e-01 6.62795961e-01 -2.05700159e-01
-1.93196610e-01 4.29776102e-01 1.06002996e-02 -3.56090516e-01
1.22147761e-02 -9.35853839e-01 -5.51322281e-01 -1.18103039e+00
5.56991063e-02 -4.99783814e-01 4.66204852e-01 -3.92921448... | [5.513589382171631, 7.944633960723877] |
aec0c9f7-85bd-4286-ba06-c5905b879793 | towards-adversarial-retinal-image-synthesis | 1701.08974 | null | http://arxiv.org/abs/1701.08974v1 | http://arxiv.org/pdf/1701.08974v1.pdf | Towards Adversarial Retinal Image Synthesis | Synthesizing images of the eye fundus is a challenging task that has been
previously approached by formulating complex models of the anatomy of the eye.
New images can then be generated by sampling a suitable parameter space. In
this work, we propose a method that learns to synthesize eye fundus images
directly from da... | ['Aurélio Campilho', 'Ana Maria Mendonça', 'Michael David Abràmoff', 'Maria Inês Meyer', 'Adrian Galdran', 'Pedro Costa', 'Meindert Niemeijer'] | 2017-01-31 | null | null | null | null | ['medical-image-generation'] | ['medical'] | [ 4.48950052e-01 5.82857966e-01 1.71292707e-01 -2.27603495e-01
-2.31431514e-01 -7.91052878e-01 5.19555569e-01 -4.45399135e-01
-1.99251711e-01 9.06504631e-01 -1.20735802e-01 -3.33386034e-01
2.96188533e-01 -7.97298491e-01 -9.50709462e-01 -7.75826156e-01
2.90177315e-01 -4.37030382e-02 2.76678145e-01 -4.01093848... | [15.590458869934082, -3.7630789279937744] |
32a38869-fd90-4f09-8c76-445a927ac35d | a-continual-learning-framework-for-adaptive | 2203.08796 | null | https://arxiv.org/abs/2203.08796v1 | https://arxiv.org/pdf/2203.08796v1.pdf | A Continual Learning Framework for Adaptive Defect Classification and Inspection | Machine-vision-based defect classification techniques have been widely adopted for automatic quality inspection in manufacturing processes. This article describes a general framework for classifying defects from high volume data batches with efficient inspection of unlabelled samples. The concept is to construct a dete... | ['Tzyy-Shuh Chang', 'Judy Jin', 'Raed Al Kontar', 'Wenbo Sun'] | 2022-03-16 | null | null | null | null | ['defect-detection'] | ['computer-vision'] | [ 2.17714369e-01 2.56237257e-02 3.87610972e-01 -5.10502636e-01
-2.04257354e-01 -2.00792938e-01 3.88673171e-02 8.56644273e-01
-1.58890989e-02 9.04542729e-02 -8.15557957e-01 -1.41582534e-01
-2.28718847e-01 -9.99944448e-01 -1.72096878e-01 -7.51274109e-01
7.83272758e-02 9.70549405e-01 4.60642457e-01 -9.64999869... | [7.375646591186523, 1.8807308673858643] |
f521d68f-0474-4f27-b7eb-46c603029cba | rbsr-efficient-and-flexible-recurrent-network | 2306.17595 | null | https://arxiv.org/abs/2306.17595v1 | https://arxiv.org/pdf/2306.17595v1.pdf | RBSR: Efficient and Flexible Recurrent Network for Burst Super-Resolution | Burst super-resolution (BurstSR) aims at reconstructing a high-resolution (HR) image from a sequence of low-resolution (LR) and noisy images, which is conducive to enhancing the imaging effects of smartphones with limited sensors. The main challenge of BurstSR is to effectively combine the complementary information fro... | ['WangMeng Zuo', 'Hongzhi Zhang', 'Shuohao Zhang', 'Zhilu Zhang', 'Renlong Wu'] | 2023-06-30 | null | null | null | null | ['super-resolution'] | ['computer-vision'] | [ 4.19031799e-01 -2.95655906e-01 -2.13229284e-01 -1.40750960e-01
-9.48676348e-01 -6.47569969e-02 1.56346574e-01 -5.80097854e-01
-1.51005834e-01 7.98254728e-01 3.27085823e-01 2.33149789e-02
-5.30572757e-02 -5.08040309e-01 -6.41221881e-01 -8.28414202e-01
1.97872773e-01 -3.92980784e-01 3.59218597e-01 -2.27499664... | [10.994829177856445, -1.901167869567871] |
63f24cd7-95ba-4aa0-ace1-d708d895a38f | safety-guided-deep-reinforcement-learning-via | 1903.02526 | null | http://arxiv.org/abs/1903.02526v2 | http://arxiv.org/pdf/1903.02526v2.pdf | Safety-Guided Deep Reinforcement Learning via Online Gaussian Process Estimation | An important facet of reinforcement learning (RL) has to do with how the
agent goes about exploring the environment. Traditional exploration strategies
typically focus on efficiency and ignore safety. However, for practical
applications, ensuring safety of the agent during exploration is crucial since
performing an uns... | ['Jiameng Fan', 'Wenchao Li'] | 2019-03-06 | null | null | null | null | ['safe-exploration'] | ['robots'] | [-0.0080493 0.366557 -0.187415 0.27551457 -0.70412034 -0.7719135
0.47170454 0.35689232 -0.6709566 1.1638695 -0.22613555 -0.6040616
-0.47777772 -0.67906135 -0.95873255 -1.0472056 -0.5401658 0.23564652
0.12012979 -0.03522148 0.20802002 0.5458145 -1.0576073 -0.7739802
0.9914283 0.911608 0.055... | [4.57480525970459, 2.258777379989624] |
43cdf866-9bc6-44a6-9859-3e9be65900e4 | shilling-black-box-review-based-recommender | 2306.16526 | null | https://arxiv.org/abs/2306.16526v1 | https://arxiv.org/pdf/2306.16526v1.pdf | Shilling Black-box Review-based Recommender Systems through Fake Review Generation | Review-Based Recommender Systems (RBRS) have attracted increasing research interest due to their ability to alleviate well-known cold-start problems. RBRS utilizes reviews to construct the user and items representations. However, in this paper, we argue that such a reliance on reviews may instead expose systems to the ... | ['Jason S. Chang', 'Hong-Han Shuai', 'Yun-Zhu Song', 'Yi-Syuan Chen', 'Hung-Yun Chiang'] | 2023-06-27 | null | null | null | null | ['review-generation'] | ['natural-language-processing'] | [-5.38024902e-02 2.71287829e-01 -1.82449698e-01 -3.16681355e-01
-6.61512315e-01 -7.27791727e-01 8.18232656e-01 -2.61054367e-01
-1.89989358e-01 7.05930293e-01 2.12492675e-01 -4.17018741e-01
4.46691602e-01 -1.02324128e+00 -6.50833726e-01 -5.09167194e-01
3.73563558e-01 1.96622357e-01 -1.20431222e-01 -1.05584574... | [6.165739059448242, 8.18825912475586] |
bea2e3bd-a49e-4f6b-a017-7ea6ee7d01fc | efficient-teacher-semi-supervised-object | 2302.07577 | null | https://arxiv.org/abs/2302.07577v3 | https://arxiv.org/pdf/2302.07577v3.pdf | Efficient Teacher: Semi-Supervised Object Detection for YOLOv5 | Semi-Supervised Object Detection (SSOD) has been successful in improving the performance of both R-CNN series and anchor-free detectors. However, one-stage anchor-based detectors lack the structure to generate high-quality or flexible pseudo labels, leading to serious inconsistency problems in SSOD. In this paper, we p... | ['Lulu Hu', 'Wenlong Guan', 'Mingtao Chen', 'Bowen Xu'] | 2023-02-15 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [-3.20420384e-01 -1.15291951e-02 -2.89161384e-01 -3.73985171e-01
-8.42524886e-01 -4.58279938e-01 3.77279311e-01 -1.76273957e-01
-6.82830930e-01 4.36025888e-01 -3.31925571e-01 8.05597473e-03
3.11475873e-01 -4.16824192e-01 -7.43930638e-01 -7.87992418e-01
3.26055855e-01 3.67472142e-01 8.23235095e-01 1.14278510... | [9.193265914916992, 1.2882637977600098] |
db5bbd5a-9ef5-40f4-a20b-706c83752905 | learning-unseen-modality-interaction | 2306.12795 | null | https://arxiv.org/abs/2306.12795v1 | https://arxiv.org/pdf/2306.12795v1.pdf | Learning Unseen Modality Interaction | Multimodal learning assumes all modality combinations of interest are available during training to learn cross-modal correspondences. In this paper, we challenge this modality-complete assumption for multimodal learning and instead strive for generalization to unseen modality combinations during inference. We pose the ... | ['Cees G. M. Snoek', 'Hazel Doughty', 'Yunhua Zhang'] | 2023-06-22 | null | null | null | null | ['video-classification', 'retrieval'] | ['computer-vision', 'methodology'] | [ 5.73255181e-01 2.48861611e-02 -3.34907115e-01 -4.86120582e-01
-1.09175670e+00 -7.27847040e-01 9.13800359e-01 -3.53668779e-02
-5.94743907e-01 8.65484893e-01 3.04663219e-02 -6.96533024e-02
-8.37178305e-02 -1.58421189e-01 -9.33100879e-01 -6.37828290e-01
1.45142794e-01 3.82054299e-01 -4.45416644e-02 -7.94318765... | [10.731208801269531, 1.688222885131836] |
9660986e-b601-41cc-907d-833f742eed7d | warppinn-cine-mr-image-registration-with | 2211.12549 | null | https://arxiv.org/abs/2211.12549v1 | https://arxiv.org/pdf/2211.12549v1.pdf | WarpPINN: Cine-MR image registration with physics-informed neural networks | Heart failure is typically diagnosed with a global function assessment, such as ejection fraction. However, these metrics have low discriminate power, failing to distinguish different types of this disease. Quantifying local deformations in the form of cardiac strain can provide helpful information, but it remains a ch... | ['Francisco Sahli Costabal', 'Daniel E. Hurtado', 'Sergio Uribe', 'Hernán Mella', 'Pablo Arratia López'] | 2022-11-22 | null | null | null | null | ['landmark-tracking'] | ['computer-vision'] | [ 1.76170945e-01 -4.49217707e-02 -2.06701038e-03 -3.73970062e-01
-5.59266567e-01 -5.41721940e-01 1.75244153e-01 4.69917729e-02
-4.29712921e-01 6.39105916e-01 1.88079894e-01 -2.62749624e-02
-6.73090816e-02 -6.82298958e-01 -4.55268145e-01 -8.17153513e-01
-5.77670932e-01 5.32511592e-01 8.77363384e-02 -4.00401681... | [13.995209693908691, -2.4768311977386475] |
5006dfa2-bb58-4c0a-b347-d030b34f60f7 | improving-open-information-extraction-via | 1905.13413 | null | https://arxiv.org/abs/1905.13413v1 | https://arxiv.org/pdf/1905.13413v1.pdf | Improving Open Information Extraction via Iterative Rank-Aware Learning | Open information extraction (IE) is the task of extracting open-domain assertions from natural language sentences. A key step in open IE is confidence modeling, ranking the extractions based on their estimated quality to adjust precision and recall of extracted assertions. We found that the extraction likelihood, a con... | ['Pengcheng Yin', 'Zhengbao Jiang', 'Graham Neubig'] | 2019-05-31 | improving-open-information-extraction-via-1 | https://aclanthology.org/P19-1523 | https://aclanthology.org/P19-1523.pdf | acl-2019-7 | ['open-information-extraction'] | ['natural-language-processing'] | [-8.37460831e-02 7.78083920e-01 -6.65131867e-01 -4.55267727e-01
-1.22560620e+00 -8.93911123e-01 5.76246977e-01 5.31872094e-01
-4.05901074e-01 1.13566875e+00 4.45440561e-01 -3.72772932e-01
1.98054351e-02 -6.82759821e-01 -8.22100341e-01 3.19009960e-01
1.12983063e-01 6.00810826e-01 2.01666951e-01 1.67197824... | [9.454195976257324, 8.58975887298584] |
b944e1a2-caab-418a-93d9-c8e2e01d2377 | chemical-reaction-aware-molecule | 2109.09888 | null | https://arxiv.org/abs/2109.09888v2 | https://arxiv.org/pdf/2109.09888v2.pdf | Chemical-Reaction-Aware Molecule Representation Learning | Molecule representation learning (MRL) methods aim to embed molecules into a real vector space. However, existing SMILES-based (Simplified Molecular-Input Line-Entry System) or GNN-based (Graph Neural Networks) MRL methods either take SMILES strings as input that have difficulty in encoding molecule structure informati... | ['Martin D. Burke', 'Jiawei Han', 'Heng Ji', 'Kyunghyun Cho', 'Xiaomeng Jin', 'Weijiang Li', 'Hongwei Wang'] | 2021-09-21 | chemical-reaction-aware-molecule-1 | https://openreview.net/forum?id=6sh3pIzKS- | https://openreview.net/pdf?id=6sh3pIzKS- | iclr-2022-4 | ['chemical-reaction-prediction'] | ['medical'] | [ 4.57780123e-01 7.11935014e-02 -4.41704392e-01 -8.39473307e-02
-3.74068201e-01 -8.50471318e-01 6.65424228e-01 7.42164612e-01
-2.53646731e-01 9.55837309e-01 5.65654878e-03 -6.35530174e-01
6.38081506e-02 -1.13557208e+00 -1.06162381e+00 -8.11504126e-01
-6.70393556e-02 1.52348682e-01 -1.48783699e-01 -2.97999471... | [4.996126651763916, 5.905185222625732] |
bc7d91dc-72aa-4d7a-b8db-945f64c2577f | transliterating-kurdish-texts-in-latin-into | 2110.12374 | null | https://arxiv.org/abs/2110.12374v1 | https://arxiv.org/pdf/2110.12374v1.pdf | Transliterating Kurdish texts in Latin into Persian-Arabic script | Kurdish is written in different scripts. The two most popular scripts are Latin and Persian-Arabic. However, not all Kurdish readers are familiar with both mentioned scripts that could be resolved by automatic transliterators. So far, the developed tools mostly transliterate Persian-Arabic scripts into Latin. We presen... | ['Hossein Hassani'] | 2021-10-24 | null | null | null | null | ['transliteration'] | ['natural-language-processing'] | [-2.91692674e-01 -5.23865260e-02 3.43357660e-02 -3.34051728e-01
-3.29070151e-01 -1.23433232e+00 5.97182691e-01 -4.67849299e-02
-4.64732468e-01 1.04985189e+00 8.69832933e-02 -8.52553487e-01
-1.87151767e-02 -5.68136811e-01 9.66547336e-03 -3.13462973e-01
6.04170442e-01 9.99504209e-01 -9.60639566e-02 -6.98152959... | [10.52642822265625, 10.521719932556152] |
853fb49d-3e6f-417e-8126-627f78706c9f | nms-threshold-matters-for-ego4d-moment | 2307.02025 | null | https://arxiv.org/abs/2307.02025v1 | https://arxiv.org/pdf/2307.02025v1.pdf | NMS Threshold matters for Ego4D Moment Queries -- 2nd place solution to the Ego4D Moment Queries Challenge 2023 | This report describes our submission to the Ego4D Moment Queries Challenge 2023. Our submission extends ActionFormer, a latest method for temporal action localization. Our extension combines an improved ground-truth assignment strategy during training and a refined version of SoftNMS at inference time. Our solution is ... | ['Yin Li', 'Fangzhou Mu', 'Lin Sui'] | 2023-07-05 | null | null | null | null | ['action-localization', 'action-recognition'] | ['computer-vision', 'computer-vision'] | [-2.25943699e-01 2.19834372e-01 -7.24344015e-01 -2.70907074e-01
-1.01144385e+00 -7.16716111e-01 8.26496065e-01 -5.21170557e-01
-8.13640654e-01 1.01411700e+00 1.03362775e+00 3.13722372e-01
1.80654034e-01 -2.85913825e-01 -7.87350476e-01 -3.92632157e-01
-4.03652698e-01 4.45889771e-01 3.52176666e-01 -1.04119420... | [8.354452133178711, 0.41934242844581604] |
988fdff7-9129-4b05-b4f5-96271be8c4c8 | disentangling-confidence-score-distribution | 2210.08830 | null | https://arxiv.org/abs/2210.08830v1 | https://arxiv.org/pdf/2210.08830v1.pdf | Disentangling Confidence Score Distribution for Out-of-Domain Intent Detection with Energy-Based Learning | Detecting Out-of-Domain (OOD) or unknown intents from user queries is essential in a task-oriented dialog system. Traditional softmax-based confidence scores are susceptible to the overconfidence issue. In this paper, we propose a simple but strong energy-based score function to detect OOD where the energy scores of OO... | ['Weiran Xu', 'Yuanmeng Yan', 'Pei Wang', 'Yutao Mou', 'Keqing He', 'Zhiyuan Zeng', 'Yanan Wu'] | 2022-10-17 | null | null | null | null | ['intent-detection'] | ['natural-language-processing'] | [-3.07829171e-01 5.68233848e-01 -6.05334222e-01 -1.03710794e+00
-9.57673311e-01 -5.53659320e-01 6.07668221e-01 2.36173496e-01
-3.40917617e-01 9.07416165e-01 2.59604782e-01 -4.02872205e-01
1.97080255e-01 -3.01830262e-01 -1.61085278e-01 -2.44719416e-01
2.16287121e-01 6.76446021e-01 2.18701679e-02 1.16174258... | [12.51302433013916, 7.703769683837891] |
1fec431b-4521-4675-9c0a-4a166d80cb29 | convolutional-neural-networks-for-facial | 1704.06756 | null | http://arxiv.org/abs/1704.06756v1 | http://arxiv.org/pdf/1704.06756v1.pdf | Convolutional Neural Networks for Facial Expression Recognition | We have developed convolutional neural networks (CNN) for a facial expression
recognition task. The goal is to classify each facial image into one of the
seven facial emotion categories considered in this study. We trained CNN models
with different depth using gray-scale images. We developed our models in Torch
and exp... | ['Shima Alizadeh', 'Azar Fazel'] | 2017-04-22 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 1.03351168e-01 1.16319619e-01 5.07783234e-01 -9.25910771e-01
2.03739569e-01 -1.17567860e-01 4.25856829e-01 -5.73020615e-02
-7.82950282e-01 6.67309582e-01 -2.57319599e-01 -4.13269550e-02
1.81878656e-01 -8.62439513e-01 -5.93468666e-01 -7.62445152e-01
-2.87134051e-01 1.47725359e-01 6.31620511e-02 -1.68150946... | [13.522807121276855, 1.8095557689666748] |
2a0a880b-a8cb-4175-b415-4b2a4ab436aa | a-photo-click-thiol-ene-collagen-based | 2304.01942 | null | https://arxiv.org/abs/2304.01942v2 | https://arxiv.org/pdf/2304.01942v2.pdf | A photo-click thiol-ene collagen-based hydrogel platform for skeletal muscle tissue engineering | UV-cured collagen-based hydrogels hold promise in skeletal muscle regeneration due to their soft elastic properties and porous architecture. However, the complex triple helix conformation of collagen and environmental conditions, i.e. molecular oxygen, pose risks to reaction controllability, wet-state integrity and rep... | ['Giuseppe Tronci', 'David J. Wood', 'Xuebin B. Yang', 'Roisin Holmes'] | 2023-03-29 | null | null | null | null | ['culture'] | ['speech'] | [ 3.48569870e-01 -3.96323204e-02 -4.14923638e-01 4.51376885e-01
-4.35810536e-01 -8.15350235e-01 8.97131711e-02 5.34664631e-01
-6.56399488e-01 9.69033957e-01 5.42810798e-01 1.98155984e-01
1.34874567e-01 -6.08621478e-01 -4.98626560e-01 -1.21231794e+00
-4.21075016e-01 8.74917284e-02 4.14374352e-01 -2.92079300... | [13.633379936218262, -3.0355491638183594] |
3f5816de-0d53-4fbd-9882-7a21d9bbb4ef | automatic-design-method-of-building-pipeline | 2305.10760 | null | https://arxiv.org/abs/2305.10760v1 | https://arxiv.org/pdf/2305.10760v1.pdf | Automatic Design Method of Building Pipeline Layout Based on Deep Reinforcement Learning | The layout design of pipelines is a critical task in the construction industry. Currently, pipeline layout is designed manually by engineers, which is time-consuming and laborious. Automating and streamlining this process can reduce the burden on engineers and save time. In this paper, we propose a method for generatin... | ['Jia-Rui Lin', 'Zhe Zheng', 'Chen Yang'] | 2023-05-18 | null | null | null | null | ['layout-design'] | ['computer-vision'] | [-1.34929761e-01 7.86702707e-02 3.59493077e-01 -2.56296605e-01
-4.69260931e-01 -1.03522384e+00 5.32775768e-04 1.48671255e-01
-2.41987184e-01 4.04756218e-01 -9.35116410e-02 -7.07817554e-01
-1.93808615e-01 -1.08296406e+00 -8.32544088e-01 -3.42739284e-01
-1.39497042e-01 4.09707695e-01 1.88483059e-01 2.69063637... | [5.495396137237549, 2.9416074752807617] |
f0d0ab7e-d556-414e-95c1-3b102a0841c4 | harnessing-the-power-of-adversarial-prompting | 2306.11648 | null | https://arxiv.org/abs/2306.11648v1 | https://arxiv.org/pdf/2306.11648v1.pdf | Harnessing the Power of Adversarial Prompting and Large Language Models for Robust Hypothesis Generation in Astronomy | This study investigates the application of Large Language Models (LLMs), specifically GPT-4, within Astronomy. We employ in-context prompting, supplying the model with up to 1000 papers from the NASA Astrophysics Data System, to explore the extent to which performance can be improved by immersing the model in domain-sp... | ['Kartheik Iyer', 'Sandor Kruk', 'Yuan-Sen Ting', 'Ioana Ciucă'] | 2023-06-20 | null | null | null | null | ['astronomy'] | ['miscellaneous'] | [ 8.54748487e-02 5.33844948e-01 -2.49835864e-01 1.22236304e-01
-8.68087590e-01 -1.07947445e+00 9.71830964e-01 7.20779672e-02
-3.56182814e-01 9.05623078e-01 4.81980771e-01 -7.86303937e-01
-2.66155392e-01 -6.01451755e-01 -9.89960790e-01 -9.72337574e-02
3.60885598e-02 1.92170218e-01 -3.17714512e-01 -1.01228736... | [10.861407279968262, 8.287883758544922] |
d769feae-1f31-4c11-a671-a4830810a56d | joint-debiased-representation-and-image | 2209.06941 | null | https://arxiv.org/abs/2209.06941v1 | https://arxiv.org/pdf/2209.06941v1.pdf | Joint Debiased Representation and Image Clustering Learning with Self-Supervision | Contrastive learning is among the most successful methods for visual representation learning, and its performance can be further improved by jointly performing clustering on the learned representations. However, existing methods for joint clustering and contrastive learning do not perform well on long-tailed data distr... | ['Mina Rezaei', 'Shekoofeh Azizi', 'Bernd Bischl', 'Emilio Dorigatti', 'JaeEun Nam', 'Shunjie-Fabian Zheng'] | 2022-09-14 | null | null | null | null | ['image-clustering'] | ['computer-vision'] | [-1.24918170e-01 -3.09547007e-01 -4.68043178e-01 -6.31387115e-01
-8.63704264e-01 -6.81434333e-01 5.21570742e-01 5.02281785e-01
-3.56302083e-01 5.85395932e-01 2.02463180e-01 -1.45932257e-01
-1.26917481e-01 -5.10601223e-01 -8.17646742e-01 -9.24525499e-01
4.70759571e-02 5.84699094e-01 -2.78824344e-02 3.89565855... | [9.517868995666504, 2.9468367099761963] |
9778ce4c-aea5-40c7-a912-4179205e557b | msdoctr-lite-a-lite-transformer-for-full-page | 2303.13931 | null | https://arxiv.org/abs/2303.13931v1 | https://arxiv.org/pdf/2303.13931v1.pdf | MSdocTr-Lite: A Lite Transformer for Full Page Multi-script Handwriting Recognition | The Transformer has quickly become the dominant architecture for various pattern recognition tasks due to its capacity for long-range representation. However, transformers are data-hungry models and need large datasets for training. In Handwritten Text Recognition (HTR), collecting a massive amount of labeled data is a... | ['Sinda Ben Salem', 'Yousri Kessentini', 'Ahmed Cheikh Rouhou', 'Marwa Dhiaf'] | 2023-03-24 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 4.33554828e-01 -3.69944900e-01 -2.15820596e-01 -4.51966316e-01
-6.98315978e-01 -6.93156719e-01 3.20735514e-01 -2.61579216e-01
-4.25119430e-01 4.99929130e-01 -2.05136314e-01 -5.83552301e-01
6.04757331e-02 -7.51802683e-01 -7.60961950e-01 -5.87542832e-01
5.84770918e-01 6.82108283e-01 6.21410310e-01 -2.14917243... | [11.859535217285156, 2.3261313438415527] |
50abc5cd-cc96-4539-a18b-f4c097abf446 | re-matching-a-fine-grained-semantic-matching | 2306.04954 | null | https://arxiv.org/abs/2306.04954v1 | https://arxiv.org/pdf/2306.04954v1.pdf | RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction | Semantic matching is a mainstream paradigm of zero-shot relation extraction, which matches a given input with a corresponding label description. The entities in the input should exactly match their hypernyms in the description, while the irrelevant contexts should be ignored when matching. However, general matching met... | ['Mingming Sun', 'Minlong Peng', 'Junzhe Wang', 'Zhongyu Wei', 'Tao Gui', 'Qi Zhang', 'Xin Zhao', 'WenYu Zhan', 'Jun Zhao'] | 2023-06-08 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [ 3.39026183e-01 2.42442936e-01 -4.93644685e-01 -3.84687752e-01
-4.93649811e-01 -2.75911480e-01 4.00215447e-01 6.61194324e-01
-4.00222182e-01 5.30219913e-01 3.25856358e-01 -9.55019444e-02
-2.14984119e-01 -1.21022880e+00 -2.23368332e-01 -3.14313531e-01
2.25393862e-01 3.72467965e-01 5.35697699e-01 -3.57219100... | [9.35133171081543, 8.562419891357422] |
12409ccb-35b8-404a-932e-30b7cb491baf | towards-head-motion-compensation-using-multi | 1807.03651 | null | http://arxiv.org/abs/1807.03651v1 | http://arxiv.org/pdf/1807.03651v1.pdf | Towards Head Motion Compensation Using Multi-Scale Convolutional Neural Networks | Head pose estimation and tracking is useful in variety of medical
applications. With the advent of RGBD cameras like Kinect, it has become
feasible to do markerless tracking by estimating the head pose directly from
the point clouds. One specific medical application is robot assisted
transcranial magnetic stimulation (... | ['Alexander Schlaefer', 'Lars Matthäus', 'Omer Rajput', 'Nils Gessert', 'Martin Gromniak'] | 2018-07-10 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-8.60052779e-02 3.31277221e-01 1.30870938e-01 -4.08854336e-01
-6.81940496e-01 -7.87819698e-02 2.02988163e-01 -8.77395943e-02
-7.33148575e-01 5.34248710e-01 1.06303461e-01 1.02376893e-01
1.33164614e-01 -2.49516234e-01 -5.59854269e-01 -7.09498405e-01
1.11770406e-01 8.32457960e-01 1.92999750e-01 -2.90903360... | [13.646628379821777, 0.22659942507743835] |
79056510-8fc8-4b4a-9856-58eea9b7ceb6 | securing-behavior-based-opinion-spam | 1811.03739 | null | http://arxiv.org/abs/1811.03739v1 | http://arxiv.org/pdf/1811.03739v1.pdf | Securing Behavior-based Opinion Spam Detection | Reviews spams are prevalent in e-commerce to manipulate product ranking and
customers decisions maliciously. While spams generated based on simple spamming
strategy can be detected effectively, hardened spammers can evade regular
detectors via more advanced spamming strategies. Previous work gave more
attention to evas... | ['Philip S. Yu', 'Sihong Xie', 'Shuaijun Ge', 'Guixiang Ma'] | 2018-11-09 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 2.81999528e-01 9.94329304e-02 -6.00607879e-02 -1.11087039e-01
3.72878951e-03 -1.10291159e+00 9.00956750e-01 4.66584004e-02
-7.12628588e-02 3.89299870e-01 -4.99917090e-01 -7.29767144e-01
3.00164986e-02 -1.17750227e+00 -5.03773689e-01 -4.20267224e-01
-3.17803733e-02 5.78846514e-01 4.98838812e-01 -8.52152407... | [7.757177829742432, 9.970415115356445] |
7575ea95-9da6-4a63-9faa-39fdd162b021 | crossget-cross-guided-ensemble-of-tokens-for | 2305.17455 | null | https://arxiv.org/abs/2305.17455v1 | https://arxiv.org/pdf/2305.17455v1.pdf | CrossGET: Cross-Guided Ensemble of Tokens for Accelerating Vision-Language Transformers | Vision-language models have achieved tremendous progress far beyond what we ever expected. However, their computational costs and latency are also dramatically growing with rapid development, making model acceleration exceedingly critical for researchers with limited resources and consumers with low-end devices. Althou... | ['Jiaqi Wang', 'Chun Yuan', 'Zhendong Yang', 'Anyi Rao', 'Chaofan Tao', 'Dachuan Shi'] | 2023-05-27 | null | null | null | null | ['visual-reasoning', 'image-captioning', 'visual-reasoning'] | ['computer-vision', 'computer-vision', 'reasoning'] | [ 3.60238492e-01 8.63616616e-02 -3.93550128e-01 -3.50534827e-01
-9.72885966e-01 -5.86326480e-01 6.51887834e-01 6.35176525e-02
-4.87974435e-01 2.46748596e-01 -8.03254247e-02 -5.45122981e-01
-2.55306643e-02 -7.22403526e-01 -7.32792497e-01 -6.92672968e-01
6.07587159e-01 5.33904910e-01 2.76406407e-01 -8.96977782... | [10.25671672821045, 0.9679552912712097] |
d6d91c79-27c5-423f-9fc6-514c2242ddba | loa-logical-optimal-actions-for-text-based-1 | 2110.10973 | null | https://arxiv.org/abs/2110.10973v1 | https://arxiv.org/pdf/2110.10973v1.pdf | LOA: Logical Optimal Actions for Text-based Interaction Games | We present Logical Optimal Actions (LOA), an action decision architecture of reinforcement learning applications with a neuro-symbolic framework which is a combination of neural network and symbolic knowledge acquisition approach for natural language interaction games. The demonstration for LOA experiments consists of ... | ['Alexander Gray', 'Ryosuke Kohita', 'Akifumi Wachi', 'Asim Munawar', 'Don Joven Agravante', 'Michiaki Tatsubori', 'Masaki Ono', 'Subhajit Chaudhury', 'Daiki Kimura'] | 2021-10-21 | loa-logical-optimal-actions-for-text-based | https://aclanthology.org/2021.acl-demo.27 | https://aclanthology.org/2021.acl-demo.27.pdf | acl-2021-5 | ['text-based-games'] | ['playing-games'] | [-3.89117420e-01 6.34858251e-01 1.46223426e-01 -1.49997398e-01
1.53905571e-01 -3.80073637e-01 6.29667819e-01 -1.96286961e-01
-2.13100240e-01 9.18700397e-01 -1.12657353e-01 -8.22264433e-01
-5.51802456e-01 -1.30513036e+00 -7.12524116e-01 -1.92954779e-01
-2.22264364e-01 9.13767695e-01 5.01779556e-01 -8.62363935... | [3.855628490447998, 1.3490887880325317] |
e3265c13-847a-4009-a9d6-1dcbae665dc5 | schema-first-learn-versatile-knowledge-graph | 2306.03659 | null | https://arxiv.org/abs/2306.03659v1 | https://arxiv.org/pdf/2306.03659v1.pdf | Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE | Knowledge graph embedding models (KGEMs) have gained considerable traction in recent years. These models learn a vector representation of knowledge graph entities and relations, a.k.a. knowledge graph embeddings (KGEs). Learning versatile KGEs is desirable as it makes them useful for a broad range of tasks. However, KG... | ['Davy Monticolo', 'Armelle Brun', 'Pierre Monnin', 'Heiko Paulheim', 'Nicolas Hubert'] | 2023-06-06 | null | null | null | null | ['graph-embedding', 'link-prediction', 'knowledge-graph-embedding', 'knowledge-graph-embeddings', 'knowledge-graph-embeddings'] | ['graphs', 'graphs', 'graphs', 'graphs', 'methodology'] | [-3.29166383e-01 4.44781274e-01 -5.42514086e-01 -2.67461807e-01
5.84326684e-02 -6.44733846e-01 4.76944208e-01 6.77640498e-01
-5.76668754e-02 3.73673320e-01 5.37325859e-01 -1.85005322e-01
-4.30677891e-01 -1.29447055e+00 -5.92863023e-01 -4.99159753e-01
-3.27057511e-01 4.62180793e-01 3.45829129e-01 -4.46282953... | [8.70962905883789, 7.875404357910156] |
41c3a014-a631-412c-975f-822a7d1e0216 | design-and-analysis-of-robust-deep-learning | 2106.09664 | null | https://arxiv.org/abs/2106.09664v1 | https://arxiv.org/pdf/2106.09664v1.pdf | Design and Analysis of Robust Deep Learning Models for Stock Price Prediction | Building predictive models for robust and accurate prediction of stock prices and stock price movement is a challenging research problem to solve. The well-known efficient market hypothesis believes in the impossibility of accurate prediction of future stock prices in an efficient stock market as the stock prices are a... | ['Sidra Mehtab', 'Jaydip Sen'] | 2021-06-17 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-9.21772063e-01 -6.15453839e-01 -2.11733595e-01 -3.16450566e-01
-4.16917235e-01 -6.47860765e-01 4.23545241e-01 -1.75720915e-01
-2.01198295e-01 9.74207044e-01 -1.00031346e-02 -8.11839998e-01
-2.64174581e-01 -1.15479076e+00 -4.84670043e-01 -6.06317818e-01
-4.24314469e-01 5.95604897e-01 5.57404533e-02 -3.74653786... | [4.449654579162598, 4.24291467666626] |
43439e2d-93a7-4597-89f4-bd79e3823fff | nsp-bert-a-prompt-based-few-shot-learner | null | null | https://aclanthology.org/2022.coling-1.286 | https://aclanthology.org/2022.coling-1.286.pdf | NSP-BERT: A Prompt-based Few-Shot Learner through an Original Pre-training Task —— Next Sentence Prediction | Using prompts to utilize language models to perform various downstream tasks, also known as prompt-based learning or prompt-learning, has lately gained significant success in comparison to the pre-train and fine-tune paradigm. Nonetheless, virtually most prompt-based methods are token-level such as PET based on mask la... | ['Hangping Qiu', 'Chao Hao', 'Yu Zheng', 'Yi Sun'] | null | null | null | null | coling-2022-10 | ['sentence-classification'] | ['natural-language-processing'] | [ 1.36961147e-01 3.03491712e-01 -3.62872392e-01 -5.20321369e-01
-1.24423647e+00 -5.24962068e-01 7.59076118e-01 6.31585121e-01
-9.29448247e-01 9.21304822e-01 2.63257951e-01 -5.40792525e-01
-8.76451880e-02 -5.12483418e-01 -5.08302450e-01 -2.87384778e-01
-6.27153218e-02 5.55946767e-01 4.14283603e-01 -4.08551514... | [10.6441650390625, 8.709274291992188] |
d1433860-dfe3-4f24-8b05-ac00d6c98aeb | core-text-improving-scene-text-detection-with | 2112.07513 | null | https://arxiv.org/abs/2112.07513v1 | https://arxiv.org/pdf/2112.07513v1.pdf | CORE-Text: Improving Scene Text Detection with Contrastive Relational Reasoning | Localizing text instances in natural scenes is regarded as a fundamental challenge in computer vision. Nevertheless, owing to the extremely varied aspect ratios and scales of text instances in real scenes, most conventional text detectors suffer from the sub-text problem that only localizes the fragments of text instan... | ['Ting Yao', 'Hongyang Chao', 'Xuehang Yang', 'Rongfeng Lai', 'Yingwei Pan', 'Jingyang Lin'] | 2021-12-14 | null | null | null | null | ['scene-text-detection', 'relational-reasoning'] | ['computer-vision', 'natural-language-processing'] | [ 2.88151115e-01 5.81309721e-02 -1.37790591e-01 -3.58068198e-01
-7.83234715e-01 -3.89095902e-01 1.00976646e+00 1.86797399e-02
-1.57810852e-01 5.35671823e-02 1.86847135e-01 -2.57945329e-01
1.52577505e-01 -6.92564726e-01 -6.44689679e-01 -6.33734345e-01
5.82812726e-01 5.72610676e-01 5.72476327e-01 -8.76148194... | [12.031734466552734, 2.2603437900543213] |
9eaedd2e-7add-4225-a27e-feff4b3dc6a4 | effective-slot-filling-via-weakly-supervised | null | null | https://ojs.aaai.org/index.php/AAAI/article/view/17643 | https://ojs.aaai.org/index.php/AAAI/article/view/17643/17450 | Effective Slot Filling via Weakly-Supervised Dual-Model Learning | Slot filling is a challenging task in Spoken Language Understanding (SLU). Supervised methods usually require large amounts of annotation to maintain desirable performance. A solution to relieve the heavy dependency on labeled data is to employ bootstrapping, which leverages unlabeled data. However, bootstrapping is kn... | ['Gang Chen', 'Sai Wu', 'Lidan Shou', 'Ke Chen', 'Jue Wang'] | 2021-05-18 | null | null | null | aaai-2021-5 | ['slot-filling'] | ['natural-language-processing'] | [ 1.62689120e-01 4.51505840e-01 -5.61566710e-01 -6.81853533e-01
-1.11323977e+00 -5.31892896e-01 3.38578612e-01 8.55811685e-02
-6.23352349e-01 1.09054995e+00 1.88661039e-01 -2.04458520e-01
4.05696541e-01 -5.67509890e-01 -6.91760838e-01 -7.17512608e-01
2.85058320e-01 7.05831468e-01 3.18394452e-01 -1.61049008... | [12.45687198638916, 7.289758205413818] |
dd9d50f5-2789-49d8-84e8-b4609a2a593e | lanit-language-driven-image-to-image | 2208.14889 | null | https://arxiv.org/abs/2208.14889v4 | https://arxiv.org/pdf/2208.14889v4.pdf | LANIT: Language-Driven Image-to-Image Translation for Unlabeled Data | Existing techniques for image-to-image translation commonly have suffered from two critical problems: heavy reliance on per-sample domain annotation and/or inability of handling multiple attributes per image. Recent truly-unsupervised methods adopt clustering approaches to easily provide per-sample one-hot domain label... | ['Seungryong Kim', 'Seokju Cho', 'Sunwoo Kim', 'Youngjung Uh', 'Jaejun Yoo', 'Soohyun Kim', 'JiHye Park'] | 2022-08-31 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Park_LANIT_Language-Driven_Image-to-Image_Translation_for_Unlabeled_Data_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Park_LANIT_Language-Driven_Image-to-Image_Translation_for_Unlabeled_Data_CVPR_2023_paper.pdf | cvpr-2023-1 | ['unsupervised-image-to-image-translation'] | ['computer-vision'] | [ 5.01426637e-01 -3.91437523e-02 -6.38242066e-01 -9.46217060e-01
-1.36812687e+00 -8.81155312e-01 8.13478708e-01 4.36473750e-02
-4.24615890e-01 5.75962603e-01 1.09962024e-01 -3.91029492e-02
1.75384298e-01 -3.18910599e-01 -7.97312260e-01 -5.86020529e-01
6.84745371e-01 1.03487396e+00 -5.41203991e-02 3.38713437... | [10.255189895629883, 1.2709052562713623] |
82424fca-5070-4459-b419-a651ec311f10 | 3d-face-reconstruction-for-forensic | 2303.11164 | null | https://arxiv.org/abs/2303.11164v1 | https://arxiv.org/pdf/2303.11164v1.pdf | 3D Face Reconstruction for Forensic Recognition -- A Survey | 3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make unclear its possible role ... | ['Gian Luca Marcialis', 'Martin Drahansky', 'Tomáš Goldmann', 'Giulia Orrù', 'Simone Maurizio La Cava'] | 2023-02-03 | null | null | null | null | ['3d-face-reconstruction', 'face-reconstruction'] | ['computer-vision', 'computer-vision'] | [ 2.02567086e-01 1.53489083e-01 -1.42556682e-01 -3.30358028e-01
-6.83231130e-02 -3.13685626e-01 4.86038625e-01 -2.06940755e-01
-3.83865714e-01 7.07718611e-01 -3.75848450e-02 -4.98914540e-01
-3.73340994e-01 -6.03291750e-01 -3.82890075e-01 -8.00636053e-01
-4.72906269e-02 1.96041211e-01 1.47159649e-02 2.32334808... | [12.810683250427246, 0.7478553056716919] |
8cf545f8-4efe-4d1d-a282-d3243316195d | neural-automated-essay-scoring-and-coherence | 1804.06898 | null | https://arxiv.org/abs/1804.06898v3 | https://arxiv.org/pdf/1804.06898v3.pdf | Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input | We demonstrate that current state-of-the-art approaches to Automated Essay Scoring (AES) are not well-suited to capturing adversarially crafted input of grammatical but incoherent sequences of sentences. We develop a neural model of local coherence that can effectively learn connectedness features between sentences, an... | ['Ted Briscoe', 'Youmna Farag', 'Helen Yannakoudakis'] | 2018-04-18 | neural-automated-essay-scoring-and-coherence-1 | https://aclanthology.org/N18-1024 | https://aclanthology.org/N18-1024.pdf | naacl-2018-6 | ['automated-essay-scoring'] | ['natural-language-processing'] | [ 3.88020724e-01 2.27777570e-01 1.96807981e-01 -5.66374600e-01
-1.09493637e+00 -9.18912530e-01 7.19022632e-01 6.25916570e-02
-1.78704605e-01 6.23284280e-01 6.28591776e-01 -5.42047381e-01
-1.84822813e-01 -7.81992853e-01 -4.28970516e-01 -1.66191667e-01
1.03308052e-01 4.24731255e-01 -9.34043229e-02 -7.22676635... | [11.312138557434082, 9.335247993469238] |
88720a66-9502-47a0-88e1-d7936fb38a03 | monai-an-open-source-framework-for-deep | 2211.02701 | null | https://arxiv.org/abs/2211.02701v1 | https://arxiv.org/pdf/2211.02701v1.pdf | MONAI: An open-source framework for deep learning in healthcare | Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and ... | ['Andrew Feng', 'Sebastien Ourselin', 'Prerna Dogra', 'Stephen Aylward', 'Klaus H. Maier-Hein', 'Keyvan Farahani', 'Haris Shuaib', 'S. Kevin Zhou', 'Ralf Floca', 'David Bericat', 'Daguang Xu', 'Holger R. Roth', 'Lee A. D. Cooper', 'Justin Kirby', 'Mona Flores', 'Jayashree Kalpathy-Cramer', 'Michael Baumgartner', 'Paul ... | 2022-11-04 | null | null | null | null | ['medical-image-detection', 'medical-image-registration'] | ['computer-vision', 'medical'] | [-3.54718976e-02 1.69099346e-01 8.19674227e-03 -1.70799032e-01
-6.29109383e-01 -2.80406386e-01 2.77006596e-01 4.43240792e-01
-4.01845761e-02 2.99487442e-01 3.82281661e-01 -5.50506175e-01
-1.62910521e-01 -7.48415291e-01 -4.12318170e-01 -6.18515134e-01
-1.67356417e-01 8.71997178e-01 -8.74644052e-03 -6.18454143... | [14.7726411819458, -2.3516926765441895] |
4eebddca-05d6-4877-a848-9e8e57bd4010 | squeezeseg-convolutional-neural-nets-with | 1710.07368 | null | http://arxiv.org/abs/1710.07368v1 | http://arxiv.org/pdf/1710.07368v1.pdf | SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud | In this paper, we address semantic segmentation of road-objects from 3D LiDAR
point clouds. In particular, we wish to detect and categorize instances of
interest, such as cars, pedestrians and cyclists. We formulate this problem as
a point- wise classification problem, and propose an end-to-end pipeline called
SqueezeS... | ['Xiangyu Yue', 'Bichen Wu', 'Kurt Keutzer', 'Alvin Wan'] | 2017-10-19 | null | null | null | null | ['robust-3d-semantic-segmentation'] | ['computer-vision'] | [ 1.93771627e-02 -4.36000936e-02 -1.26647592e-01 -8.43289733e-01
-9.91731346e-01 -4.42157269e-01 4.85467315e-01 -9.70484242e-02
-5.15291572e-01 4.18621480e-01 -6.41307414e-01 -4.73240972e-01
3.81613135e-01 -1.20248532e+00 -1.14909112e+00 -1.84481949e-01
-1.15269810e-01 1.02471161e+00 7.16283619e-01 -7.57472068... | [8.103917121887207, -2.5702691078186035] |
8399cfb9-61ea-42da-87d2-a2008e73999f | deformable-registration-using-average | 1907.09670 | null | https://arxiv.org/abs/1907.09670v1 | https://arxiv.org/pdf/1907.09670v1.pdf | Deformable Registration Using Average Geometric Transformations for Brain MR Images | Accurate registration of medical images is vital for doctor's diagnosis and quantitative analysis. In this paper, we propose a new deformable medical image registration method based on average geometric transformations and VoxelMorph CNN architecture. We compute the differential geometric information including Jacobian... | ['Zicong Zhou', 'Yongpei Zhu', 'Guojun Liao', 'Kehong Yuan'] | 2019-07-23 | null | null | null | null | ['deformable-medical-image-registration'] | ['medical'] | [-1.35895044e-01 -3.04038879e-02 1.99716672e-01 -6.05258167e-01
-7.32799768e-01 -3.23083639e-01 2.99808741e-01 -8.06154460e-02
-7.09719539e-01 5.47872782e-01 2.42081195e-01 5.68004809e-02
3.80505100e-02 -6.26507699e-01 -4.27780330e-01 -7.50534534e-01
-3.28307629e-01 6.45379245e-01 4.86460388e-01 -3.16122860... | [14.008770942687988, -2.5443954467773438] |
b4a1f214-cf8f-4375-b983-367f178efe4c | secure-and-privacy-preserving-automated-end | 2211.07643 | null | https://arxiv.org/abs/2211.07643v1 | https://arxiv.org/pdf/2211.07643v1.pdf | Secure and Privacy-Preserving Automated End-to-End Integrated IoT-Edge-Artificial Intelligence-Blockchain Monitoring System for Diabetes Mellitus Prediction | Diabetes Mellitus, one of the leading causes of death worldwide, has no cure till date and can lead to severe health complications, such as retinopathy, limb amputation, cardiovascular diseases, and neuronal disease, if left untreated. Consequently, it becomes crucial to take precautionary measures to avoid/predict the... | ['Rajiv Janardhanan', 'Priya Ranjan', 'Juma Al Kaabi', 'Huned Materwala', 'Alain Hennebelle', 'Leila Ismail'] | 2022-11-13 | null | null | null | null | ['diabetes-prediction'] | ['medical'] | [ 3.62690955e-01 -1.29236430e-01 -6.29018962e-01 -3.77451450e-01
-2.28428274e-01 -1.27974689e-01 4.17880386e-01 7.07449496e-01
-1.97509438e-01 1.13609183e+00 1.17021389e-01 -7.13387191e-01
-3.80584270e-01 -8.27589810e-01 -3.86357576e-01 -9.20159757e-01
-1.41328976e-01 5.05555451e-01 -2.83905774e-01 1.02106854... | [8.407461166381836, 4.971554756164551] |
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