paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
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e2aca4bd-8f05-4269-a643-7307b55dcf04 | unsupervised-sentence-textual-similarity-with | 2210.02284 | null | https://arxiv.org/abs/2210.02284v1 | https://arxiv.org/pdf/2210.02284v1.pdf | Unsupervised Sentence Textual Similarity with Compositional Phrase Semantics | Measuring Sentence Textual Similarity (STS) is a classic task that can be applied to many downstream NLP applications such as text generation and retrieval. In this paper, we focus on unsupervised STS that works on various domains but only requires minimal data and computational resources. Theoretically, we propose a l... | ['Yong Zhang', 'Jiaheng Dou', 'ZiHao Wang'] | 2022-10-05 | null | https://aclanthology.org/2022.coling-1.441 | https://aclanthology.org/2022.coling-1.441.pdf | coling-2022-10 | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [ 6.35344088e-01 -2.67126888e-01 -8.73114392e-02 -3.66912693e-01
-1.01671422e+00 -6.64631367e-01 6.88981712e-01 5.86089313e-01
-6.27371788e-01 4.70669627e-01 7.29164600e-01 -5.22495568e-01
-2.84490347e-01 -4.26561803e-01 -4.17955637e-01 -5.39753914e-01
2.13874027e-01 3.86805207e-01 5.13945758e-01 -3.27546269... | [11.047858238220215, 8.874863624572754] |
54f30c7d-6f62-46e3-a231-eeb259ed08b2 | 1st-place-solution-for-ava-kinetics-crossover | 2006.09116 | null | https://arxiv.org/abs/2006.09116v1 | https://arxiv.org/pdf/2006.09116v1.pdf | 1st place solution for AVA-Kinetics Crossover in AcitivityNet Challenge 2020 | This technical report introduces our winning solution to the spatio-temporal action localization track, AVA-Kinetics Crossover, in ActivityNet Challenge 2020. Our entry is mainly based on Actor-Context-Actor Relation Network. We describe technical details for the new AVA-Kinetics dataset, together with some experimenta... | ['Manyuan Zhang', 'Ziyi Lin', 'Yu Liu', 'Siyu Chen', 'Junting Pan', 'Hao Shao', 'Hongsheng Li', 'Jing Shao', 'Guanglu Song'] | 2020-06-16 | null | null | null | null | ['spatio-temporal-action-localization'] | ['computer-vision'] | [-3.50812495e-01 -1.08495422e-01 -6.21139050e-01 3.05445455e-02
-7.07266510e-01 -7.57026136e-01 7.70830393e-01 -1.38632074e-01
-4.83388722e-01 9.21205938e-01 7.26270974e-01 -1.64434895e-01
-2.62352049e-01 -1.86002880e-01 -4.91861224e-01 -6.05029881e-01
-7.19837725e-01 5.26423901e-02 4.57096785e-01 -2.32834563... | [8.388585090637207, 0.4816572070121765] |
e46c6049-3485-4957-a679-2c8955e4a7e5 | requirement-analysis-for-an-artificial | 2110.12464 | null | https://arxiv.org/abs/2110.12464v2 | https://arxiv.org/pdf/2110.12464v2.pdf | Requirement analysis for an artificial intelligence model for the diagnosis of the COVID-19 from chest X-ray data | There are multiple papers published about different AI models for the COVID-19 diagnosis with promising results. Unfortunately according to the reviews many of the papers do not reach the level of sophistication needed for a clinically usable model. In this paper I go through multiple review papers, guidelines, and oth... | ['Tuomo Kalliokoski'] | 2021-10-24 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [-1.60170779e-01 9.73949805e-02 -2.28052646e-01 -5.52596211e-01
-4.81955111e-01 -2.88718641e-01 1.72448292e-01 3.12183350e-01
5.35241328e-02 5.28565407e-01 2.32331529e-01 -9.71237421e-01
-9.83669996e-01 -3.41362834e-01 -1.11538142e-01 -4.29246724e-01
1.09945498e-01 1.05024087e+00 7.96426609e-02 2.40148142... | [8.469444274902344, 5.77777099609375] |
135b43c8-95d3-4994-97cb-ba2332c1a9e3 | a-monaural-speech-enhancement-method-for | 1906.08415 | null | https://arxiv.org/abs/1906.08415v1 | https://arxiv.org/pdf/1906.08415v1.pdf | A Monaural Speech Enhancement Method for Robust Small-Footprint Keyword Spotting | Robustness against noise is critical for keyword spotting (KWS) in real-world environments. To improve the robustness, a speech enhancement front-end is involved. Instead of treating the speech enhancement as a separated preprocessing before the KWS system, in this study, a pre-trained speech enhancement front-end and ... | ['HUI ZHANG', 'Zhihao Du', 'Yue Gu', 'Xueliang Zhang'] | 2019-06-20 | null | null | null | null | ['small-footprint-keyword-spotting'] | ['speech'] | [ 3.48007619e-01 4.15740535e-02 3.95866275e-01 -3.12485516e-01
-6.45649016e-01 -3.34236659e-02 2.07553521e-01 -1.90343615e-02
-7.24491835e-01 1.45124421e-01 3.48862261e-01 -6.37221634e-01
1.00348398e-01 -6.61060631e-01 -2.60021180e-01 -7.51287401e-01
3.99224371e-01 -8.11441541e-01 4.01554316e-01 -4.40844208... | [14.815231323242188, 5.955252170562744] |
9e52f603-8f56-4a7c-a7a2-65e254ae8c79 | spidr-sdf-based-neural-point-fields-for | 2210.08398 | null | https://arxiv.org/abs/2210.08398v3 | https://arxiv.org/pdf/2210.08398v3.pdf | SPIDR: SDF-based Neural Point Fields for Illumination and Deformation | Neural radiance fields (NeRFs) have recently emerged as a promising approach for 3D reconstruction and novel view synthesis. However, NeRF-based methods encode shape, reflectance, and illumination implicitly and this makes it challenging for users to manipulate these properties in the rendered images explicitly. Existi... | ['Yushi Guan', 'Nandita Vijaykumar', 'Chen Yang', 'Haoda Li', 'Jiahao Zhang', 'Ruofan Liang'] | 2022-10-15 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 5.04030764e-01 -4.02699262e-01 5.48421085e-01 -5.55068433e-01
-7.64216408e-02 -5.13127208e-01 4.03948516e-01 -2.94065565e-01
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4.14914906e-01 1.58848330e-01 1.88862979e-01 -1.81131989... | [9.634037971496582, -3.1195993423461914] |
99aadd74-41a7-4979-9068-0040ebbea69d | tell-me-why-explanations-support-learning-of | 2112.03753 | null | https://arxiv.org/abs/2112.03753v3 | https://arxiv.org/pdf/2112.03753v3.pdf | Tell me why! Explanations support learning relational and causal structure | Inferring the abstract relational and causal structure of the world is a major challenge for reinforcement-learning (RL) agents. For humans, language--particularly in the form of explanations--plays a considerable role in overcoming this challenge. Here, we show that language can play a similar role for deep RL agents ... | ['Felix Hill', 'Jane X. Wang', 'Neil C. Rabinowitz', 'Adam Santoro', 'Chen Yan', 'James L. McClelland', 'Allison C. Tam', 'Stephanie C. Y. Chan', 'Ishita Dasgupta', 'Nicholas A. Roy', 'Andrew K. Lampinen'] | 2021-12-07 | null | null | null | null | ['odd-one-out'] | ['reasoning'] | [ 9.07360285e-04 3.86036843e-01 -4.23862308e-01 -2.83608526e-01
-2.99830973e-01 -5.99889159e-01 8.42435420e-01 4.15194988e-01
-3.85325909e-01 1.01782739e+00 5.82453370e-01 -4.69389856e-01
-2.95480579e-01 -8.42896461e-01 -9.15789545e-01 -3.22761148e-01
-3.73828590e-01 6.38979912e-01 -1.24913335e-01 -4.77321833... | [4.257334232330322, 1.2208791971206665] |
5bc372e1-a178-483a-bde8-8da8357c9b6e | ocatari-object-centric-atari-2600 | 2306.08649 | null | https://arxiv.org/abs/2306.08649v1 | https://arxiv.org/pdf/2306.08649v1.pdf | OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments | Cognitive science and psychology suggest that object-centric representations of complex scenes are a promising step towards enabling efficient abstract reasoning from low-level perceptual features. Yet, most deep reinforcement learning approaches rely on only pixel-based representations that do not capture the composit... | ['Kristian Kersting', 'Sebastian Sztwiertnia', 'Bjarne Gregori', 'Jannis Blüml', 'Quentin Delfosse'] | 2023-06-14 | null | null | null | null | ['atari-games'] | ['playing-games'] | [-1.96670741e-01 -1.42768979e-01 -3.24713066e-02 -1.90516278e-01
-2.56178796e-01 -6.02005005e-01 8.51333916e-01 -1.53518561e-02
-5.35153925e-01 6.13076627e-01 2.17306033e-01 -2.10908115e-01
-7.43397176e-02 -8.73829603e-01 -8.09260428e-01 -4.34873819e-01
-1.11154400e-01 4.62613553e-01 3.52575570e-01 -6.74302101... | [4.1944074630737305, 1.1579225063323975] |
7e6a2a9d-7b56-4788-b2e6-3d80af10923a | unsupervised-deep-homography-a-fast-and | 1709.03966 | null | http://arxiv.org/abs/1709.03966v3 | http://arxiv.org/pdf/1709.03966v3.pdf | Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model | Homography estimation between multiple aerial images can provide relative
pose estimation for collaborative autonomous exploration and monitoring. The
usage on a robotic system requires a fast and robust homography estimation
algorithm. In this study, we propose an unsupervised learning algorithm that
trains a Deep Con... | ['Steven W. Chen', 'Ty Nguyen', 'Camillo J. Taylor', 'Vijay Kumar', 'Shreyas S. Shivakumar'] | 2017-09-12 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 2.09485054e-01 -1.15305185e-01 -7.56547600e-02 -4.19510782e-01
-9.75687504e-02 -5.93257427e-01 4.66288149e-01 -2.55357713e-01
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-5.87810755e-01 -1.08485198e+00 -8.76835346e-01 -3.81709427e-01
-3.28372210e-01 5.29792666e-01 7.61548057e-02 -1.78591460... | [7.8733649253845215, -2.0742275714874268] |
d09d3337-4189-4671-a575-d1290b27e9e3 | distributed-cooperative-control-and | 2208.13412 | null | https://arxiv.org/abs/2208.13412v3 | https://arxiv.org/pdf/2208.13412v3.pdf | Distributed Cooperative Control and Optimization of Connected Automated Vehicles Platoon Against Cut-in Behaviors of Social Drivers | Connected automated vehicles (CAVs) have brought new opportunities to improve traffic throughput and reduce energy consumption. However, the uncertain lane-change behaviors (LCBs) of surrounding vehicles (SVs) as an uncontrollable factor significantly threaten the driving safety and the consistent movement of a group o... | ['Rong Su', 'Bohui Wang'] | 2022-08-29 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-3.89366567e-01 4.47305083e-01 -1.39760509e-01 5.17475270e-02
2.60830913e-02 -5.28095603e-01 5.38200378e-01 -1.20326683e-01
-1.65520534e-01 9.36668873e-01 -5.42723596e-01 -6.27943337e-01
-3.81956518e-01 -9.85413074e-01 -4.51734185e-01 -1.08484519e+00
-2.47621611e-01 4.81425762e-01 7.77058721e-01 -4.24185932... | [5.539227485656738, 1.6179099082946777] |
028152c5-613a-4900-91c0-9bed80305ca2 | comparison-of-genres-in-word-sense | null | null | https://aclanthology.org/2020.clib-1.17 | https://aclanthology.org/2020.clib-1.17.pdf | Comparison of Genres in Word Sense Disambiguation using Automatically Generated Text Collections | The best approaches in Word Sense Disambiguation (WSD) are supervised and rely on large amounts of hand-labelled data, which is not always available and costly to create. In our work we describe an approach that is used to create an automatically labelled collection based on the monosemous relatives (related unambiguou... | ['Natalia Loukachevitch', 'Angelina Bolshina'] | null | null | null | null | clib-2020-9 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [-3.14866640e-02 -9.99256968e-03 -1.64529398e-01 -2.78736174e-01
-5.96973360e-01 -1.01579618e+00 6.28564537e-01 1.00990534e+00
-1.07664216e+00 1.28001547e+00 4.47480530e-01 -4.23252493e-01
-5.47902465e-01 -8.15729856e-01 9.39711854e-02 -3.04649711e-01
1.14719406e-01 9.10574496e-01 4.22756344e-01 -1.09105837... | [10.241291999816895, 9.288840293884277] |
afa6c9cf-3e6b-4392-959d-50d4d9310771 | bidirectional-transformer-reranker-for | 2305.13000 | null | https://arxiv.org/abs/2305.13000v1 | https://arxiv.org/pdf/2305.13000v1.pdf | Bidirectional Transformer Reranker for Grammatical Error Correction | Pre-trained seq2seq models have achieved state-of-the-art results in the grammatical error correction task. However, these models still suffer from a prediction bias due to their unidirectional decoding. Thus, we propose a bidirectional Transformer reranker (BTR), that re-estimates the probability of each candidate sen... | ['Manabu Okumura', 'Hidetaka Kamigaito', 'Ying Zhang'] | 2023-05-22 | null | null | null | null | ['grammatical-error-correction'] | ['natural-language-processing'] | [ 3.07355195e-01 3.35786253e-01 1.19264098e-02 -4.93046284e-01
-1.16869271e+00 -2.90513188e-01 3.00458610e-01 1.30217791e-01
-6.95565879e-01 9.23362911e-01 2.97313869e-01 -4.02601063e-01
3.23778719e-01 -6.86561465e-01 -9.88919616e-01 -4.79261011e-01
2.64086962e-01 3.89697373e-01 1.35400578e-01 -4.33831692... | [11.189090728759766, 10.523324966430664] |
17f2f628-944a-4791-a275-2586b913d6f8 | an-adaptive-volatility-method-for | 2303.01855 | null | https://arxiv.org/abs/2303.01855v1 | https://arxiv.org/pdf/2303.01855v1.pdf | An adaptive volatility method for probabilistic forecasting and its application to the M6 financial forecasting competition | In this note, we address the problem of probabilistic forecasting using an adaptive volatility method based on classical time-varying volatility models and stochastic optimization algorithms. These principles were successfully applied in the recent M6 financial forecasting competition for both probabilistic forecasting... | ['Nicklas Werge', 'Joseph de Vilmarest'] | 2023-03-03 | null | null | null | null | ['stochastic-optimization'] | ['methodology'] | [-4.25067514e-01 -2.65630633e-01 3.11120510e-01 -5.30056596e-01
-6.78398192e-01 -8.89537394e-01 1.07360756e+00 -2.38106221e-01
-2.07562700e-01 7.51034617e-01 7.74472654e-02 -7.36648202e-01
-5.12727976e-01 -8.02635074e-01 -1.68271616e-01 -9.51801062e-01
-4.90552783e-02 7.76904106e-01 8.08919445e-02 -2.70739317... | [4.953858375549316, 4.051660537719727] |
c76339aa-dd3d-4366-bc0a-72919633e688 | greedy-transition-based-dependency-parsing-1 | 2007.04686 | null | https://arxiv.org/abs/2007.04686v1 | https://arxiv.org/pdf/2007.04686v1.pdf | Greedy Transition-Based Dependency Parsing with Discrete and Continuous Supertag Features | We study the effect of rich supertag features in greedy transition-based dependency parsing. While previous studies have shown that sparse boolean features representing the 1-best supertag of a word can improve parsing accuracy, we show that we can get further improvements by adding a continuous vector representation o... | ['Ali Basirat', 'Joakim Nivre'] | 2020-07-09 | null | null | null | null | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-2.23049790e-01 4.25291687e-01 -2.62755841e-01 -8.95407081e-01
-1.20737326e+00 -6.38208568e-01 1.72228009e-01 4.85025495e-01
-7.35638559e-01 7.90264428e-01 3.91412824e-01 -6.20753169e-01
1.81650713e-01 -9.48853970e-01 -4.55843836e-01 -6.03547752e-01
-4.89662111e-01 4.66332197e-01 4.82812375e-01 -6.50444388... | [10.344884872436523, 9.720023155212402] |
bbdfcb2c-9df0-40fb-b0b1-58961320c028 | domain-transfer-through-image-to-image | 2307.00479 | null | https://arxiv.org/abs/2307.00479v1 | https://arxiv.org/pdf/2307.00479v1.pdf | Domain Transfer Through Image-to-Image Translation for Uncertainty-Aware Prostate Cancer Classification | Prostate Cancer (PCa) is often diagnosed using High-resolution 3.0 Tesla(T) MRI, which has been widely established in clinics. However, there are still many medical centers that use 1.5T MRI units in the actual diagnostic process of PCa. In the past few years, deep learning-based models have been proven to be efficient... | ['Parvin Mousavi', 'Robert Siemens', 'Alexandre Menard', 'Jason Izard', 'Amoon Jamzad', 'Meng Zhou'] | 2023-07-02 | null | null | null | null | ['image-to-image-translation', 'image-to-image-translation'] | ['computer-vision', 'miscellaneous'] | [ 3.87363940e-01 3.22462529e-01 -2.14493766e-01 -6.10943079e-01
-1.48029721e+00 -6.87589824e-01 2.89779007e-01 2.52548575e-01
-4.14177597e-01 6.14023507e-01 -1.09143592e-01 -4.68119800e-01
-2.45966390e-01 -5.29303074e-01 -6.52394414e-01 -9.00593758e-01
-2.75141478e-01 8.99812937e-01 1.49154037e-01 5.01168370... | [14.6195707321167, -2.3797786235809326] |
ce61cb6c-268f-4962-99b1-5ae48e0f7c2f | integrity-and-junkiness-failure-handling-for | 2304.09287 | null | https://arxiv.org/abs/2304.09287v1 | https://arxiv.org/pdf/2304.09287v1.pdf | Integrity and Junkiness Failure Handling for Embedding-based Retrieval: A Case Study in Social Network Search | Embedding based retrieval has seen its usage in a variety of search applications like e-commerce, social networking search etc. While the approach has demonstrated its efficacy in tasks like semantic matching and contextual search, it is plagued by the problem of uncontrollable relevance. In this paper, we conduct an a... | ['Pramodh Karanth Prabhakar', 'Hao Fu', 'Guangdeng Liao', 'Shuai Ding', 'Chiyao Shen', 'Yunxi Guo', 'Wenping Wang'] | 2023-04-18 | null | null | null | null | ['text-matching'] | ['natural-language-processing'] | [-2.12754719e-02 1.84123844e-01 -2.77491957e-01 5.09381294e-02
-8.14673722e-01 -8.43981326e-01 9.95863080e-01 4.52159107e-01
-6.60395265e-01 4.76678401e-01 4.33530837e-01 -6.52895868e-01
-8.29432130e-01 -4.38669533e-01 -2.52502918e-01 -2.98594117e-01
2.79329177e-02 3.77954066e-01 4.13801819e-01 -6.49511456... | [11.380078315734863, 7.56084680557251] |
158c93cb-1fe9-4600-92e7-76e800139973 | pushing-the-limits-of-unsupervised-unit | 2306.08920 | null | https://arxiv.org/abs/2306.08920v1 | https://arxiv.org/pdf/2306.08920v1.pdf | Pushing the Limits of Unsupervised Unit Discovery for SSL Speech Representation | The excellent generalization ability of self-supervised learning (SSL) for speech foundation models has garnered significant attention. HuBERT is a successful example that utilizes offline clustering to convert speech features into discrete units for a masked language modeling pretext task. However, simply clustering f... | ['Xie Chen', 'Chao Zhang', 'Yu Wang', 'Guanrou Yang', 'Zhisheng Zheng', 'Ziyang Ma'] | 2023-06-15 | null | null | null | null | ['clustering'] | ['methodology'] | [ 1.36233136e-01 2.24693090e-01 -2.67375499e-01 -8.55397344e-01
-1.09469557e+00 -7.21314788e-01 8.11462760e-01 2.64018834e-01
-6.36038601e-01 5.43633461e-01 3.42905432e-01 -7.18897283e-01
9.39069316e-02 -4.83173400e-01 -5.52161813e-01 -4.40577447e-01
-9.65791047e-02 5.42274177e-01 3.20579171e-01 -1.18266195... | [14.428496360778809, 6.604561805725098] |
66feb5d4-3673-496c-96ce-338623fc4859 | improving-code-generation-by-training-with | 2303.16749 | null | https://arxiv.org/abs/2303.16749v1 | https://arxiv.org/pdf/2303.16749v1.pdf | Improving Code Generation by Training with Natural Language Feedback | The potential for pre-trained large language models (LLMs) to use natural language feedback at inference time has been an exciting recent development. We build upon this observation by formalizing an algorithm for learning from natural language feedback at training time instead, which we call Imitation learning from La... | ['Ethan Perez', 'Kyunghyun Cho', 'Samuel R. Bowman', 'Jun Shern Chan', 'Jon Ander Campos', 'Tomasz Korbak', 'Jérémy Scheurer', 'Angelica Chen'] | 2023-03-28 | null | null | null | null | ['program-synthesis'] | ['computer-code'] | [ 3.43133174e-02 3.41899425e-01 -2.25396484e-01 -4.40988660e-01
-1.01240993e+00 -6.62079513e-01 6.05574846e-01 1.43000960e-01
-6.64702713e-01 6.68179512e-01 -4.77434322e-02 -8.59943748e-01
4.99339283e-01 -6.54416502e-01 -1.59377742e+00 -5.40758632e-02
-2.17731103e-01 2.66474336e-01 9.51370373e-02 -1.16910823... | [8.057413101196289, 7.6296067237854] |
4e18952b-5609-46ec-b753-33c18e9c82b8 | ai-in-pursuit-of-happiness-finding-only | 1911.05187 | null | https://arxiv.org/abs/1911.05187v1 | https://arxiv.org/pdf/1911.05187v1.pdf | AI in Pursuit of Happiness, Finding Only Sadness: Multi-Modal Facial Emotion Recognition Challenge | The importance of automated Facial Emotion Recognition (FER) grows the more common human-machine interactions become, which will only continue to increase dramatically with time. A common method to describe human sentiment or feeling is the categorical model the `7 basic emotions', consisting of `Angry', `Disgust', `Fe... | ['Carl Norman'] | 2019-10-24 | null | null | null | null | ['facial-emotion-recognition'] | ['computer-vision'] | [ 4.06455621e-02 -2.57379979e-01 1.73959285e-01 -6.62139654e-01
-3.73915583e-01 -3.06318402e-01 4.87997442e-01 -1.13820046e-01
-4.91032213e-01 5.55273592e-01 3.27986807e-01 3.05418670e-01
6.52569234e-02 -2.51241654e-01 -1.26257241e-01 -6.42011702e-01
-4.67720121e-01 -1.22988440e-01 -1.46611676e-01 -6.92395568... | [13.558692932128906, 2.1156373023986816] |
a44c450d-d5cb-4ac6-a5aa-f87a3305a6ca | bootstrap-your-own-prior-towards-distribution | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Yang_Bootstrap_Your_Own_Prior_Towards_Distribution-Agnostic_Novel_Class_Discovery_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Yang_Bootstrap_Your_Own_Prior_Towards_Distribution-Agnostic_Novel_Class_Discovery_CVPR_2023_paper.pdf | Bootstrap Your Own Prior: Towards Distribution-Agnostic Novel Class Discovery | Novel Class Discovery (NCD) aims to discover unknown classes without any annotation, by exploiting the transferable knowledge already learned from a base set of known classes. Existing works hold an impractical assumption that the novel class distribution prior is uniform, yet neglect the imbalanced nature of real-... | ['Hanwang Zhang', 'Cheng Deng', 'Liancheng Wang', 'Muli Yang'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['novel-class-discovery', 'novel-class-discovery'] | ['computer-vision', 'methodology'] | [ 3.47595841e-01 1.81382939e-01 -6.53191388e-01 -4.87062126e-01
-8.53134096e-01 -5.14206767e-01 4.20609564e-01 2.88410485e-01
-6.41418919e-02 1.18607962e+00 -2.02450454e-01 -1.64111659e-01
-1.54031992e-01 -8.48644018e-01 -6.69850230e-01 -9.20869708e-01
3.41038495e-01 8.83779526e-01 3.31822187e-01 2.77436465... | [9.395710945129395, 3.7038326263427734] |
001c34a4-3440-4e13-a598-4d2fff91e379 | comparing-abstractive-summaries-generated-by | 2303.17650 | null | https://arxiv.org/abs/2303.17650v2 | https://arxiv.org/pdf/2303.17650v2.pdf | Evaluating and Detecting ChatGPT's Responses on Abstractive Summarization | Large Language Models (LLMs) have gathered significant attention due to their impressive performance on a variety of tasks. ChatGPT, developed by OpenAI, is a recent addition to the family of language models and is being called a disruptive technology by a few, owing to its human-like text-generation capabilities. Alth... | ['Vincent Wade', 'Mayank Soni'] | 2023-03-30 | null | null | null | null | ['abstractive-text-summarization'] | ['natural-language-processing'] | [-8.04921612e-02 3.05178106e-01 -4.20699060e-01 3.44619416e-02
-1.06406212e+00 -5.52888751e-01 1.08372378e+00 6.19668782e-01
-1.32199720e-01 6.67967141e-01 7.28999555e-01 -5.24246871e-01
5.59114143e-02 -3.68607670e-01 -1.06888965e-01 -1.22090384e-01
1.35700017e-01 5.29148042e-01 2.23952562e-01 -1.35522597... | [11.947612762451172, 9.04207992553711] |
4a0885f6-9be9-4e5f-97ed-a04cbde86cf8 | covsegnet-a-multi-encoder-decoder | 2012.01473 | null | https://arxiv.org/abs/2012.01473v1 | https://arxiv.org/pdf/2012.01473v1.pdf | CovSegNet: A Multi Encoder-Decoder Architecture for Improved Lesion Segmentation of COVID-19 Chest CT Scans | Automatic lung lesions segmentation of chest CT scans is considered a pivotal stage towards accurate diagnosis and severity measurement of COVID-19. Traditional U-shaped encoder-decoder architecture and its variants suffer from diminutions of contextual information in pooling/upsampling operations with increased semant... | ['Sun-Yuan Kung', 'Shaikh Anowarul Fattah', 'Md Awsafur Rahman', 'Tanvir Mahmud'] | 2020-12-02 | null | null | null | null | ['covid-19-image-segmentation'] | ['computer-vision'] | [ 5.01440585e-01 3.20431627e-02 -4.97449748e-02 -2.42186800e-01
-1.13819683e+00 -3.78229626e-04 1.42572954e-01 4.20253724e-01
-7.17029572e-01 4.74299431e-01 1.24830469e-01 -3.38196635e-01
-1.12750009e-01 -7.79694378e-01 -3.67741704e-01 -7.69227445e-01
-1.79702695e-02 1.53318748e-01 7.52810001e-01 2.06737947... | [15.165205001831055, -2.1720025539398193] |
f601d1df-f1ec-4094-8691-a14f0c9f7c83 | cross-modal-compression-towards-human | 2209.02574 | null | https://arxiv.org/abs/2209.02574v1 | https://arxiv.org/pdf/2209.02574v1.pdf | Cross Modal Compression: Towards Human-comprehensible Semantic Compression | Traditional image/video compression aims to reduce the transmission/storage cost with signal fidelity as high as possible. However, with the increasing demand for machine analysis and semantic monitoring in recent years, semantic fidelity rather than signal fidelity is becoming another emerging concern in image/video c... | ['Wen Gao', 'Siwei Ma', 'Xinfeng Zhang', 'Chuanmin Jia', 'Jiguo Li'] | 2022-09-06 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 8.74971986e-01 -2.66254216e-01 -1.50436893e-01 -2.40423337e-01
-7.36902237e-01 -2.76437432e-01 5.85699618e-01 -4.42807004e-02
-1.49433270e-01 5.41637957e-01 3.84078562e-01 5.85885122e-02
-3.11452180e-01 -7.57782102e-01 -7.84219384e-01 -3.50563198e-01
2.10329831e-01 -1.37818471e-01 1.49943382e-01 -4.63927723... | [11.2887544631958, -1.7411006689071655] |
1227845f-c392-41c9-8602-b15a2d648306 | pre-training-transformers-for-molecular | 2207.02724 | null | https://arxiv.org/abs/2207.02724v1 | https://arxiv.org/pdf/2207.02724v1.pdf | Pre-training Transformers for Molecular Property Prediction Using Reaction Prediction | Molecular property prediction is essential in chemistry, especially for drug discovery applications. However, available molecular property data is often limited, encouraging the transfer of information from related data. Transfer learning has had a tremendous impact in fields like Computer Vision and Natural Language P... | ['Erik Ylipää', 'Maria Bånkestad', 'Johan Broberg'] | 2022-07-06 | null | null | null | null | ['molecular-property-prediction'] | ['miscellaneous'] | [ 4.84708279e-01 -1.98458359e-01 -6.64183259e-01 -3.44521970e-01
-7.88377106e-01 -5.03407240e-01 5.66055417e-01 1.03883195e+00
-5.67994952e-01 1.43046868e+00 3.38190705e-01 -7.34927833e-01
-3.37923802e-02 -7.69070864e-01 -1.00389373e+00 -8.52441013e-01
-3.65991831e-01 2.69320369e-01 1.82548061e-01 -1.09446257... | [5.05210018157959, 5.8477301597595215] |
935f57d7-82b3-4f7d-9dd6-dba7abc0da4e | movieclip-visual-scene-recognition-in-movies | 2210.11065 | null | https://arxiv.org/abs/2210.11065v2 | https://arxiv.org/pdf/2210.11065v2.pdf | MovieCLIP: Visual Scene Recognition in Movies | Longform media such as movies have complex narrative structures, with events spanning a rich variety of ambient visual scenes. Domain specific challenges associated with visual scenes in movies include transitions, person coverage, and a wide array of real-life and fictional scenarios. Existing visual scene datasets in... | ['Shrikanth Narayanan', 'Huisheng Wang', 'Kree Cole-McLaughlin', 'Yin Cui', 'Haoyang Zhang', 'Krishna Somandepalli', 'Rajat Hebbar', 'Digbalay Bose'] | 2022-10-20 | null | null | null | null | ['scene-recognition', 'genre-classification'] | ['computer-vision', 'computer-vision'] | [ 1.83142185e-01 -6.51475549e-01 -4.32705164e-01 -5.66141069e-01
-7.27305472e-01 -1.37563694e+00 6.88840985e-01 7.85131902e-02
-3.21810842e-01 2.62382865e-01 7.26454318e-01 1.21823043e-01
3.55970442e-01 -1.54807195e-01 -7.58325815e-01 -1.69688806e-01
4.07271162e-02 -1.20942466e-01 5.06142974e-01 -2.57075205... | [10.303550720214844, 0.7997976541519165] |
bb953425-7179-44f2-b568-f1b99df719c1 | aerial-scene-understanding-in-the-wild-multi | 2104.11200 | null | https://arxiv.org/abs/2104.11200v1 | https://arxiv.org/pdf/2104.11200v1.pdf | Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory Networks | Aerial scene recognition is a fundamental visual task and has attracted an increasing research interest in the last few years. Most of current researches mainly deploy efforts to categorize an aerial image into one scene-level label, while in real-world scenarios, there often exist multiple scenes in a single image. Th... | ['Xiao Xiang Zhu', 'Konrad Heidler', 'Jianzhe Lin', 'Lichao Moua', 'Yuansheng Hua'] | 2021-04-22 | null | null | null | null | ['scene-recognition'] | ['computer-vision'] | [ 7.26794124e-01 -5.59761584e-01 -2.22336501e-02 -4.98902410e-01
-6.03569925e-01 -5.71455359e-01 2.90206075e-01 1.97481781e-01
-3.70083183e-01 2.52451152e-01 -4.25501436e-01 -1.19879708e-01
-7.84482211e-02 -8.42086732e-01 -7.98526227e-01 -5.52240610e-01
3.03602636e-01 8.97333845e-02 3.70410174e-01 2.01509014... | [9.784725189208984, 1.7184507846832275] |
6076925f-81a2-4421-814f-f603d13a386b | le-hgr-a-lightweight-and-efficient-rgb-based | 2001.05654 | null | https://arxiv.org/abs/2001.05654v1 | https://arxiv.org/pdf/2001.05654v1.pdf | LE-HGR: A Lightweight and Efficient RGB-based Online Gesture Recognition Network for Embedded AR Devices | Online hand gesture recognition (HGR) techniques are essential in augmented reality (AR) applications for enabling natural human-to-computer interaction and communication. In recent years, the consumer market for low-cost AR devices has been rapidly growing, while the technology maturity in this domain is still limited... | ['Jiafang Wang', 'Mingyang Li', 'Jian Gu', 'Hongwei Xie', 'Baitao Shao'] | 2020-01-16 | null | null | null | null | ['hand-detection', '3d-part-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.06854612e-01 -6.01207376e-01 -2.96162516e-01 3.57032046e-02
-7.49019444e-01 -3.28063667e-01 2.05611467e-01 -5.14494002e-01
-7.02777028e-01 3.56411934e-01 -2.23409042e-01 -3.80511791e-01
-2.06792757e-01 -6.61572516e-01 -7.03176081e-01 -3.81489694e-01
2.62663011e-02 1.05797797e-01 4.93819207e-01 -6.21317551... | [6.67405366897583, -0.11719825863838196] |
04dc29fc-cf3d-44fc-bb5b-4c55e29a7480 | weight-excitation-built-in-attention | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/7039_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123750086.pdf | Weight Excitation: Built-in Attention Mechanisms in Convolutional Neural Networks | We propose novel approaches for simultaneously identifying important weights of a convolutional neural network (ConvNet) and providing more attention to the important weights during training. More formally, we identify two characteristics of a weight, its magnitude and its location, which can be linked with the importa... | ['Md Mafijul Islam Bhuiyan', 'Peng Dai', 'Juwei Lu', 'Wei Li', 'Niamul Quader'] | null | null | null | null | eccv-2020-8 | ['deep-attention', '3d-object-classification', '3d-classification', '3d-human-action-recognition', 'deep-attention', 'attention-score-prediction'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'natural-language-processing', 'time-series'] | [ 2.59822786e-01 1.65998936e-01 -6.03190251e-02 -3.31768245e-01
-3.65789026e-01 -3.39276940e-01 5.86241305e-01 1.20939188e-01
-1.02534199e+00 5.64810514e-01 3.96196425e-01 -3.18038434e-01
-8.56597349e-02 -6.76218510e-01 -1.03822267e+00 -7.10790455e-01
4.82248887e-02 -3.61516178e-02 4.15277600e-01 -1.71357259... | [9.026493072509766, 2.369802236557007] |
251607b9-538a-4b4c-8c46-b4177a970b36 | contrastive-mean-teacher-for-domain-adaptive | 2305.03034 | null | https://arxiv.org/abs/2305.03034v1 | https://arxiv.org/pdf/2305.03034v1.pdf | Contrastive Mean Teacher for Domain Adaptive Object Detectors | Object detectors often suffer from the domain gap between training (source domain) and real-world applications (target domain). Mean-teacher self-training is a powerful paradigm in unsupervised domain adaptation for object detection, but it struggles with low-quality pseudo-labels. In this work, we identify the intrigu... | ['Yu-Xiong Wang', 'Liang-Yan Gui', 'Dhiraj Joshi', 'Shengcao Cao'] | 2023-05-04 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Cao_Contrastive_Mean_Teacher_for_Domain_Adaptive_Object_Detectors_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cao_Contrastive_Mean_Teacher_for_Domain_Adaptive_Object_Detectors_CVPR_2023_paper.pdf | cvpr-2023-1 | ['pseudo-label'] | ['miscellaneous'] | [ 3.55599433e-01 2.42702752e-01 -3.29976678e-01 -4.52720970e-01
-1.11567318e+00 -6.17300868e-01 7.94172645e-01 7.65654584e-03
-4.48430777e-01 5.90784252e-01 -2.15892330e-01 5.51511347e-02
-1.12234443e-01 -4.79911327e-01 -8.51291358e-01 -7.69586802e-01
9.36708227e-02 7.58019865e-01 6.33318543e-01 -2.13735759... | [9.366785049438477, 1.4798976182937622] |
95aeedbc-e2f3-4872-82cd-9183c770bbb3 | high-performance-automatic-categorization-and | 2202.08965 | null | https://arxiv.org/abs/2202.08965v1 | https://arxiv.org/pdf/2202.08965v1.pdf | High-performance automatic categorization and attribution of inventory catalogs | Techniques of machine learning for automatic text categorization are applied and adapted for the problem of inventory catalog data attribution, with different approaches explored and optimal solution addressing the tradeoff between accuracy and performance is selected. | ['Anton Kolonin'] | 2022-02-09 | null | null | null | null | ['text-categorization'] | ['natural-language-processing'] | [-1.73135065e-02 -1.63966373e-01 -1.02448750e+00 -6.95989966e-01
-6.43536031e-01 -6.32895589e-01 5.58812559e-01 7.53477097e-01
-5.55327594e-01 1.40706658e-01 4.23407614e-01 -2.06871033e-01
-5.86103201e-01 -3.62848401e-01 1.96897820e-01 -1.87647909e-01
2.75110066e-01 1.12097764e+00 5.35033122e-02 -1.25948757... | [9.995992660522461, 6.579092025756836] |
c2c357e4-8f63-48c7-a4b4-2599424077aa | unmasked-teacher-towards-training-efficient | 2303.16058 | null | https://arxiv.org/abs/2303.16058v1 | https://arxiv.org/pdf/2303.16058v1.pdf | Unmasked Teacher: Towards Training-Efficient Video Foundation Models | Video Foundation Models (VFMs) have received limited exploration due to high computational costs and data scarcity. Previous VFMs rely on Image Foundation Models (IFMs), which face challenges in transferring to the video domain. Although VideoMAE has trained a robust ViT from limited data, its low-level reconstruction ... | ['Yu Qiao', 'LiMin Wang', 'Yinan He', 'Yi Wang', 'Yizhuo Li', 'Yali Wang', 'Kunchang Li'] | 2023-03-28 | null | null | null | null | ['action-classification', 'video-question-answering', 'video-retrieval', 'spatio-temporal-action-localization'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 2.82036692e-01 -2.18818873e-01 -4.74603444e-01 -3.37367415e-01
-9.31786180e-01 -3.68858755e-01 4.72308606e-01 -4.13409740e-01
-4.06034201e-01 2.38775909e-01 3.12215060e-01 -3.30075920e-01
2.92346776e-01 -2.86465466e-01 -1.11403871e+00 -5.11226177e-01
1.84211314e-01 2.78849363e-01 3.35740507e-01 -1.67235360... | [9.940741539001465, 0.8601009249687195] |
1ff3cce1-080a-4e25-96a5-c9e1fe48a2a0 | visualizing-evidence-for-alzheimers-disease | 1903.07317 | null | http://arxiv.org/abs/1903.07317v1 | http://arxiv.org/pdf/1903.07317v1.pdf | Visualizing evidence for Alzheimer's disease in deep neural networks trained on structural MRI data | Deep neural networks have led to state-of-the-art results in many medical
imaging tasks including Alzheimer's disease (AD) detection based on structural
magnetic resonance imaging (MRI) data. However, the network decisions are often
perceived as being highly non-transparent making it difficult to apply these
algorithms... | ['Moritz Böhle', 'Martin Weygandt', 'Kerstin Ritter', 'Fabian Eitel'] | 2019-03-18 | null | null | null | null | ['2d-human-pose-estimation'] | ['computer-vision'] | [ 1.42650619e-01 3.85642767e-01 -2.97317151e-02 -6.01175666e-01
-2.36995786e-01 -2.52167881e-01 6.36523843e-01 5.84788322e-01
-4.83303934e-01 7.74012804e-01 4.33921069e-01 -5.59943557e-01
-5.75707734e-01 -7.85053432e-01 -3.99129301e-01 -7.41826892e-01
-7.42554843e-01 3.86255771e-01 1.29282340e-01 2.09082793... | [14.168574333190918, -1.777555227279663] |
15c6ccec-6e21-499f-83bf-4ac2fa80302d | paying-attention-to-multiscale-feature-maps | 2103.11247 | null | https://arxiv.org/abs/2103.11247v1 | https://arxiv.org/pdf/2103.11247v1.pdf | Paying Attention to Multiscale Feature Maps in Multimodal Image Matching | We propose an attention-based approach for multimodal image patch matching using a Transformer encoder attending to the feature maps of a multiscale Siamese CNN. Our encoder is shown to efficiently aggregate multiscale image embeddings while emphasizing task-specific appearance-invariant image cues. We also introduce a... | ['Yosi Keller', 'Aviad Moreshet'] | 2021-03-20 | null | null | null | null | ['multimodal-patch-matching', 'patch-matching'] | ['computer-vision', 'computer-vision'] | [ 3.28332692e-01 1.04652122e-01 -1.78872898e-01 -3.85376930e-01
-1.51900482e+00 -2.45024949e-01 6.45415127e-01 -6.00982420e-02
-5.29036343e-01 2.17890024e-01 3.28207821e-01 3.77502926e-02
2.08907247e-01 -4.38179106e-01 -1.17057979e+00 -4.43369061e-01
1.41328394e-01 3.00481737e-01 1.15443781e-01 -3.44158053... | [10.728009223937988, 1.3421988487243652] |
94eb3162-fbb6-442e-a829-2f43fd314fb1 | diverse-difficult-and-odd-instances-d2o-a-new | 2301.12527 | null | https://arxiv.org/abs/2301.12527v1 | https://arxiv.org/pdf/2301.12527v1.pdf | Diverse, Difficult, and Odd Instances (D2O): A New Test Set for Object Classification | Test sets are an integral part of evaluating models and gauging progress in object recognition, and more broadly in computer vision and AI. Existing test sets for object recognition, however, suffer from shortcomings such as bias towards the ImageNet characteristics and idiosyncrasies (e.g., ImageNet-V2), being limited... | ['Ali Borji'] | 2023-01-29 | null | null | null | null | ['object-recognition', 'miscellaneous'] | ['computer-vision', 'miscellaneous'] | [ 2.31311873e-01 -1.89354256e-01 -1.28237903e-01 -4.67748761e-01
-5.97098291e-01 -8.62494051e-01 9.21390772e-01 -9.23743024e-02
-5.53889930e-01 3.46146077e-01 -1.55028939e-01 -4.82891649e-01
-1.31894842e-01 -7.01674104e-01 -8.57389271e-01 -4.97970670e-01
-4.96761352e-02 4.26780313e-01 4.99176115e-01 -1.51760340... | [9.734122276306152, 2.23158597946167] |
a4824820-0ac3-4f74-8c44-3e1200816bc3 | detrex-benchmarking-detection-transformers | 2306.07265 | null | https://arxiv.org/abs/2306.07265v2 | https://arxiv.org/pdf/2306.07265v2.pdf | detrex: Benchmarking Detection Transformers | The DEtection TRansformer (DETR) algorithm has received considerable attention in the research community and is gradually emerging as a mainstream approach for object detection and other perception tasks. However, the current field lacks a unified and comprehensive benchmark specifically tailored for DETR-based models.... | ['Lei Zhang', 'Jianwei Yang', 'Yuhui Yuan', 'Xianbiao Qi', 'Zhaoyang Zeng', 'Jianan Wang', 'He Cao', 'Hongyang Li', 'Ding Jia', 'Xingyu Liao', 'Jie Yang', 'Ailing Zeng', 'Hao Zhang', 'Feng Li', 'Shilong Liu', 'Tianhe Ren'] | 2023-06-12 | null | null | null | null | ['pose-estimation'] | ['computer-vision'] | [ 1.23080038e-01 -3.51232767e-01 -3.77791315e-01 -4.17043924e-01
-7.93518603e-01 -6.79478467e-01 6.42479122e-01 5.70640974e-02
-1.95536226e-01 3.28299738e-02 -1.72212258e-01 -1.45982638e-01
3.11108768e-01 -6.56465113e-01 -6.34739339e-01 -4.93745208e-01
2.11134315e-01 3.62818301e-01 6.07001483e-01 1.63693234... | [8.971108436584473, 0.09494529664516449] |
18993254-c755-4a62-b49d-f8de867f3106 | general-place-recognition-survey-towards-the | 2209.04497 | null | https://arxiv.org/abs/2209.04497v1 | https://arxiv.org/pdf/2209.04497v1.pdf | General Place Recognition Survey: Towards the Real-world Autonomy Age | Place recognition is the fundamental module that can assist Simultaneous Localization and Mapping (SLAM) in loop-closure detection and re-localization for long-term navigation. The place recognition community has made astonishing progress over the last $20$ years, and this has attracted widespread research interest and... | ['Sebastian Scherer', 'Howie Choset', 'Changliu Liu', 'Micheal Milford', 'Guoquan Huang', 'Abulikemu Abuduweili', 'Ivan Cisneros', 'Shiqi Zhao', 'Peng Yin'] | 2022-09-09 | null | null | null | null | ['simultaneous-localization-and-mapping', 'loop-closure-detection'] | ['computer-vision', 'computer-vision'] | [-1.45110562e-01 -3.99742007e-01 -1.84090823e-01 -5.67108393e-01
-6.05547428e-01 -7.35482156e-01 6.05420172e-01 1.57496408e-01
-7.47637630e-01 7.36015320e-01 -2.55934387e-01 -5.40436767e-02
-1.89953804e-01 -5.45120835e-01 -8.48353505e-01 -5.80952287e-01
-5.15469968e-01 3.94550681e-01 3.67589027e-01 -3.93628657... | [7.307878494262695, -2.048964023590088] |
667aed3f-7812-461e-ad25-074980824b8c | tdiot-target-driven-inference-for-deep-video | 2103.11017 | null | https://arxiv.org/abs/2103.11017v2 | https://arxiv.org/pdf/2103.11017v2.pdf | TDIOT: Target-driven Inference for Deep Video Object Tracking | Recent tracking-by-detection approaches use deep object detectors as target detection baseline, because of their high performance on still images. For effective video object tracking, object detection is integrated with a data association step performed by either a custom design inference architecture or an end-to-end ... | ['Bilge Gunsel', 'Ozgun Cirakman', 'Llukman Cerkezi', 'Filiz Gurkan'] | 2021-03-19 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [-2.11764961e-01 -3.83144945e-01 -2.63609171e-01 -4.29438539e-02
-4.32683796e-01 -3.76158446e-01 5.44989884e-01 -1.38102495e-03
-7.60480225e-01 2.77004331e-01 -3.18964392e-01 7.71187320e-02
1.19856037e-01 -4.67577159e-01 -8.40180099e-01 -7.35486567e-01
7.50653353e-03 3.69931281e-01 9.81073022e-01 1.19030721... | [6.358063697814941, -2.1259827613830566] |
b9e0dc2d-41ee-4d80-8836-5e088964377a | differentially-private-learning-does-not | 2010.12112 | null | https://arxiv.org/abs/2010.12112v4 | https://arxiv.org/pdf/2010.12112v4.pdf | Investigating Membership Inference Attacks under Data Dependencies | Training machine learning models on privacy-sensitive data has become a popular practice, driving innovation in ever-expanding fields. This has opened the door to new attacks that can have serious privacy implications. One such attack, the Membership Inference Attack (MIA), exposes whether or not a particular data poin... | ['Matthew Rafuse', 'Simon Oya', 'Urs Hengartner', 'Florian Kerschbaum', 'Ian Goldberg', 'Lindsey Tulloch', 'Thomas Humphries'] | 2020-10-23 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [ 2.18132019e-01 1.34540871e-01 -5.08145802e-02 -6.30541027e-01
-5.38476825e-01 -1.08140433e+00 4.22891796e-01 3.32410485e-01
-5.96380949e-01 7.63616979e-01 -2.93695539e-01 -6.98537230e-01
-6.72282875e-02 -9.79163587e-01 -8.10713470e-01 -9.01460052e-01
-3.37989956e-01 1.67813733e-01 2.58503295e-02 9.40068886... | [5.949230670928955, 7.056524753570557] |
3ac55f96-3b58-444e-bd9c-66d72fa95935 | confess-a-framework-for-single-source-cross | null | null | https://openreview.net/forum?id=zRJu6mU2BaE | https://openreview.net/pdf?id=zRJu6mU2BaE | ConFeSS: A Framework for Single Source Cross-Domain Few-Shot Learning | Most current few-shot learning methods train a model from abundantly labeled base category data and then transfer and adapt the model to sparsely labeled novel category data. These methods mostly generalize well on novel categories from the same domain as the base categories but perform poorly for distant domain catego... | ['Fatih Porikli', 'Sungrack Yun', 'Debasmit Das'] | 2021-09-29 | null | null | null | iclr-2022-4 | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 5.08107364e-01 -1.05153047e-01 -4.83560354e-01 -6.68528914e-01
-9.78575885e-01 -5.02252340e-01 8.55952144e-01 2.57121861e-01
-4.51635003e-01 8.33102643e-01 7.97865465e-02 3.14462274e-01
-1.83900177e-01 -9.55106497e-01 -6.54432476e-01 -6.44398570e-01
9.40530598e-02 5.61862826e-01 8.15270662e-01 -3.21258098... | [10.050186157226562, 3.037947177886963] |
00883efc-8471-4a25-8b62-de8580e40edd | bootstrapped-edge-count-tests-for | 2304.13848 | null | https://arxiv.org/abs/2304.13848v1 | https://arxiv.org/pdf/2304.13848v1.pdf | Bootstrapped Edge Count Tests for Nonparametric Two-Sample Inference Under Heterogeneity | Nonparametric two-sample testing is a classical problem in inferential statistics. While modern two-sample tests, such as the edge count test and its variants, can handle multivariate and non-Euclidean data, contemporary gargantuan datasets often exhibit heterogeneity due to the presence of latent subpopulations. Direc... | ['Gourab Mukherjee', 'Bhaswar B. Bhattacharya', 'Trambak Banerjee'] | 2023-04-26 | null | null | null | null | ['hypothesis-testing', 'hypothesis-testing'] | ['methodology', 'miscellaneous'] | [ 2.20918715e-01 -2.17607155e-01 -2.69680917e-01 -2.45559901e-01
-5.95697284e-01 -4.70508099e-01 3.94286752e-01 2.59282082e-01
-3.41593891e-01 1.23324609e+00 -2.40796313e-01 -7.73440361e-01
-4.10642684e-01 -9.52625871e-01 -5.09036362e-01 -6.78101778e-01
-1.81516364e-01 7.54532814e-01 1.33536488e-01 3.31420183... | [7.277248859405518, 4.389183521270752] |
ebd329aa-10e0-40e9-8769-edb8cebc9232 | mssrnet-manipulating-sequential-style | 2306.07994 | null | https://arxiv.org/abs/2306.07994v1 | https://arxiv.org/pdf/2306.07994v1.pdf | MSSRNet: Manipulating Sequential Style Representation for Unsupervised Text Style Transfer | Unsupervised text style transfer task aims to rewrite a text into target style while preserving its main content. Traditional methods rely on the use of a fixed-sized vector to regulate text style, which is difficult to accurately convey the style strength for each individual token. In fact, each token of a text contai... | ['Qi Liu', 'Zhou Zhao', 'Yazheng Yang'] | 2023-06-12 | null | null | null | null | ['style-transfer', 'text-style-transfoer'] | ['computer-vision', 'natural-language-processing'] | [ 4.76646185e-01 -2.12269530e-01 -1.03001647e-01 -3.48026752e-01
-5.55647075e-01 -9.34598148e-01 5.23448706e-01 -3.38671193e-03
-3.85646313e-01 7.73665190e-01 2.27606729e-01 -5.73862232e-02
2.01145604e-01 -7.97252238e-01 -5.92041075e-01 -7.18684733e-01
6.73469007e-01 1.73757836e-01 3.76492068e-02 -4.67414409... | [11.498167991638184, -0.5032269954681396] |
25e7c302-2b39-47a8-8035-0e3981d87ac6 | frozen-pretrained-transformers-for-neural | null | null | https://aclanthology.org/2021.mtsummit-at4ssl.10/ | https://aclanthology.org/2021.mtsummit-at4ssl.10.pdf | Frozen Pretrained Transformers for Neural Sign Language Translation | One of the major challenges in sign language translation from a sign language to a spoken language is the lack of parallel corpora. Recent works have achieved promising results on the RWTH-PHOENIX-Weather 2014T dataset, which consists of over eight thousand parallel sentences between German sign language and German. ... | ['Joni Dambre', 'Mieke Van Herreweghe', 'Severine Verlinden', 'Paloma Rabaey', 'Marija Pizurica', "Karel D'Oosterlinck", 'Mathieu De Coster'] | 2021-08-20 | null | null | null | international-workshop-on-automatic | ['sign-language-translation'] | ['computer-vision'] | [ 2.50780255e-01 -4.25773859e-02 -2.00194433e-01 -4.11669880e-01
-1.17499292e+00 -4.06479090e-01 7.77487576e-01 -9.70632792e-01
-6.94231927e-01 6.22244418e-01 7.24330366e-01 -3.70810777e-01
4.72735196e-01 -2.93596953e-01 -9.48103070e-01 -3.90121400e-01
3.18869770e-01 8.09462667e-01 3.73162895e-01 -5.06066263... | [9.217089653015137, -6.543366432189941] |
ce8b2a46-6112-4c50-b0c8-928e8380b978 | inversion-by-direct-iteration-an-alternative | 2303.11435 | null | https://arxiv.org/abs/2303.11435v3 | https://arxiv.org/pdf/2303.11435v3.pdf | Inversion by Direct Iteration: An Alternative to Denoising Diffusion for Image Restoration | Inversion by Direct Iteration (InDI) is a new formulation for supervised image restoration that avoids the so-called ``regression to the mean'' effect and produces more realistic and detailed images than existing regression-based methods. It does this by gradually improving image quality in small steps, similar to gene... | ['Peyman Milanfar', 'Mauricio Delbracio'] | 2023-03-20 | null | null | null | null | ['deblurring'] | ['computer-vision'] | [ 7.22226441e-01 8.89497921e-02 3.40028971e-01 -1.88072488e-01
-9.98035073e-01 -2.11316451e-01 7.20142126e-01 -4.87319291e-01
-1.94433734e-01 8.66240144e-01 3.93553734e-01 -7.48579651e-02
-3.84771377e-01 -6.60006583e-01 -8.06822300e-01 -1.33643198e+00
2.82468349e-01 2.60658771e-01 -9.89253446e-02 -2.70208776... | [11.641335487365723, -2.344101905822754] |
30c3f8b1-a707-430d-809e-f795522e50d2 | optical-flow-distillation-towards-efficient | 2007.05146 | null | https://arxiv.org/abs/2007.05146v2 | https://arxiv.org/pdf/2007.05146v2.pdf | Optical Flow Distillation: Towards Efficient and Stable Video Style Transfer | Video style transfer techniques inspire many exciting applications on mobile devices. However, their efficiency and stability are still far from satisfactory. To boost the transfer stability across frames, optical flow is widely adopted, despite its high computational complexity, e.g. occupying over 97% inference time.... | ['Chunjing Xu', 'Yiman Zhang', 'Yunhe Wang', 'Xinghao Chen', 'Han Shu', 'Chang Xu'] | 2020-07-10 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/13_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123510613.pdf | eccv-2020-8 | ['video-style-transfer'] | ['computer-vision'] | [ 3.70912641e-01 1.90155907e-03 -2.62016594e-01 -1.91863596e-01
-3.21160227e-01 -5.42419374e-01 4.64865863e-01 -4.28756744e-01
-4.45966631e-01 1.09691024e+00 -1.18493237e-01 -3.21502835e-01
3.22789736e-02 -7.99124241e-01 -1.05540645e+00 -7.92174459e-01
1.41209066e-01 -2.02321745e-02 3.34593415e-01 -4.31505106... | [10.816245079040527, -1.1866010427474976] |
08f5ab1e-3719-43c5-9fe2-ea8cbe49ab57 | generalized-content-preserving-warps-for | 1809.06783 | null | http://arxiv.org/abs/1809.06783v1 | http://arxiv.org/pdf/1809.06783v1.pdf | Generalized Content-Preserving Warps for Image Stitching | Local misalignment caused by global homography is a common issue in image
stitching task. Content-Preserving Warping (CPW) is a typical method to deal
with this issue, in which geometric and photometric constraints are imposed to
guide the warping process. One of its essential condition however, is colour
consistency, ... | ['Kai Chen', 'Jian Yao', 'Jingmin Tu'] | 2018-09-18 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [ 7.50354767e-01 -4.68386680e-01 1.15782894e-01 1.87875882e-01
-3.11836243e-01 -7.69807756e-01 6.84656739e-01 -4.13719684e-01
-9.79675204e-02 4.11332756e-01 9.26441997e-02 3.03831846e-02
-4.90425043e-02 -5.07377088e-01 -5.00868559e-01 -1.06859326e+00
4.29422528e-01 1.84285700e-01 4.87519622e-01 -3.42683792... | [9.397333145141602, -2.3349509239196777] |
594a4407-dbd0-4f30-b0e4-2808aca112a0 | towards-suicide-prevention-from-bipolar | 2307.00995 | null | https://arxiv.org/abs/2307.00995v1 | https://arxiv.org/pdf/2307.00995v1.pdf | Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning | Bipolar disorder (BD) is closely associated with an increased risk of suicide. However, while the prior work has revealed valuable insight into understanding the behavior of BD patients on social media, little attention has been paid to developing a model that can predict the future suicidality of a BD patient. Therefo... | ['Jinyoung Han', 'Seungbae Kim', 'Hyolim Jeon', 'Sejung Son', 'Daeun Lee'] | 2023-07-03 | null | null | null | null | ['multi-task-learning'] | ['methodology'] | [-1.74349815e-01 1.90802589e-01 -2.39686057e-01 -7.94516563e-01
-7.01718271e-01 1.79960385e-01 2.51272291e-01 7.42809713e-01
-4.59416717e-01 5.22771180e-01 7.16456473e-01 2.85924882e-01
-3.28286350e-01 -5.09889781e-01 5.87971628e-01 -4.10358250e-01
-4.79092449e-01 8.62922907e-01 -3.87709290e-01 -2.54833430... | [8.770743370056152, 10.129104614257812] |
5f703ae0-567d-4574-9c1f-ba8163d0bb05 | agent-centric-risk-assessment-accident | 1705.06560 | null | http://arxiv.org/abs/1705.06560v1 | http://arxiv.org/pdf/1705.06560v1.pdf | Agent-Centric Risk Assessment: Accident Anticipation and Risky Region Localization | For survival, a living agent must have the ability to assess risk (1) by
temporally anticipating accidents before they occur, and (2) by spatially
localizing risky regions in the environment to move away from threats. In this
paper, we take an agent-centric approach to study the accident anticipation and
risky region l... | ['Fu-Hsiang Chan', 'Shih-Han Chou', 'Kuo-Hao Zeng', 'Min Sun', 'Juan Carlos Niebles'] | 2017-05-18 | agent-centric-risk-assessment-accident-1 | http://openaccess.thecvf.com/content_cvpr_2017/html/Zeng_Agent-Centric_Risk_Assessment_CVPR_2017_paper.html | http://openaccess.thecvf.com/content_cvpr_2017/papers/Zeng_Agent-Centric_Risk_Assessment_CVPR_2017_paper.pdf | cvpr-2017-7 | ['accident-anticipation'] | ['computer-vision'] | [ 3.50343257e-01 -4.14094150e-01 8.13190192e-02 -1.74206883e-01
-7.23631620e-01 -4.47546661e-01 1.02813423e+00 2.18661383e-01
-7.23661602e-01 6.46709383e-01 6.38930321e-01 -2.63289332e-01
2.19932012e-02 -5.77206314e-01 -5.74135840e-01 -9.66065586e-01
-6.56059206e-01 2.43519515e-01 2.99979001e-01 -3.84181067... | [8.118729591369629, 0.5520936846733093] |
a5383262-8cfa-4ec2-bd07-b69901388d7e | a-universal-unbiased-method-for | 2306.11343 | null | https://arxiv.org/abs/2306.11343v2 | https://arxiv.org/pdf/2306.11343v2.pdf | A Universal Unbiased Method for Classification from Aggregate Observations | In conventional supervised classification, true labels are required for individual instances. However, it could be prohibitive to collect the true labels for individual instances, due to privacy concerns or unaffordable annotation costs. This motivates the study on classification from aggregate observations (CFAO), whe... | ['Heng Tao Shen', 'Xiaofeng Zhu', 'Gang Niu', 'Tongliang Liu', 'Bo Han', 'Lei Feng', 'Zixi Wei'] | 2023-06-20 | null | null | null | null | ['classification-1', 'multiple-instance-learning'] | ['methodology', 'methodology'] | [ 2.96082228e-01 5.02910674e-01 -3.15257788e-01 -7.99205899e-01
-8.12507093e-01 -1.86258614e-01 -2.63529811e-02 5.06053329e-01
-3.23825508e-01 1.14180422e+00 -3.51599902e-01 -1.04203716e-01
-2.83982933e-01 -9.09402966e-01 -5.30882895e-01 -1.05159175e+00
3.98387089e-02 3.39173704e-01 -1.69737071e-01 4.05019015... | [9.096138000488281, 4.1873884201049805] |
4e79c89c-42a3-429d-9497-77e61069427f | vtlayout-fusion-of-visual-and-text-features | 2108.13297 | null | https://arxiv.org/abs/2108.13297v1 | https://arxiv.org/pdf/2108.13297v1.pdf | VTLayout: Fusion of Visual and Text Features for Document Layout Analysis | Documents often contain complex physical structures, which make the Document Layout Analysis (DLA) task challenging. As a pre-processing step for content extraction, DLA has the potential to capture rich information in historical or scientific documents on a large scale. Although many deep-learning-based methods from c... | ['Qing Wang', 'Lin Shi', 'Jun Hu', 'Shuaiqun Pan', 'Xuyan Ma', 'Shoubin Li'] | 2021-08-12 | null | null | null | null | ['document-layout-analysis'] | ['computer-vision'] | [-2.32558772e-01 -4.64230955e-01 3.28783467e-02 -5.34052588e-02
-4.95822459e-01 -5.42631686e-01 8.54846001e-01 2.93642670e-01
-7.23583251e-02 1.91542298e-01 5.33145927e-02 -4.65738505e-01
-2.26499364e-01 -8.39281440e-01 -4.22964364e-01 -9.04017568e-01
2.44012758e-01 7.01101720e-02 2.69557506e-01 2.11447570... | [11.745874404907227, 2.4149482250213623] |
1fdaeb3e-70dd-45f3-a54e-744d248a1cbe | detecting-everything-in-the-open-world | 2303.11749 | null | https://arxiv.org/abs/2303.11749v2 | https://arxiv.org/pdf/2303.11749v2.pdf | Detecting Everything in the Open World: Towards Universal Object Detection | In this paper, we formally address universal object detection, which aims to detect every scene and predict every category. The dependence on human annotations, the limited visual information, and the novel categories in the open world severely restrict the universality of traditional detectors. We propose UniDetector,... | ['Shengjin Wang', 'Hengshuang Zhao', 'Antonio Torralba', 'Ser-Nam Lim', 'Xi Chen', 'YaLi Li', 'Zhenyu Wang'] | 2023-03-21 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Detecting_Everything_in_the_Open_World_Towards_Universal_Object_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Detecting_Everything_in_the_Open_World_Towards_Universal_Object_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['open-world-object-detection'] | ['computer-vision'] | [-3.89308408e-02 2.72382461e-02 -2.71693230e-01 -1.40832826e-01
-7.67171144e-01 -8.01581144e-01 8.27011287e-01 8.82954821e-02
-4.32651669e-01 3.51273805e-01 -4.32982855e-02 -1.57465741e-01
2.79747277e-01 -6.38754308e-01 -7.38547027e-01 -6.27198875e-01
1.37653068e-01 3.54012519e-01 8.52048516e-01 -1.24886245... | [9.549468040466309, 1.5091547966003418] |
a4dafa29-e704-4bd0-bd33-89d27ae8f173 | synthesizing-adversarial-negative-responses | 2106.05894 | null | https://arxiv.org/abs/2106.05894v1 | https://arxiv.org/pdf/2106.05894v1.pdf | Synthesizing Adversarial Negative Responses for Robust Response Ranking and Evaluation | Open-domain neural dialogue models have achieved high performance in response ranking and evaluation tasks. These tasks are formulated as a binary classification of responses given in a dialogue context, and models generally learn to make predictions based on context-response content similarity. However, over-reliance ... | ['Jeffrey P. Bigham', 'Yulia Tsvetkov', 'Prakhar Gupta'] | 2021-06-10 | null | https://aclanthology.org/2021.findings-acl.338 | https://aclanthology.org/2021.findings-acl.338.pdf | findings-acl-2021-8 | ['dialogue-evaluation'] | ['natural-language-processing'] | [ 3.74946237e-01 5.07726490e-01 -5.71327694e-02 -1.09615624e+00
-1.11592162e+00 -8.14874172e-01 1.10383844e+00 1.86786667e-01
-4.81393754e-01 1.00884056e+00 7.39435375e-01 -1.77516535e-01
1.29221439e-01 -6.08625352e-01 8.90646782e-03 6.58138609e-03
2.67223001e-01 9.90420759e-01 -2.57554967e-02 -9.70310628... | [12.729866981506348, 8.145323753356934] |
dc08d566-80cc-4ed5-8f9d-5afdf45cfe13 | exploiting-unsupervised-pre-training-and | 1906.09325 | null | https://arxiv.org/abs/1906.09325v1 | https://arxiv.org/pdf/1906.09325v1.pdf | Exploiting Unsupervised Pre-training and Automated Feature Engineering for Low-resource Hate Speech Detection in Polish | This paper presents our contribution to PolEval 2019 Task 6: Hate speech and bullying detection. We describe three parallel approaches that we followed: fine-tuning a pre-trained ULMFiT model to our classification task, fine-tuning a pre-trained BERT model to our classification task, and using the TPOT library to find ... | ['Marcin Możejko', 'Przemysław Sadownik', 'Rafał Rolczyński', 'Tomasz Korbak', 'Renard Korzeniowski'] | 2019-06-17 | null | null | null | null | ['automated-feature-engineering'] | ['methodology'] | [-3.37049216e-01 9.28445086e-02 5.14186285e-02 -6.47960007e-01
-7.69702375e-01 -5.70111394e-01 2.00071126e-01 -2.14202940e-01
-3.59833390e-01 4.00248408e-01 5.71811870e-02 -4.20695305e-01
-2.44951248e-02 -1.73924882e-02 -6.96930960e-02 -2.20243439e-01
8.34436640e-02 6.53194308e-01 4.78084356e-01 -4.00227994... | [8.823739051818848, 10.601326942443848] |
7ceb6d11-a504-4469-a9cd-1ce849f7a13c | ecg-based-blood-pressure-estimation-using | 2008.10099 | null | https://arxiv.org/abs/2008.10099v1 | https://arxiv.org/pdf/2008.10099v1.pdf | ECG-Based Blood Pressure Estimation Using Mechano-Electric Coupling Concept | The Electrocardiograph signal represents the heart's electrical activity while blood pressure results from the heart's mechanical activity. Previous studies have investigated how the heart's electrical and mechanical activities are related and have referred to their relationship as the Mechano-Electric Coupling term. A... | ['Yadollah Ghorbani', 'Maryam Moghadam', 'Mohammad Hemmati', 'Mohammad Firouzmand', 'Mostafa Charmi', 'Seyedeh Somayyeh Mousavi'] | 2020-08-23 | null | null | null | null | ['blood-pressure-estimation'] | ['medical'] | [ 1.47930071e-01 -1.66876853e-01 -1.08463168e-01 -3.70104402e-01
1.18928835e-01 -2.47411355e-01 -3.65774706e-02 2.17545837e-01
-6.38383985e-01 1.01308143e+00 -4.69189063e-02 -3.04462224e-01
-2.03601345e-01 -8.37443352e-01 -1.33936450e-01 -6.00070298e-01
-1.97097316e-01 2.47813642e-01 -1.18110493e-01 2.23578960... | [14.082152366638184, 2.993879556655884] |
2907fad8-56a3-4583-9a8e-cc88e2939cc8 | pyramid-architecture-for-multi-scale | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Nie_Pyramid_Architecture_for_Multi-Scale_Processing_in_Point_Cloud_Segmentation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Nie_Pyramid_Architecture_for_Multi-Scale_Processing_in_Point_Cloud_Segmentation_CVPR_2022_paper.pdf | Pyramid Architecture for Multi-Scale Processing in Point Cloud Segmentation | Semantic segmentation of point cloud data is a critical task for autonomous driving and other applications. Recent advances of point cloud segmentation are mainly driven by new designs of local aggregation operators and point sampling methods. Unlike image segmentation, few efforts have been made to understand the ... | ['Xiaofeng Ren', 'Ling Wang', 'Rui Lan', 'Dong Nie'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['point-cloud-segmentation'] | ['computer-vision'] | [-5.28827943e-02 -1.31838545e-02 -9.09362361e-02 -5.12210250e-01
-6.37162924e-01 -5.47133088e-01 4.97213036e-01 1.89947724e-01
-3.45707148e-01 2.41424978e-01 -1.85031474e-01 -2.85299867e-01
-1.19469419e-01 -9.82706904e-01 -9.55981374e-01 -3.39249581e-01
-1.68591648e-01 5.74216545e-01 7.95675755e-01 -4.30283338... | [7.993488311767578, -3.240955352783203] |
c00762b9-43b4-4183-9417-5e936a6d68f4 | smart-sentiment-analysis-based-search-engine | 2306.09777 | null | https://arxiv.org/abs/2306.09777v1 | https://arxiv.org/pdf/2306.09777v1.pdf | Smart Sentiment Analysis-based Search Engine Classification Intelligence | Search engines are widely used for finding information on the internet. However, there are limitations in the current search approach, such as providing popular but not necessarily relevant results. This research addresses the issue of polysemy in search results by implementing a search function that determines the sen... | ['Mike Nkongolo'] | 2023-06-16 | null | null | null | null | ['clustering', 'classification-1', 'sentiment-analysis'] | ['methodology', 'methodology', 'natural-language-processing'] | [-8.17947865e-01 -3.73300195e-01 -5.44677377e-01 1.18584700e-01
-4.43496227e-01 -7.50474513e-01 6.66109264e-01 5.10603845e-01
-7.68736064e-01 3.93034726e-01 1.84882358e-01 -2.22543344e-01
-6.25749171e-01 -9.92857575e-01 -3.06421548e-01 -3.66501153e-01
2.08768263e-01 5.35071611e-01 -3.59624438e-02 -6.34323120... | [10.6874418258667, 6.913955211639404] |
9587fecb-c34f-4879-966a-cdde28fed08f | an-empirical-evaluation-of-word-embedding | null | null | https://ieeexplore.ieee.org/document/9392437 | https://ieeexplore.ieee.org/document/9392437 | An Empirical Evaluation of Word Embedding Models for Subjectivity Analysis Tasks | It is a clearly established fact that good categorization results are heavily dependent on representation techniques. Text representation is a necessity that must be fulfilled before working on any text analysis task since it creates a baseline which even advanced machine learning models fail to compensate. This paper ... | ['Shashank Shekhar', 'Priya Kamath', 'Geetha Maiya', 'Ritika Nandi'] | 2021-04-06 | null | null | null | ieee-international-conference-on-advances-in | ['subjectivity-analysis'] | ['natural-language-processing'] | [-6.31621620e-03 2.59769917e-01 -4.20843333e-01 -5.52055359e-01
-5.85998476e-01 -4.32631701e-01 1.10888577e+00 6.85833395e-01
-3.22316945e-01 3.04197341e-01 6.00638211e-01 -4.60665196e-01
-3.94724101e-01 -8.36145937e-01 1.30489677e-01 -3.19699466e-01
1.50243700e-01 8.47980499e-01 -9.28434804e-02 -7.83628643... | [10.371785163879395, 8.763903617858887] |
e4940bcd-ac7a-4568-a07d-72a7321bdc65 | semi-supervised-formality-style-transfer | 2010.05090 | null | https://arxiv.org/abs/2010.05090v1 | https://arxiv.org/pdf/2010.05090v1.pdf | Semi-supervised Formality Style Transfer using Language Model Discriminator and Mutual Information Maximization | Formality style transfer is the task of converting informal sentences to grammatically-correct formal sentences, which can be used to improve performance of many downstream NLP tasks. In this work, we propose a semi-supervised formality style transfer model that utilizes a language model-based discriminator to maximize... | ['Diyi Yang', 'Kunal Chawla'] | 2020-10-10 | null | https://aclanthology.org/2020.findings-emnlp.212 | https://aclanthology.org/2020.findings-emnlp.212.pdf | findings-of-the-association-for-computational | ['formality-style-transfer', 'semi-supervised-formality-style-transfer'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.88487828e-01 4.10358638e-01 -1.95785761e-02 -9.91990328e-01
-1.06619298e+00 -7.89120376e-01 6.91342592e-01 -1.19893827e-01
-5.74693680e-01 1.06734121e+00 2.40483657e-01 -4.22665805e-01
5.41861475e-01 -8.19625020e-01 -7.24550486e-01 -2.21756771e-01
5.34912229e-01 5.63657761e-01 -1.22329779e-01 -3.50084424... | [11.527729034423828, 9.47980785369873] |
24dd2821-2bab-4489-a1d3-4ab9b8b5fa3d | vibration-control-of-a-rotating-cantilever | 2004.11703 | null | http://arxiv.org/abs/2004.11703v1 | http://arxiv.org/pdf/2004.11703v1.pdf | Vibration control of a rotating cantilever beam using piezoelectric actuator and feedback linearization method | The vibration of various structures such as blades of turbines, helicopters,
and all kinds of rotating robot arms can damage the structures and disrupt
their performance and balance. Thus, investigation of the reduction and control
of vibration of these structures is significant. In this paper, the coupled and
nonlinea... | [] | 2020-03-23 | null | null | null | null | ['cantilever-beam'] | ['miscellaneous'] | [-1.15966298e-01 3.29393059e-01 2.88299322e-01 5.69451690e-01
5.56614518e-01 -6.01118267e-01 -1.32683322e-01 -9.08125818e-01
1.19040301e-02 7.05980957e-01 -1.98369905e-01 6.59968331e-03
-2.92226940e-01 -5.19731581e-01 -3.46752286e-01 -1.42902350e+00
1.36891112e-01 -5.49695909e-01 1.83397427e-01 -5.08533478... | [5.597653388977051, 2.7145426273345947] |
950cb202-c7ac-45d2-88fa-165defdb0c81 | headposr-end-to-end-trainable-head-pose | 2202.03548 | null | https://arxiv.org/abs/2202.03548v1 | https://arxiv.org/pdf/2202.03548v1.pdf | HeadPosr: End-to-end Trainable Head Pose Estimation using Transformer Encoders | In this paper, HeadPosr is proposed to predict the head poses using a single RGB image. \textit{HeadPosr} uses a novel architecture which includes a transformer encoder. In concrete, it consists of: (1) backbone; (2) connector; (3) transformer encoder; (4) prediction head. The significance of using a transformer encode... | ['Naina Dhingra'] | 2022-02-07 | null | null | null | null | ['head-pose-estimation'] | ['computer-vision'] | [-6.26852289e-02 5.34210503e-01 1.37130126e-01 -1.84323221e-01
-8.25537026e-01 2.64855381e-02 6.68794274e-01 6.39713416e-03
-7.74501562e-01 6.62644804e-01 3.77066761e-01 1.54100582e-02
-1.58403113e-01 -7.21790493e-01 -1.16255796e+00 -8.41938317e-01
-3.60537350e-01 5.85723579e-01 5.33989549e-01 -3.46606851... | [13.683554649353027, 0.28112515807151794] |
4c709c42-df50-49b3-be5c-fe737c825c06 | unsupervised-anomaly-detection-via-nonlinear | 2306.09441 | null | https://arxiv.org/abs/2306.09441v1 | https://arxiv.org/pdf/2306.09441v1.pdf | Unsupervised Anomaly Detection via Nonlinear Manifold Learning | Anomalies are samples that significantly deviate from the rest of the data and their detection plays a major role in building machine learning models that can be reliably used in applications such as data-driven design and novelty detection. The majority of existing anomaly detection methods either are exclusively deve... | ['Ramin Bostanabad', 'Zahra Zanjani Foumani', 'Mehdi Shishehbor', 'Amin Yousefpour'] | 2023-06-15 | null | null | null | null | ['anomaly-detection', 'unsupervised-anomaly-detection'] | ['methodology', 'methodology'] | [-3.8378332e-02 5.2028079e-02 4.2194940e-02 -3.6297888e-01
-6.8875837e-01 -4.5896593e-01 8.8180280e-01 2.5971356e-01
1.9973171e-01 7.8931510e-02 1.0455915e-01 -3.9930162e-01
-3.8310912e-01 -6.5318984e-01 -5.3857225e-01 -9.8464018e-01
-3.0640244e-01 5.8892262e-01 -5.2048061e-02 2.6132631e-01
3.4068105e-01... | [7.7035393714904785, 2.3330531120300293] |
66a6da17-8a76-4cde-a928-30f1423fe9c1 | toward-knowledge-driven-speech-based-models | 2210.02527 | null | https://arxiv.org/abs/2210.02527v1 | https://arxiv.org/pdf/2210.02527v1.pdf | Toward Knowledge-Driven Speech-Based Models of Depression: Leveraging Spectrotemporal Variations in Speech Vowels | Psychomotor retardation associated with depression has been linked with tangible differences in vowel production. This paper investigates a knowledge-driven machine learning (ML) method that integrates spectrotemporal information of speech at the vowel-level to identify the depression. Low-level speech descriptors are ... | ['Theodora Chaspari', 'Kexin Feng'] | 2022-10-05 | null | null | null | null | ['vowel-classification'] | ['audio'] | [ 2.70248353e-01 3.34878236e-01 -3.31621498e-01 -8.36257756e-01
-6.48276508e-01 -1.89687267e-01 2.28741154e-01 5.74418128e-01
-1.66946128e-01 3.74901175e-01 8.45378816e-01 -2.31614590e-01
-4.72920984e-01 -3.82457495e-01 -2.60577053e-01 -5.97682416e-01
-1.17259659e-01 1.52586073e-01 -3.99442464e-01 -2.95517981... | [13.868674278259277, 5.647998809814453] |
eab0a0d3-1979-42ea-8a24-bf1350fb24df | target-driven-one-shot-unsupervised-domain | 2305.04628 | null | https://arxiv.org/abs/2305.04628v1 | https://arxiv.org/pdf/2305.04628v1.pdf | Target-driven One-Shot Unsupervised Domain Adaptation | In this paper, we introduce a novel framework for the challenging problem of One-Shot Unsupervised Domain Adaptation (OSUDA), which aims to adapt to a target domain with only a single unlabeled target sample. Unlike existing approaches that rely on large labeled source and unlabeled target data, our Target-driven One-S... | ['Vittorio Murino', 'Alessio Del Bue', 'Pietro Morerio', 'Suvarna Kishorkumar Kadam', 'Julio Ivan Davila Carrazco'] | 2023-05-08 | null | null | null | null | ['unsupervised-domain-adaptation'] | ['methodology'] | [ 4.84745294e-01 2.07814336e-01 -3.01998764e-01 -6.76840663e-01
-9.59999681e-01 -6.50481462e-01 8.33297729e-01 -2.07220986e-02
-3.24111789e-01 7.65217662e-01 1.94000974e-01 -2.62530651e-02
3.89613152e-01 -5.80945551e-01 -5.69248974e-01 -5.12887836e-01
6.14569068e-01 8.32372248e-01 4.74458545e-01 -3.04076523... | [10.127700805664062, 2.7854702472686768] |
547dbb40-b8c0-4137-983e-742b29cb5bd1 | label-cleaning-multiple-instance-learning | 2109.10778 | null | https://arxiv.org/abs/2109.10778v2 | https://arxiv.org/pdf/2109.10778v2.pdf | Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images | Annotating cancerous regions in whole-slide images (WSIs) of pathology samples plays a critical role in clinical diagnosis, biomedical research, and machine learning algorithms development. However, generating exhaustive and accurate annotations is labor-intensive, challenging, and costly. Drawing only coarse and appro... | ['Aleksander S. Popel', 'Aaron W. James', 'Sintawat Wangsiricharoen', 'Carla Saoud', 'Jeremias Sulam', 'Zhenzhen Wang'] | 2021-09-22 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 6.53053939e-01 5.12647212e-01 -3.96834284e-01 -3.44635397e-01
-1.52158332e+00 -4.83864546e-01 -2.40288582e-02 7.02998161e-01
-5.13127744e-01 8.67842615e-01 -2.71024168e-01 -2.76926607e-01
-4.01366726e-02 -5.20132959e-01 -6.70832694e-01 -1.18418503e+00
3.33467454e-01 7.82359123e-01 4.61707860e-01 2.90777624... | [14.990372657775879, -2.931941270828247] |
119e5b98-fdc0-49ec-80f0-5c85289b9a6f | inverse-preference-learning-preference-based | 2305.15363 | null | https://arxiv.org/abs/2305.15363v1 | https://arxiv.org/pdf/2305.15363v1.pdf | Inverse Preference Learning: Preference-based RL without a Reward Function | Reward functions are difficult to design and often hard to align with human intent. Preference-based Reinforcement Learning (RL) algorithms address these problems by learning reward functions from human feedback. However, the majority of preference-based RL methods na\"ively combine supervised reward models with off-th... | ['Dorsa Sadigh', 'Joey Hejna'] | 2023-05-24 | null | null | null | null | ['continuous-control'] | ['playing-games'] | [-7.31220022e-02 1.88527387e-02 -7.25177705e-01 -2.93864310e-01
-8.04864883e-01 -9.40403044e-01 3.69657218e-01 7.05105364e-02
-7.35681951e-01 1.00546408e+00 3.47209513e-01 -3.76736671e-01
-4.02800560e-01 -4.84067738e-01 -5.85050225e-01 -5.17195284e-01
-2.57606983e-01 6.47663116e-01 1.20996669e-01 -3.68660331... | [4.044167518615723, 1.7925009727478027] |
2b0119c7-9937-4566-95db-dcdd14ee1110 | integrated-multi-omics-analysis-using | 1908.06278 | null | https://arxiv.org/abs/1908.06278v1 | https://arxiv.org/pdf/1908.06278v1.pdf | Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification | Different aspects of a clinical sample can be revealed by multiple types of omics data. Integrated analysis of multi-omics data provides a comprehensive view of patients, which has the potential to facilitate more accurate clinical decision making. However, omics data are normally high dimensional with large number of ... | ['Xiao-Yu Zhang', 'Yike Guo', 'Xian Yang', 'Jingqing Zhang', 'Chengliang Dai', 'Kai Sun'] | 2019-08-17 | null | null | null | null | ['tumour-classification'] | ['medical'] | [-6.14642957e-03 -1.75928846e-01 -3.56042653e-01 -3.38905305e-01
-5.52809000e-01 -2.12270632e-01 3.48912895e-01 4.78981555e-01
-2.66437024e-01 4.88451213e-01 1.10307336e-01 -1.09198891e-01
-2.97878712e-01 -8.84182394e-01 -2.52353370e-01 -1.30408716e+00
1.72415346e-01 7.25325465e-01 -4.62404579e-01 4.09437388... | [6.030148983001709, 5.692865371704102] |
9198c37d-5c38-4661-89c7-a1c57d4e65bd | deep-high-resolution-representation-learning | 1902.09212 | null | http://arxiv.org/abs/1902.09212v1 | http://arxiv.org/pdf/1902.09212v1.pdf | Deep High-Resolution Representation Learning for Human Pose Estimation | This is an official pytorch implementation of Deep High-Resolution
Representation Learning for Human Pose Estimation. In this work, we are
interested in the human pose estimation problem with a focus on learning
reliable high-resolution representations. Most existing methods recover
high-resolution representations from... | ['Ke Sun', 'Bin Xiao', 'Jingdong Wang', 'Dong Liu'] | 2019-02-25 | deep-high-resolution-representation-learning-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Sun_Deep_High-Resolution_Representation_Learning_for_Human_Pose_Estimation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Sun_Deep_High-Resolution_Representation_Learning_for_Human_Pose_Estimation_CVPR_2019_paper.pdf | cvpr-2019-6 | ['2d-human-pose-estimation'] | ['computer-vision'] | [-1.12776786e-01 7.03886449e-02 -3.67276222e-02 -2.91988015e-01
-1.21488619e+00 -1.72926068e-01 3.75365436e-01 6.91737533e-02
-4.28536952e-01 7.89619386e-01 4.56824362e-01 5.91497898e-01
-1.23255759e-01 -8.16792130e-01 -9.02385235e-01 -4.64554012e-01
-2.39179403e-01 7.58636355e-01 4.44818616e-01 -4.27233249... | [7.091564178466797, -0.8728415966033936] |
609291dc-bc04-49ae-9247-b261b5d2945e | cache-transition-systems-for-graph-parsing | null | null | https://aclanthology.org/J18-1004 | https://aclanthology.org/J18-1004.pdf | Cache Transition Systems for Graph Parsing | Motivated by the task of semantic parsing, we describe a transition system that generalizes standard transition-based dependency parsing techniques to generate a graph rather than a tree. Our system includes a cache with fixed size m, and we characterize the relationship between the parameter m and the class of graphs ... | ['Xiaochang Peng', 'Giorgio Satta', 'Daniel Gildea'] | 2018-03-01 | null | null | null | cl-2018-3 | ['tree-decomposition', 'transition-based-dependency-parsing'] | ['graphs', 'natural-language-processing'] | [ 2.80005366e-01 8.11077356e-01 -3.81433189e-01 -6.71205580e-01
-9.39962327e-01 -7.75960267e-01 3.94636869e-01 5.93048632e-01
-9.60708410e-02 5.93107462e-01 3.41424882e-01 -8.71563315e-01
3.64000112e-01 -1.29978859e+00 -7.72231877e-01 3.43072116e-02
-2.23208025e-01 7.78711796e-01 9.69242871e-01 -5.78336865... | [10.338287353515625, 9.560135841369629] |
a1505763-d44a-44f9-9045-d28325679356 | unsupervised-segmentation-incorporating-shape | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Kim_Unsupervised_Segmentation_Incorporating_Shape_Prior_via_Generative_Adversarial_Networks_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Kim_Unsupervised_Segmentation_Incorporating_Shape_Prior_via_Generative_Adversarial_Networks_ICCV_2021_paper.pdf | Unsupervised Segmentation Incorporating Shape Prior via Generative Adversarial Networks | We present an image segmentation algorithm that is developed in an unsupervised deep learning framework. The delineation of object boundaries often fails due to the nuisance factors such as illumination changes and occlusions. Thus, we initially propose an unsupervised image decomposition algorithm to obtain an int... | ['Byung-Woo Hong', 'Dahye Kim'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['unsupervised-image-decomposition'] | ['computer-vision'] | [ 6.98523521e-01 3.98769855e-01 3.41788352e-01 -4.24398661e-01
-4.51799899e-01 -5.49736202e-01 4.18679684e-01 1.59094781e-01
-4.01213765e-01 7.08630383e-01 -2.98631936e-01 1.02588855e-01
-1.68354824e-01 -9.46917653e-01 -8.98028493e-01 -9.71448123e-01
3.31282824e-01 3.96356255e-01 2.37265602e-02 6.24680072... | [10.277364730834961, 0.1702558696269989] |
13bcfa8d-455d-4e1e-841d-4ce92c6a20f8 | derived-metrics-for-the-game-of-go-intrinsic | 2009.01606 | null | https://arxiv.org/abs/2009.01606v3 | https://arxiv.org/pdf/2009.01606v3.pdf | Derived metrics for the game of Go -- intrinsic network strength assessment and cheat-detection | The widespread availability of superhuman AI engines is changing how we play the ancient game of Go. The open-source software packages developed after the AlphaGo series shifted focus from producing strong playing entities to providing tools for analyzing games. Here we describe two ways of how the innovations of the s... | ['Antti Törmänen', 'Attila Egri-Nagy'] | 2020-09-03 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-2.54270673e-01 -4.21273917e-01 -1.73107237e-01 -8.98076445e-02
-3.19177151e-01 -9.07960236e-01 4.37214613e-01 -5.61401844e-02
-1.00135159e+00 6.31141424e-01 1.87137038e-01 -3.05257440e-01
-3.46571267e-01 -9.44162369e-01 -2.76382923e-01 -5.65125763e-01
-2.15950623e-01 3.26056778e-01 5.18667340e-01 -6.68573439... | [3.4663586616516113, 1.4218956232070923] |
d0943b7b-5812-46eb-8f1a-f57c80b7dea3 | case-based-reasoning-for-better-1 | 2110.08470 | null | https://arxiv.org/abs/2110.08470v3 | https://arxiv.org/pdf/2110.08470v3.pdf | Case-based Reasoning for Better Generalization in Textual Reinforcement Learning | Text-based games (TBG) have emerged as promising environments for driving research in grounded language understanding and studying problems like generalization and sample efficiency. Several deep reinforcement learning (RL) methods with varying architectures and learning schemes have been proposed for TBGs. However, th... | ['Mrinmaya Sachan', 'Keerthiram Murugesan', 'Shehzaad Dhuliawala', 'Mattia Atzeni'] | 2021-10-16 | case-based-reasoning-for-better | https://openreview.net/forum?id=ZDaSIkWT-AP | https://openreview.net/pdf?id=ZDaSIkWT-AP | iclr-2022-4 | ['text-based-games'] | ['playing-games'] | [-1.70707196e-01 2.82590330e-01 -2.45681658e-01 -3.65002811e-01
-7.43470848e-01 -4.65110749e-01 8.97471845e-01 1.80670351e-01
-8.54097605e-01 1.01074123e+00 3.39030206e-01 -4.35478777e-01
-3.44637066e-01 -1.14380109e+00 -6.02559805e-01 -7.97124505e-01
-1.60468712e-01 1.03509390e+00 9.33591053e-02 -7.30715871... | [3.891289472579956, 1.5166534185409546] |
0dc10e5b-e2bd-4142-93b1-78be27d961c9 | deep-contextual-attention-for-human-object | 1910.07721 | null | https://arxiv.org/abs/1910.07721v1 | https://arxiv.org/pdf/1910.07721v1.pdf | Deep Contextual Attention for Human-Object Interaction Detection | Human-object interaction detection is an important and relatively new class of visual relationship detection tasks, essential for deeper scene understanding. Most existing approaches decompose the problem into object localization and interaction recognition. Despite showing progress, these approaches only rely on the a... | ['Muhammad Haris Khan', 'Jorma Laaksonen', 'Fahad Shahbaz Khan', 'Rao Muhammad Anwer', 'Yanwei Pang', 'Tiancai Wang', 'Ling Shao'] | 2019-10-17 | deep-contextual-attention-for-human-object-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Deep_Contextual_Attention_for_Human-Object_Interaction_Detection_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Deep_Contextual_Attention_for_Human-Object_Interaction_Detection_ICCV_2019_paper.pdf | iccv-2019-10 | ['visual-relationship-detection'] | ['computer-vision'] | [ 2.67692983e-01 -1.84514940e-01 -1.92119583e-01 -2.02394709e-01
-3.46357703e-01 -3.80292118e-01 8.29206824e-01 4.69252944e-01
-4.16468501e-01 4.41898227e-01 1.68341994e-01 5.43981828e-02
8.60205591e-02 -2.94409424e-01 -6.26107335e-01 -5.05606592e-01
-1.44090012e-01 5.21311164e-01 7.65097678e-01 -6.62928075... | [9.806012153625488, 1.438041090965271] |
168027f6-6612-489c-a282-bfcb9faba14c | my-boli-code-mixed-marathi-english-corpora | 2306.14030 | null | https://arxiv.org/abs/2306.14030v1 | https://arxiv.org/pdf/2306.14030v1.pdf | My Boli: Code-mixed Marathi-English Corpora, Pretrained Language Models and Evaluation Benchmarks | The research on code-mixed data is limited due to the unavailability of dedicated code-mixed datasets and pre-trained language models. In this work, we focus on the low-resource Indian language Marathi which lacks any prior work in code-mixing. We present L3Cube-MeCorpus, a large code-mixed Marathi-English (Mr-En) corp... | ['Raviraj Joshi', 'Shantanu Patankar', 'Aditya Kane', 'Omkar Gokhale', 'Tanmay Chavan'] | 2023-06-24 | null | null | null | null | ['language-identification', 'benchmarking', 'hate-speech-detection', 'sentiment-analysis', 'language-identification', 'benchmarking'] | ['audio', 'miscellaneous', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'robots'] | [-2.89751410e-01 -1.83112681e-01 -3.09558250e-02 -1.58279076e-01
-1.52377510e+00 -6.79254651e-01 4.96053040e-01 -1.53371707e-01
-4.52610552e-01 4.36705798e-01 4.43857163e-02 -7.56718874e-01
3.05081367e-01 -2.01100931e-01 -7.41735935e-01 -3.87460470e-01
-4.41186838e-02 5.73264122e-01 5.22242440e-03 -6.05617523... | [9.217996597290039, 10.498470306396484] |
b357ebbf-6561-4784-8d0c-fbdfcf85bfcc | dit-self-supervised-pre-training-for-document | 2203.02378 | null | https://arxiv.org/abs/2203.02378v3 | https://arxiv.org/pdf/2203.02378v3.pdf | DiT: Self-supervised Pre-training for Document Image Transformer | Image Transformer has recently achieved significant progress for natural image understanding, either using supervised (ViT, DeiT, etc.) or self-supervised (BEiT, MAE, etc.) pre-training techniques. In this paper, we propose \textbf{DiT}, a self-supervised pre-trained \textbf{D}ocument \textbf{I}mage \textbf{T}ransforme... | ['Furu Wei', 'Cha Zhang', 'Lei Cui', 'Tengchao Lv', 'Yiheng Xu', 'Junlong Li'] | 2022-03-04 | null | null | null | null | ['document-image-classification', 'document-layout-analysis', 'table-detection', 'document-ai'] | ['computer-vision', 'computer-vision', 'miscellaneous', 'natural-language-processing'] | [ 5.51623046e-01 -2.84260392e-01 -9.98851061e-02 -3.98332119e-01
-8.04125428e-01 -9.56221700e-01 7.65132129e-01 1.84317362e-02
-1.98844865e-01 2.69516200e-01 -1.23936355e-01 -4.99901861e-01
1.27348602e-01 -4.41417187e-01 -7.52485156e-01 -6.83707356e-01
1.89644024e-01 3.70105624e-01 -5.67177795e-02 -7.98858851... | [11.711808204650879, 2.289491653442383] |
40141dca-a873-4b44-b391-8a91be2eb329 | goal-conditioned-video-prediction | null | null | https://openreview.net/forum?id=B1g79grKPr | https://openreview.net/pdf?id=B1g79grKPr | Goal-Conditioned Video Prediction | Many processes can be concisely represented as a sequence of events leading from a starting state to an end state. Given raw ingredients, and a finished cake, an experienced chef can surmise the recipe. Building upon this intuition, we propose a new class of visual generative models: goal-conditioned predictors (GCP).... | ['Sergey Levine', 'Chelsea Finn', 'Dinesh Jayaraman', 'Frederik Ebert', 'Karl Pertsch', 'Oleh Rybkin'] | 2019-09-25 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 3.26537400e-01 2.13675410e-01 -2.76283622e-01 -1.23843282e-01
-6.19616747e-01 -6.80090010e-01 9.21040475e-01 -2.40388960e-01
1.50457844e-01 7.19067454e-01 5.75154841e-01 -1.32147640e-01
1.75412938e-01 -8.20126593e-01 -1.26539242e+00 -6.61845744e-01
-1.95953071e-01 3.22392941e-01 -1.05100982e-01 8.58374536... | [10.64317798614502, -0.5460270643234253] |
2076e6b2-6b27-4fcc-b101-1da271cd1b64 | deepsentipers-novel-deep-learning-models | 2004.05328 | null | https://arxiv.org/abs/2004.05328v1 | https://arxiv.org/pdf/2004.05328v1.pdf | DeepSentiPers: Novel Deep Learning Models Trained Over Proposed Augmented Persian Sentiment Corpus | This paper focuses on how to extract opinions over each Persian sentence-level text. Deep learning models provided a new way to boost the quality of the output. However, these architectures need to feed on big annotated data as well as an accurate design. To best of our knowledge, we do not merely suffer from lack of w... | ['Parsa Abbasi Sarabestani', 'Seyed Abolghasem Mirroshandel', 'Javad PourMostafa Roshan Sharami'] | 2020-04-11 | null | null | null | null | ['persian-sentiment-anlysis'] | ['natural-language-processing'] | [-9.56690311e-02 3.77822429e-01 -2.14769378e-01 -7.34821558e-01
-5.59827626e-01 -4.04014677e-01 4.42443669e-01 7.52656907e-02
-6.88245893e-01 1.02249444e+00 3.44036549e-01 -4.12630647e-01
3.08721721e-01 -1.05268359e+00 -3.87258440e-01 -4.05099660e-01
2.94626653e-01 6.39507711e-01 8.11668485e-03 -7.81342685... | [11.379756927490234, 6.875241756439209] |
3825fc75-0e07-4a96-b4c5-61ad24e9c817 | consac-robust-multi-model-fitting-by | 2001.02643 | null | https://arxiv.org/abs/2001.02643v3 | https://arxiv.org/pdf/2001.02643v3.pdf | CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus | We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements. Applications include finding multiple vanishing points in man-made scenes, fitting planes to architectural imagery, or estimating multiple rigid motions within the same sequence. In contrast to previous works, w... | ['Florian Kluger', 'Michael Ying Yang', 'Eric Brachmann', 'Carsten Rother', 'Hanno Ackermann', 'Bodo Rosenhahn'] | 2020-01-08 | consac-robust-multi-model-fitting-by-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Kluger_CONSAC_Robust_Multi-Model_Fitting_by_Conditional_Sample_Consensus_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Kluger_CONSAC_Robust_Multi-Model_Fitting_by_Conditional_Sample_Consensus_CVPR_2020_paper.pdf | cvpr-2020-6 | ['homography-estimation'] | ['computer-vision'] | [ 3.80141914e-01 -3.37289393e-01 -1.84304714e-01 -2.81548709e-01
-1.03347278e+00 -6.71892941e-01 5.78058779e-01 -2.30679154e-01
-3.74474198e-01 2.76868612e-01 -2.76040226e-01 7.29898587e-02
-3.03239107e-01 -3.09928060e-01 -9.40458059e-01 -5.25153756e-01
1.99392825e-01 1.05805445e+00 3.18126470e-01 -4.43571471... | [7.838505744934082, -2.3000361919403076] |
aa9ec860-0347-4c69-82f3-5f88939dc0e1 | v-net-fully-convolutional-neural-networks-for | 1606.04797 | null | http://arxiv.org/abs/1606.04797v1 | http://arxiv.org/pdf/1606.04797v1.pdf | V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation | Convolutional Neural Networks (CNNs) have been recently employed to solve
problems from both the computer vision and medical image analysis fields.
Despite their popularity, most approaches are only able to process 2D images
while most medical data used in clinical practice consists of 3D volumes. In
this work we propo... | ['Seyed-Ahmad Ahmadi', 'Fausto Milletari', 'Nassir Navab'] | 2016-06-15 | null | null | null | null | ['volumetric-medical-image-segmentation'] | ['medical'] | [ 4.95201558e-01 4.56402063e-01 2.44882330e-02 -6.15349233e-01
-6.13777876e-01 -4.46537435e-01 5.16252518e-01 7.43849456e-01
-9.19981122e-01 5.51874042e-01 -1.93695992e-01 -3.76678199e-01
4.44014296e-02 -7.45893836e-01 -5.79687357e-01 -4.82276291e-01
-3.97301704e-01 9.33952212e-01 2.70759791e-01 1.14379831... | [14.436193466186523, -2.5249786376953125] |
0c4849b7-4a3b-48d1-b54d-2fb754a3934e | from-speech-to-speech-translation-to | 2001.06785 | null | https://arxiv.org/abs/2001.06785v3 | https://arxiv.org/pdf/2001.06785v3.pdf | From Speech-to-Speech Translation to Automatic Dubbing | We present enhancements to a speech-to-speech translation pipeline in order to perform automatic dubbing. Our architecture features neural machine translation generating output of preferred length, prosodic alignment of the translation with the original speech segments, neural text-to-speech with fine tuning of the dur... | ['Roberto Barra-Chicote', 'Robert Enyedi', 'Umut Isik', 'Ritwik Giri', 'Marcello Federico', 'Hassan Sawaf', 'Arvindh Krishnaswamy'] | 2020-01-19 | from-speech-to-speech-translation-to-1 | https://aclanthology.org/2020.iwslt-1.31 | https://aclanthology.org/2020.iwslt-1.31.pdf | ws-2020-7 | ['speech-to-speech-translation'] | ['speech'] | [ 5.78635156e-01 6.70355618e-01 3.80423754e-01 -4.66175199e-01
-1.55280328e+00 -6.52746022e-01 2.94658571e-01 4.21993807e-02
1.48881171e-02 5.21753907e-01 8.41107547e-01 -3.64352405e-01
5.08912504e-01 -2.26628944e-01 -6.90829694e-01 -3.99062634e-01
4.21755284e-01 4.40739065e-01 3.92291648e-03 -3.42062891... | [14.617754936218262, 6.958740711212158] |
1e0311e3-9ec3-4211-b71e-f7f9c9293a5a | spatial-temporal-sequential-hypergraph-1 | 2201.02435 | null | https://arxiv.org/abs/2201.02435v2 | https://arxiv.org/pdf/2201.02435v2.pdf | Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning | Crime prediction is crucial for public safety and resource optimization, yet is very challenging due to two aspects: i) the dynamics of criminal patterns across time and space, crime events are distributed unevenly on both spatial and temporal domains; ii) time-evolving dependencies between different types of crimes (e... | ['Tianyi Chen', 'Xiyue Zhang', 'Liefeng Bo', 'Peng Dai', 'Yong Xu', 'Chao Huang', 'Lianghao Xia'] | 2022-01-07 | spatial-temporal-sequential-hypergraph | https://www.ijcai.org/proceedings/2021/225 | https://www.ijcai.org/proceedings/2021/0225.pdf | ijcai-2021-8 | ['crime-prediction'] | ['miscellaneous'] | [-4.11375239e-02 -4.35641319e-01 -2.46766493e-01 -4.19114798e-01
-1.73548058e-01 -3.27474952e-01 6.02895081e-01 7.54760861e-01
-3.60522956e-01 5.10927141e-01 6.42235279e-01 -3.00929368e-01
-7.18056560e-01 -1.36016774e+00 -3.56992006e-01 -2.40425766e-01
-6.24984086e-01 4.46575969e-01 5.86253583e-01 -3.57497752... | [6.682809829711914, 2.0773050785064697] |
6b369295-1b2e-4664-b830-f4ea568cdef7 | layoutdiffusion-improving-graphic-layout | 2303.11589 | null | https://arxiv.org/abs/2303.11589v1 | https://arxiv.org/pdf/2303.11589v1.pdf | LayoutDiffusion: Improving Graphic Layout Generation by Discrete Diffusion Probabilistic Models | Creating graphic layouts is a fundamental step in graphic designs. In this work, we present a novel generative model named LayoutDiffusion for automatic layout generation. As layout is typically represented as a sequence of discrete tokens, LayoutDiffusion models layout generation as a discrete denoising diffusion proc... | ['Dongmei Zhang', 'Jian-Guang Lou', 'Shizhao Sun', 'Jiaqi Guo', 'Junyi Zhang'] | 2023-03-21 | null | null | null | null | ['layout-design'] | ['computer-vision'] | [-6.34243935e-02 -2.15354972e-02 6.77881464e-02 -3.43985379e-01
-6.00041330e-01 -7.09928215e-01 7.07058668e-01 2.59973351e-02
-5.69283068e-02 6.33810759e-01 3.82589340e-01 -3.48830283e-01
-9.89070982e-02 -8.86927247e-01 -1.04645848e+00 -8.11499834e-01
9.53630656e-02 5.44609129e-01 -4.37997244e-02 2.23463234... | [11.32269287109375, -0.21739383041858673] |
9ac5fb1f-b040-475a-8c01-715a56f00262 | audioldm-text-to-audio-generation-with-latent | 2301.12503 | null | https://arxiv.org/abs/2301.12503v2 | https://arxiv.org/pdf/2301.12503v2.pdf | AudioLDM: Text-to-Audio Generation with Latent Diffusion Models | Text-to-audio (TTA) system has recently gained attention for its ability to synthesize general audio based on text descriptions. However, previous studies in TTA have limited generation quality with high computational costs. In this study, we propose AudioLDM, a TTA system that is built on a latent space to learn the c... | ['Mark D. Plumbley', 'Wenwu Wang', 'Danilo Mandic', 'Xubo Liu', 'Xinhao Mei', 'Yi Yuan', 'Zehua Chen', 'Haohe Liu'] | 2023-01-29 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 1.77062616e-01 -2.35574573e-01 1.43250227e-01 -1.45128548e-01
-1.29276955e+00 -5.93357444e-01 5.63052595e-01 -2.23033905e-01
1.28746152e-01 2.90203482e-01 5.18564284e-01 -1.01417176e-01
1.60277024e-01 -7.69664228e-01 -7.40164518e-01 -7.24363863e-01
5.78660443e-02 2.25323230e-01 -3.79016176e-02 -5.16333431... | [15.445962905883789, 5.655033111572266] |
f0e48b52-d8b6-4955-b43e-74cce507536b | recommending-on-graphs-a-comprehensive-review | 2212.12230 | null | https://arxiv.org/abs/2212.12230v2 | https://arxiv.org/pdf/2212.12230v2.pdf | Recommending on graphs: a comprehensive review from a data perspective | Recent advances in graph-based learning approaches have demonstrated their effectiveness in modelling users' preferences and items' characteristics for Recommender Systems (RSS). Most of the data in RSS can be organized into graphs where various objects (e.g., users, items, and attributes) are explicitly or implicitly ... | ['Jon Atle Gulla', 'Peng Liu', 'Lemei Zhang'] | 2022-12-23 | null | null | null | null | ['network-embedding'] | ['methodology'] | [-1.99801654e-01 2.46333942e-01 -8.31349075e-01 -4.63381737e-01
1.91583801e-02 -4.68181670e-01 2.51564324e-01 8.03110898e-01
3.19600105e-01 4.85216588e-01 5.17425954e-01 -5.42403579e-01
-7.20464826e-01 -1.08791602e+00 -3.48758072e-01 -4.00756270e-01
-5.98950982e-01 3.52975279e-01 1.03087472e-02 -4.65135008... | [10.162166595458984, 5.670096397399902] |
5cfd2fe0-2ef0-40e0-8e07-cc982725cb7f | bp-net-efficient-deep-learning-for-continuous | 2111.14558 | null | https://arxiv.org/abs/2111.14558v1 | https://arxiv.org/pdf/2111.14558v1.pdf | BP-Net: Efficient Deep Learning for Continuous Arterial Blood Pressure Estimation using Photoplethysmogram | Blood pressure (BP) is one of the most influential bio-markers for cardiovascular diseases and stroke; therefore, it needs to be regularly monitored to diagnose and prevent any advent of medical complications. Current cuffless approaches to continuous BP monitoring, though non-invasive and unobtrusive, involve explicit... | ['Vineeth Vijayaraghavan', 'Nitish Kumar M', 'Abhishek K', 'Poojah G', 'Vedanth S', 'Rishi Vardhan K'] | 2021-11-29 | null | null | null | null | ['blood-pressure-estimation'] | ['medical'] | [-8.87255296e-02 1.45406321e-01 2.30090767e-01 -5.54270744e-01
-4.72743064e-01 -4.65633124e-01 -2.08712265e-01 1.64123178e-01
-2.31274143e-01 1.10561478e+00 1.37215406e-01 -6.45160139e-01
1.00222550e-01 -8.83126736e-01 -3.60469252e-01 -4.77828920e-01
-3.68126452e-01 2.44358238e-02 -8.21093097e-02 2.96043634... | [14.087676048278809, 2.948641300201416] |
817ca623-95db-4995-b799-d78feb741bbc | improving-dataset-distillation | 1910.02551 | null | https://arxiv.org/abs/1910.02551v3 | https://arxiv.org/pdf/1910.02551v3.pdf | Soft-Label Dataset Distillation and Text Dataset Distillation | Dataset distillation is a method for reducing dataset sizes by learning a small number of synthetic samples containing all the information of a large dataset. This has several benefits like speeding up model training, reducing energy consumption, and reducing required storage space. Currently, each synthetic sample is ... | ['Matthias Schonlau', 'Ilia Sucholutsky'] | 2019-10-06 | null | null | null | null | ['data-summarization'] | ['miscellaneous'] | [ 4.32141602e-01 8.07818249e-02 -4.79950339e-01 -7.00027466e-01
-1.08150053e+00 -7.10623741e-01 3.64400297e-01 2.49226078e-01
-7.77074337e-01 7.69096911e-01 -5.47512248e-02 -2.45402277e-01
4.85400587e-01 -8.16663861e-01 -9.63165581e-01 -5.98373890e-01
3.23140264e-01 5.32641113e-01 -1.62454341e-02 2.39994422... | [9.29942798614502, 2.5974016189575195] |
345f4b8a-6694-4064-a689-f5368b77dcb4 | graph-based-semi-supervised-learning-approach | null | null | https://aclanthology.org/L18-1624 | https://aclanthology.org/L18-1624.pdf | Graph Based Semi-Supervised Learning Approach for Tamil POS tagging | null | ['Uthayasanker Thayasivam', 'Surangika Ranathunga', 'Mokanarangan Thayaparan'] | 2018-05-01 | graph-based-semi-supervised-learning-approach-1 | https://aclanthology.org/L18-1624 | https://aclanthology.org/L18-1624.pdf | lrec-2018-5 | ['graph-similarity'] | ['graphs'] | [-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... | [-7.21641731262207, 3.6164743900299072] |
56b2a15e-2426-40d3-aedb-2eb7698621c2 | dynamic-compressive-sensing-based-on-rls-for | 2304.11838 | null | https://arxiv.org/abs/2304.11838v2 | https://arxiv.org/pdf/2304.11838v2.pdf | Dynamic Compressive Sensing based on RLS for Underwater Acoustic Communications | Sparse structures are widely recognized and utilized in channel estimation. Two typical mechanisms, namely proportionate updating (PU) and zero-attracting (ZA) techniques, achieve better performance, but their computational complexity are higher than non-sparse counterparts. In this paper, we propose a DCS technique ba... | ['Zhen Qin'] | 2023-04-24 | null | null | null | null | ['compressive-sensing'] | ['computer-vision'] | [ 4.53307480e-01 -4.87711042e-01 -4.16173749e-02 2.14285012e-02
-8.68499637e-01 -1.95279032e-01 -5.78976944e-02 1.14966959e-01
-4.83525187e-01 7.61955261e-01 1.24971710e-01 -3.47270787e-01
-1.61261603e-01 -4.43332165e-01 -4.65269744e-01 -8.58604312e-01
-4.43871170e-01 -3.85886073e-01 2.42324010e-01 -1.74708828... | [6.454378604888916, 1.3502943515777588] |
8821d851-f5ab-4689-a2b2-25f1dbc44bad | towards-a-practical-lip-to-speech-conversion | 2104.14467 | null | https://arxiv.org/abs/2104.14467v1 | https://arxiv.org/pdf/2104.14467v1.pdf | Towards a practical lip-to-speech conversion system using deep neural networks and mobile application frontend | Articulatory-to-acoustic (forward) mapping is a technique to predict speech using various articulatory acquisition techniques as input (e.g. ultrasound tongue imaging, MRI, lip video). The advantage of lip video is that it is easily available and affordable: most modern smartphones have a front camera. There are alread... | ['Tamás Gábor Csapó', 'Frigyes Viktor Arthur'] | 2021-04-29 | null | null | null | null | ['lip-to-speech-synthesis'] | ['computer-vision'] | [-1.75601505e-02 3.10607761e-01 -5.95125020e-01 -3.27787876e-01
-9.99603391e-01 -1.48927480e-01 2.66807377e-01 -6.64515078e-01
-1.57789096e-01 5.77299297e-01 4.39581484e-01 -4.94896114e-01
2.84616888e-01 -2.30612472e-01 -6.45030022e-01 -4.91848558e-01
3.58233064e-01 2.81011939e-01 5.75429387e-02 1.33681595... | [14.297337532043457, 5.013840675354004] |
91856649-2ae2-4929-9d84-3f6b40977f98 | adaptive-model-predictive-control-by-learning | 2203.06783 | null | https://arxiv.org/abs/2203.06783v2 | https://arxiv.org/pdf/2203.06783v2.pdf | Adaptive Model Predictive Control by Learning Classifiers | Stochastic model predictive control has been a successful and robust control framework for many robotics tasks where the system dynamics model is slightly inaccurate or in the presence of environment disturbances. Despite the successes, it is still unclear how to best adjust control parameters to the current task in th... | ['Fabio Ramos', 'Rafael Oliveira', 'Rel Guzman'] | 2022-03-13 | null | null | null | null | ['density-ratio-estimation', 'bayesian-optimisation'] | ['methodology', 'methodology'] | [ 5.38766444e-01 2.34339774e-01 -2.62077481e-01 1.16439201e-01
-6.94140613e-01 -5.39352298e-01 7.41093278e-01 -6.51105791e-02
-4.31216389e-01 1.00462866e+00 -3.41841042e-01 -1.96126521e-01
-8.31678212e-01 -3.08154225e-01 -8.81349683e-01 -9.58819926e-01
6.98447227e-02 7.16802061e-01 2.96312809e-01 6.90264627... | [4.87252140045166, 2.3393614292144775] |
97aae79e-ece8-4d95-963d-26ee5bdf8310 | cocktail-mixing-multi-modality-controls-for | 2306.00964 | null | https://arxiv.org/abs/2306.00964v1 | https://arxiv.org/pdf/2306.00964v1.pdf | Cocktail: Mixing Multi-Modality Controls for Text-Conditional Image Generation | Text-conditional diffusion models are able to generate high-fidelity images with diverse contents. However, linguistic representations frequently exhibit ambiguous descriptions of the envisioned objective imagery, requiring the incorporation of additional control signals to bolster the efficacy of text-guided diffusion... | ['Tat-Jen Cham', 'DaCheng Tao', 'Chaoyue Wang', 'Chuanxia Zheng', 'Daqing Liu', 'Jianbin Zheng', 'Minghui Hu'] | 2023-06-01 | null | null | null | null | ['conditional-image-generation'] | ['computer-vision'] | [ 5.51487148e-01 2.00783357e-01 9.73056257e-02 -3.77495331e-03
-3.91899258e-01 -6.69908047e-01 1.32413495e+00 -2.21990287e-01
-2.04197600e-01 5.19688785e-01 4.48191881e-01 1.36098385e-01
-2.35791162e-01 -8.59234393e-01 -6.38139784e-01 -1.18248105e+00
3.22822601e-01 1.74487889e-01 1.55478045e-01 -4.01506782... | [11.613719940185547, -0.4872269630432129] |
a41ab0b4-181b-44be-9be5-2a4ed29133e7 | a-stochastic-metapopulation-state-space | 2106.07919 | null | https://arxiv.org/abs/2106.07919v1 | https://arxiv.org/pdf/2106.07919v1.pdf | A stochastic metapopulation state-space approach to modeling and estimating Covid-19 spread | Mathematical models are widely recognized as an important tool for analyzing and understanding the dynamics of infectious disease outbreaks, predict their future trends, and evaluate public health intervention measures for disease control and elimination. We propose a novel stochastic metapopulation state-space model f... | ['Ulisses Braga-Neto', 'Martial Ndeffo-Mbah', 'Durward Cator III', 'Yukun Tan'] | 2021-06-15 | null | null | null | null | ['metaheuristic-optimization'] | ['methodology'] | [ 2.17204124e-01 -6.51403546e-01 8.77636597e-02 9.37080979e-02
-2.95861393e-01 -1.83521986e-01 6.17029905e-01 3.07686538e-01
-3.42950404e-01 9.12773013e-01 3.05387557e-01 -3.15942496e-01
-8.63126457e-01 -6.71986401e-01 -1.72986791e-01 -9.62445915e-01
-8.42521548e-01 7.41560459e-01 -2.26966456e-01 -1.83706172... | [5.997599124908447, 4.3704118728637695] |
ebc2076a-7ed6-4e1c-b54e-36a7c2410e47 | optimal-scalarizations-for-sublinear | 2307.03288 | null | https://arxiv.org/abs/2307.03288v1 | https://arxiv.org/pdf/2307.03288v1.pdf | Optimal Scalarizations for Sublinear Hypervolume Regret | Scalarization is a general technique that can be deployed in any multiobjective setting to reduce multiple objectives into one, such as recently in RLHF for training reward models that align human preferences. Yet some have dismissed this classical approach because linear scalarizations are known to miss concave region... | ['Qiuyi Zhang'] | 2023-07-06 | null | null | null | null | ['bayesian-optimization'] | ['methodology'] | [ 4.55202349e-03 4.35752302e-01 -4.88646805e-01 -4.30052161e-01
-1.51354396e+00 -1.07230413e+00 -6.00407980e-02 1.53688997e-01
-6.95310950e-01 1.14248300e+00 1.20871194e-01 -4.10613060e-01
-1.20739543e+00 -5.86593747e-01 -8.83219004e-01 -9.86317933e-01
-2.35360980e-01 9.03865635e-01 -3.50017250e-01 -1.09393850... | [4.552755832672119, 3.3419384956359863] |
22478d34-32b1-43d5-9676-19567a6f4bca | blended-diffusion-for-text-driven-editing-of | 2111.14818 | null | https://arxiv.org/abs/2111.14818v2 | https://arxiv.org/pdf/2111.14818v2.pdf | Blended Diffusion for Text-driven Editing of Natural Images | Natural language offers a highly intuitive interface for image editing. In this paper, we introduce the first solution for performing local (region-based) edits in generic natural images, based on a natural language description along with an ROI mask. We achieve our goal by leveraging and combining a pretrained languag... | ['Ohad Fried', 'Dani Lischinski', 'Omri Avrahami'] | 2021-11-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Avrahami_Blended_Diffusion_for_Text-Driven_Editing_of_Natural_Images_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Avrahami_Blended_Diffusion_for_Text-Driven_Editing_of_Natural_Images_CVPR_2022_paper.pdf | cvpr-2022-1 | ['text-guided-image-editing', 'zero-shot-text-to-image-generation'] | ['computer-vision', 'natural-language-processing'] | [ 5.35450518e-01 8.00957903e-02 3.54487032e-01 -2.53985524e-01
-5.70300221e-01 -7.72607446e-01 8.90514433e-01 -3.67889404e-02
-3.87190700e-01 4.69787478e-01 4.49431717e-01 -8.39549452e-02
2.88330913e-01 -6.30898774e-01 -7.80161202e-01 -5.65248847e-01
4.20825481e-01 1.10256476e-02 3.64035189e-01 -2.39598647... | [11.405402183532715, -0.5176979899406433] |
0821e528-5652-4ef9-8874-30bb8232653d | crowd-robot-interaction-crowd-aware-robot | 1809.08835 | null | http://arxiv.org/abs/1809.08835v2 | http://arxiv.org/pdf/1809.08835v2.pdf | Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning | Mobility in an effective and socially-compliant manner is an essential yet
challenging task for robots operating in crowded spaces. Recent works have
shown the power of deep reinforcement learning techniques to learn socially
cooperative policies. However, their cooperation ability deteriorates as the
crowd grows since... | ['Changan Chen', 'Alexandre Alahi', 'Yuejiang Liu', 'Sven Kreiss'] | 2018-09-24 | null | null | null | null | ['human-dynamics'] | ['computer-vision'] | [-4.91021663e-01 5.08904397e-01 3.38763535e-01 -1.57179907e-01
-7.29595823e-03 1.34610087e-02 5.93343198e-01 -1.03095397e-01
-9.22707558e-01 1.18998468e+00 4.36761707e-01 2.60785908e-01
-2.06617117e-01 -6.18418455e-01 -7.33536661e-01 -9.04549599e-01
-7.32880831e-01 7.39971638e-01 4.75550234e-01 -9.08996880... | [4.794475078582764, 0.9858051538467407] |
a81e2c7b-ec4d-46f2-b0b9-5bb873ee6e41 | a-systematic-study-on-object-recognition | 2305.02085 | null | https://arxiv.org/abs/2305.02085v1 | https://arxiv.org/pdf/2305.02085v1.pdf | A Systematic Study on Object Recognition Using Millimeter-wave Radar | Due to its light and weather-independent sensing, millimeter-wave (MMW) radar is essential in smart environments. Intelligent vehicle systems and industry-grade MMW radars have integrated such capabilities. Industry-grade MMW radars are expensive and hard to get for community-purpose smart environment applications. How... | ['Nirmalya Roy', 'Biplab Pal', 'Marc Conn', 'Zahid Hasan', 'Emon Dey', 'Mohammad Saeid Anwar', 'Avijoy Chakma', 'Maloy Kumar Devnath'] | 2023-05-03 | null | null | null | null | ['object-recognition', 'object-localization'] | ['computer-vision', 'computer-vision'] | [ 2.42180377e-01 -4.32081431e-01 7.84523785e-02 -8.26703608e-01
-4.44788158e-01 -4.01561856e-01 4.40169990e-01 -4.94185477e-01
-4.91906762e-01 9.17192578e-01 -2.34462455e-01 -4.80852395e-01
-2.63482243e-01 -8.51945460e-01 -1.79577351e-01 -8.94177139e-01
-2.75553539e-02 2.52109706e-01 -4.22092192e-02 5.71264103... | [7.800446510314941, -1.2803525924682617] |
656e3edb-2a2e-4a87-80b5-753bcc8531c1 | multi-class-graph-clustering-via-approximated | 2306.08617 | null | https://arxiv.org/abs/2306.08617v1 | https://arxiv.org/pdf/2306.08617v1.pdf | Multi-class Graph Clustering via Approximated Effective $p$-Resistance | This paper develops an approximation to the (effective) $p$-resistance and applies it to multi-class clustering. Spectral methods based on the graph Laplacian and its generalization to the graph $p$-Laplacian have been a backbone of non-euclidean clustering techniques. The advantage of the $p$-Laplacian is that the par... | ['Mark Herbster', 'Shota Saito'] | 2023-06-14 | null | null | null | null | ['graph-clustering', 'clustering'] | ['graphs', 'methodology'] | [-1.54930770e-01 2.54334003e-01 -1.80263877e-01 -1.87343992e-02
-5.21185875e-01 -6.43882751e-01 -1.49778128e-01 2.56342083e-01
-2.91820586e-01 1.88613862e-01 -2.73207545e-01 -3.95737320e-01
-5.75379014e-01 -1.09542310e+00 -6.04463696e-01 -8.20164084e-01
-8.90131116e-01 3.62299591e-01 2.59867996e-01 -5.31007573... | [7.082196235656738, 5.098977565765381] |
260e2854-edf8-4714-9b16-f28cbb4ef013 | variational-policy-gradient-method-for | 2007.02151 | null | https://arxiv.org/abs/2007.02151v1 | https://arxiv.org/pdf/2007.02151v1.pdf | Variational Policy Gradient Method for Reinforcement Learning with General Utilities | In recent years, reinforcement learning (RL) systems with general goals beyond a cumulative sum of rewards have gained traction, such as in constrained problems, exploration, and acting upon prior experiences. In this paper, we consider policy optimization in Markov Decision Problems, where the objective is a general c... | ['Mengdi Wang', 'Csaba Szepesvari', 'Amrit Singh Bedi', 'Junyu Zhang', 'Alec Koppel'] | 2020-07-04 | null | http://proceedings.neurips.cc/paper/2020/hash/30ee748d38e21392de740e2f9dc686b6-Abstract.html | http://proceedings.neurips.cc/paper/2020/file/30ee748d38e21392de740e2f9dc686b6-Paper.pdf | neurips-2020-12 | ['variational-monte-carlo'] | ['miscellaneous'] | [-1.05659150e-01 4.55941170e-01 -6.08703494e-01 3.10052961e-01
-1.03944659e+00 -7.58472919e-01 2.83954948e-01 2.88144294e-02
-9.74985063e-01 1.39887595e+00 6.64212480e-02 -5.91986120e-01
-4.38898683e-01 -5.17184198e-01 -7.78177083e-01 -1.15899026e+00
-1.40401438e-01 4.27178741e-01 -2.47423500e-01 -1.93692312... | [4.241131782531738, 2.5760788917541504] |
93eb11c1-d429-4c23-9047-4bf2dbda8e87 | auxiliary-tasks-and-exploration-enable | 2104.04112 | null | https://arxiv.org/abs/2104.04112v2 | https://arxiv.org/pdf/2104.04112v2.pdf | Auxiliary Tasks and Exploration Enable ObjectNav | ObjectGoal Navigation (ObjectNav) is an embodied task wherein agents are to navigate to an object instance in an unseen environment. Prior works have shown that end-to-end ObjectNav agents that use vanilla visual and recurrent modules, e.g. a CNN+RNN, perform poorly due to overfitting and sample inefficiency. This has ... | ['Erik Wijmans', 'Abhishek Das', 'Dhruv Batra', 'Joel Ye'] | 2021-04-08 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [-3.94111753e-01 2.35067576e-01 1.95141420e-01 -1.39937580e-01
-5.03562987e-01 -6.71371043e-01 6.94936275e-01 -2.33674049e-01
-7.88251579e-01 5.35994947e-01 3.06126595e-01 -2.10259303e-01
-6.10692911e-02 -5.27417660e-01 -9.29199934e-01 -5.35379112e-01
-5.40059805e-01 5.83255768e-01 -4.34470996e-02 -4.40431297... | [4.500492095947266, 0.6341466903686523] |
36b3707d-510c-4b5c-bc74-a1e339575f47 | generate-then-select-open-ended-visual | 2305.18842 | null | https://arxiv.org/abs/2305.18842v1 | https://arxiv.org/pdf/2305.18842v1.pdf | Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge | The open-ended Visual Question Answering (VQA) task requires AI models to jointly reason over visual and natural language inputs using world knowledge. Recently, pre-trained Language Models (PLM) such as GPT-3 have been applied to the task and shown to be powerful world knowledge sources. However, these methods suffer ... | ['Bing Xiang', 'Dan Roth', 'Patrick Ng', 'Vittorio Castelli', 'Zhiguo Wang', 'William Yang Wang', 'Alexander Hanbo Li', 'Yuhao Zhang', 'Henghui Zhu', 'Pramuditha Perera', 'Gukyeong Kwon', 'Sheng Zhang', 'Xingyu Fu'] | 2023-05-30 | null | null | null | null | ['answer-selection'] | ['natural-language-processing'] | [-4.07001749e-02 3.55643362e-01 -1.28945261e-01 -3.11823070e-01
-1.25834596e+00 -1.00916660e+00 7.79111862e-01 1.90211877e-01
-3.28321368e-01 5.11388540e-01 3.31007510e-01 -4.65889722e-01
1.86417177e-01 -8.74502718e-01 -9.45790172e-01 -6.90530390e-02
3.91696841e-01 9.34189796e-01 5.60586870e-01 -3.38090122... | [10.881878852844238, 1.8471035957336426] |
8eb6951a-d317-43c7-a6a5-366c57a42bca | language-model-as-an-annotator-exploring | 2105.12544 | null | https://arxiv.org/abs/2105.12544v2 | https://arxiv.org/pdf/2105.12544v2.pdf | Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization | Current dialogue summarization systems usually encode the text with a number of general semantic features (e.g., keywords and topics) to gain more powerful dialogue modeling capabilities. However, these features are obtained via open-domain toolkits that are dialog-agnostic or heavily relied on human annotations. In th... | ['Ting Liu', 'Bing Qin', 'Libo Qin', 'Xiaocheng Feng', 'Xiachong Feng'] | 2021-05-26 | null | https://aclanthology.org/2021.acl-long.117 | https://aclanthology.org/2021.acl-long.117.pdf | acl-2021-5 | ['conversational-response-generation'] | ['natural-language-processing'] | [ 2.90764570e-01 7.80554235e-01 -1.52576491e-01 -5.64485908e-01
-8.83603454e-01 -7.37147272e-01 1.20569348e+00 1.47720933e-01
-2.12862819e-01 1.08880734e+00 8.65419269e-01 -7.09218234e-02
4.51247007e-01 -6.71885848e-01 -3.07838563e-02 -2.08390921e-01
2.60590047e-01 8.41042697e-01 3.30137223e-01 -9.33201969... | [12.70919418334961, 8.147547721862793] |
f3db9a4c-7b10-4b39-add2-d6f8c2086389 | ecg-atk-gan-robustness-against-adversarial | 2110.09983 | null | https://arxiv.org/abs/2110.09983v3 | https://arxiv.org/pdf/2110.09983v3.pdf | ECG-ATK-GAN: Robustness against Adversarial Attacks on ECGs using Conditional Generative Adversarial Networks | Automating arrhythmia detection from ECG requires a robust and trusted system that retains high accuracy under electrical disturbances. Many machine learning approaches have reached human-level performance in classifying arrhythmia from ECGs. However, these architectures are vulnerable to adversarial attacks, which can... | ['Xingjun Ma', 'Alireza Tavakkoli', 'Sharif Amit Kamran', 'Khondker Fariha Hossain'] | 2021-10-17 | null | null | null | null | ['arrhythmia-detection'] | ['medical'] | [ 5.32056630e-01 9.34992135e-02 3.34154725e-01 -2.96211869e-01
-1.20806956e+00 -1.12301123e+00 -4.29764502e-02 1.28671052e-02
4.01928872e-02 9.42383111e-01 -2.18319163e-01 -3.91507715e-01
1.04891257e-02 -6.21964514e-01 -8.09582949e-01 -7.27893710e-01
-5.58296919e-01 2.05968723e-01 -4.15504724e-02 -6.28750920... | [14.308389663696289, 3.15895414352417] |
1140664c-bf0c-4c96-a895-69206b4bacea | calibration-tests-beyond-classification-1 | 2210.13355 | null | https://arxiv.org/abs/2210.13355v1 | https://arxiv.org/pdf/2210.13355v1.pdf | Calibration tests beyond classification | Most supervised machine learning tasks are subject to irreducible prediction errors. Probabilistic predictive models address this limitation by providing probability distributions that represent a belief over plausible targets, rather than point estimates. Such models can be a valuable tool in decision-making under unc... | ['Dave Zachariah', 'Fredrik Lindsten', 'David Widmann'] | 2022-10-21 | calibration-tests-beyond-classification | https://openreview.net/forum?id=-bxf89v3Nx | https://openreview.net/pdf?id=-bxf89v3Nx | iclr-2021-1 | ['decision-making-under-uncertainty', 'decision-making-under-uncertainty'] | ['medical', 'reasoning'] | [ 2.31613070e-01 2.58128822e-01 -7.04121709e-01 -8.25089037e-01
-9.76292670e-01 -5.90152323e-01 4.57702279e-01 5.19222617e-01
-1.34189636e-01 9.02358234e-01 -2.56827712e-01 -5.19521177e-01
-7.17234433e-01 -9.53080475e-01 -5.74587882e-01 -7.00471163e-01
6.17750250e-02 5.90708792e-01 1.05976984e-01 2.57620156... | [7.876157283782959, 4.2327656745910645] |
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