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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1c38f3a6-3836-482a-9f89-db18e14f019c | model-error-propagation-via-learned | 2104.08695 | null | https://arxiv.org/abs/2104.08695v2 | https://arxiv.org/pdf/2104.08695v2.pdf | Model Error Propagation via Learned Contraction Metrics for Safe Feedback Motion Planning of Unknown Systems | We present a method for contraction-based feedback motion planning of locally incrementally exponentially stabilizable systems with unknown dynamics that provides probabilistic safety and reachability guarantees. Given a dynamics dataset, our method learns a deep control-affine approximation of the dynamics. To find a ... | ['Dmitry Berenson', 'Necmiye Ozay', 'Glen Chou'] | 2021-04-18 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [-2.47409418e-01 8.18806827e-01 -3.76951426e-01 2.92128295e-01
-1.05564320e+00 -9.52610195e-01 4.49231058e-01 1.42563097e-02
-2.67820358e-01 7.58470714e-01 -5.23827560e-02 -4.61324751e-01
-3.08179051e-01 -5.01089454e-01 -1.53312767e+00 -8.69170487e-01
-6.03740275e-01 7.81124175e-01 5.04867077e-01 -3.02408665... | [4.800153732299805, 2.1332266330718994] |
9db87700-3efe-48e3-bbd1-1d7d463ce358 | evaluating-the-evaluators-are-current-few | 2307.02732 | null | https://arxiv.org/abs/2307.02732v1 | https://arxiv.org/pdf/2307.02732v1.pdf | Evaluating the Evaluators: Are Current Few-Shot Learning Benchmarks Fit for Purpose? | Numerous benchmarks for Few-Shot Learning have been proposed in the last decade. However all of these benchmarks focus on performance averaged over many tasks, and the question of how to reliably evaluate and tune models trained for individual tasks in this regime has not been addressed. This paper presents the first i... | ['Henry Gouk', 'Timothy Hospedales', 'Luísa Shimabucoro'] | 2023-07-06 | null | null | null | null | ['model-selection', 'few-shot-learning'] | ['methodology', 'methodology'] | [ 1.54871956e-01 -2.80728847e-01 -2.89415747e-01 -4.60254997e-01
-1.23040926e+00 -4.17733341e-01 9.16795611e-01 2.34295279e-01
-6.45769060e-01 7.80203104e-01 -5.05058393e-02 -1.40436649e-01
-2.13026583e-01 -4.07541096e-01 -4.24247146e-01 -6.62103057e-01
1.75651744e-01 7.25804508e-01 5.99203765e-01 -2.19552740... | [9.883331298828125, 3.328824520111084] |
9b1feab8-d3df-460d-8b04-6a2998ae4f76 | data-efficient-weakly-supervised-learning-for-1 | 2012.14345 | null | https://arxiv.org/abs/2012.14345v2 | https://arxiv.org/pdf/2012.14345v2.pdf | From Handheld to Unconstrained Object Detection: a Weakly-supervised On-line Learning Approach | Deep Learning (DL) based methods for object detection achieve remarkable performance at the cost of computationally expensive training and extensive data labeling. Robots embodiment can be exploited to mitigate this burden by acquiring automatically annotated training data via a natural interaction with a human showing... | ['Raffaello Camoriano', 'Andrea Maracani', 'Lorenzo Natale', 'Lorenzo Rosasco', 'Vadim Tikhanoff', 'Giulia Pasquale', 'Elisa Maiettini'] | 2020-12-28 | null | null | null | null | ['line-detection'] | ['computer-vision'] | [ 5.67810953e-01 7.91678011e-01 -7.00055435e-02 -3.29332739e-01
-5.02036333e-01 -6.01167738e-01 7.36505628e-01 4.28947866e-01
-1.04740143e+00 2.66967535e-01 -6.46196246e-01 5.39121730e-03
-1.23007774e-01 -4.69590396e-01 -9.20992434e-01 -6.46991611e-01
-5.85264899e-02 1.02490091e+00 7.52890527e-01 -1.32379904... | [7.770370960235596, -1.0431602001190186] |
340223a0-978a-4aa1-8cf3-75f7712162a1 | the-information-retrieval-experiment-platform | 2305.18932 | null | https://arxiv.org/abs/2305.18932v1 | https://arxiv.org/pdf/2305.18932v1.pdf | The Information Retrieval Experiment Platform | We integrate ir_datasets, ir_measures, and PyTerrier with TIRA in the Information Retrieval Experiment Platform (TIREx) to promote more standardized, reproducible, scalable, and even blinded retrieval experiments. Standardization is achieved when a retrieval approach implements PyTerrier's interfaces and the input and ... | ['Martin Potthast', 'Matthias Hagen', 'Benno Stein', 'Janek Bevendorff', 'Simon Reich', 'Niklas Deckers', 'Sean MacAvaney', 'Jan Heinrich Reimer', 'Maik Fröbe'] | 2023-05-30 | null | null | null | null | ['information-retrieval'] | ['natural-language-processing'] | [-4.05970037e-01 -6.84483647e-01 -4.44664480e-03 -2.26542965e-01
-1.40561771e+00 -1.20443857e+00 5.32786191e-01 1.48944899e-01
-8.86129677e-01 3.51645321e-01 -7.18874261e-02 -5.54751635e-01
-2.22649395e-01 -5.35275102e-01 -3.84027988e-01 -5.63718319e-01
-5.60914092e-02 7.83609867e-01 1.08369552e-01 -5.20187654... | [11.005821228027344, 8.437318801879883] |
9b9d3e81-3141-47b7-9f92-a8107293baed | a-new-cervical-cytology-dataset-for-nucleus | 1811.09651 | null | http://arxiv.org/abs/1811.09651v1 | http://arxiv.org/pdf/1811.09651v1.pdf | A New Cervical Cytology Dataset for Nucleus Detection and Image Classification (Cervix93) and Methods for Cervical Nucleus Detection | Analyzing Pap cytology slides is an important tasks in detecting and grading
precancerous and cancerous cervical cancer stages. Processing cytology images
usually involve segmenting nuclei and overlapping cells. We introduce a
cervical cytology dataset that can be used to evaluate nucleus detection, as
well as image cl... | ['Hady Ahmady Phoulady', 'Peter R. Mouton'] | 2018-11-23 | null | null | null | null | ['cervical-nucleus-detection'] | ['medical'] | [ 3.41377258e-01 2.10878104e-01 -2.58420318e-01 -2.70933151e-01
-1.01887691e+00 -8.30459297e-01 4.40301597e-01 5.19938350e-01
-6.24094367e-01 6.45100236e-01 -3.30513239e-01 -8.44354153e-01
3.53521675e-01 -9.32868183e-01 -5.48501670e-01 -1.21933305e+00
1.41824499e-01 7.15366721e-01 5.72064281e-01 4.09670323... | [15.068462371826172, -3.119831085205078] |
e314afd7-8c48-4f12-9890-763f48264ecc | a-fully-dense-and-globally-consistent-3d-map | 1705.06524 | null | http://arxiv.org/abs/1705.06524v1 | http://arxiv.org/pdf/1705.06524v1.pdf | A fully dense and globally consistent 3D map reconstruction approach for GI tract to enhance therapeutic relevance of the endoscopic capsule robot | In the gastrointestinal (GI) tract endoscopy field, ingestible wireless
capsule endoscopy is emerging as a novel, minimally invasive diagnostic
technology for inspection of the GI tract and diagnosis of a wide range of
diseases and pathologies. Since the development of this technology, medical
device companies and many... | ['Redhwan Jamiruddin', 'Metin Sitti', 'Yusuf Yigit Pilavci', 'Mehmet Turan', 'Helder Araujo', 'Ender Konukoglu'] | 2017-05-18 | null | null | null | null | ['image-stitching'] | ['computer-vision'] | [-3.66568595e-01 2.19830543e-01 2.15645581e-01 1.29366130e-01
-1.75912932e-01 -1.11684704e+00 -1.59706715e-02 3.06656450e-01
-2.15379834e-01 7.34901130e-02 -2.17232034e-02 -4.63923484e-01
1.78854138e-01 -5.52733719e-01 -5.08800268e-01 -5.76686442e-01
-3.41457486e-01 4.20774668e-01 5.39137244e-01 -1.21402293... | [13.991437911987305, -3.1795732975006104] |
0e884671-99b6-46ed-8f70-750d795cad9b | restore-from-restored-video-restoration-with | 2003.04279 | null | https://arxiv.org/abs/2003.04279v3 | https://arxiv.org/pdf/2003.04279v3.pdf | Restore from Restored: Video Restoration with Pseudo Clean Video | In this study, we propose a self-supervised video denoising method called "restore-from-restored." This method fine-tunes a pre-trained network by using a pseudo clean video during the test phase. The pseudo clean video is obtained by applying a noisy video to the baseline network. By adopting a fully convolutional neu... | ['Donghyeon Cho', 'Seunghwan Lee', 'Tae Hyun Kim', 'Jiwon Kim'] | 2020-03-09 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Lee_Restore_From_Restored_Video_Restoration_With_Pseudo_Clean_Video_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Lee_Restore_From_Restored_Video_Restoration_With_Pseudo_Clean_Video_CVPR_2021_paper.pdf | cvpr-2021-1 | ['video-denoising', 'video-restoration'] | ['computer-vision', 'computer-vision'] | [ 1.73496023e-01 -3.58064026e-01 1.13333009e-01 -1.87864110e-01
-8.13204229e-01 -3.43131840e-01 3.56676638e-01 -5.34127772e-01
-3.11597317e-01 7.07935512e-01 4.37991410e-01 1.25377178e-01
1.04780532e-01 -6.91257000e-01 -1.14479613e+00 -9.98985529e-01
2.08174381e-02 -3.28626543e-01 3.12456600e-02 -4.32975888... | [11.326371192932129, -2.0812439918518066] |
fdeeab70-ce4d-4858-9daf-899bb35b4537 | massive-feature-extraction-for-explaining-and | 2108.00846 | null | https://arxiv.org/abs/2108.00846v2 | https://arxiv.org/pdf/2108.00846v2.pdf | Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale | Statistical analyses and descriptive characterizations are sometimes assumed to be offering information on time series forecastability. Despite the scientific interest suggested by such assumptions, the relationships between descriptive time series features (e.g., temporal dependence, entropy, seasonality, trend and li... | ['Elena Volpi', 'Salvatore Grimaldi', 'Ilias G. Pechlivanidis', 'Hristos Tyralis', 'Georgia Papacharalampous'] | 2021-07-25 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-3.01520616e-01 -2.37871125e-01 -2.36373425e-01 -3.58045280e-01
-5.27034588e-02 -8.96541834e-01 1.08586276e+00 3.59368950e-01
-2.40059868e-01 1.01959789e+00 4.82596993e-01 -8.42149794e-01
-3.61861616e-01 -1.11690497e+00 -1.33046791e-01 -8.77140284e-01
-7.73068488e-01 -7.42055029e-02 -1.69837698e-01 -5.97533047... | [6.558010578155518, 2.978684425354004] |
877d3290-b838-4ecb-a195-11d993ed5b3a | unified-analysis-of-sgd-type-methods | 2303.16502 | null | https://arxiv.org/abs/2303.16502v1 | https://arxiv.org/pdf/2303.16502v1.pdf | Unified analysis of SGD-type methods | This note focuses on a simple approach to the unified analysis of SGD-type methods from (Gorbunov et al., 2020) for strongly convex smooth optimization problems. The similarities in the analyses of different stochastic first-order methods are discussed along with the existing extensions of the framework. The limitation... | ['Eduard Gorbunov'] | 2023-03-29 | null | null | null | null | ['type'] | ['speech'] | [-2.84438580e-01 2.56655842e-01 5.44830449e-02 -3.35395545e-01
-9.47209656e-01 -3.08015913e-01 4.97835994e-01 -3.70890468e-01
-2.86799312e-01 1.31848109e+00 2.25190327e-01 -1.45451650e-01
-4.08058733e-01 -9.05997772e-03 -2.72619516e-01 -1.17915893e+00
-3.12027454e-01 2.66529500e-01 -6.28534798e-03 -5.97576678... | [6.878398418426514, 4.296334743499756] |
8f752da2-776a-4771-8142-d22a4fe4f842 | reducing-spurious-correlations-for-aspect | 2303.02846 | null | https://arxiv.org/abs/2303.02846v2 | https://arxiv.org/pdf/2303.02846v2.pdf | Reducing Spurious Correlations for Aspect-Based Sentiment Analysis with Variational Information Bottleneck and Contrastive Learning | Deep learning techniques have dominated the literature on aspect-based sentiment analysis (ABSA), yielding state-of-the-art results. However, these deep models generally suffer from spurious correlation problems between input features and output labels, which creates significant barriers to robustness and generalizatio... | ['Ruifeng Xu', 'Qingshan Jiang', 'Min Yang', 'Mingshan Chang'] | 2023-03-06 | null | null | null | null | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [ 2.20459148e-01 -8.37359130e-02 -1.58054277e-01 -5.05735457e-01
-5.62893808e-01 -2.41711140e-01 4.00129229e-01 8.59678239e-02
-3.53140771e-01 5.13475180e-01 5.06463014e-02 -1.10646509e-01
-2.57420510e-01 -7.72325456e-01 -5.83762109e-01 -1.01820040e+00
2.13447079e-01 2.56511688e-01 1.09069198e-01 -2.96868533... | [10.160872459411621, 4.794378757476807] |
fad6cff7-d7e7-4065-a0e9-00a6e9fd184e | jointly-learning-author-and-annotated | null | null | https://aclanthology.org/R19-1080 | https://aclanthology.org/R19-1080.pdf | Jointly Learning Author and Annotated Character N-gram Embeddings: A Case Study in Literary Text | An author{'}s way of presenting a story through his/her writing style has a great impact on whether the story will be liked by readers or not. In this paper, we learn representations for authors of literary texts together with representations for character n-grams annotated with their functional roles. We train a neura... | ["Fabio A. Gonz{\\'a}lez", 'Deepthi Mave', 'Suraj Maharjan', 'Thamar Solorio', 'Prasha Shrestha', 'Manuel Montes'] | 2019-09-01 | null | null | null | ranlp-2019-9 | ['genre-classification'] | ['computer-vision'] | [ 8.87318235e-03 -1.59452930e-01 -5.29039145e-01 -3.52245301e-01
-2.99611419e-01 -9.26781595e-01 9.58642185e-01 4.57252622e-01
-5.10569334e-01 4.19972867e-01 9.79502201e-01 -2.12239280e-01
4.96518984e-02 -8.20926070e-01 -4.69687879e-01 4.39819060e-02
6.39288843e-01 7.71605611e-01 -2.84098238e-01 -3.64309549... | [10.509781837463379, 10.242204666137695] |
18c7a862-1df4-4eb5-870d-85df5a4f6ff4 | local-latin-hypercube-refinement-for-multi | 2108.08890 | null | https://arxiv.org/abs/2108.08890v2 | https://arxiv.org/pdf/2108.08890v2.pdf | Local Latin Hypercube Refinement for Multi-objective Design Uncertainty Optimization | Optimizing the reliability and the robustness of a design is important but often unaffordable due to high sample requirements. Surrogate models based on statistical and machine learning methods are used to increase the sample efficiency. However, for higher dimensional or multi-modal systems, surrogate models may also ... | ['Tamara Nestorović', 'Dirk Roos', 'Can Bogoclu'] | 2021-08-19 | null | null | null | null | ['robust-design'] | ['miscellaneous'] | [ 7.30624720e-02 -1.40498713e-01 -1.93997025e-01 -1.23800956e-01
-9.80125070e-01 -3.11950237e-01 3.62171561e-01 3.10506761e-01
-2.82991797e-01 1.45612264e+00 -3.89001705e-02 -2.55489379e-01
-8.05086315e-01 -6.17478549e-01 -5.45947790e-01 -1.02936184e+00
-1.96713746e-01 6.05915070e-01 -2.23671451e-01 -6.11177795... | [6.112616539001465, 3.491797685623169] |
dce291f3-d546-4ffc-9a53-741487a2c1ac | check-your-facts-and-try-again-improving | 2302.12813 | null | https://arxiv.org/abs/2302.12813v3 | https://arxiv.org/pdf/2302.12813v3.pdf | Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback | Large language models (LLMs), such as ChatGPT, are able to generate human-like, fluent responses for many downstream tasks, e.g., task-oriented dialog and question answering. However, applying LLMs to real-world, mission-critical applications remains challenging mainly due to their tendency to generate hallucinations a... | ['Jianfeng Gao', 'Weizhu Chen', 'Zhou Yu', 'Lars Liden', 'Qiuyuan Huang', 'Yu Hu', 'Yujia Xie', 'Hao Cheng', 'Pengcheng He', 'Michel Galley', 'Baolin Peng'] | 2023-02-24 | null | null | null | null | ['open-domain-question-answering'] | ['natural-language-processing'] | [-3.79754990e-01 6.23198688e-01 1.88811332e-01 -5.15100300e-01
-6.46930397e-01 -6.37794673e-01 5.03941715e-01 1.21923164e-01
-3.99265617e-01 8.51541996e-01 6.03566825e-01 -4.15678024e-01
1.31402031e-01 -6.49209857e-01 2.90211644e-02 1.10878408e-01
5.16381264e-01 7.55739093e-01 2.80733019e-01 -7.06249475... | [12.749370574951172, 7.916416168212891] |
4f222796-a441-4bb9-a8a9-5f39943009a5 | rule-neural-symbolic-knowledge-graph | 2210.14905 | null | https://arxiv.org/abs/2210.14905v1 | https://arxiv.org/pdf/2210.14905v1.pdf | RulE: Neural-Symbolic Knowledge Graph Reasoning with Rule Embedding | Knowledge graph (KG) reasoning is an important problem for knowledge graphs. It predicts missing links by reasoning on existing facts. Knowledge graph embedding (KGE) is one of the most popular methods to address this problem. It embeds entities and relations into low-dimensional vectors and uses the learned entity/rel... | ['Muhan Zhang', 'Yitao Liang', 'Song-Chun Zhu', 'Xiaojuan Tang'] | 2022-10-24 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-1.41312525e-01 8.64790082e-01 -6.74541771e-01 -3.56477797e-01
2.87786156e-01 -3.98787737e-01 2.84110665e-01 3.27241719e-01
6.18002079e-02 8.47633183e-01 1.48371950e-01 -7.83246756e-01
-6.48025334e-01 -1.53158951e+00 -1.05147350e+00 -2.05338985e-01
-1.74679160e-01 5.88176608e-01 2.19916955e-01 -4.41276222... | [8.802894592285156, 7.773520469665527] |
bcb66d31-1a4c-4674-8f32-33a55b9d9ebc | interpretable-and-scalable-graphical-models | 2301.06021 | null | https://arxiv.org/abs/2301.06021v1 | https://arxiv.org/pdf/2301.06021v1.pdf | Interpretable and Scalable Graphical Models for Complex Spatio-temporal Processes | This thesis focuses on data that has complex spatio-temporal structure and on probabilistic graphical models that learn the structure in an interpretable and scalable manner. We target two research areas of interest: Gaussian graphical models for tensor-variate data and summarization of complex time-varying texts using... | ['Yu Wang'] | 2023-01-15 | null | null | null | null | ['weather-forecasting', 'topic-models'] | ['miscellaneous', 'natural-language-processing'] | [-4.81367409e-01 -2.53691345e-01 6.03656590e-01 -4.19753343e-01
-3.72177482e-01 -5.30512154e-01 7.10265934e-01 3.21118869e-02
1.61923002e-02 5.72639704e-01 3.18991095e-01 -3.66831958e-01
-9.15768206e-01 -5.74002683e-01 -2.80921668e-01 -1.01262784e+00
-7.06549048e-01 5.74602067e-01 -2.40021572e-01 -1.88645154... | [6.743375778198242, 3.6609628200531006] |
0cd78773-900b-4d07-b41d-024eb3f2fbc6 | securing-voice-driven-interfaces-against-fake | 1902.06782 | null | http://arxiv.org/abs/1902.06782v1 | http://arxiv.org/pdf/1902.06782v1.pdf | Securing Voice-driven Interfaces against Fake (Cloned) Audio Attacks | Voice cloning technologies have found applications in a variety of areas
ranging from personalized speech interfaces to advertisement, robotics, and so
on. Existing voice cloning systems are capable of learning speaker
characteristics and use trained models to synthesize a person's voice from only
a few audio samples. ... | [] | 2019-02-18 | null | null | null | null | ['voice-cloning'] | ['speech'] | [ 8.52366462e-02 9.46026444e-02 8.49100109e-03 -3.30745608e-01
-1.07696891e+00 -4.84796703e-01 3.94355685e-01 -1.98811993e-01
2.13313363e-02 6.59162641e-01 1.73908740e-01 -1.92024037e-01
1.66962117e-01 -4.59356844e-01 -3.21286798e-01 -6.07062042e-01
-1.84528247e-01 2.74583876e-01 2.31710926e-01 -1.31923407... | [14.612217903137207, 6.194589614868164] |
2a655d95-377c-45e9-98f5-5d61200b89f8 | histopathology-slide-indexing-and-search-are | 2306.17019 | null | https://arxiv.org/abs/2306.17019v1 | https://arxiv.org/pdf/2306.17019v1.pdf | Histopathology Slide Indexing and Search: Are We There Yet? | The search and retrieval of digital histopathology slides is an important task that has yet to be solved. In this case study, we investigate the clinical readiness of three state-of-the-art histopathology slide search engines, Yottixel, SISH, and RetCCL, on three patients with solid tumors. We provide a qualitative ass... | ['Jacob M. Luber', 'Jitin Makker', 'Chace Moleta', 'Manfred Huber', 'Amir Hajighasemi', 'MD Jillur Rahman Saurav', 'Parisa Boodaghi Malidarreh', 'Jai Prakash Veerla', 'Mohammad Sadegh Nasr', 'Helen H. Shang'] | 2023-06-29 | null | null | null | null | ['image-retrieval', 'retrieval'] | ['computer-vision', 'methodology'] | [ 5.55075333e-02 -2.22593114e-01 -3.86035323e-01 -4.10045572e-02
-1.59420598e+00 -8.15099239e-01 3.43031913e-01 8.38723540e-01
-4.26476926e-01 5.52377820e-01 2.35664696e-01 -8.68108928e-01
-4.65780407e-01 -2.56608903e-01 -1.16461657e-01 -7.91294336e-01
1.51615947e-01 6.33767605e-01 4.13594604e-01 6.07294776... | [15.127228736877441, -3.0724735260009766] |
f65e50b2-3020-4beb-b264-ae0e0632633d | diva-a-dirichlet-process-based-incremental | 2305.14067 | null | https://arxiv.org/abs/2305.14067v2 | https://arxiv.org/pdf/2305.14067v2.pdf | DIVA: A Dirichlet Process Based Incremental Deep Clustering Algorithm via Variational Auto-Encoder | Generative model-based deep clustering frameworks excel in classifying complex data, but are limited in handling dynamic and complex features because they require prior knowledge of the number of clusters. In this paper, we propose a nonparametric deep clustering framework that employs an infinite mixture of Gaussians ... | ['Alois Knoll', 'Kai Huang', 'Xiaojie Su', 'Hang Su', 'Yuqi Yun', 'Yuan Meng', 'Zhenshan Bing'] | 2023-05-23 | null | null | null | null | ['nonparametric-deep-clustering', 'deep-clustering', 'deep-clustering'] | ['methodology', 'miscellaneous', 'natural-language-processing'] | [-6.65387928e-01 -4.05445367e-01 7.63276070e-02 -4.74283159e-01
-5.80968678e-01 -5.60156226e-01 9.29867387e-01 5.28165977e-03
-4.34887826e-01 1.65581167e-01 -2.91887503e-02 -1.81530073e-01
-4.60913451e-03 -9.16438520e-01 -6.10645533e-01 -8.04573596e-01
-3.36871296e-02 9.83496249e-01 1.47839412e-01 2.59482890... | [9.105490684509277, 3.2671148777008057] |
2c0bae22-b9f9-4f49-958d-869d3f0db144 | multi-layer-feature-aggregation-for-deep | 2011.02572 | null | https://arxiv.org/abs/2011.02572v1 | https://arxiv.org/pdf/2011.02572v1.pdf | Multi-layer Feature Aggregation for Deep Scene Parsing Models | Scene parsing from images is a fundamental yet challenging problem in visual content understanding. In this dense prediction task, the parsing model assigns every pixel to a categorical label, which requires the contextual information of adjacent image patches. So the challenge for this learning task is to simultaneous... | ['Qiang Wu', 'Jian Zhang', 'Jun Zhou', 'Yongsheng Gao', 'Litao Yu'] | 2020-11-04 | null | null | null | null | ['scene-parsing'] | ['computer-vision'] | [ 3.17685932e-01 -1.86371431e-02 -1.83751971e-01 -8.37430179e-01
-5.21474063e-01 -3.35579365e-01 3.72217059e-01 9.00622904e-02
-1.94594592e-01 2.01433152e-01 9.05111879e-02 -4.97014672e-02
4.73827310e-02 -1.08465993e+00 -8.04760635e-01 -8.12412679e-01
2.67690688e-01 -1.21738092e-04 5.67204237e-01 1.62899539... | [9.59613037109375, 0.39626631140708923] |
d1edc83d-0dd0-4107-a97a-4671dfbe0509 | mstream-fast-streaming-multi-aspect-group | 2009.08451 | null | https://arxiv.org/abs/2009.08451v4 | https://arxiv.org/pdf/2009.08451v4.pdf | MSTREAM: Fast Anomaly Detection in Multi-Aspect Streams | Given a stream of entries in a multi-aspect data setting i.e., entries having multiple dimensions, how can we detect anomalous activities in an unsupervised manner? For example, in the intrusion detection setting, existing work seeks to detect anomalous events or edges in dynamic graph streams, but this does not allow ... | ['Bryan Hooi', 'Arjit Jain', 'Siddharth Bhatia', 'Ritesh Kumar', 'Pan Li'] | 2020-09-17 | null | null | null | null | ['group-anomaly-detection'] | ['methodology'] | [-1.39387948e-02 -2.59673417e-01 -5.99862896e-02 -7.11803511e-02
-8.23232755e-02 -5.03683627e-01 5.76788008e-01 1.27752864e+00
-1.49115965e-01 2.48974279e-01 1.28323913e-01 -4.54939008e-01
-3.68177980e-01 -1.07753468e+00 -4.08466488e-01 -3.86494637e-01
-1.03785634e+00 8.32870722e-01 8.64556372e-01 -1.35801077... | [6.657393455505371, 5.700028419494629] |
5d9a21e9-ea67-4006-a8c3-51f8303418ab | uncertainty-aware-adaptation-for-self | 2203.15293 | null | https://arxiv.org/abs/2203.15293v1 | https://arxiv.org/pdf/2203.15293v1.pdf | Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose Estimation | The advances in monocular 3D human pose estimation are dominated by supervised techniques that require large-scale 2D/3D pose annotations. Such methods often behave erratically in the absence of any provision to discard unfamiliar out-of-distribution data. To this end, we cast the 3D human pose learning as an unsupervi... | ['R. Venkatesh Babu', 'Anirban Chakraborty', 'Varun Jampani', 'Pradyumna YM', 'Siddharth Seth', 'Jogendra Nath Kundu'] | 2022-03-29 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Kundu_Uncertainty-Aware_Adaptation_for_Self-Supervised_3D_Human_Pose_Estimation_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Kundu_Uncertainty-Aware_Adaptation_for_Self-Supervised_3D_Human_Pose_Estimation_CVPR_2022_paper.pdf | cvpr-2022-1 | ['monocular-3d-human-pose-estimation', 'unsupervised-3d-human-pose-estimation', 'weakly-supervised-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 1.08615644e-01 4.87249643e-01 1.17590785e-01 -6.55034423e-01
-8.49931061e-01 -4.57832545e-01 5.78743458e-01 -1.38958529e-01
-6.21081352e-01 9.39629376e-01 1.29455200e-03 2.90398151e-01
-1.93379279e-02 -4.40069765e-01 -1.01773608e+00 -5.89873075e-01
-2.16437615e-02 1.14458871e+00 2.56048769e-01 -7.34092072... | [7.021215915679932, -0.9913513660430908] |
d2ac6b81-5e44-4415-bfb9-b25dd469c8f9 | heuristics-for-image-generation-from-scene | null | null | https://openreview.net/forum?id=r1eqsNXgdV | https://openreview.net/pdf?id=r1eqsNXgdV | Heuristics for Image Generation from Scene Graphs | Generating realistic images from scene graphs requires neural networks to be able to reason about object relationships and compositionality. Learning a sufficiently rich representation to facilitate this reasoning is challenging due to dataset limitations. Synthetic scene graphs from COCO only have basic geometric rela... | ['Hanlin Tang', 'Alexei Bastidas', 'Anahita Bhiwandiwalla', 'Subarna Tripathi'] | 2019-03-20 | null | null | null | iclr-workshop-lld-2019 | ['image-generation-from-scene-graphs'] | ['computer-vision'] | [ 5.97272873e-01 5.66178679e-01 4.73037884e-02 -4.87354547e-01
-3.82682502e-01 -8.04209173e-01 7.65870512e-01 2.83402473e-01
-8.80422816e-03 4.62545663e-01 2.11339414e-01 -3.67954642e-01
2.08327785e-01 -1.04743886e+00 -1.09792733e+00 -1.18332408e-01
8.69974867e-02 6.36255980e-01 3.13207090e-01 -2.33176798... | [10.45615005493164, 1.4320600032806396] |
9af4beb2-d813-45da-b11a-1158ba257dc7 | uav-human-a-large-benchmark-for-human | 2104.00946 | null | https://arxiv.org/abs/2104.00946v4 | https://arxiv.org/pdf/2104.00946v4.pdf | UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles | Human behavior understanding with unmanned aerial vehicles (UAVs) is of great significance for a wide range of applications, which simultaneously brings an urgent demand of large, challenging, and comprehensive benchmarks for the development and evaluation of UAV-based models. However, existing benchmarks have limitati... | ['Zhiheng Li', 'Wenqian Wang', 'Yun Ni', 'Wei zhang', 'Jun Liu', 'Tianjiao Li'] | 2021-04-02 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Li_UAV-Human_A_Large_Benchmark_for_Human_Behavior_Understanding_With_Unmanned_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Li_UAV-Human_A_Large_Benchmark_for_Human_Behavior_Understanding_With_Unmanned_CVPR_2021_paper.pdf | cvpr-2021-1 | ['pedestrian-attribute-recognition'] | ['computer-vision'] | [-7.70947244e-03 -6.95812106e-01 5.73579744e-02 -4.01084185e-01
-9.77459475e-02 -7.71638036e-01 3.24846596e-01 -4.49775010e-01
-4.00721163e-01 6.38627410e-01 5.76619655e-02 1.62581444e-01
-6.03969395e-02 -6.06410384e-01 -5.64217031e-01 -7.47609258e-01
-1.93183482e-01 6.63326010e-02 -6.13230541e-02 -3.00592721... | [7.787271976470947, -0.6631615161895752] |
2d01cbe4-0cbe-40e6-b00e-ef95a90f571b | neilf-inter-reflectable-light-fields-for | 2303.17147 | null | https://arxiv.org/abs/2303.17147v1 | https://arxiv.org/pdf/2303.17147v1.pdf | NeILF++: Inter-Reflectable Light Fields for Geometry and Material Estimation | We present a novel differentiable rendering framework for joint geometry, material, and lighting estimation from multi-view images. In contrast to previous methods which assume a simplified environment map or co-located flashlights, in this work, we formulate the lighting of a static scene as one neural incident light ... | ['Long Quan', 'Yanghai Tsin', 'David McKinnon', 'Tian Fang', 'Jingbo Liu', 'Shiwei Li', 'Yao Yao', 'Jingyang Zhang'] | 2023-03-30 | null | null | null | null | ['lighting-estimation'] | ['computer-vision'] | [ 6.11363292e-01 -2.53195047e-01 8.09816718e-01 -4.15204763e-01
-3.60594898e-01 -4.25159484e-01 7.85984337e-01 -2.12980792e-01
-1.55091688e-01 4.56830829e-01 -5.62072098e-02 -1.19994450e-02
-4.13603559e-02 -1.15046930e+00 -8.50253284e-01 -9.89276528e-01
7.17114985e-01 3.65268737e-01 1.83170885e-01 -2.04336867... | [9.746244430541992, -3.062335252761841] |
7aa72e49-97d1-45b8-acef-14646f8b3e23 | apple-of-sodom-hidden-backdoors-in-superior | 2210.11082 | null | https://arxiv.org/abs/2210.11082v1 | https://arxiv.org/pdf/2210.11082v1.pdf | Apple of Sodom: Hidden Backdoors in Superior Sentence Embeddings via Contrastive Learning | This paper finds that contrastive learning can produce superior sentence embeddings for pre-trained models but is also vulnerable to backdoor attacks. We present the first backdoor attack framework, BadCSE, for state-of-the-art sentence embeddings under supervised and unsupervised learning settings. The attack manipula... | ['Zhonghai Wu', 'Qingni Shen', 'Shiqing Ma', 'Shengfang Zhai', 'Baisong Xin', 'Xiaoyi Chen'] | 2022-10-20 | null | null | null | null | ['sentence-embeddings', 'sentence-embeddings'] | ['methodology', 'natural-language-processing'] | [ 1.52050018e-01 3.26503009e-01 -4.93614912e-01 -3.18650633e-01
-8.70005906e-01 -9.67348576e-01 7.60348320e-01 2.78583348e-01
-5.68176031e-01 3.41948837e-01 4.13882822e-01 -6.56845331e-01
3.73979926e-01 -7.67182469e-01 -6.70028448e-01 -6.35914564e-01
-1.83359161e-01 1.75807141e-02 2.44093612e-01 -3.49281013... | [10.65715217590332, 8.551119804382324] |
19d420bb-45be-4a50-b317-e9da6eac8674 | solution-existence-uniqueness-and-stability | 2305.03330 | null | https://arxiv.org/abs/2305.03330v1 | https://arxiv.org/pdf/2305.03330v1.pdf | Solution existence, uniqueness, and stability of discrete basis sinograms in multispectral CT | This work investigates conditions for quantitative image reconstruction in multispectral computed tomography (MSCT), which remains a topic of active research. In MSCT, one seeks to obtain from data the spatial distribution of linear attenuation coefficient, referred to as a virtual monochromatic image (VMI), at a given... | ['Chong Chen', 'Xiaochuan Pan', 'Yu Gao'] | 2023-05-05 | null | null | null | null | ['image-reconstruction'] | ['computer-vision'] | [ 6.09500945e-01 -1.22750901e-01 1.23290338e-01 -8.77634529e-03
-7.40715921e-01 -1.72999993e-01 2.06650019e-01 -3.65851939e-01
-2.34585479e-01 7.94968843e-01 -1.98359072e-01 -3.78133386e-01
-4.99456584e-01 -6.36180937e-01 -3.05576921e-01 -1.10361898e+00
2.08325580e-01 5.98348260e-01 7.60017633e-02 1.65907532... | [12.842198371887207, -2.7114062309265137] |
98ae413b-7601-4497-85f4-dc1cf2a17990 | one-shot-transfer-learning-for-population | 2108.06228 | null | https://arxiv.org/abs/2108.06228v2 | https://arxiv.org/pdf/2108.06228v2.pdf | One-shot Transfer Learning for Population Mapping | Fine-grained population distribution data is of great importance for many applications, e.g., urban planning, traffic scheduling, epidemic modeling, and risk control. However, due to the limitations of data collection, including infrastructure density, user privacy, and business security, such fine-grained data is hard... | ['Yong Li', 'Tong Xia', 'Yingheng Wang', 'Jie Feng', 'Erzhuo Shao'] | 2021-08-13 | null | null | null | null | ['population-mapping'] | ['computer-vision'] | [-3.17715883e-01 -1.08631708e-01 -3.64428759e-01 -1.08131453e-01
-6.77947223e-01 -1.26498908e-01 5.86069405e-01 1.39750287e-01
-3.12705845e-01 1.24196112e+00 3.00199479e-01 -3.08707207e-01
-4.01966125e-01 -1.68813610e+00 -6.30587101e-01 -8.06202233e-01
3.07396144e-01 7.30079651e-01 3.94957095e-01 -4.52501625... | [6.458844184875488, 2.0231592655181885] |
00aebf75-58ee-4dad-8a90-33f8b276e36f | spatial-dual-modality-graph-reasoning-for-key | 2103.14470 | null | https://arxiv.org/abs/2103.14470v1 | https://arxiv.org/pdf/2103.14470v1.pdf | Spatial Dual-Modality Graph Reasoning for Key Information Extraction | Key information extraction from document images is of paramount importance in office automation. Conventional template matching based approaches fail to generalize well to document images of unseen templates, and are not robust against text recognition errors. In this paper, we propose an end-to-end Spatial Dual-Modali... | ['Wayne Zhang', 'Chenhao Lin', 'Xiaoyu Yue', 'Zhanghui Kuang', 'Hongbin Sun'] | 2021-03-26 | null | null | null | null | ['template-matching', 'key-information-extraction'] | ['computer-vision', 'natural-language-processing'] | [ 5.55299699e-01 -1.17896445e-01 4.14209329e-02 -1.16004176e-01
-7.51903713e-01 -9.13802445e-01 8.48249614e-01 1.51496962e-01
5.67630865e-02 2.82777905e-01 2.00390115e-01 -3.11459839e-01
-5.37052214e-01 -6.10674679e-01 -6.13174498e-01 -5.49372554e-01
3.06395262e-01 6.01637006e-01 5.47065377e-01 -2.11702421... | [11.596575736999512, 2.2969391345977783] |
70ad9fb3-e369-47bb-ba48-34410048d3ea | ensemble-transfer-learning-for-the-prediction | 2005.09572 | null | https://arxiv.org/abs/2005.09572v1 | https://arxiv.org/pdf/2005.09572v1.pdf | Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response | Transfer learning has been shown to be effective in many applications in which training data for the target problem are limited but data for a related (source) problem are abundant. In this paper, we apply transfer learning to the prediction of anti-cancer drug response. Previous transfer learning studies for drug resp... | ['Alexander Partin', 'Yvonne A. Evrard', 'Yitan Zhu', 'Thomas Brettin', 'Maulik Shukla', 'Fangfang Xia', 'Rick Stevens', 'James H. Doroshow', 'Hyunseung Yoo'] | 2020-05-13 | null | null | null | null | ['drug-response-prediction'] | ['medical'] | [ 4.01028395e-01 -2.53579468e-01 -6.03104174e-01 -2.24394768e-01
-6.57134175e-01 -2.30873480e-01 4.40282732e-01 2.51399785e-01
-2.75986493e-01 1.23486197e+00 2.36578472e-03 -7.96748281e-01
-3.61940861e-01 -9.91870105e-01 -5.61411023e-01 -1.02630591e+00
1.70545503e-01 7.49244988e-01 1.27165243e-01 -3.15231979... | [5.693985939025879, 5.698042392730713] |
100b74cb-64c2-48aa-b821-ab57fcd430a9 | multi-classification-using-one-versus-one | 2306.09668 | null | https://arxiv.org/abs/2306.09668v1 | https://arxiv.org/pdf/2306.09668v1.pdf | Multi-Classification using One-versus-One Deep Learning Strategy with Joint Probability Estimates | The One-versus-One (OvO) strategy is an approach of multi-classification models which focuses on training binary classifiers between each pair of classes. While the OvO strategy takes advantage of balanced training data, the classification accuracy is usually hindered by the voting mechanism to combine all binary class... | ['Lingjia Dai', 'Raymond HonFu Chan', 'Anthony Hei-Long Chan'] | 2023-06-16 | null | null | null | null | ['classification-1'] | ['methodology'] | [ 1.56567842e-02 5.28331064e-02 -5.80942154e-01 -8.45835745e-01
-9.42214251e-01 5.79762738e-03 3.37378800e-01 3.53027403e-01
-3.69458526e-01 8.94386232e-01 -5.03292501e-01 -2.31112745e-02
-6.78002894e-01 -7.96247184e-01 -3.87688190e-01 -1.09867072e+00
3.34934115e-01 6.33337140e-01 -2.03810558e-01 3.02648127... | [8.995534896850586, 3.968574047088623] |
b4a8573e-f84e-4492-8d0c-f0ca0096d664 | responding-to-challenge-call-of-machine | 2111.14354 | null | https://arxiv.org/abs/2111.14354v1 | https://arxiv.org/pdf/2111.14354v1.pdf | Responding to Challenge Call of Machine Learning Model Development in Diagnosing Respiratory Disease Sounds | In this study, a machine learning model was developed for automatically detecting respiratory system sounds such as sneezing and coughing in disease diagnosis. The automatic model and approach development of breath sounds, which carry valuable information, results in early diagnosis and treatment. A successful machine ... | ['Negin Melek'] | 2021-11-29 | null | null | null | null | ['environmental-sound-classification', 'sound-classification'] | ['audio', 'audio'] | [ 3.36668827e-02 -5.86477697e-01 1.81339532e-01 1.59299240e-01
-2.96561509e-01 -4.95220631e-01 5.22682033e-02 3.20690513e-01
-3.96066338e-01 7.53911555e-01 2.10452020e-01 -4.45513040e-01
-4.20614004e-01 -6.62024021e-01 1.06274650e-01 -7.14054704e-01
-9.68652666e-02 9.91558060e-02 3.04419905e-01 1.26261935... | [14.493413925170898, 3.84393310546875] |
1747dc6e-734a-442f-9485-4750853050f1 | widening-the-dialogue-workflow-modeling | 2011.08334 | null | https://arxiv.org/abs/2011.08334v1 | https://arxiv.org/pdf/2011.08334v1.pdf | Widening the Dialogue Workflow Modeling Bottleneck in Ontology-Based Personal Assistants | We present a new approach to dialogue specification for Virtual Personal Assistants (VPAs) based on so-called dialogue workflow graphs, with several demonstrated advantages over current ontology-based methods. Our new dialogue specification language (DSL) enables customers to more easily participate in the VPA modeling... | ['Andreas Kathol', 'Girish Acharya', 'Edgar Kalns', 'Michael Wessel'] | 2020-11-16 | null | null | null | null | ['dialogue-management'] | ['natural-language-processing'] | [-2.10313842e-01 1.38459384e+00 -3.69150341e-02 -6.21499717e-01
-1.64808959e-01 -6.95826769e-01 9.77679431e-01 1.97149336e-01
-1.95207864e-01 5.87648392e-01 6.33250654e-01 -6.84400082e-01
-4.27315116e-01 -7.42734075e-01 2.61595577e-01 3.16938847e-01
3.78682405e-01 9.34275866e-01 3.40508729e-01 -9.15174365... | [12.86535930633545, 7.9540605545043945] |
d13cc4b8-656d-4c73-92c4-5567253fb739 | representer-point-selection-for-explaining-1 | 2305.20002 | null | https://arxiv.org/abs/2305.20002v2 | https://arxiv.org/pdf/2305.20002v2.pdf | Representer Point Selection for Explaining Regularized High-dimensional Models | We introduce a novel class of sample-based explanations we term high-dimensional representers, that can be used to explain the predictions of a regularized high-dimensional model in terms of importance weights for each of the training samples. Our workhorse is a novel representer theorem for general regularized high-di... | ['Pradeep Ravikumar', 'Cho-Jui Hsieh', 'Hsiang-Fu Yu', 'Eli Chien', 'Jiong Zhang', 'Che-Ping Tsai'] | 2023-05-31 | null | null | null | null | ['collaborative-filtering'] | ['miscellaneous'] | [ 3.44849974e-01 7.71351933e-01 -5.89664102e-01 -5.34305871e-01
-4.80078489e-01 -9.85438526e-02 6.20383441e-01 -2.37371512e-02
4.94733453e-01 5.51699638e-01 8.23688447e-01 -3.14382166e-01
-8.80455136e-01 -6.02083921e-01 -7.65435100e-01 -5.70358276e-01
-2.78755248e-01 9.17380035e-01 -4.31561977e-01 -1.14426687... | [9.582032203674316, 5.546529293060303] |
9ecebad4-0285-4573-9ff7-14aee44fd5c1 | hybrid-contrastive-learning-with-cluster | 2201.11995 | null | https://arxiv.org/abs/2201.11995v2 | https://arxiv.org/pdf/2201.11995v2.pdf | Hybrid Contrastive Learning with Cluster Ensemble for Unsupervised Person Re-identification | Unsupervised person re-identification (ReID) aims to match a query image of a pedestrian to the images in gallery set without supervision labels. The most popular approaches to tackle unsupervised person ReID are usually performing a clustering algorithm to yield pseudo labels at first and then exploit the pseudo label... | ['Chun-Guang Li', 'Mingkun Li', 'He Sun'] | 2022-01-28 | null | null | null | null | ['unsupervised-person-re-identification', 'clustering-ensemble'] | ['computer-vision', 'graphs'] | [-4.39089537e-02 -4.28350687e-01 1.44896761e-01 -6.32870793e-01
-8.40813220e-01 -1.52795598e-01 7.82193124e-01 1.77997530e-01
-8.09547484e-01 5.60032308e-01 1.97065637e-01 3.24161887e-01
-3.57842058e-01 -7.01682150e-01 -6.29467547e-01 -1.06615579e+00
1.24103315e-01 5.92945099e-01 -4.47116904e-02 1.55964077... | [14.816292762756348, 1.0690934658050537] |
ed14773f-0b2a-49c7-8a31-c7e5ae0cddf7 | calib-anything-zero-training-lidar-camera | 2306.02656 | null | https://arxiv.org/abs/2306.02656v1 | https://arxiv.org/pdf/2306.02656v1.pdf | Calib-Anything: Zero-training LiDAR-Camera Extrinsic Calibration Method Using Segment Anything | The research on extrinsic calibration between Light Detection and Ranging(LiDAR) and camera are being promoted to a more accurate, automatic and generic manner. Since deep learning has been employed in calibration, the restrictions on the scene are greatly reduced. However, data driven method has the drawback of low tr... | ['Yikang Li', 'Guohang Yan', 'Zhaotong Luo'] | 2023-06-05 | null | null | null | null | ['camera-calibration'] | ['computer-vision'] | [-1.23552373e-03 -4.01164144e-01 -6.31744638e-02 -8.05168211e-01
-5.28451860e-01 -4.48014617e-01 4.00444239e-01 -5.05395949e-01
-3.94905686e-01 5.03379047e-01 -2.76263028e-01 1.26827821e-01
-8.64640772e-02 -8.31782818e-01 -6.61403418e-01 -7.44977236e-01
6.75208390e-01 5.76467454e-01 3.39733183e-01 8.70196372... | [8.2136812210083, -2.3839685916900635] |
6eb342b5-3024-4cc2-aa76-56698bdf922a | marinevrs-marine-video-retrieval-system-with | 2306.04593 | null | https://arxiv.org/abs/2306.04593v1 | https://arxiv.org/pdf/2306.04593v1.pdf | MarineVRS: Marine Video Retrieval System with Explainability via Semantic Understanding | Building a video retrieval system that is robust and reliable, especially for the marine environment, is a challenging task due to several factors such as dealing with massive amounts of dense and repetitive data, occlusion, blurriness, low lighting conditions, and abstract queries. To address these challenges, we pres... | ['Sai-Kit Yeung', 'Tuan-Anh Vu', 'Hai Nguyen-Truong', 'Tan-Sang Ha'] | 2023-06-07 | null | null | null | null | ['video-retrieval'] | ['computer-vision'] | [-2.22751766e-01 -7.16248035e-01 4.47538972e-01 -1.98501691e-01
-5.44345915e-01 -1.02993286e+00 3.13564956e-01 3.19142461e-01
-6.48985207e-01 3.10280174e-01 6.21623844e-02 -2.27392316e-02
-2.88207501e-01 -7.13573098e-01 -5.66274881e-01 -6.14274979e-01
-2.89649040e-01 8.84527490e-02 6.84125960e-01 -2.88093537... | [10.06053352355957, 0.5915743708610535] |
367044b7-5058-4329-9b13-c92baa664ac2 | privacy-protection-drone-patrol-system-based | 2005.14390 | null | https://arxiv.org/abs/2005.14390v1 | https://arxiv.org/pdf/2005.14390v1.pdf | Privacy-Protection Drone Patrol System based on Face Anonymization | The robot market has been growing significantly and is expected to become 1.5 times larger in 2024 than what it was in 2019. Robots have attracted attention of security companies thanks to their mobility. These days, for security robots, unmanned aerial vehicles (UAVs) have quickly emerged by highlighting their advanta... | ['Hyun Jong Yang', 'Yeongjun Kim', 'Hyeonsu Lyu', 'Myeung Un Kim', 'Harim Lee'] | 2020-05-29 | null | null | null | null | ['face-anonymization'] | ['computer-vision'] | [ 2.09714606e-01 2.00697348e-01 1.58169106e-01 -3.59654576e-01
-3.37326080e-01 -8.48288774e-01 5.48782706e-01 -5.29941499e-01
-7.41828501e-01 9.70986545e-01 -3.37970942e-01 3.69355120e-02
-1.32916734e-01 -9.12623644e-01 -8.11290324e-01 -9.24194932e-01
5.91830090e-02 1.12769438e-03 2.56167003e-03 -3.28930378... | [12.679730415344238, 0.8174795508384705] |
336cdd45-485f-4452-8236-c857dd0b4891 | a-federated-approach-for-hate-speech | 2302.09243 | null | https://arxiv.org/abs/2302.09243v1 | https://arxiv.org/pdf/2302.09243v1.pdf | A Federated Approach for Hate Speech Detection | Hate speech detection has been the subject of high research attention, due to the scale of content created on social media. In spite of the attention and the sensitive nature of the task, privacy preservation in hate speech detection has remained under-studied. The majority of research has focused on centralised machin... | ['Zeerak Talat', 'Jash Mehta', 'Deep Gandhi', 'Jay Gala'] | 2023-02-18 | null | null | null | null | ['hate-speech-detection'] | ['natural-language-processing'] | [-2.32750215e-02 2.63942689e-01 1.25273198e-01 4.26758267e-03
-7.23181546e-01 -8.18612516e-01 7.26967692e-01 5.25126219e-01
-3.39103997e-01 6.01300001e-01 4.31809574e-01 -3.40195775e-01
-1.54288625e-02 -2.58630961e-01 -3.00352812e-01 -5.80375850e-01
5.19868024e-02 -3.67664248e-01 8.61062780e-02 -2.23216981... | [8.709792137145996, 10.528307914733887] |
99deb7ee-795a-4b4a-be17-84d2bc5eaf79 | collective-opinion-target-extraction-in | null | null | https://aclanthology.org/D13-1189 | https://aclanthology.org/D13-1189.pdf | Collective Opinion Target Extraction in Chinese Microblogs | null | ['Xinjie Zhou', 'Xiaojun Wan', 'Jianguo Xiao'] | 2013-10-01 | null | null | null | emnlp-2013-10 | ['stock-market-prediction'] | ['time-series'] | [-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... | [-7.3204779624938965, 3.821566581726074] |
7f0efaf9-2707-445f-8b16-0c3d091d468a | reconstruction-cognizant-graph-sampling-using | 1811.03206 | null | http://arxiv.org/abs/1811.03206v2 | http://arxiv.org/pdf/1811.03206v2.pdf | Reconstruction-Cognizant Graph Sampling using Gershgorin Disc Alignment | Graph sampling with noise is a fundamental problem in graph signal processing
(GSP). Previous works assume an unbiased least square (LS) signal
reconstruction scheme and select samples greedily via expensive extreme
eigenvector computation. A popular biased scheme using graph Laplacian
regularization (GLR) solves a sys... | [] | 2019-02-16 | null | null | null | null | ['graph-sampling'] | ['graphs'] | [ 3.93022567e-01 2.07235068e-01 -1.87483028e-01 6.35353848e-02
-7.97249258e-01 -2.85830706e-01 -4.46543545e-01 -2.20823094e-01
-2.06077367e-01 5.45841157e-01 6.77183121e-02 -3.53988558e-01
-4.80342358e-01 -5.29738486e-01 -5.28730392e-01 -8.39699030e-01
-3.54104459e-01 -1.71190515e-01 -1.69128716e-01 -1.34420797... | [6.985772609710693, 4.605802536010742] |
690d31cd-b15a-4c34-a266-2314e4c84ace | directional-deep-embedding-and-appearance | 2002.06736 | null | https://arxiv.org/abs/2002.06736v1 | https://arxiv.org/pdf/2002.06736v1.pdf | Directional Deep Embedding and Appearance Learning for Fast Video Object Segmentation | Most recent semi-supervised video object segmentation (VOS) methods rely on fine-tuning deep convolutional neural networks online using the given mask of the first frame or predicted masks of subsequent frames. However, the online fine-tuning process is usually time-consuming, limiting the practical use of such methods... | ['Yingjie Yin', 'Xingang Wang', 'Lei Zhang', 'De Xu'] | 2020-02-17 | null | null | null | null | ['one-shot-visual-object-segmentation'] | ['computer-vision'] | [-1.61447451e-01 -1.91744417e-01 -2.63707161e-01 -4.79712278e-01
-6.81450427e-01 -4.56893831e-01 5.19538894e-02 -3.51366222e-01
-5.16436100e-01 2.02153102e-01 -3.33098799e-01 -2.40938500e-01
3.53711486e-01 -7.56010234e-01 -1.04211235e+00 -7.87552893e-01
2.50964999e-01 2.58853406e-01 7.81700432e-01 1.77779615... | [9.216024398803711, -0.10751084238290787] |
3f046458-96ef-4533-a983-ac9fdede076d | distant-reading-in-digital-humanities-case | null | null | https://aclanthology.org/2022.lrec-1.356 | https://aclanthology.org/2022.lrec-1.356.pdf | Distant Reading in Digital Humanities: Case Study on the Serbian Part of the ELTeC Collection | In this paper we present the Serbian part of the ELTeC multilingual corpus of novels written in the time period 1840-1920. The corpus is being built in order to test various distant reading methods and tools with the aim of re-thinking the European literary history. We present the various steps that led to the producti... | ['Milica Ikonić Nešić', 'Mihailo Skoric', 'Dusko Vitas', 'Branislava Šandrih Todorović', 'Cvetana Krstev', 'Ranka Stanković'] | null | null | null | null | lrec-2022-6 | ['lemmatization'] | ['natural-language-processing'] | [-1.04926765e-01 -1.12237498e-01 -5.79930423e-03 -2.59463638e-01
-6.71880484e-01 -9.83148694e-01 1.13033712e+00 6.58186793e-01
-9.39129174e-01 1.16558850e+00 7.14701056e-01 -4.02765065e-01
-3.10145736e-01 -5.96100092e-01 -1.80594042e-01 -2.23815814e-01
1.69764906e-01 8.83596599e-01 3.88322979e-01 -6.75127447... | [10.352624893188477, 10.211751937866211] |
8875951a-7a72-4ba3-9a0a-4f33908b58f9 | a-novel-neural-network-model-for-joint-pos | 1705.05952 | null | http://arxiv.org/abs/1705.05952v2 | http://arxiv.org/pdf/1705.05952v2.pdf | A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing | We present a novel neural network model that learns POS tagging and
graph-based dependency parsing jointly. Our model uses bidirectional LSTMs to
learn feature representations shared for both POS tagging and dependency
parsing tasks, thus handling the feature-engineering problem. Our extensive
experiments, on 19 langua... | ['Mark Johnson', 'Mark Dras', 'Dat Quoc Nguyen'] | 2017-05-16 | a-novel-neural-network-model-for-joint-pos-1 | https://aclanthology.org/K17-3014 | https://aclanthology.org/K17-3014.pdf | conll-2017-8 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-3.80874187e-01 5.18981934e-01 -6.49920046e-01 -7.14034736e-01
-1.08858681e+00 -7.98150718e-01 2.78542101e-01 2.51843005e-01
-3.03153425e-01 8.19974065e-01 5.26822507e-01 -8.39657545e-01
3.51551950e-01 -6.44105017e-01 -7.72209883e-01 -3.27948153e-01
-4.56657410e-01 6.66237056e-01 3.49123210e-01 -7.57221133... | [10.304308891296387, 9.695919036865234] |
a327b95d-e3d8-4556-93d5-0dafaed92682 | movie-recommendation-system-using-composite | 2212.00139 | null | https://arxiv.org/abs/2212.00139v2 | https://arxiv.org/pdf/2212.00139v2.pdf | Movie Recommendation System using Composite Ranking | In today's world, abundant digital content like e-books, movies, videos and articles are available for consumption. It is daunting to review everything accessible and decide what to watch next. Consequently, digital media providers want to capitalise on this confusion and tackle it to increase user engagement, eventual... | ['Aashal Kamdar', 'Irish Mehta'] | 2022-11-30 | null | null | null | null | ['movie-recommendation'] | ['miscellaneous'] | [-1.66891679e-01 -3.52397203e-01 -2.91556597e-01 -3.03387731e-01
-2.83844680e-01 -7.70936787e-01 6.43371999e-01 7.36454070e-01
-5.53943336e-01 1.50524378e-01 5.51931381e-01 -2.78511733e-01
-3.86378527e-01 -7.64057875e-01 -2.15511769e-02 -4.71255928e-01
2.96060741e-01 -8.24599415e-02 3.96763563e-01 -5.88296354... | [10.164013862609863, 5.81782865524292] |
472d7fa3-f7d1-4ea6-a41b-5218a96ed3e9 | segmenting-medical-instruments-in-minimally | 2203.11358 | null | https://arxiv.org/abs/2203.11358v1 | https://arxiv.org/pdf/2203.11358v1.pdf | Segmenting Medical Instruments in Minimally Invasive Surgeries using AttentionMask | Precisely locating and segmenting medical instruments in images of minimally invasive surgeries, medical instrument segmentation, is an essential first step for several tasks in medical image processing. However, image degradations, small instruments, and the generalization between different surgery types make medical ... | ['Simone Frintrop', 'Rüdiger Schmitz', 'Alexander Michael Gerlach', 'Christian Wilms'] | 2022-03-21 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [ 6.31136656e-01 2.66081840e-01 -3.94035816e-01 -7.07440674e-02
-8.17490518e-01 -6.97863758e-01 2.19630420e-01 1.87767386e-01
-5.74483991e-01 2.57208526e-01 5.27425706e-02 -3.78200948e-01
4.39307429e-02 -2.05495745e-01 -9.40508246e-01 -3.66708338e-01
1.52832806e-01 5.75417995e-01 2.28761688e-01 -5.53052127... | [14.250678062438965, -2.946519374847412] |
f3fc0da1-a5ce-4740-b4d8-3f3591ff54aa | a-neural-network-based-on-device-learning | 1907.10147 | null | https://arxiv.org/abs/1907.10147v5 | https://arxiv.org/pdf/1907.10147v5.pdf | A Neural Network-Based On-device Learning Anomaly Detector for Edge Devices | Semi-supervised anomaly detection is an approach to identify anomalies by learning the distribution of normal data. Backpropagation neural networks (i.e., BP-NNs) based approaches have recently drawn attention because of their good generalization capability. In a typical situation, BP-NN-based models are iteratively op... | ['Hiroki Matsutani', 'Masaaki Kondo', 'Mineto Tsukada'] | 2019-07-23 | null | null | null | null | ['supervised-anomaly-detection', 'semi-supervised-anomaly-detection'] | ['computer-vision', 'computer-vision'] | [-2.72613466e-01 -5.15928626e-01 -9.20705050e-02 -4.20938909e-01
1.99789867e-01 -1.57840312e-01 4.34584208e-02 2.38894969e-01
-6.08607531e-01 5.06955087e-01 -7.68846333e-01 -7.48891294e-01
1.66917503e-01 -6.77829862e-01 -7.56273210e-01 -7.13104367e-01
-3.91944572e-02 2.89004415e-01 5.27701318e-01 8.54731649... | [7.974139213562012, 2.610874891281128] |
545a2365-71a1-4751-b1da-a9626b87fc86 | g2mf-wa-geometric-multi-model-fitting-with | 2001.06965 | null | https://arxiv.org/abs/2001.06965v1 | https://arxiv.org/pdf/2001.06965v1.pdf | G2MF-WA: Geometric Multi-Model Fitting with Weakly Annotated Data | In this paper we attempt to address the problem of geometric multi-model fitting with resorting to a few weakly annotated (WA) data points, which has been sparsely studied so far. In weak annotating, most of the manual annotations are supposed to be correct yet inevitably mixed with incorrect ones. The WA data can be n... | ['Xi Yang', 'Katsuya Hotta', 'Xuequan Lu', 'Chao Zhang'] | 2020-01-20 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [ 3.26590449e-01 4.76337165e-01 -2.67546982e-01 -2.18958393e-01
-8.04149032e-01 -2.59700805e-01 2.25589484e-01 1.63866282e-01
-1.27950534e-01 4.88607109e-01 -1.54017389e-01 1.07680604e-01
-2.64968108e-02 -7.88341880e-01 -7.44865239e-01 -7.49054372e-01
4.49859828e-01 1.05520797e+00 5.51943064e-01 1.84011221... | [7.8446125984191895, -2.6739697456359863] |
a4256ed9-4e01-49b6-8592-0cd5c55ec63e | video-reenactment-as-inductive-bias-for | 2102.00324 | null | https://arxiv.org/abs/2102.00324v3 | https://arxiv.org/pdf/2102.00324v3.pdf | Video Reenactment as Inductive Bias for Content-Motion Disentanglement | Independent components within low-dimensional representations are essential inputs in several downstream tasks, and provide explanations over the observed data. Video-based disentangled factors of variation provide low-dimensional representations that can be identified and used to feed task-specific models. We introduc... | ['Adín Ramírez Rivera', 'Juan F. Hernández Albarracín'] | 2021-01-30 | null | null | null | null | ['motion-disentanglement'] | ['computer-vision'] | [ 4.53468896e-02 -3.31073910e-01 -6.13058269e-01 -2.25846484e-01
-8.16584349e-01 -8.14289153e-01 9.27948117e-01 -5.28581560e-01
-3.26391906e-01 4.90199685e-01 1.19867158e+00 -9.06524807e-02
-1.07377864e-01 -1.16031945e-01 -1.00066638e+00 -6.87238097e-01
-9.09877941e-02 4.49502282e-02 -3.84292424e-01 1.59752011... | [8.726700782775879, 0.3338398337364197] |
70256078-e9a9-44f5-b52a-da0156ef67e2 | unsupervised-machine-learning-for-networking | 1709.06599 | null | http://arxiv.org/abs/1709.06599v1 | http://arxiv.org/pdf/1709.06599v1.pdf | Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges | While machine learning and artificial intelligence have long been applied in
networking research, the bulk of such works has focused on supervised learning.
Recently there has been a rising trend of employing unsupervised machine
learning using unstructured raw network data to improve network performance and
provide se... | ['Ala Al-Fuqaha', 'Kok-Lim Alvin Yau', 'Aunn Raza', 'Amir Hussain', 'Junaid Qadir', 'Yehia Elkhatib', 'Muhammad Usama', 'Hunain Arif'] | 2017-09-19 | null | null | null | null | ['traffic-classification'] | ['miscellaneous'] | [ 3.65283817e-01 1.45429760e-01 -8.78980517e-01 -6.01946831e-01
-6.96424469e-02 -2.11893380e-01 3.12694550e-01 6.68588877e-02
-4.49869573e-01 7.94452250e-01 -4.48890090e-01 -6.74464464e-01
-4.47208315e-01 -8.72289777e-01 -1.55684084e-01 -5.40403962e-01
-5.37576020e-01 4.85262126e-01 3.14841658e-01 7.58892670... | [5.059884071350098, 7.2165045738220215] |
cd86cefe-f73d-418e-924b-c3da1143abe8 | lance-stress-testing-visual-models-by | 2305.19164 | null | https://arxiv.org/abs/2305.19164v1 | https://arxiv.org/pdf/2305.19164v1.pdf | LANCE: Stress-testing Visual Models by Generating Language-guided Counterfactual Images | We propose an automated algorithm to stress-test a trained visual model by generating language-guided counterfactual test images (LANCE). Our method leverages recent progress in large language modeling and text-based image editing to augment an IID test set with a suite of diverse, realistic, and challenging test image... | ['Judy Hoffman', 'Prithvijit Chattopadhyay', 'Sriram Yenamandra', 'Viraj Prabhu'] | 2023-05-30 | null | null | null | null | ['text-based-image-editing'] | ['computer-vision'] | [ 6.90341830e-01 5.46346158e-02 -6.61406144e-02 -5.29654145e-01
-7.48928189e-01 -8.23080957e-01 1.21074378e+00 -4.09226388e-01
-6.93360627e-01 8.97528172e-01 1.96162254e-01 -6.80435061e-01
3.17701757e-01 -2.98903197e-01 -1.07723999e+00 -1.41661474e-02
1.09440498e-01 4.44357663e-01 -1.37266964e-01 1.52921632... | [10.350812911987305, 2.047618865966797] |
2bbb6631-fe4d-4202-8b11-6b09a043938d | discrete-and-continuous-action-representation | 1912.11077 | null | https://arxiv.org/abs/1912.11077v1 | https://arxiv.org/pdf/1912.11077v1.pdf | Discrete and Continuous Action Representation for Practical RL in Video Games | While most current research in Reinforcement Learning (RL) focuses on improving the performance of the algorithms in controlled environments, the use of RL under constraints like those met in the video game industry is rarely studied. Operating under such constraints, we propose Hybrid SAC, an extension of the Soft Act... | ['Adrien Logut', 'Eloi Alonso', 'Olivier Delalleau', 'Maxim Peter'] | 2019-12-23 | null | null | null | null | ['control-with-prametrised-actions'] | ['playing-games'] | [-4.08400185e-02 3.21885884e-01 -3.11140478e-01 4.23961356e-02
-4.43706900e-01 -5.55552363e-01 6.69291019e-01 -1.68083906e-01
-8.54313552e-01 1.10489118e+00 7.65718594e-02 -3.74862283e-01
-3.15957487e-01 -5.96392751e-01 -6.62913024e-01 -7.17795372e-01
-2.23450497e-01 5.69812417e-01 5.67724884e-01 -5.83619833... | [4.1116557121276855, 2.223191261291504] |
42838037-fce5-42f7-a35a-08057438cfed | memecap-a-dataset-for-captioning-and | 2305.13703 | null | https://arxiv.org/abs/2305.13703v1 | https://arxiv.org/pdf/2305.13703v1.pdf | MemeCap: A Dataset for Captioning and Interpreting Memes | Memes are a widely popular tool for web users to express their thoughts using visual metaphors. Understanding memes requires recognizing and interpreting visual metaphors with respect to the text inside or around the meme, often while employing background knowledge and reasoning abilities. We present the task of meme c... | ['Vered Shwartz', 'EunJeong Hwang'] | 2023-05-23 | null | null | null | null | ['image-captioning', 'meme-captioning'] | ['computer-vision', 'natural-language-processing'] | [-1.11925565e-01 1.87243700e-01 1.20027684e-01 3.97718232e-03
-1.01946540e-01 -8.75374496e-01 1.11145771e+00 4.15779680e-01
-2.89930284e-01 3.99665177e-01 6.98088109e-01 -6.25621200e-01
5.56596339e-01 -6.35173917e-01 -8.20967317e-01 -4.38175797e-02
4.75616008e-01 4.37163949e-01 -1.61876589e-01 -5.08327961... | [10.928168296813965, 1.5321999788284302] |
ddce3747-32b9-4298-8ef3-7f800f3c69c6 | deep-q-learning-versus-proximal-policy | 2306.01451 | null | https://arxiv.org/abs/2306.01451v1 | https://arxiv.org/pdf/2306.01451v1.pdf | Deep Q-Learning versus Proximal Policy Optimization: Performance Comparison in a Material Sorting Task | This paper presents a comparison between two well-known deep Reinforcement Learning (RL) algorithms: Deep Q-Learning (DQN) and Proximal Policy Optimization (PPO) in a simulated production system. We utilize a Petri Net (PN)-based simulation environment, which was previously proposed in related work. The performance of ... | ['Simon Hirländer', 'Stefan Wegenkittl', 'Reuf Kozlica'] | 2023-06-02 | null | null | null | null | ['q-learning'] | ['methodology'] | [-8.47847760e-02 5.38408011e-02 -2.39353597e-01 2.49263585e-01
-2.94730306e-01 -5.66089272e-01 5.86711764e-01 5.04065096e-01
-4.41712737e-01 1.18829882e+00 -1.22118220e-01 -3.35018069e-01
-7.01252162e-01 -8.29010725e-01 -7.31955111e-01 -8.21274936e-01
-6.53176665e-01 4.45033878e-01 8.40611979e-02 -4.41211343... | [4.32283878326416, 2.056790828704834] |
9ac78507-01d5-42d3-970b-f40c77a9b364 | improving-efficient-neural-ranking-models | 2010.02666 | null | https://arxiv.org/abs/2010.02666v2 | https://arxiv.org/pdf/2010.02666v2.pdf | Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation | Retrieval and ranking models are the backbone of many applications such as web search, open domain QA, or text-based recommender systems. The latency of neural ranking models at query time is largely dependent on the architecture and deliberate choices by their designers to trade-off effectiveness for higher efficiency... | ['Allan Hanbury', 'Mete Sertkan', 'Michael Schröder', 'Sophia Althammer', 'Sebastian Hofstätter'] | 2020-10-06 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [-1.96503177e-02 -1.69172928e-01 -2.66234845e-01 -5.31778574e-01
-1.50361037e+00 -1.02311897e+00 5.45878291e-01 2.61558354e-01
-6.75463140e-01 6.82922959e-01 1.53404653e-01 -4.51309890e-01
-8.76039922e-01 -6.52064919e-01 -8.68475735e-01 -3.17615569e-01
-7.66628832e-02 1.26332521e+00 6.30852580e-01 -7.53417671... | [11.411840438842773, 7.575625419616699] |
12b4e112-93d7-41fc-97a8-1856620cc74d | byel-bootstrap-on-your-emotion-latent | 2207.10003 | null | https://arxiv.org/abs/2207.10003v2 | https://arxiv.org/pdf/2207.10003v2.pdf | BYEL : Bootstrap Your Emotion Latent | With the improved performance of deep learning, the number of studies trying to apply deep learning to human emotion analysis is increasing rapidly. But even with this trend going on, it is still difficult to obtain high-quality images and annotations. For this reason, the Learning from Synthetic Data (LSD) Challenge, ... | ['Sejoon Lim', 'Hwangyu Lim', 'Hyungjun Lee'] | 2022-07-20 | null | null | null | null | ['emotion-classification', 'emotion-classification'] | ['computer-vision', 'natural-language-processing'] | [ 7.34144822e-02 2.85615008e-02 9.50950831e-02 -6.90887034e-01
-6.68755114e-01 -3.35115790e-01 5.68441570e-01 -8.90314057e-02
-5.44747770e-01 7.27804780e-01 8.57929215e-02 5.45249403e-01
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1.24374919e-01 3.00166696e-01 -2.08497569e-01 -2.26843745... | [13.574974060058594, 1.864148497581482] |
de280eb0-29fc-4eb5-aeef-db4ea011dee5 | reasoning-over-public-and-private-data-in | 2203.11027 | null | https://arxiv.org/abs/2203.11027v1 | https://arxiv.org/pdf/2203.11027v1.pdf | Reasoning over Public and Private Data in Retrieval-Based Systems | Users and organizations are generating ever-increasing amounts of private data from a wide range of sources. Incorporating private data is important to personalize open-domain applications such as question-answering, fact-checking, and personal assistants. State-of-the-art systems for these tasks explicitly retrieve re... | ['Christopher Ré', 'Jacob Kahn', 'Angela Fan', 'Patrick Lewis', 'Simran Arora'] | 2022-03-14 | null | null | null | null | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 1.17906496e-01 3.45972419e-01 -1.12195231e-01 -6.83523893e-01
-1.86815500e+00 -1.12089038e+00 7.22698152e-01 3.45915318e-01
-5.89339316e-01 6.82808697e-01 3.32265556e-01 -4.16250557e-01
-3.21502209e-01 -7.03082085e-01 -6.46431446e-01 -5.03901303e-01
3.72604877e-01 8.46895218e-01 1.84522077e-01 -3.94708216... | [6.067076206207275, 7.025417327880859] |
1c499078-f487-4f1d-b543-c0231c721f6f | psiminer-a-tool-for-mining-rich-abstract | 2103.12778 | null | https://arxiv.org/abs/2103.12778v1 | https://arxiv.org/pdf/2103.12778v1.pdf | PSIMiner: A Tool for Mining Rich Abstract Syntax Trees from Code | The application of machine learning algorithms to source code has grown in the past years. Since these algorithms are quite sensitive to input data, it is not surprising that researchers experiment with input representations. Nowadays, a popular starting point to represent code is abstract syntax trees (ASTs). Abstract... | ['Timofey Bryksin', 'Vladimir Kovalenko', 'Egor Bogomolov', 'Egor Spirin'] | 2021-03-23 | null | null | null | null | ['method-name-prediction'] | ['natural-language-processing'] | [ 6.54289871e-02 2.03680564e-02 -3.62236500e-01 -4.85697716e-01
-1.20903507e-01 -6.13652050e-01 4.21798736e-01 7.58844316e-01
-5.04591549e-03 3.02795410e-01 -9.20647532e-02 -8.72578263e-01
2.08611831e-01 -9.78803873e-01 -5.81141174e-01 -3.62637714e-02
-2.80181170e-01 2.66940504e-01 6.03499591e-01 -1.65704623... | [7.719442844390869, 7.79136848449707] |
6fd2d158-6d09-4ec7-9255-8685b0c7dfd5 | an-exploratory-study-of-masked-face | 2306.08549 | null | https://arxiv.org/abs/2306.08549v1 | https://arxiv.org/pdf/2306.08549v1.pdf | An Exploratory Study of Masked Face Recognition with Machine Learning Algorithms | Automated face recognition is a widely adopted machine learning technology for contactless identification of people in various processes such as automated border control, secure login to electronic devices, community surveillance, tracking school attendance, workplace clock in and clock out. Using face masks have becom... | ['Mustafa Atay', 'Megh Pudyel'] | 2023-06-14 | null | null | null | null | ['face-recognition'] | ['computer-vision'] | [ 6.75978303e-01 -3.25479060e-01 -1.63930416e-01 -2.69935519e-01
-3.14773053e-01 -5.20169616e-01 6.47724390e-01 -4.66908634e-01
-3.53531629e-01 9.16686177e-01 -1.01566255e-01 -3.50553930e-01
-1.85100883e-01 -4.66046721e-01 -3.04533958e-01 -1.03319943e+00
-5.11472821e-02 1.56290695e-01 -9.99384969e-02 4.74633723... | [13.23256778717041, 0.9135958552360535] |
172655de-c751-4084-8b14-2228b40a6724 | voxlingua107-a-dataset-for-spoken-language | null | null | https://arxiv.org/pdf/2011.12998.pdf | https://arxiv.org/abs/2011.12998 | VOXLINGUA107: A DATASET FOR SPOKEN LANGUAGE RECOGNITION | This paper investigates the use of automatically collected web audio data for the task of spoken language recognition. We generate semi-random search phrases from language-specific Wikipedia data that are then used to retrieve videos from YouTube for 107 languages. Speech activity detection and speaker diarization are ... | ['Tanel Alumae', 'Jorgen Valk'] | 2020-11-25 | null | null | null | null | ['spoken-language-identification'] | ['speech'] | [ 8.68403614e-02 5.54545373e-02 -3.23976696e-01 -5.46728373e-01
-1.62621844e+00 -7.79457510e-01 5.73874652e-01 2.04378031e-02
-7.57535279e-01 6.86852694e-01 7.05216825e-01 4.53516804e-02
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5.30953370e-02 5.11841714e-01 3.10978800e-01 2.53583014... | [14.247608184814453, 6.243347644805908] |
9f6da0f4-8f76-4378-b681-6b30561c6b06 | can-gpt-4-perform-neural-architecture-search | 2304.10970 | null | https://arxiv.org/abs/2304.10970v3 | https://arxiv.org/pdf/2304.10970v3.pdf | Can GPT-4 Perform Neural Architecture Search? | We investigate the potential of GPT-4~\cite{gpt4} to perform Neural Architecture Search (NAS) -- the task of designing effective neural architectures. Our proposed approach, \textbf{G}PT-4 \textbf{E}nhanced \textbf{N}eural arch\textbf{I}tect\textbf{U}re \textbf{S}earch (GENIUS), leverages the generative capabilities of... | ['Samuel Albanie', 'Chang Xu', 'Chen Qian', 'Fei Wang', 'Shan You', 'Xiu Su', 'Mingkai Zheng'] | 2023-04-21 | null | null | null | null | ['architecture-search'] | ['methodology'] | [-2.37129480e-02 3.78735393e-01 -8.69495273e-02 -2.05835074e-01
-9.61872637e-01 -6.46593034e-01 2.04552189e-01 -1.18731961e-01
-5.22934139e-01 5.82508922e-01 1.30805776e-01 -6.88245475e-01
-4.63803262e-01 -4.73458380e-01 -8.39221954e-01 -3.59251440e-01
-1.82922855e-01 5.09307742e-01 -4.34700936e-01 -5.35360992... | [8.515413284301758, 3.3704922199249268] |
a18d956a-3526-41f0-aa5b-c370a719d934 | efficient-open-domain-multi-hop-question | 2305.13691 | null | https://arxiv.org/abs/2305.13691v1 | https://arxiv.org/pdf/2305.13691v1.pdf | Efficient Open Domain Multi-Hop Question Answering with Few-Shot Data Synthesis | Few-shot learning for open domain multi-hop question answering typically relies on large language models (LLMs). While powerful, LLMs are inefficient at the inference time. We propose a data synthesis framework for multi-hop question answering that allows for improving smaller language models with less than 10 human-an... | ['Wen-tau Yih', 'Xilun Chen', 'Mingda Chen'] | 2023-05-23 | null | null | null | null | ['multi-hop-question-answering', 'fact-verification'] | ['knowledge-base', 'natural-language-processing'] | [-3.01281046e-02 4.88450170e-01 -4.63599205e-01 -2.58352250e-01
-1.91159928e+00 -7.08885610e-01 7.10417092e-01 3.75915885e-01
-5.06612778e-01 7.15761662e-01 2.71481216e-01 -6.81955159e-01
-1.25896651e-02 -9.32639837e-01 -9.14180517e-01 3.02136719e-01
3.07990551e-01 8.06307495e-01 7.37239122e-01 -4.67896879... | [11.275789260864258, 8.015871047973633] |
9aaa27b5-cdb2-45b5-a387-3846fe566757 | ernie-vil-2-0-multi-view-contrastive-learning | 2209.15270 | null | https://arxiv.org/abs/2209.15270v1 | https://arxiv.org/pdf/2209.15270v1.pdf | ERNIE-ViL 2.0: Multi-view Contrastive Learning for Image-Text Pre-training | Recent Vision-Language Pre-trained (VLP) models based on dual encoder have attracted extensive attention from academia and industry due to their superior performance on various cross-modal tasks and high computational efficiency. They attempt to learn cross-modal representation using contrastive learning on image-text ... | ['Haifeng Wang', 'Hua Wu', 'Hao Tian', 'Yu Sun', 'Weichong Yin', 'Bin Shan'] | 2022-09-30 | null | null | null | null | ['zero-shot-cross-modal-retrieval'] | ['miscellaneous'] | [-5.82028031e-02 -6.21166945e-01 -3.10514420e-01 -3.31596345e-01
-1.61797833e+00 -7.58430958e-01 9.04535353e-01 -2.48924494e-01
-4.06979501e-01 4.21620041e-01 4.34446245e-01 1.10196916e-03
1.49248112e-02 -5.30559838e-01 -7.81323433e-01 -5.92467427e-01
4.18431997e-01 3.88938218e-01 -8.57499391e-02 -1.40526459... | [10.877264022827148, 1.3822917938232422] |
6f3f97f6-f2cc-4c51-826e-c1783b7d7e98 | subclass-balancing-contrastive-learning-for | 2306.15925 | null | https://arxiv.org/abs/2306.15925v1 | https://arxiv.org/pdf/2306.15925v1.pdf | Subclass-balancing Contrastive Learning for Long-tailed Recognition | Long-tailed recognition with imbalanced class distribution naturally emerges in practical machine learning applications. Existing methods such as data reweighing, resampling, and supervised contrastive learning enforce the class balance with a price of introducing imbalance between instances of head class and tail clas... | ['Tianyi Zhou', 'Haonan Wang', 'Jieyu Zhang', 'Chengkai Hou'] | 2023-06-28 | null | null | null | null | ['contrastive-learning', 'contrastive-learning'] | ['computer-vision', 'methodology'] | [ 1.08427428e-01 1.93954092e-02 -6.92171037e-01 -6.88551128e-01
-5.69598317e-01 -3.42429817e-01 4.32402194e-01 4.94179100e-01
-2.18202114e-01 8.10803115e-01 1.07155941e-01 -1.90216023e-02
-2.12365478e-01 -9.77800608e-01 -6.26629889e-01 -9.80206072e-01
1.71100304e-01 7.30520368e-01 2.88306952e-01 -4.56198491... | [9.486102104187012, 3.453401803970337] |
fc647f39-93e7-4a8a-a2e1-b345d07974e1 | pyretri-a-pytorch-based-library-for | 2005.02154 | null | https://arxiv.org/abs/2005.02154v2 | https://arxiv.org/pdf/2005.02154v2.pdf | PyRetri: A PyTorch-based Library for Unsupervised Image Retrieval by Deep Convolutional Neural Networks | Despite significant progress of applying deep learning methods to the field of content-based image retrieval, there has not been a software library that covers these methods in a unified manner. In order to fill this gap, we introduce PyRetri, an open source library for deep learning based unsupervised image retrieval.... | ['Xian-Sheng Hua', 'Xiu-Shen Wei', 'Ren-Jie Song', 'Yuehu Liu', 'Benyi Hu', 'Yazhou Yao'] | 2020-05-02 | null | null | null | null | ['content-based-image-retrieval'] | ['computer-vision'] | [-2.94693172e-01 -5.52046299e-01 -2.53704548e-01 -2.14659408e-01
-9.09147501e-01 -4.34765041e-01 6.76592946e-01 2.80294716e-01
-6.30331337e-01 1.09891430e-01 -1.87285185e-01 -1.05028443e-01
-3.99946600e-01 -7.91853726e-01 -1.55597776e-01 -5.78018963e-01
7.94547275e-02 5.14450312e-01 2.12640651e-02 -1.81682006... | [10.74798583984375, 0.5581743717193604] |
ae7d707e-7517-4a3d-ad13-ec30238b6dde | drive-deep-reinforced-accident-anticipation | 2107.10189 | null | https://arxiv.org/abs/2107.10189v2 | https://arxiv.org/pdf/2107.10189v2.pdf | DRIVE: Deep Reinforced Accident Anticipation with Visual Explanation | Traffic accident anticipation aims to accurately and promptly predict the occurrence of a future accident from dashcam videos, which is vital for a safety-guaranteed self-driving system. To encourage an early and accurate decision, existing approaches typically focus on capturing the cues of spatial and temporal contex... | ['Yu Kong', 'Qi Yu', 'Wentao Bao'] | 2021-07-21 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Bao_DRIVE_Deep_Reinforced_Accident_Anticipation_With_Visual_Explanation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Bao_DRIVE_Deep_Reinforced_Accident_Anticipation_With_Visual_Explanation_ICCV_2021_paper.pdf | iccv-2021-1 | ['accident-anticipation'] | ['computer-vision'] | [-7.90434852e-02 3.33105996e-02 -1.02124847e-01 -3.46256405e-01
-5.58333874e-01 1.98408246e-01 3.68985593e-01 -1.53504929e-03
-2.28395998e-01 5.57118475e-01 4.51539963e-01 -4.44146007e-01
6.25949651e-02 -3.74300689e-01 -6.91308796e-01 -6.39555335e-01
1.98624268e-01 4.64627258e-02 4.96233374e-01 -2.97157973... | [7.611192226409912, 0.18610325455665588] |
22a8b769-27aa-40e7-9b9c-fdced91205b8 | towards-automatic-face-to-face-translation-1 | 2003.00418 | null | https://arxiv.org/abs/2003.00418v1 | https://arxiv.org/pdf/2003.00418v1.pdf | Towards Automatic Face-to-Face Translation | In light of the recent breakthroughs in automatic machine translation systems, we propose a novel approach that we term as "Face-to-Face Translation". As today's digital communication becomes increasingly visual, we argue that there is a need for systems that can automatically translate a video of a person speaking in ... | ['Vinay Namboodiri', 'Jerin Philip', 'C. V. Jawahar', 'Abhishek Jha', 'Rudrabha Mukhopadhyay', 'Prajwal K R'] | 2020-03-01 | towards-automatic-face-to-face-translation | https://dl.acm.org/doi/10.1145/3343031.3351066 | https://dl.acm.org/doi/pdf/10.1145/3343031.3351066?download=true | acm-multimedia-2019-2019-10 | ['face-to-face-translation', 'lip-sync', 'speech-to-speech-translation'] | ['computer-vision', 'computer-vision', 'speech'] | [ 7.82987848e-02 4.24868539e-02 -2.76108757e-02 -4.36719805e-01
-1.51393819e+00 -6.72493637e-01 6.19793713e-01 -6.19008839e-01
1.24235734e-01 5.93735397e-01 5.67243457e-01 -4.04903412e-01
6.36063933e-01 -9.46732834e-02 -6.16053700e-01 -2.42215291e-01
4.75751102e-01 4.97369587e-01 -1.85063139e-01 -2.07113132... | [13.26745891571045, -0.36202722787857056] |
2f3a6e5e-43da-48e2-9af7-1ff21156aaeb | residual-q-learning-offline-and-online-policy | 2306.09526 | null | https://arxiv.org/abs/2306.09526v1 | https://arxiv.org/pdf/2306.09526v1.pdf | Residual Q-Learning: Offline and Online Policy Customization without Value | Imitation Learning (IL) is a widely used framework for learning imitative behavior from demonstrations. It is especially appealing for solving complex real-world tasks where handcrafting reward function is difficult, or when the goal is to mimic human expert behavior. However, the learned imitative policy can only foll... | ['Wei Zhan', 'Masayoshi Tomizuka', 'Jean Mercat', 'Haruki Nishimura', 'Chen Tang', 'Chenran Li'] | 2023-06-15 | null | null | null | null | ['q-learning', 'imitation-learning'] | ['methodology', 'methodology'] | [ 7.40017295e-02 -2.43876074e-02 -4.05737042e-01 -5.17098978e-02
-6.12564385e-01 -6.96057200e-01 4.29318339e-01 -1.98750958e-01
-7.63360918e-01 8.08562517e-01 -6.29138052e-02 -4.09289449e-01
-2.48772696e-01 -4.36968923e-01 -7.23160744e-01 -9.56887424e-01
2.52871364e-01 3.33047956e-01 1.33631915e-01 -1.46804914... | [4.20743465423584, 1.9024670124053955] |
96e205aa-5437-451a-8876-f9c4809ab377 | towards-diverse-temporal-grounding-under | 2303.06545 | null | https://arxiv.org/abs/2303.06545v1 | https://arxiv.org/pdf/2303.06545v1.pdf | Towards Diverse Temporal Grounding under Single Positive Labels | Temporal grounding aims to retrieve moments of the described event within an untrimmed video by a language query. Typically, existing methods assume annotations are precise and unique, yet one query may describe multiple moments in many cases. Hence, simply taking it as a one-vs-one mapping task and striving to match s... | ['Chuanping Hu', 'Yanjun Chen', 'Chongyang Zhang', 'Hao Zhou'] | 2023-03-12 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [-7.28627713e-03 -1.29362077e-01 -7.21985519e-01 -5.83239496e-01
-1.37447882e+00 -5.94853938e-01 6.42712951e-01 7.51105994e-02
-4.14323509e-01 5.37803650e-01 -2.16446426e-02 1.45424113e-01
-3.70541625e-02 -3.60845596e-01 -8.77459049e-01 -5.76290011e-01
-1.74204260e-01 6.00422382e-01 1.77789211e-01 3.36721629... | [9.95577335357666, 0.7089518904685974] |
05bef623-e4a1-4d07-90dd-946c2ab3b75a | diffdreamer-consistent-single-view-perpetual | 2211.12131 | null | https://arxiv.org/abs/2211.12131v2 | https://arxiv.org/pdf/2211.12131v2.pdf | DiffDreamer: Towards Consistent Unsupervised Single-view Scene Extrapolation with Conditional Diffusion Models | Scene extrapolation -- the idea of generating novel views by flying into a given image -- is a promising, yet challenging task. For each predicted frame, a joint inpainting and 3D refinement problem has to be solved, which is ill posed and includes a high level of ambiguity. Moreover, training data for long-range scene... | ['Gordon Wetzstein', 'Luc van Gool', 'Anton Obukhov', 'Mohamad Shahbazi', 'Songyou Peng', 'Eric Ryan Chan', 'Shengqu Cai'] | 2022-11-22 | null | null | null | null | ['perpetual-view-generation'] | ['computer-vision'] | [ 4.67435598e-01 2.72972416e-02 6.79612309e-02 -4.97603208e-01
-7.66484857e-01 -6.86470211e-01 6.29031062e-01 -7.50398695e-01
-6.03653081e-02 7.42672861e-01 4.17400122e-01 -8.50426480e-02
1.96279079e-01 -6.30345643e-01 -1.01595247e+00 -6.51741028e-01
4.89922076e-01 4.16554302e-01 6.88417107e-02 -1.17586656... | [9.277788162231445, -3.091217517852783] |
0d708b8f-8fb9-4f8f-81d3-855988aa7923 | end-to-end-temporal-relation-extraction-in | null | null | https://ceur-ws.org/Vol-3370/paper2.pdf | https://ceur-ws.org/Vol-3370/paper2.pdf | End-to-End Temporal Relation Extraction in the Clinical Domain | Temporal relation extraction is an important task in the clinical domain, as it allows a better understanding of the temporal context of clinical events. In this paper, we present an end-to end temporal relation extraction system for the clinical domain, using the i2b2 2012 Temporal Relation challenge as a benchmark. I... | ['Begoña Altuna', 'José Javier Saiz'] | 2023-04-02 | null | null | null | proceedings-of-the-text2story-23-workshop | ['temporal-relation-extraction', 'joint-entity-and-relation-extraction'] | ['natural-language-processing', 'natural-language-processing'] | [ 6.79136068e-02 3.21777374e-01 -5.92690766e-01 -2.99535632e-01
-7.84925878e-01 -6.03630960e-01 5.45231521e-01 9.21090424e-01
-4.39104676e-01 8.82685184e-01 5.03102362e-01 -5.10134876e-01
-6.19464517e-01 -5.81416726e-01 -4.46989387e-02 -3.06755483e-01
-8.30627084e-01 6.10947728e-01 3.20549339e-01 -1.79332793... | [8.576155662536621, 9.028803825378418] |
d87d3ae1-0f10-4b10-b635-087a0ea712c7 | swigs-a-swift-guided-sampling-method | null | null | http://openaccess.thecvf.com/content_cvpr_2013/html/Fragoso_SWIGS_A_Swift_2013_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2013/papers/Fragoso_SWIGS_A_Swift_2013_CVPR_paper.pdf | SWIGS: A Swift Guided Sampling Method | We present SWIGS, a Swift and efficient Guided Sampling method for robust model estimation from image feature correspondences. Our method leverages the accuracy of our new confidence measure (MR-Rayleigh), which assigns a correctness-confidence to a putative correspondence in an online fashion. MR-Rayleigh is inspired ... | ['Victor Fragoso', 'Matthew Turk'] | 2013-06-01 | null | null | null | cvpr-2013-6 | ['homography-estimation'] | ['computer-vision'] | [ 3.92486125e-01 7.53919482e-02 -4.09362018e-01 -1.56065613e-01
-1.25968170e+00 -3.79391164e-01 9.16749775e-01 -1.35229960e-01
1.00158066e-01 4.12805706e-01 1.41005293e-01 -1.28406078e-01
-2.46343374e-01 -5.51302791e-01 -5.71507275e-01 -4.44083989e-01
-1.44418702e-01 4.40451533e-01 3.23548019e-01 1.68404311... | [7.946898460388184, -2.2384400367736816] |
a1247da4-211d-4c87-b857-f35c3e8ee621 | shape-aware-masking-for-inpainting-in-medical | 2207.05787 | null | https://arxiv.org/abs/2207.05787v1 | https://arxiv.org/pdf/2207.05787v1.pdf | Shape-Aware Masking for Inpainting in Medical Imaging | Inpainting has recently been proposed as a successful deep learning technique for unsupervised medical image model discovery. The masks used for inpainting are generally independent of the dataset and are not tailored to perform on different given classes of anatomy. In this work, we introduce a method for generating s... | ['Nassir Navab', 'Azade Farshad', 'Yousef Yeganeh'] | 2022-07-12 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [ 5.96454561e-01 4.00238991e-01 -6.80107996e-02 -5.40648401e-01
-5.14909387e-01 -3.77091825e-01 3.63102853e-01 2.12467194e-01
-2.75057763e-01 8.17811072e-01 5.38863912e-02 -2.27359645e-02
-1.25812098e-01 -6.59417033e-01 -8.10817420e-01 -7.28621602e-01
-7.13453395e-03 6.62363052e-01 2.90429115e-01 -2.57058218... | [14.394068717956543, -2.2329506874084473] |
cd1f62fe-b0fd-4f5e-9b12-82903d388270 | discovery-of-governing-equations-with | 2009.11500 | null | https://arxiv.org/abs/2009.11500v1 | https://arxiv.org/pdf/2009.11500v1.pdf | Discovery of Governing Equations with Recursive Deep Neural Networks | Model discovery based on existing data has been one of the major focuses of mathematical modelers for decades. Despite tremendous achievements of model identification from adequate data, how to unravel the models from limited data is less resolved. In this paper, we focus on the model discovery problem when the data is... | ['Jia Zhao', 'Jarrod Mau'] | 2020-09-24 | null | null | null | null | ['model-discovery'] | ['miscellaneous'] | [-8.80248845e-02 -4.21664715e-01 -1.22329526e-01 4.00290042e-02
-2.09182501e-01 -2.70947993e-01 3.94072533e-01 -2.84860898e-02
-2.39543065e-01 9.98743773e-01 -5.65921009e-01 -4.02157575e-01
-6.79992497e-01 -6.73524499e-01 -3.92633438e-01 -9.44069982e-01
-7.57578462e-02 5.62174201e-01 -7.44161904e-02 -2.23413825... | [6.535553455352783, 3.3160996437072754] |
d9c05d18-714b-4101-8bcd-f7922012756c | revisiting-temporal-alignment-for-video | 2111.15288 | null | https://arxiv.org/abs/2111.15288v2 | https://arxiv.org/pdf/2111.15288v2.pdf | Revisiting Temporal Alignment for Video Restoration | Long-range temporal alignment is critical yet challenging for video restoration tasks. Recently, some works attempt to divide the long-range alignment into several sub-alignments and handle them progressively. Although this operation is helpful in modeling distant correspondences, error accumulation is inevitable due t... | ['Jiangbo Lu', 'Xiaoguang Han', 'Liying Lu', 'Wenbo Li', 'Kun Zhou'] | 2021-11-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhou_Revisiting_Temporal_Alignment_for_Video_Restoration_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhou_Revisiting_Temporal_Alignment_for_Video_Restoration_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-super-resolution', 'video-restoration'] | ['computer-vision', 'computer-vision'] | [ 1.63109124e-01 -6.08172178e-01 -7.22776949e-02 -3.39689106e-01
-8.16959023e-01 -2.94214725e-01 4.45159853e-01 -1.99936360e-01
-2.36602336e-01 6.38688028e-01 6.68050170e-01 3.63258198e-02
-7.03805983e-02 -5.52984118e-01 -5.51185250e-01 -7.41227150e-01
-2.84510572e-02 -1.20143004e-01 5.09318411e-01 -2.56057680... | [11.081963539123535, -1.9490182399749756] |
10140519-bd8c-4b2b-a352-6b75691a13fd | normalising-non-standardised-orthography-in | null | null | https://aclanthology.org/D19-5518 | https://aclanthology.org/D19-5518.pdf | Normalising Non-standardised Orthography in Algerian Code-switched User-generated Data | We work with Algerian, an under-resourced non-standardised Arabic variety, for which we compile a new parallel corpus consisting of user-generated textual data matched with normalised and corrected human annotations following data-driven and our linguistically motivated standard. We use an end-to-end deep neural model ... | ['Jean-Philippe Bernardy', 'Simon Dobnik', 'Wafia Adouane'] | 2019-11-01 | null | null | null | ws-2019-11 | ['spelling-correction'] | ['natural-language-processing'] | [ 6.11998022e-01 -5.33613339e-02 3.12971979e-01 -8.42732549e-01
-8.23905110e-01 -5.66971660e-01 5.42107403e-01 6.91591561e-01
-1.08734190e+00 4.66737628e-01 4.89267617e-01 -2.88560838e-01
2.78659135e-01 -6.46697521e-01 -5.67376316e-01 -3.75092387e-01
-2.36799866e-02 7.81229973e-01 -2.71278560e-01 -7.18880057... | [10.84343147277832, 10.429732322692871] |
daf3fb80-9251-4210-99fc-9d173bcf7445 | simple-dataset-for-proof-method | 2004.10667 | null | https://arxiv.org/abs/2004.10667v3 | https://arxiv.org/pdf/2004.10667v3.pdf | Simple Dataset for Proof Method Recommendation in Isabelle/HOL (Dataset Description) | Recently, a growing number of researchers have applied machine learning to assist users of interactive theorem provers. However, the expressive nature of underlying logics and esoteric structures of proof documents impede machine learning practitioners, who often do not have much expertise in formal logic, let alone Is... | ['Yutaka Nagashima'] | 2020-04-21 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [-1.94705576e-02 3.44924182e-01 -5.52096009e-01 -2.06489086e-01
-7.78166056e-01 -9.98939872e-01 4.80357319e-01 3.79840821e-01
8.96923319e-02 9.11278248e-01 -3.48473370e-01 -1.40394008e+00
-2.99610138e-01 -8.64671707e-01 -7.67258465e-01 1.41592324e-01
-2.86296546e-01 5.70207715e-01 1.70723692e-01 -2.52058625... | [8.939105987548828, 7.046802520751953] |
06c004f7-8cd8-462b-a7c3-68d731c1894c | capdet-unifying-dense-captioning-and-open | 2303.02489 | null | https://arxiv.org/abs/2303.02489v3 | https://arxiv.org/pdf/2303.02489v3.pdf | CapDet: Unifying Dense Captioning and Open-World Detection Pretraining | Benefiting from large-scale vision-language pre-training on image-text pairs, open-world detection methods have shown superior generalization ability under the zero-shot or few-shot detection settings. However, a pre-defined category space is still required during the inference stage of existing methods and only the ob... | ['Xiaodan Liang', 'Shen Zhao', 'Wei zhang', 'Pengzhen Ren', 'Hang Xu', 'Jianhua Han', 'Youpeng Wen', 'Yanxin Long'] | 2023-03-04 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Long_CapDet_Unifying_Dense_Captioning_and_Open-World_Detection_Pretraining_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Long_CapDet_Unifying_Dense_Captioning_and_Open-World_Detection_Pretraining_CVPR_2023_paper.pdf | cvpr-2023-1 | ['dense-captioning'] | ['computer-vision'] | [ 7.20597580e-02 3.66272628e-01 -1.28057867e-01 -3.23825121e-01
-1.14592493e+00 -4.00522828e-01 6.52208090e-01 -9.78993718e-03
-3.80478173e-01 5.67225635e-01 9.86263826e-02 -1.48607483e-02
6.34579301e-01 -7.54007638e-01 -9.81350303e-01 -5.05656779e-01
2.62626141e-01 5.69490790e-01 6.84895456e-01 -1.39193773... | [9.715231895446777, 1.48170006275177] |
47a6fc63-7ebe-492e-a029-ad18afc60fa1 | learning-semantic-sentence-representations | 1903.11393 | null | http://arxiv.org/abs/1903.11393v1 | http://arxiv.org/pdf/1903.11393v1.pdf | Learning semantic sentence representations from visually grounded language without lexical knowledge | Current approaches to learning semantic representations of sentences often
use prior word-level knowledge. The current study aims to leverage visual
information in order to capture sentence level semantics without the need for
word embeddings. We use a multimodal sentence encoder trained on a corpus of
images with matc... | ['Stefan Frank', 'Danny Merkx'] | 2019-03-27 | null | null | null | null | ['learning-semantic-representations', 'grounded-language-learning'] | ['methodology', 'natural-language-processing'] | [ 3.87786537e-01 5.89108430e-02 -9.93635207e-02 -4.59575444e-01
-8.43921900e-01 -4.45833296e-01 1.06075120e+00 6.29984081e-01
-7.96116769e-01 2.30531305e-01 5.65016210e-01 -1.59128122e-02
2.33074799e-01 -6.20928466e-01 -7.94263244e-01 -3.87559861e-01
2.05917284e-01 1.85253575e-01 1.94799170e-01 -3.18477809... | [10.71645736694336, 1.6642910242080688] |
8f315dda-f422-412f-abf7-f873e6549d6e | explaining-black-box-android-malware | 1803.03544 | null | http://arxiv.org/abs/1803.03544v2 | http://arxiv.org/pdf/1803.03544v2.pdf | Explaining Black-box Android Malware Detection | Machine-learning models have been recently used for detecting malicious
Android applications, reporting impressive performances on benchmark datasets,
even when trained only on features statically extracted from the application,
such as system calls and permissions. However, recent findings have highlighted
the fragili... | ['Battista Biggio', 'Giorgio Giacinto', 'Fabio Roli', 'Marco Melis', 'Davide Maiorca'] | 2018-03-09 | null | null | null | null | ['android-malware-detection'] | ['miscellaneous'] | [ 3.19352537e-01 1.03727378e-01 -5.52516222e-01 -1.05762340e-01
-4.13266957e-01 -8.74667525e-01 6.62726820e-01 1.57300651e-01
-1.19496644e-01 4.80794996e-01 -1.34604305e-01 -9.00183141e-01
3.35188173e-02 -5.33436835e-01 -8.48051488e-01 -4.78310347e-01
-5.05419970e-01 -2.34193802e-02 4.71179694e-01 -1.89004123... | [14.421866416931152, 9.681358337402344] |
7e7aae77-4fa5-4107-8f33-73ab16417c61 | solar-sinkhorn-label-refinery-for-imbalanced | 2209.10365 | null | https://arxiv.org/abs/2209.10365v1 | https://arxiv.org/pdf/2209.10365v1.pdf | SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning | Partial-label learning (PLL) is a peculiar weakly-supervised learning task where the training samples are generally associated with a set of candidate labels instead of single ground truth. While a variety of label disambiguation methods have been proposed in this domain, they normally assume a class-balanced scenario ... | ['Junbo Zhao', 'Gang Chen', 'Lei Feng', 'YUREN MAO', 'Yixuan Li', 'Mingxuan Xia', 'Haobo Wang'] | 2022-09-21 | null | null | null | null | ['partial-label-learning'] | ['methodology'] | [ 3.10327560e-01 5.74799627e-02 -7.33152390e-01 -6.57271028e-01
-1.32549417e+00 -6.69049561e-01 8.10204327e-01 2.16298297e-01
-4.07052308e-01 1.01337767e+00 -1.89714417e-01 -1.67733565e-01
-1.35706559e-01 -4.62853968e-01 -5.41418672e-01 -9.19942260e-01
2.75006026e-01 8.57395351e-01 3.58898342e-01 3.00257295... | [9.41832160949707, 4.040194034576416] |
098c0161-b732-4531-8ee5-1a80b9dee827 | pcred-zero-shot-relation-triplet-extraction | 2211.14477 | null | https://arxiv.org/abs/2211.14477v2 | https://arxiv.org/pdf/2211.14477v2.pdf | PCRED: Zero-shot Relation Triplet Extraction with Potential Candidate Relation Selection and Entity Boundary Detection | Zero-shot relation triplet extraction (ZeroRTE) aims to extract relation triplets from unstructured texts under the zero-shot setting, where the relation sets at the training and testing stages are disjoint. Previous state-of-the-art method handles this challenging task by leveraging pretrained language models to gener... | ['Hui Zhao', 'Yunqi Zhang', 'Gang Zhao', 'Dongxu Li', 'Yuquan Lan'] | 2022-11-26 | null | null | null | null | ['boundary-detection', 'zero-shot-relation-triplet-extraction'] | ['computer-vision', 'natural-language-processing'] | [ 1.61703929e-01 5.69270551e-01 -4.71436262e-01 -1.47053435e-01
-6.88189507e-01 -2.49621555e-01 6.86853111e-01 4.10256594e-01
-3.59678000e-01 6.99725270e-01 5.09954356e-02 -2.26557046e-01
-7.59121701e-02 -1.07789719e+00 -3.79492134e-01 -3.60072583e-01
1.76784262e-01 7.13159025e-01 5.37333786e-01 -6.30934238... | [9.281166076660156, 8.5518159866333] |
b282b767-b635-4a13-bb53-43c581fa6b70 | exploratory-hidden-markov-factor-models-for | 2202.12819 | null | https://arxiv.org/abs/2202.12819v2 | https://arxiv.org/pdf/2202.12819v2.pdf | Exploratory Hidden Markov Factor Models for Longitudinal Mobile Health Data: Application to Adverse Posttraumatic Neuropsychiatric Sequelae | Adverse posttraumatic neuropsychiatric sequelae (APNS) are common among veterans and millions of Americans after traumatic exposures, resulting in substantial burdens for trauma survivors and society. Despite numerous studies conducted on APNS over the past decades, there has been limited progress in understanding the ... | ['Rui Song', 'Ronald Kessler', 'Samuel McLean', 'Donglin Zeng', 'Xinming An', 'Lin Ge'] | 2022-02-25 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 3.37206721e-01 -4.42872196e-01 -5.26552260e-01 -1.89673617e-01
-9.32824612e-01 -4.37500745e-01 1.11355729e-01 4.66917306e-01
-9.02074814e-01 5.89592874e-01 5.38843751e-01 -6.07636809e-01
-5.50762594e-01 -3.32348168e-01 -3.73943776e-01 -2.61462301e-01
-4.01495457e-01 2.78608531e-01 -2.53921747e-01 2.96121776... | [8.07808780670166, 5.669051647186279] |
c2c30ffa-f337-4da5-a0d1-5f41594ac9cb | weakly-supervised-instance-segmentation-by-1 | 2007.09397 | null | https://arxiv.org/abs/2007.09397v1 | https://arxiv.org/pdf/2007.09397v1.pdf | Weakly Supervised Instance Segmentation by Learning Annotation Consistent Instances | Recent approaches for weakly supervised instance segmentations depend on two components: (i) a pseudo label generation model that provides instances which are consistent with a given annotation; and (ii) an instance segmentation model, which is trained in a supervised manner using the pseudo labels as ground-truth. Unl... | ['M. Pawan Kumar', 'C. V. Jawahar', 'Aditya Arun'] | 2020-07-18 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6083_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123730256.pdf | eccv-2020-8 | ['weakly-supervised-instance-segmentation', 'image-level-supervised-instance-segmentation'] | ['computer-vision', 'computer-vision'] | [ 5.91090143e-01 7.52545774e-01 -3.55216324e-01 -8.73368025e-01
-1.51992738e+00 -8.67080152e-01 7.54715204e-01 2.63462454e-01
-4.34118956e-01 8.37693393e-01 9.09577031e-03 2.65087157e-01
2.14635924e-01 -6.30123973e-01 -1.03354847e+00 -6.41530931e-01
1.87354982e-01 1.02974689e+00 5.22583842e-01 3.87414575... | [9.536582946777344, 0.5544496178627014] |
b99037b2-930e-49eb-aa43-af5886cdde8c | digitalexposome-quantifying-the-urban | 2101.12615 | null | https://arxiv.org/abs/2101.12615v1 | https://arxiv.org/pdf/2101.12615v1.pdf | DigitalExposome: Quantifying the Urban Environment Influence on Wellbeing based on Real-Time Multi-Sensor Fusion and Deep Belief Network | In this paper, we define the term 'DigitalExposome' as a conceptual framework that takes us closer towards understanding the relationship between environment, personal characteristics, behaviour and wellbeing using multimodel mobile sensing technology. Specifically, we simultaneously collected (for the first time) mult... | ['Kieran Woodward', 'Eiman Kanjo', 'Thomas Johnson'] | 2021-01-29 | null | null | null | null | ['heart-rate-variability'] | ['medical'] | [ 8.20299014e-02 -1.78181902e-01 1.06398299e-01 -1.31538495e-01
-1.93121910e-01 -2.82525659e-01 2.77372062e-01 7.24760890e-01
-2.64320940e-01 1.09269345e+00 3.87869239e-01 -1.64919585e-01
-2.69757837e-01 -1.06439435e+00 -2.30835095e-01 -5.57888448e-01
-2.67675459e-01 -2.25392267e-01 -3.77531528e-01 -9.24136117... | [13.721720695495605, 3.091951608657837] |
91524e59-17f0-47aa-986d-2624aa92aff0 | learning-to-generate-questions-by-enhancing | 2212.12192 | null | https://arxiv.org/abs/2212.12192v1 | https://arxiv.org/pdf/2212.12192v1.pdf | Learning to Generate Questions by Enhancing Text Generation with Sentence Selection | We introduce an approach for the answer-aware question generation problem. Instead of only relying on the capability of strong pre-trained language models, we observe that the information of answers and questions can be found in some relevant sentences in the context. Based on that, we design a model which includes two... | ['Minh-Tien Nguyen', 'Hung Le', 'Nguyen Hong Son', 'Do Hoang Thai Duong'] | 2022-12-23 | null | null | null | null | ['question-generation'] | ['natural-language-processing'] | [ 2.15022862e-01 7.27158546e-01 -2.83072013e-02 -4.85554904e-01
-1.27518749e+00 -7.45268166e-01 8.28984439e-01 2.05341473e-01
-1.20030910e-01 7.25797117e-01 8.17295492e-01 -3.20251018e-01
2.31229201e-01 -1.09539211e+00 -6.29458666e-01 -1.58559158e-01
3.56740564e-01 3.90079588e-01 4.10979837e-01 -4.87553984... | [11.442005157470703, 8.198563575744629] |
74512684-e4d5-4864-91c8-811770395b30 | multi-objective-reinforcement-learning-based | 2009.13854 | null | https://arxiv.org/abs/2009.13854v1 | https://arxiv.org/pdf/2009.13854v1.pdf | Multi-objective Reinforcement Learning based approach for User-Centric Power Optimization in Smart Home Environments | Smart homes require every device inside them to be connected with each other at all times, which leads to a lot of power wastage on a daily basis. As the devices inside a smart home increase, it becomes difficult for the user to control or operate every individual device optimally. Therefore, users generally rely on po... | ['Arun Balaji Buduru', 'Siddhant Bhambri', 'Saurabh Gupta', 'Ponnurangam Kumaraguru', 'Karan Dhingra'] | 2020-09-29 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [-1.78121448e-01 6.47872090e-02 -4.59474951e-01 -2.59337515e-01
-2.73067713e-01 -2.50908166e-01 -2.38130122e-01 1.87152684e-01
-2.39038855e-01 1.00471711e+00 1.11996591e-01 -9.62393358e-02
-5.12415528e-01 -9.20728505e-01 -2.03562543e-01 -1.03562438e+00
-1.61149159e-01 4.01489317e-01 -4.21110019e-02 -1.14021644... | [5.8184428215026855, 2.484769582748413] |
a11b5bad-c2c6-48c4-a523-b57266045107 | from-the-one-judge-of-the-whole-typed | 2306.04170 | null | https://arxiv.org/abs/2306.04170v1 | https://arxiv.org/pdf/2306.04170v1.pdf | From the One, Judge of the Whole: Typed Entailment Graph Construction with Predicate Generation | Entailment Graphs (EGs) have been constructed based on extracted corpora as a strong and explainable form to indicate context-independent entailment relations in natural languages. However, EGs built by previous methods often suffer from the severe sparsity issues, due to limited corpora available and the long-tail phe... | ['Dongyan Zhao', 'Yansong Feng', 'Zhibin Chen'] | 2023-06-07 | null | null | null | null | ['graph-construction'] | ['graphs'] | [ 4.16749328e-01 4.53769356e-01 -3.57975394e-01 -4.85570133e-01
-8.26739252e-01 -6.29492640e-01 7.78518677e-01 2.92907387e-01
4.34348024e-02 7.81946301e-01 3.57353956e-01 -6.70275033e-01
1.01804741e-01 -1.02568150e+00 -1.07064533e+00 7.67516419e-02
-7.92465135e-02 6.97519481e-01 1.99460268e-01 -2.83829242... | [9.585201263427734, 8.349262237548828] |
84b85930-7103-45c0-8d8a-7e08ce5d93b4 | jointly-modeling-aspect-and-sentiment-with | 2004.06427 | null | https://arxiv.org/abs/2004.06427v1 | https://arxiv.org/pdf/2004.06427v1.pdf | Jointly Modeling Aspect and Sentiment with Dynamic Heterogeneous Graph Neural Networks | Target-Based Sentiment Analysis aims to detect the opinion aspects (aspect extraction) and the sentiment polarities (sentiment detection) towards them. Both the previous pipeline and integrated methods fail to precisely model the innate connection between these two objectives. In this paper, we propose a novel dynamic ... | ['Xu sun', 'Yunfang Wu', 'Shu Liu', 'Qi Su', 'Wei Li'] | 2020-04-14 | null | null | null | null | ['aspect-extraction'] | ['natural-language-processing'] | [ 1.15542440e-02 3.36535603e-01 -5.05708277e-01 -7.54199743e-01
-3.79283428e-01 -9.18185830e-01 7.91043162e-01 2.08781287e-01
1.06975958e-02 2.02062458e-01 3.97185624e-01 -2.10377023e-01
2.10167959e-01 -7.76912391e-01 -2.33124524e-01 -5.88890374e-01
7.15606660e-02 4.98007834e-01 3.09071660e-01 -7.54468560... | [11.466547012329102, 6.666269779205322] |
fe3c79e9-50d1-4d44-bd83-2bfdf3239a09 | adversarial-attacks-on-adversarial-bandits | 2301.12595 | null | https://arxiv.org/abs/2301.12595v1 | https://arxiv.org/pdf/2301.12595v1.pdf | Adversarial Attacks on Adversarial Bandits | We study a security threat to adversarial multi-armed bandits, in which an attacker perturbs the loss or reward signal to control the behavior of the victim bandit player. We show that the attacker is able to mislead any no-regret adversarial bandit algorithm into selecting a suboptimal target arm in every but sublinea... | ['Zhijin Zhou', 'Yuzhe ma'] | 2023-01-30 | null | null | null | null | ['multi-armed-bandits'] | ['miscellaneous'] | [ 1.94745094e-01 3.05047095e-01 -2.76374161e-01 5.71614206e-02
-8.57656598e-01 -1.77916813e+00 1.62890479e-01 -9.72525626e-02
-4.20582652e-01 6.36992216e-01 -3.11079055e-01 -1.02053642e+00
-5.42599380e-01 -9.37292278e-01 -1.28125119e+00 -9.47665572e-01
-1.87864900e-01 6.62823319e-01 2.27499418e-02 -1.85101822... | [4.595784664154053, 3.4563419818878174] |
c61b305d-7af5-4281-af44-6fdec836011d | learning-an-image-based-motion-context-for | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Leal-Taixe_Learning_an_Image-based_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Leal-Taixe_Learning_an_Image-based_2014_CVPR_paper.pdf | Learning an Image-based Motion Context for Multiple People Tracking | We present a novel method for multiple people tracking that leverages a generalized model for capturing interactions among individuals. At the core of our model lies a learned dictionary of interaction feature strings which capture relationships between the motions of targets. These feature strings, created from low-l... | ['Laura Leal-Taixe', 'Silvio Savarese', 'Michele Fenzi', 'Alina Kuznetsova', 'Bodo Rosenhahn'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['multiple-people-tracking'] | ['computer-vision'] | [-9.30703655e-02 -2.85447061e-01 -2.10481659e-02 -2.35599726e-01
-1.55922681e-01 -6.40859008e-01 1.00640237e+00 -9.26814228e-03
-4.00778860e-01 6.46962821e-01 4.33684438e-01 3.88011813e-01
7.88415968e-02 -7.66070783e-01 -7.05916941e-01 -5.59085667e-01
-4.61879820e-01 5.20635784e-01 6.76785827e-01 -1.93167523... | [6.410713195800781, -1.9406425952911377] |
14ec015e-f213-40a9-bf68-af08fdb8e7bb | multilayer-hypergraph-clustering-using-the | 2301.11657 | null | https://arxiv.org/abs/2301.11657v2 | https://arxiv.org/pdf/2301.11657v2.pdf | Multilayer hypergraph clustering using the aggregate similarity matrix | We consider the community recovery problem on a multilayer variant of the hypergraph stochastic block model (HSBM). Each layer is associated with an independent realization of a d-uniform HSBM on N vertices. Given the similarity matrix containing the aggregated number of hyperedges incident to each pair of vertices, th... | ['Lasse Leskelä', 'B. R. Vinay Kumar', 'Konstantin Avrachenkov', 'Kalle Alaluusua'] | 2023-01-27 | null | null | null | null | ['stochastic-block-model'] | ['graphs'] | [ 1.66908041e-01 4.32581216e-01 -4.04112756e-01 -1.29004493e-01
-5.49411237e-01 -4.29482847e-01 1.35376632e-01 2.22528890e-01
9.65352654e-02 8.35036874e-01 2.97827214e-01 -3.57606560e-02
-6.27885580e-01 -9.89604592e-01 -8.29146981e-01 -1.11039853e+00
-5.50437212e-01 1.19840431e+00 -1.78655103e-01 5.52367494... | [6.878824234008789, 5.106053352355957] |
7b538f9d-ddd4-4476-8369-cee0f2402d31 | gan-based-disentanglement-learning-for-chest | 2110.09134 | null | https://arxiv.org/abs/2110.09134v1 | https://arxiv.org/pdf/2110.09134v1.pdf | GAN-based disentanglement learning for chest X-ray rib suppression | Clinical evidence has shown that rib-suppressed chest X-rays (CXRs) can improve the reliability of pulmonary disease diagnosis. However, previous approaches on generating rib-suppressed CXR face challenges in preserving details and eliminating rib residues. We hereby propose a GAN-based disentanglement learning framewo... | ['S. Kevin Zhou', 'Cheng Peng', 'Yuanyuan Lyu', 'Luyi Han'] | 2021-10-18 | null | null | null | null | ['lung-disease-classification'] | ['medical'] | [ 6.62441790e-01 -8.31642002e-02 -3.41205746e-01 -2.38582268e-02
-1.22382438e+00 -4.64515775e-01 2.94120252e-01 -4.80595142e-01
-8.31918642e-02 9.74587560e-01 4.47836310e-01 -4.91633654e-01
8.13108869e-03 -8.28749955e-01 -5.01188278e-01 -9.54499483e-01
3.62050354e-01 4.69572037e-01 1.45767927e-01 2.20617995... | [13.794649124145508, -2.3807730674743652] |
248169c4-f062-4970-8847-daaeea65b19f | implicit-motion-compensated-network-for | 2204.02791 | null | https://arxiv.org/abs/2204.02791v1 | https://arxiv.org/pdf/2204.02791v1.pdf | Implicit Motion-Compensated Network for Unsupervised Video Object Segmentation | Unsupervised video object segmentation (UVOS) aims at automatically separating the primary foreground object(s) from the background in a video sequence. Existing UVOS methods either lack robustness when there are visually similar surroundings (appearance-based) or suffer from deterioration in the quality of their predi... | ['Zhengguo Li', 'Zhong Liu', 'Xingming Wu', 'Weihai Chen', 'Lin Xi'] | 2022-04-06 | null | null | null | null | ['motion-compensation', 'unsupervised-video-object-segmentation'] | ['computer-vision', 'computer-vision'] | [ 2.68551797e-01 -3.34220558e-01 -9.62110087e-02 -1.85059890e-01
-2.51016825e-01 -2.19625786e-01 1.05087206e-01 -1.10377252e-01
-5.57441711e-01 5.95400155e-01 -8.86831284e-02 -1.15963048e-03
4.04195525e-02 -7.10337281e-01 -5.94794452e-01 -7.40563691e-01
1.04120567e-01 4.75289002e-02 9.63006079e-01 1.98543638... | [9.226518630981445, -0.28244030475616455] |
c76ffba7-e804-48bf-9c38-1eec19c90453 | in-search-for-a-generalizable-method-for | 2302.06658 | null | https://arxiv.org/abs/2302.06658v2 | https://arxiv.org/pdf/2302.06658v2.pdf | In Search for a Generalizable Method for Source Free Domain Adaptation | Source-free domain adaptation (SFDA) is compelling because it allows adapting an off-the-shelf model to a new domain using only unlabelled data. In this work, we apply existing SFDA techniques to a challenging set of naturally-occurring distribution shifts in bioacoustics, which are very different from the ones commonl... | ['Eleni Triantafillou', 'Vincent Dumoulin', 'Bart van Merriënboer', 'Tom Denton', 'Malik Boudiaf'] | 2023-02-13 | null | null | null | null | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 2.54477650e-01 -4.19941872e-01 7.60027766e-02 -4.05488342e-01
-7.54110038e-01 -9.17156339e-01 7.96851695e-01 -2.36013174e-01
-6.84078097e-01 7.21135736e-01 2.45631605e-01 -2.79794037e-02
-3.71067226e-02 -1.72069445e-01 -5.92717707e-01 -9.87039447e-01
4.71270010e-02 5.08064210e-01 6.60142303e-01 -3.51775229... | [10.173888206481934, 2.8841826915740967] |
d9faa591-bd57-4c62-9c1e-10af7ad02353 | statistical-depth-functions-for-ranking | 2201.08105 | null | https://arxiv.org/abs/2201.08105v1 | https://arxiv.org/pdf/2201.08105v1.pdf | Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications | The concept of median/consensus has been widely investigated in order to provide a statistical summary of ranking data, i.e. realizations of a random permutation $\Sigma$ of a finite set, $\{1,\; \ldots,\; n\}$ with $n\geq 1$ say. As it sheds light onto only one aspect of $\Sigma$'s distribution $P$, it may neglect oth... | ['Pavlo Mozharovskyi', 'Ekhine Irurozki', 'Stéphan Clémençon', 'Morgane Goibert'] | 2022-01-20 | null | null | null | null | ['novel-concepts'] | ['reasoning'] | [ 2.35853001e-01 2.30604149e-02 -2.82774083e-02 -6.57669127e-01
-7.07809389e-01 -7.89338589e-01 4.73586202e-01 2.20393956e-01
-4.48468894e-01 7.63080895e-01 1.06217027e-01 -3.21767896e-01
-1.41606069e+00 -8.89105558e-01 -2.23689988e-01 -1.01017559e+00
-6.82082832e-01 6.34764671e-01 -1.44855037e-01 -5.00941694... | [7.376430034637451, 4.366192817687988] |
c29559be-6827-4b20-b4fb-13dd075a12f1 | sketch-an-anchor-sub-epoch-fast-model | 2303.16769 | null | https://arxiv.org/abs/2303.16769v1 | https://arxiv.org/pdf/2303.16769v1.pdf | Sketch-an-Anchor: Sub-epoch Fast Model Adaptation for Zero-shot Sketch-based Image Retrieval | Sketch-an-Anchor is a novel method to train state-of-the-art Zero-shot Sketch-based Image Retrieval (ZSSBIR) models in under an epoch. Most studies break down the problem of ZSSBIR into two parts: domain alignment between images and sketches, inherited from SBIR, and generalization to unseen data, inherent to the zero-... | ['Moacir Antonelli Ponti', 'Leo Sampaio Ferraz Ribeiro'] | 2023-03-29 | null | null | null | null | ['sketch-based-image-retrieval'] | ['computer-vision'] | [ 1.60976008e-01 -3.42723519e-01 -6.90428257e-01 -3.27300966e-01
-1.36168051e+00 -5.91175914e-01 9.46659803e-01 -2.81913638e-01
-2.80692995e-01 2.11051941e-01 2.70756692e-01 2.46032536e-01
-4.89416480e-01 -5.66504896e-01 -6.61353528e-01 -5.00316560e-01
1.67653903e-01 6.57969832e-01 3.41479808e-01 -4.49480951... | [11.562582969665527, 0.6737768054008484] |
6ed55145-7676-4199-af2d-0a3da321060f | classification-attention-for-chinese-ner | null | null | https://openreview.net/forum?id=B1gUn24tPr | https://openreview.net/pdf?id=B1gUn24tPr | Classification Attention for Chinese NER | The character-based model, such as BERT, has achieved remarkable success in Chinese named entity recognition (NER). However, such model would likely miss the overall information of the entity words. In this paper, we propose to combine priori entity information with BERT. Instead of relying on additional lexicons or pr... | ['PeiYang', 'FanYang', 'Yuchen Ge'] | 2019-09-25 | null | null | null | null | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [-5.50714672e-01 1.22013271e-01 3.70894186e-02 -3.57670754e-01
-5.95606983e-01 -5.58153808e-01 5.00734210e-01 1.23939544e-01
-1.13630998e+00 6.69223487e-01 4.26766306e-01 -4.19984460e-01
3.62062931e-01 -8.76545370e-01 -2.81819522e-01 -2.19820544e-01
1.02347143e-01 3.01351905e-01 2.67209768e-01 -1.35663569... | [9.819540977478027, 9.797547340393066] |
998e5671-0c1d-4a35-8b8f-790cfa8be754 | connecting-the-dots-a-comprehensive | 2307.00455 | null | https://arxiv.org/abs/2307.00455v1 | https://arxiv.org/pdf/2307.00455v1.pdf | Connecting the Dots: A Comprehensive Literature Review on Low and Medium-Voltage Cables, Fault Types, and Digital Signal Processing Techniques for Fault Location | The review begins with an exploration of acceptable cable types guided by local standards. It then investigates typical cable faults, including insulation degradation, conductor faults, and ground faults, providing insights into their characteristics, causes, and detection methods. Furthermore, the manuscript surveys t... | ['Sanjay Bahadoorsingh', 'Shankar Ramharack'] | 2023-07-02 | null | null | null | null | ['management'] | ['miscellaneous'] | [-7.53123611e-02 -6.89259827e-01 -1.94863245e-01 -1.53882310e-01
-7.42393851e-01 -9.31788564e-01 -4.33308303e-01 1.04547702e-01
5.18595099e-01 8.98341298e-01 -6.40112311e-02 -4.73563910e-01
-8.08324277e-01 -3.07216316e-01 -3.28374684e-01 -9.28931296e-01
-7.99832523e-01 3.04281920e-01 1.25742093e-01 -3.15904990... | [6.3170647621154785, 2.453106641769409] |
08d17758-9315-40d5-a648-ee89ccea5aee | disentangled-pre-training-for-image-matting | 2304.00784 | null | https://arxiv.org/abs/2304.00784v1 | https://arxiv.org/pdf/2304.00784v1.pdf | Disentangled Pre-training for Image Matting | Image matting requires high-quality pixel-level human annotations to support the training of a deep model in recent literature. Whereas such annotation is costly and hard to scale, significantly holding back the development of the research. In this work, we make the first attempt towards addressing this problem, by pro... | ['Jianbo Jiao', 'Yunchao Wei', 'Ling Chen', 'Gang Yu', 'Zilong Huang', 'Yanda Li'] | 2023-04-03 | null | null | null | null | ['image-matting'] | ['computer-vision'] | [ 6.01739943e-01 4.49297488e-01 -5.56067675e-02 -5.34763098e-01
-7.85516977e-01 -3.68445128e-01 8.93351316e-01 -1.40089661e-01
-4.81471777e-01 6.21738374e-01 1.40607744e-01 -3.01562548e-01
1.87640756e-01 -4.92303044e-01 -1.12542307e+00 -7.86297917e-01
2.87648916e-01 6.18839025e-01 1.29395306e-01 -7.45192617... | [10.74098014831543, -0.9421555995941162] |
796712f9-5c73-4e2c-be37-bc43e95e7f60 | pc-hmr-pose-calibration-for-3d-human-mesh | 2103.09009 | null | https://arxiv.org/abs/2103.09009v2 | https://arxiv.org/pdf/2103.09009v2.pdf | PC-HMR: Pose Calibration for 3D Human Mesh Recovery from 2D Images/Videos | The end-to-end Human Mesh Recovery (HMR) approach has been successfully used for 3D body reconstruction. However, most HMR-based frameworks reconstruct human body by directly learning mesh parameters from images or videos, while lacking explicit guidance of 3D human pose in visual data. As a result, the generated mesh ... | ['Yu Qiao', 'Zhipeng Zhou', 'Zhe Wang', 'Junhao Zhang', 'Yali Wang', 'Tianyu Luan'] | 2021-03-16 | null | null | null | null | ['human-mesh-recovery'] | ['computer-vision'] | [-1.62510201e-01 2.38996029e-01 -1.15023822e-01 -1.10446764e-02
-7.49654412e-01 -2.37532347e-01 1.59901723e-01 -2.31158897e-01
-1.67792454e-01 3.11050475e-01 -1.14203326e-01 1.80500269e-01
4.28619571e-02 -8.10461462e-01 -1.04240930e+00 -3.35483372e-01
1.84848055e-01 9.64271843e-01 5.33169866e-01 -3.72146636... | [7.0012946128845215, -1.1393630504608154] |
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