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76b89547-5a7e-491a-9d9c-1381c3b8e362 | clipsitu-effectively-leveraging-clip-for | 2307.00586 | null | https://arxiv.org/abs/2307.00586v1 | https://arxiv.org/pdf/2307.00586v1.pdf | ClipSitu: Effectively Leveraging CLIP for Conditional Predictions in Situation Recognition | Situation Recognition is the task of generating a structured summary of what is happening in an image using an activity verb and the semantic roles played by actors and objects. In this task, the same activity verb can describe a diverse set of situations as well as the same actor or object category can play a diverse ... | ['Basura Fernando', 'Dhruv Verma', 'Debaditya Roy'] | 2023-07-02 | null | null | null | null | ['grounded-situation-recognition'] | ['computer-vision'] | [ 6.10919118e-01 3.17993969e-01 -3.84307206e-01 -5.15563428e-01
-5.55643082e-01 -7.98014998e-01 1.22585225e+00 2.68855929e-01
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2.46423945e-01 2.42785692e-01 1.92357540e-01 -2.59156317... | [10.543034553527832, 1.4296187162399292] |
27b7f301-6f62-42e6-a296-3c3d4d8e897c | on-training-deep-networks-for-satellite-image | 1906.06697 | null | https://arxiv.org/abs/1906.06697v1 | https://arxiv.org/pdf/1906.06697v1.pdf | On training deep networks for satellite image super-resolution | The capabilities of super-resolution reconstruction (SRR)---techniques for enhancing image spatial resolution---have been recently improved significantly by the use of deep convolutional neural networks. Commonly, such networks are learned using huge training sets composed of original images alongside their low-resolut... | ['Szymon Piechaczek', 'Pawel Benecki', 'Krzysztof Hrynczenko', 'Jakub Nalepa', 'Daniel Kostrzewa', 'Michal Kawulok'] | 2019-06-16 | null | null | null | null | ['satellite-image-super-resolution'] | ['computer-vision'] | [ 4.29831982e-01 -2.89274037e-01 -2.63528097e-02 -2.23387361e-01
-6.55706584e-01 -7.50368461e-02 6.22035205e-01 -3.05470556e-01
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-1.48626149e-01 7.47959837e-02 1.48103252e-01 -6.62950635... | [10.490184783935547, -1.9518300294876099] |
1c789cb0-c14d-4ea5-b44e-c2bb1f124986 | asymmetric-quantum-decision-making | 2305.02117 | null | https://arxiv.org/abs/2305.02117v1 | https://arxiv.org/pdf/2305.02117v1.pdf | Asymmetric quantum decision-making | Collective decision-making is crucial to information and communication systems. Decision conflicts among agents hinder the maximization of potential utilities of the entire system. Quantum processes can realize conflict-free joint decisions among two agents using the entanglement of photons or quantum interference of o... | ['Makoto Naruse', 'Ryoichi Horisaki', 'Tomoki Yamagami', 'Guillaume Bachelier', 'Jonathan Laurent', 'Etsuo Segawa', 'Nicolas Chauvet', 'André Röhm', 'Hiroaki Shinkawa', 'Honoka Shiratori'] | 2023-05-03 | null | null | null | null | ['ethics'] | ['miscellaneous'] | [ 1.63043365e-01 3.17024410e-01 -1.99640751e-01 3.96212414e-02
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1.17629431e-01 2.92451710e-01 -4.62687194e-01 -3.16328019... | [5.584012508392334, 4.788204669952393] |
2169cf63-6e83-47f6-84bb-2cfb7a548f68 | a-novel-viewport-adaptive-motion-compensation | 2202.13892 | null | https://arxiv.org/abs/2202.13892v1 | https://arxiv.org/pdf/2202.13892v1.pdf | A Novel Viewport-Adaptive Motion Compensation Technique for Fisheye Video | Although fisheye cameras are in high demand in many application areas due to their large field of view, many image and video signal processing tasks such as motion compensation suffer from the introduced strong radial distortions. A recently proposed projection-based approach takes the fisheye projection into account t... | ['André Kaup', 'Christian Herglotz', 'Andy Regensky'] | 2022-02-28 | null | null | null | null | ['motion-compensation'] | ['computer-vision'] | [ 2.44292393e-01 -3.21734697e-01 1.27909347e-01 -2.28314921e-01
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6.45379126e-01 -2.25807786e-01 6.73866510e-01 -2.28867799... | [9.122180938720703, -2.44962739944458] |
c561ed38-015a-4950-96d5-d3f0be9bbcc3 | finite-state-script-normalization-and | null | null | https://aclanthology.org/2021.eacl-demos.3 | https://aclanthology.org/2021.eacl-demos.3.pdf | Finite-state script normalization and processing utilities: The Nisaba Brahmic library | This paper presents an open-source library for efficient low-level processing of ten major South Asian Brahmic scripts. The library provides a flexible and extensible framework for supporting crucial operations on Brahmic scripts, such as NFC, visual normalization, reversible transliteration, and validity checks, imple... | ['Brian Roark', 'Alexander Gutkin', 'Lawrence Wolf-Sonkin', 'Cibu Johny'] | 2021-04-01 | null | null | null | eacl-2021-2 | ['transliteration'] | ['natural-language-processing'] | [ 5.42088985e-01 -1.50161281e-01 -2.21724570e-01 -8.80180299e-01
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5.04116535e-01 5.18532157e-01 -1.12358652e-01 -2.98685700... | [10.464888572692871, 10.25110149383545] |
1c853903-ac13-4972-99f0-8c8a4fbaae98 | discovering-intrinsic-spatial-temporal-logic | 2306.12244 | null | https://arxiv.org/abs/2306.12244v1 | https://arxiv.org/pdf/2306.12244v1.pdf | Discovering Intrinsic Spatial-Temporal Logic Rules to Explain Human Actions | We propose a logic-informed knowledge-driven modeling framework for human movements by analyzing their trajectories. Our approach is inspired by the fact that human actions are usually driven by their intentions or desires, and are influenced by environmental factors such as the spatial relationships with surrounding o... | ['Shuang Li', 'Chao Yang', 'Chengzhi Cao'] | 2023-06-21 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [ 3.85548845e-02 3.13008279e-01 -5.65287292e-01 -5.77634692e-01
1.00077644e-01 -2.44638398e-01 7.67472386e-01 1.57950874e-02
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-1.33496404e-01 5.54652989e-01 3.26337039e-01 -2.59091407... | [7.406071186065674, 0.6564618349075317] |
1eb8e0c0-775e-4912-ae66-7299a413b1fa | unediting-detecting-disfluencies-without | null | null | https://aclanthology.info/papers/N15-1161/n15-1161 | https://www.aclweb.org/anthology/N15-1161 | Unediting: Detecting Disfluencies Without Careful Transcripts | null | ['Victoria Zayats', 'Mari Ostendorf', 'Hannaneh Hajishirzi'] | 2015-05-01 | null | null | null | hlt-2015-5 | ['electrical-engineering'] | ['miscellaneous'] | [-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01
-8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01
-5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01
-2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01
-7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302... | [-1.5391420125961304, 15.869156837463379] |
866d2f36-516f-470a-8af3-2d21e8cdc5fb | enhancing-learnability-of-classification | 1912.04453 | null | https://arxiv.org/abs/1912.04453v1 | https://arxiv.org/pdf/1912.04453v1.pdf | Enhancing Learnability of classification algorithms using simple data preprocessing in fMRI scans of Alzheimer's disease | Alzheimer's Disease (AD) is the most common type of dementia. In all leading countries, it is one of the primary reasons of death in senior citizens. Currently, it is diagnosed by calculating the MSME score and by the manual study of MRI Scan. Also, different machine learning methods are utilized for automatic diagnosi... | ['Rekh Ram Janghel', 'Rishu Garg', 'Yogesh Rathore'] | 2019-12-10 | null | null | null | null | ['3d-classification'] | ['computer-vision'] | [-1.50231589e-02 -2.43550792e-01 2.24265784e-01 -5.95457971e-01
-5.41503370e-01 -2.69520968e-01 3.10150504e-01 1.87442407e-01
-9.11737740e-01 1.09416878e+00 1.68481752e-01 -2.53624707e-01
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-1.23672776e-01 4.85409737e-01 6.24326229e-01 1.61495745... | [14.157700538635254, -1.7526860237121582] |
beb2357c-8e43-452f-8063-ddfdd04503bc | border-an-oriented-rectangles-approach-to | null | null | http://openaccess.thecvf.com/content_cvpr_2016/html/Chan_BORDER_An_Oriented_CVPR_2016_paper.html | http://openaccess.thecvf.com/content_cvpr_2016/papers/Chan_BORDER_An_Oriented_CVPR_2016_paper.pdf | BORDER: An Oriented Rectangles Approach to Texture-Less Object Recognition | This paper presents an algorithm coined BORDER (Bounding Oriented-Rectangle Descriptors for Enclosed Regions) for texture-less object recognition. By fusing a regional object encompassment concept with descriptor-based pipelines, we extend local-patches into scalable object-sized oriented rectangles for optimal object ... | ['Qian Kemao', 'Jimmy Addison Lee', 'Jacob Chan'] | 2016-06-01 | null | null | null | cvpr-2016-6 | ['line-segment-detection'] | ['computer-vision'] | [ 9.51376110e-02 -3.00695449e-01 -1.28903717e-01 -3.47200632e-01
-1.02628183e+00 -6.08245254e-01 5.78956008e-01 4.01471853e-01
-1.22135587e-01 6.79024234e-02 -3.00553679e-01 2.08202764e-01
-1.67168826e-01 -5.10830164e-01 -4.21308607e-01 -5.40103316e-01
-1.61583766e-01 5.25640130e-01 7.37896502e-01 -1.94606051... | [7.868295669555664, -2.2570960521698] |
28701b4a-9fe8-4558-b81c-92d1fef6e98e | learning-node-embeddings-via-summary-graphs-a | 2207.01189 | null | https://arxiv.org/abs/2207.01189v1 | https://arxiv.org/pdf/2207.01189v1.pdf | Learning node embeddings via summary graphs: a brief theoretical analysis | Graph representation learning plays an important role in many graph mining applications, but learning embeddings of large-scale graphs remains a problem. Recent works try to improve scalability via graph summarization -- i.e., they learn embeddings on a smaller summary graph, and then restore the node embeddings of the... | ['Xueqi Cheng', 'HuaWei Shen', 'Danai Koutra', 'Shenghua Liu', 'Houquan Zhou'] | 2022-07-04 | null | null | null | null | ['graph-mining'] | ['graphs'] | [ 1.81041248e-02 6.44222140e-01 -5.55335343e-01 6.86122628e-04
-2.06253007e-01 -4.43321824e-01 7.03650713e-02 7.66168237e-01
-6.85756048e-03 2.69156903e-01 3.24380189e-01 -5.85045993e-01
-3.88689637e-01 -1.06242955e+00 -6.04751229e-01 -4.28027749e-01
-6.06836855e-01 5.01665294e-01 3.44575256e-01 -2.04268649... | [7.133735179901123, 6.092657089233398] |
710d8813-ea67-4657-b25d-2172fd8cf3a2 | ecg-heartbeat-classification-a-deep | 1805.00794 | null | http://arxiv.org/abs/1805.00794v2 | http://arxiv.org/pdf/1805.00794v2.pdf | ECG Heartbeat Classification: A Deep Transferable Representation | Electrocardiogram (ECG) can be reliably used as a measure to monitor the
functionality of the cardiovascular system. Recently, there has been a great
attention towards accurate categorization of heartbeats. While there are many
commonalities between different ECG conditions, the focus of most studies has
been classifyi... | ['Shayan Fazeli', 'Mohammad Kachuee', 'Majid Sarrafzadeh'] | 2018-04-19 | null | null | null | null | ['myocardial-infarction-detection', 'arrhythmia-detection', 'heartbeat-classification', 'electrocardiography-ecg'] | ['medical', 'medical', 'medical', 'methodology'] | [ 1.51785374e-01 -7.69520253e-02 2.18638584e-01 -6.06530726e-01
-5.50546348e-01 -3.62056106e-01 1.10752083e-01 4.78742033e-01
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-2.43910998e-01 -6.85992062e-01 -1.22674540e-01 -4.90024626e-01
-1.66760132e-01 4.24233049e-01 -2.74971485e-01 7.82300606... | [14.323248863220215, 3.3144633769989014] |
64a4f6a5-e7e4-486c-85f4-9d1b697cca67 | fine-grained-analysis-of-propaganda-in-news | 1910.02517 | null | https://arxiv.org/abs/1910.02517v1 | https://arxiv.org/pdf/1910.02517v1.pdf | Fine-Grained Analysis of Propaganda in News Articles | Propaganda aims at influencing people's mindset with the purpose of advancing a specific agenda. Previous work has addressed propaganda detection at the document level, typically labelling all articles from a propagandistic news outlet as propaganda. Such noisy gold labels inevitably affect the quality of any learning ... | ['Alberto Barrón-Cedeño', 'Giovanni Da San Martino', 'Rostislav Petrov', 'Preslav Nakov', 'Seunghak Yu'] | 2019-10-06 | null | null | null | null | ['propaganda-detection'] | ['natural-language-processing'] | [ 3.16335678e-01 3.19319814e-01 -7.29676306e-01 -1.87919185e-01
-8.99928093e-01 -5.77664137e-01 1.30751777e+00 4.56806034e-01
-4.17267501e-01 7.30855286e-01 1.10967600e+00 -6.48846269e-01
3.92947108e-01 -7.95423329e-01 -7.21347809e-01 -4.07545954e-01
3.65956515e-01 3.67972165e-01 2.26707347e-02 -3.19262981... | [8.506986618041992, 10.600865364074707] |
65e075f7-1b16-4e1f-a526-db701b63db70 | better-query-graph-selection-for-knowledge | 2204.12662 | null | https://arxiv.org/abs/2204.12662v1 | https://arxiv.org/pdf/2204.12662v1.pdf | Better Query Graph Selection for Knowledge Base Question Answering | This paper presents a novel approach based on semantic parsing to improve the performance of Knowledge Base Question Answering (KBQA). Specifically, we focus on how to select an optimal query graph from a candidate set so as to retrieve the answer from knowledge base (KB). In our approach, we first propose to linearize... | ['Wenliang Chen', 'Yonghui Jia'] | 2022-04-27 | null | null | null | null | ['knowledge-base-question-answering'] | ['natural-language-processing'] | [ 4.55130078e-02 3.74510914e-01 -2.20627680e-01 -3.42232317e-01
-1.18966949e+00 -9.49079812e-01 1.19037054e-01 2.41566941e-01
-2.89829850e-01 7.84160554e-01 1.03217393e-01 -4.86622125e-01
-1.41696885e-01 -1.23495603e+00 -9.39135790e-01 -2.56191492e-02
3.67801994e-01 8.46754193e-01 1.17606592e+00 -5.73525250... | [10.487818717956543, 7.943595886230469] |
48c8266e-34a5-405e-8f24-238715a213ad | generalizing-to-evolving-domains-with-latent | 2205.07649 | null | https://arxiv.org/abs/2205.07649v2 | https://arxiv.org/pdf/2205.07649v2.pdf | Generalizing to Evolving Domains with Latent Structure-Aware Sequential Autoencoder | Domain generalization aims to improve the generalization capability of machine learning systems to out-of-distribution (OOD) data. Existing domain generalization techniques embark upon stationary and discrete environments to tackle the generalization issue caused by OOD data. However, many real-world tasks in non-stati... | ['Haoliang Li', 'Shiqi Wang', 'Tiexin Qin'] | 2022-05-16 | null | null | null | null | ['evolving-domain-generalization'] | ['computer-vision'] | [ 3.33073825e-01 -3.81584942e-01 4.78018634e-02 -4.19424683e-01
-2.17368841e-01 -4.52323914e-01 4.39134359e-01 -7.82684311e-02
-2.06768855e-01 7.83798516e-01 -1.25439927e-01 -1.81893095e-01
-4.72264200e-01 -6.13943458e-01 -6.60590947e-01 -1.02455294e+00
-2.02860072e-01 3.19012135e-01 7.66495168e-02 -1.99883237... | [10.303328514099121, 3.0186173915863037] |
d9ef9ebd-4d5c-41e8-8b2a-fa6a653ebe64 | mri-images-analysis-method-for-early-stage | 2012.00830 | null | https://arxiv.org/abs/2012.00830v1 | https://arxiv.org/pdf/2012.00830v1.pdf | MRI Images Analysis Method for Early Stage Alzheimer's Disease Detection | Alzheimer's disease is a neurogenerative disease that alters memories, cognitive functions leading to death. Early diagnosis of the disease, by detection of the preliminary stage, called Mild Cognitive Impairment (MCI), remains a challenging issue. In this respect, we introduce, in this paper, a powerful classification... | ['Amira ben Rabeh', 'Taoufik Yeferny', 'Achraf Ben Miled'] | 2020-11-27 | null | null | null | null | ['alzheimer-s-disease-detection'] | ['medical'] | [-1.54558554e-01 -2.71670669e-01 2.50017732e-01 -4.84274179e-01
-2.83420205e-01 9.26458929e-03 3.98678690e-01 -8.44162256e-02
-9.30978715e-01 1.15195775e+00 3.37977380e-01 -8.36759731e-02
-1.31898254e-01 -6.60305619e-01 -1.85467139e-01 -4.55224633e-01
-6.21684253e-01 5.34324050e-01 3.55712831e-01 -4.51501198... | [14.1611967086792, -1.751629114151001] |
1e92f91a-8cc0-4e2c-ac7c-96d4bff3fb2a | toward-educator-focused-automated-scoring | 2112.11973 | null | https://arxiv.org/abs/2112.11973v1 | https://arxiv.org/pdf/2112.11973v1.pdf | Toward Educator-focused Automated Scoring Systems for Reading and Writing | This paper presents methods for improving automated essay scoring with techniques that address the computational trade-offs of self-attention and document length. To make Automated Essay Scoring (AES) more useful to practitioners, researchers must overcome the challenges of data and label availability, authentic and ex... | ['Mike Hardy'] | 2021-12-22 | null | null | null | null | ['automated-essay-scoring'] | ['natural-language-processing'] | [ 2.27263600e-01 -1.26781330e-01 -3.70059848e-01 -6.43074393e-01
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-5.78264594e-01 -5.43957591e-01 -2.18575805e-01 -5.69771826e-02
5.93705833e-01 5.10161102e-01 1.55033134e-02 -4.16984111... | [11.31344985961914, 9.359574317932129] |
506e201a-f419-4b91-b8c5-a622fc270aa5 | aligning-synthetic-medical-images-with | 2306.12438 | null | https://arxiv.org/abs/2306.12438v1 | https://arxiv.org/pdf/2306.12438v1.pdf | Aligning Synthetic Medical Images with Clinical Knowledge using Human Feedback | Generative models capable of capturing nuanced clinical features in medical images hold great promise for facilitating clinical data sharing, enhancing rare disease datasets, and efficiently synthesizing annotated medical images at scale. Despite their potential, assessing the quality of synthetic medical images remain... | ['Ahmed M. Alaa', 'Atul Butte', 'Gregory M. Goldgof', 'Shenghuan Sun'] | 2023-06-16 | null | null | null | null | ['clinical-knowledge'] | ['miscellaneous'] | [ 5.07311106e-01 4.62724537e-01 -3.32942195e-02 -3.28043491e-01
-1.10440707e+00 -6.84948504e-01 4.41968441e-01 2.16231450e-01
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5.06342098e-04 8.10132563e-01 3.68875153e-02 3.01780134... | [14.293296813964844, -1.9216371774673462] |
31e7df59-db6e-455c-b330-dd2432064ef5 | eend-ss-joint-end-to-end-neural-speaker | 2203.17068 | null | https://arxiv.org/abs/2203.17068v2 | https://arxiv.org/pdf/2203.17068v2.pdf | EEND-SS: Joint End-to-End Neural Speaker Diarization and Speech Separation for Flexible Number of Speakers | In this paper, we present a novel framework that jointly performs three tasks: speaker diarization, speech separation, and speaker counting. Our proposed framework integrates speaker diarization based on end-to-end neural diarization (EEND) models, speaker counting with encoder-decoder based attractors (EDA), and speec... | ['Soumi Maiti', 'Yong Xu', 'Shi-Xiong Zhang', 'Meng Yu', 'Chunlei Zhang', 'Shinji Watanabe', 'Yushi Ueda'] | 2022-03-31 | null | null | null | null | ['speech-separation'] | ['speech'] | [-4.84543666e-02 -1.88287601e-01 3.16634178e-01 -5.09529233e-01
-1.11524498e+00 -6.55965447e-01 5.41293025e-01 -3.09418142e-01
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6.35698959e-02 4.61008936e-01 2.19259243e-02 1.75861746... | [14.650282859802246, 6.12100076675415] |
1a71df22-240c-4d95-a782-9ed13f32bcde | towards-accurate-human-motion-prediction-via | 2305.04443 | null | https://arxiv.org/abs/2305.04443v1 | https://arxiv.org/pdf/2305.04443v1.pdf | Towards Accurate Human Motion Prediction via Iterative Refinement | Human motion prediction aims to forecast an upcoming pose sequence given a past human motion trajectory. To address the problem, in this work we propose FreqMRN, a human motion prediction framework that takes into account both the kinematic structure of the human body and the temporal smoothness nature of motion. Speci... | ['Girish Chowdhary', 'Jiarui Sun'] | 2023-05-08 | null | null | null | null | ['motion-prediction', 'human-motion-prediction'] | ['computer-vision', 'time-series'] | [ 2.48480700e-02 1.70588624e-02 -3.88214707e-01 -1.34249911e-01
-5.60847640e-01 -4.67776554e-03 5.09114325e-01 -2.61767685e-01
-5.51086664e-01 6.94318831e-01 7.75192857e-01 1.91077664e-01
1.01963738e-02 -4.18720543e-01 -6.14954114e-01 -4.41074073e-01
-2.95895398e-01 2.53906846e-01 5.40208995e-01 -1.08181655... | [7.281731128692627, -0.25001752376556396] |
3235edc5-e75b-4601-a31e-0452cadc6503 | adapting-a-language-model-while-preserving | 2301.08986 | null | https://arxiv.org/abs/2301.08986v1 | https://arxiv.org/pdf/2301.08986v1.pdf | Adapting a Language Model While Preserving its General Knowledge | Domain-adaptive pre-training (or DA-training for short), also known as post-training, aims to train a pre-trained general-purpose language model (LM) using an unlabeled corpus of a particular domain to adapt the LM so that end-tasks in the domain can give improved performances. However, existing DA-training methods are... | ['Bing Liu', 'Lei Shu', 'Hu Xu', 'Haowei Lin', 'Yijia Shao', 'Zixuan Ke'] | 2023-01-21 | null | null | null | null | ['general-knowledge'] | ['miscellaneous'] | [ 1.22217394e-01 3.48756820e-01 -2.13511914e-01 -4.40324754e-01
-5.16241431e-01 -6.88531995e-01 7.80917943e-01 -2.23835781e-01
-6.16558492e-01 1.07351625e+00 4.65182036e-01 -3.59031767e-01
1.89547822e-01 -3.81273180e-01 -6.06476724e-01 -7.08986163e-01
5.91732085e-01 7.14481175e-01 5.30778408e-01 -2.03513294... | [10.471186637878418, 8.116803169250488] |
073bdf6e-7dcd-4aab-ba4a-afde7fa571e7 | regularizing-class-wise-predictions-via-self | 2003.13964 | null | https://arxiv.org/abs/2003.13964v2 | https://arxiv.org/pdf/2003.13964v2.pdf | Regularizing Class-wise Predictions via Self-knowledge Distillation | Deep neural networks with millions of parameters may suffer from poor generalization due to overfitting. To mitigate the issue, we propose a new regularization method that penalizes the predictive distribution between similar samples. In particular, we distill the predictive distribution between different samples of th... | ['Jinwoo Shin', 'Kimin Lee', 'Jongjin Park', 'Sukmin Yun'] | 2020-03-31 | regularizing-class-wise-predictions-via-self-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Yun_Regularizing_Class-Wise_Predictions_via_Self-Knowledge_Distillation_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Yun_Regularizing_Class-Wise_Predictions_via_Self-Knowledge_Distillation_CVPR_2020_paper.pdf | cvpr-2020-6 | ['self-knowledge-distillation'] | ['computer-vision'] | [ 9.61300954e-02 6.94347918e-02 -1.46950498e-01 -8.90826344e-01
-3.27213883e-01 -6.49164081e-01 3.32880855e-01 -1.28820926e-01
-4.89701331e-01 8.51849735e-01 -2.59033620e-01 -3.04039270e-01
6.51122332e-02 -6.61530077e-01 -1.07040441e+00 -8.68870497e-01
4.77803111e-01 6.56705946e-02 3.21921229e-01 1.59027606... | [9.3204927444458, 3.750525951385498] |
1644bc53-de76-4fed-a7fc-f87005baa24a | galaxy-a-generative-pre-trained-model-for | 2111.14592 | null | https://arxiv.org/abs/2111.14592v8 | https://arxiv.org/pdf/2111.14592v8.pdf | GALAXY: A Generative Pre-trained Model for Task-Oriented Dialog with Semi-Supervised Learning and Explicit Policy Injection | Pre-trained models have proved to be powerful in enhancing task-oriented dialog systems. However, current pre-training methods mainly focus on enhancing dialog understanding and generation tasks while neglecting the exploitation of dialog policy. In this paper, we propose GALAXY, a novel pre-trained dialog model that e... | ['Yongbin Li', 'Jian Sun', 'Luo Si', 'Fei Huang', 'Min Yang', 'Peng Jiang', 'Dermot Liu', 'Zheng Cao', 'Yuchuan Wu', 'Yinhe Zheng', 'Yinpei Dai', 'Wanwei He'] | 2021-11-29 | null | null | null | null | ['end-to-end-dialogue-modelling'] | ['natural-language-processing'] | [-2.58202672e-01 5.43090701e-01 -3.07529241e-01 -8.46168578e-01
-8.55730653e-01 -6.76203370e-01 9.05328631e-01 -5.29343374e-02
-5.83830595e-01 9.85949039e-01 6.61722064e-01 -2.82660782e-01
2.98477262e-01 -4.83470112e-01 -9.42747444e-02 -2.91637957e-01
4.57739472e-01 1.09819055e+00 4.46196586e-01 -7.49532700... | [12.83259391784668, 7.984288692474365] |
9f432228-6a2e-4e2b-a400-52fdc8e95a0a | lung-cancer-diagnosis-using-deep-attention | 2104.14655 | null | https://arxiv.org/abs/2104.14655v2 | https://arxiv.org/pdf/2104.14655v2.pdf | Lung Cancer Diagnosis Using Deep Attention Based on Multiple Instance Learning and Radiomics | Early diagnosis of lung cancer is a key intervention for the treatment of lung cancer computer aided diagnosis (CAD) can play a crucial role. However, most published CAD methods treat lung cancer diagnosis as a lung nodule classification problem, which does not reflect clinical practice, where clinicians diagnose a pat... | ['Inigo Bermejo', 'Leonard Wee', 'Andre Dekker', 'Zhenwei Shi', 'Chong Zhang', 'Haiyan Zeng', 'Junhua Chen'] | 2021-04-29 | null | null | null | null | ['deep-attention', 'lung-cancer-diagnosis', 'lung-nodule-classification', 'deep-attention'] | ['computer-vision', 'medical', 'medical', 'natural-language-processing'] | [-4.30319160e-02 2.82540798e-01 -5.52788079e-01 -2.77556688e-01
-8.74744475e-01 -1.43121064e-01 3.00830513e-01 3.13282549e-01
-2.28461102e-01 6.94115222e-01 1.21561281e-01 -4.79455471e-01
-2.44941592e-01 -8.87716293e-01 -4.49091315e-01 -7.98832178e-01
3.33463758e-01 6.58270657e-01 1.04969785e-01 3.79640698... | [15.357486724853516, -2.169893264770508] |
ced714a5-1edc-4f5a-9bed-04c1736a414e | optimal-01-matrix-completion-with | 2209.04373 | null | https://arxiv.org/abs/2209.04373v1 | https://arxiv.org/pdf/2209.04373v1.pdf | Optimal $(0,1)$-Matrix Completion with Majorization Ordered Objectives (To the memory of Pravin Varaiya) | We propose and examine two optimal $(0,1)$-matrix completion problems with majorization ordered objectives. They elevate the seminal study by Gale and Ryser from feasibility to optimality in partial order programming (POP), referring to optimization with partially ordered objectives. We showcase their applications in e... | ['Li Qiu', 'Keyou You', 'Wei Chen', 'Yanfang Mo'] | 2022-09-09 | null | null | null | null | ['matrix-completion', 'portfolio-optimization'] | ['methodology', 'time-series'] | [ 3.22185427e-01 2.69020319e-01 -6.32314458e-02 -1.64000049e-01
-6.27711415e-01 -1.01915658e+00 1.63237229e-01 5.01535058e-01
-3.36948335e-01 6.03205502e-01 -7.25649521e-02 -5.62940776e-01
-1.11007428e+00 -5.93427062e-01 -8.83363426e-01 -8.91119063e-01
-4.23553109e-01 1.03801632e+00 -4.23142433e-01 -3.33530873... | [6.555875778198242, 4.77060079574585] |
75858124-1c26-49c6-bb71-f2b92a60a062 | gum-net-unsupervised-geometric-matching-for | null | null | http://openaccess.thecvf.com/content_CVPR_2020/html/Zeng_Gum-Net_Unsupervised_Geometric_Matching_for_Fast_and_Accurate_3D_Subtomogram_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Zeng_Gum-Net_Unsupervised_Geometric_Matching_for_Fast_and_Accurate_3D_Subtomogram_CVPR_2020_paper.pdf | Gum-Net: Unsupervised Geometric Matching for Fast and Accurate 3D Subtomogram Image Alignment and Averaging | We propose a Geometric unsupervised matching Net-work (Gum-Net) for finding the geometric correspondence between two images with application to 3D subtomogram alignment and averaging. Subtomogram alignment is the most important task in cryo-electron tomography (cryo-ET), a revolutionary 3D imaging technique for visuali... | [' Min Xu', 'Xiangrui Zeng'] | 2020-06-01 | null | null | null | cvpr-2020-6 | ['geometric-matching', 'electron-tomography'] | ['computer-vision', 'medical'] | [ 5.73602080e-01 -3.51102769e-01 4.44890589e-01 -3.62895638e-01
-9.36251640e-01 -4.51371491e-01 4.25635189e-01 2.38819197e-01
-4.94650632e-01 5.76366782e-01 -1.20455064e-01 -2.15816513e-01
-1.93500221e-01 -4.01683837e-01 -8.52107286e-01 -1.11839700e+00
-2.17408046e-01 8.07455719e-01 1.02330536e-01 -4.54377457... | [13.52585220336914, -3.0498239994049072] |
980a3e39-60c1-411e-b86d-8e8d04cb882f | sparse-lidar-assisted-self-supervised-stereo | 2112.15355 | null | https://arxiv.org/abs/2112.15355v1 | https://arxiv.org/pdf/2112.15355v1.pdf | Sparse LiDAR Assisted Self-supervised Stereo Disparity Estimation | Deep stereo matching has made significant progress in recent years. However, state-of-the-art methods are based on expensive 4D cost volume, which limits their use in real-world applications. To address this issue, 3D correlation maps and iterative disparity updates have been proposed. Regarding that in real-world plat... | ['Zhengguo Li', 'Peter C. Y. Chen', 'Xingming Wu', 'Weihai Chen', 'Xiaoming Zhao'] | 2021-12-31 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [-6.32582903e-02 -2.23526821e-01 -2.75875151e-01 -5.06854475e-01
-1.60562117e-02 1.08972289e-01 3.71700227e-01 -1.61142811e-01
-6.46775424e-01 6.87236965e-01 -4.74089801e-01 -1.88103288e-01
1.46805674e-01 -1.04789698e+00 -7.91554272e-01 -5.16935706e-01
1.95026785e-01 4.24283713e-01 6.42520905e-01 -2.12779135... | [8.485450744628906, -2.3708674907684326] |
5c03fe43-23b6-4194-a7b2-d1cf685827a8 | incomplete-multi-view-clustering-via-graph | 1809.05998 | null | http://arxiv.org/abs/1809.05998v1 | http://arxiv.org/pdf/1809.05998v1.pdf | Incomplete Multi-view Clustering via Graph Regularized Matrix Factorization | Clustering with incomplete views is a challenge in multi-view clustering. In
this paper, we provide a novel and simple method to address this issue.
Specifically, the proposed method simultaneously exploits the local information
of each view and the complementary information among views to learn the common
latent repre... | ['Jie Wen', 'Zuofeng Zhong', 'Yong Xu', 'Zheng Zhang'] | 2018-09-17 | null | null | null | null | ['incomplete-multi-view-clustering'] | ['computer-vision'] | [-2.62182236e-01 -2.95555472e-01 -3.86325091e-01 -1.99395180e-01
-5.27704477e-01 -5.38730860e-01 3.49672049e-01 1.85568035e-01
-8.70113671e-02 1.87164053e-01 8.16951469e-02 2.75014102e-01
-3.82720590e-01 -6.23436451e-01 -7.57673010e-02 -1.24439740e+00
4.81757492e-01 2.67802507e-01 2.88996883e-02 2.70109653... | [8.225054740905762, 4.601219654083252] |
6c233ea5-7786-4516-b362-dad5b5b522a7 | integrating-connection-search-in-graph | 2208.04802 | null | https://arxiv.org/abs/2208.04802v1 | https://arxiv.org/pdf/2208.04802v1.pdf | Integrating connection search in graph queries | Graph data management and querying has many practical applications. When graphs are very heterogeneous and/or users are unfamiliar with their structure, they may need to find how two or more groups of nodes are connected in a graph, even when users are not able to describe the connections. This is only partially suppor... | ['Madhulika Mohanty', 'Ioana Manolescu', 'Angelos Christos Anadiotis'] | 2022-08-09 | null | null | null | null | ['steiner-tree-problem'] | ['graphs'] | [ 3.35204862e-02 5.16635031e-02 -2.25481734e-01 -5.65972552e-02
-2.75338918e-01 -9.40265954e-01 1.45630494e-01 9.45180595e-01
-1.62876606e-01 5.95284522e-01 -2.69913703e-01 -6.96359634e-01
-7.19709277e-01 -1.48930442e+00 -4.76767838e-01 1.36933126e-03
-6.39967322e-01 9.76549268e-01 1.07553256e+00 -3.21883738... | [7.204141616821289, 5.634772300720215] |
795127f0-bfd3-44c6-ad53-2660cd5d6918 | localizing-small-apples-in-complex-apple | 2202.11372 | null | https://arxiv.org/abs/2202.11372v1 | https://arxiv.org/pdf/2202.11372v1.pdf | Localizing Small Apples in Complex Apple Orchard Environments | The localization of fruits is an essential first step in automated agricultural pipelines for yield estimation or fruit picking. One example of this is the localization of apples in images of entire apple trees. Since the apples are very small objects in such scenarios, we tackle this problem by adapting the object pro... | ['Simone Frintrop', 'Robert Johanson', 'Christian Wilms'] | 2022-02-23 | null | null | null | null | ['object-proposal-generation'] | ['computer-vision'] | [ 2.74419844e-01 1.42889097e-01 2.71360930e-02 -3.57956082e-01
-3.91650647e-01 -1.06462491e+00 5.27706683e-01 6.55171216e-01
-2.56922454e-01 1.06108576e-01 -3.95096987e-01 -1.69056937e-01
-9.00283456e-02 -8.70135784e-01 -8.88127267e-01 -5.68466246e-01
-1.28426567e-01 5.98597646e-01 9.48642075e-01 -1.17610637... | [9.068378448486328, -1.432457447052002] |
52ac3ad8-77d7-4476-9e9e-2e3075eb7b29 | non-intrusive-load-monitoring-nilm-using-deep | 2306.05017 | null | https://arxiv.org/abs/2306.05017v1 | https://arxiv.org/pdf/2306.05017v1.pdf | Non-Intrusive Load Monitoring (NILM) using Deep Neural Networks: A Review | Demand-side management now encompasses more residential loads. To efficiently apply demand response strategies, it's essential to periodically observe the contribution of various domestic appliances to total energy consumption. Non-intrusive load monitoring (NILM), also known as load disaggregation, is a method for dec... | ['Abouzar Estebsari', 'Roozbeh Rajabi', 'Mohammad Irani Azad'] | 2023-06-08 | null | null | null | null | ['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'total-energy', 'non-intrusive-load-monitoring'] | ['knowledge-base', 'miscellaneous', 'miscellaneous', 'time-series'] | [-3.50146383e-01 -4.38415140e-01 -4.51949030e-01 -5.92949867e-01
-6.20372832e-01 -4.95104700e-01 4.73261774e-01 -4.29657176e-02
2.09166586e-01 7.50573397e-01 4.60495085e-01 -2.33643338e-01
-2.40353614e-01 -1.10282993e+00 -4.22117449e-02 -1.14714539e+00
-5.94289228e-02 6.33105755e-01 -7.37796307e-01 8.35102499... | [16.051790237426758, 7.571531772613525] |
651010cd-a768-42c4-8c26-a4d726e8891d | learning-term-embeddings-for-taxonomic | null | null | https://aclanthology.org/D16-1039 | https://aclanthology.org/D16-1039.pdf | Learning Term Embeddings for Taxonomic Relation Identification Using Dynamic Weighting Neural Network | null | ['See Kiong Ng', 'Anh Tuan Luu', 'Yi Tay', 'Siu Cheung Hui'] | 2016-11-01 | null | null | null | emnlp-2016-11 | ['learning-word-embeddings'] | ['methodology'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.355753421783447, 3.682218551635742] |
f6c44c8c-94c1-405d-b4ae-df50ef0bde6d | learning-to-generate-text-grounded-mask-for | 2212.00785 | null | https://arxiv.org/abs/2212.00785v2 | https://arxiv.org/pdf/2212.00785v2.pdf | Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs | We tackle open-world semantic segmentation, which aims at learning to segment arbitrary visual concepts in images, by using only image-text pairs without dense annotations. Existing open-world segmentation methods have shown impressive advances by employing contrastive learning (CL) to learn diverse visual concepts and... | ['Byungseok Roh', 'Jonghwan Mun', 'Junbum Cha'] | 2022-12-01 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Cha_Learning_To_Generate_Text-Grounded_Mask_for_Open-World_Semantic_Segmentation_From_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Cha_Learning_To_Generate_Text-Grounded_Mask_for_Open-World_Semantic_Segmentation_From_CVPR_2023_paper.pdf | cvpr-2023-1 | ['unsupervised-semantic-segmentation-with', 'zero-shot-segmentation'] | ['computer-vision', 'computer-vision'] | [ 6.05044723e-01 2.37574026e-01 -3.71205181e-01 -5.07966161e-01
-1.16267788e+00 -6.75230920e-01 4.86564368e-01 -1.05118491e-01
-5.27208924e-01 2.89362818e-01 -1.18370362e-01 -1.39096037e-01
3.77906859e-01 -6.44367933e-01 -9.27498281e-01 -6.28373265e-01
4.99898970e-01 6.26662493e-01 7.39528835e-01 -9.56312194... | [9.714420318603516, 0.7795840501785278] |
1bc46737-0f28-458e-b98a-45bb44f85d34 | eda-easy-data-augmentation-techniques-for | 1901.11196 | null | https://arxiv.org/abs/1901.11196v2 | https://arxiv.org/pdf/1901.11196v2.pdf | EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks | We present EDA: easy data augmentation techniques for boosting performance on text classification tasks. EDA consists of four simple but powerful operations: synonym replacement, random insertion, random swap, and random deletion. On five text classification tasks, we show that EDA improves performance for both convolu... | ['Kai Zou', 'Jason Wei'] | 2019-01-31 | eda-easy-data-augmentation-techniques-for-1 | https://aclanthology.org/D19-1670 | https://aclanthology.org/D19-1670.pdf | ijcnlp-2019-11 | ['text-augmentation', 'subjectivity-analysis'] | ['natural-language-processing', 'natural-language-processing'] | [ 1.31533086e-01 -1.17387697e-01 -5.23500264e-01 -5.03554165e-01
-4.32214260e-01 -4.11289304e-01 7.68213928e-01 3.98319572e-01
-9.95136499e-01 7.98004687e-01 2.51544774e-01 -8.87997031e-01
3.00990492e-01 -5.24338484e-01 -3.36791366e-01 -1.66048214e-01
2.96322145e-02 6.28056705e-01 -3.24416846e-01 -6.43077195... | [10.801412582397461, 7.97304630279541] |
3dd8e516-b029-43d1-a2b9-1bf64b4be9e0 | unsupervised-ct-metal-artifact-learning-using | 2007.03480 | null | https://arxiv.org/abs/2007.03480v1 | https://arxiv.org/pdf/2007.03480v1.pdf | Unsupervised CT Metal Artifact Learning using Attention-guided beta-CycleGAN | Metal artifact reduction (MAR) is one of the most important research topics in computed tomography (CT). With the advance of deep learning technology for image reconstruction,various deep learning methods have been also suggested for metal artifact removal, among which supervised learning methods are most popular. Howe... | ['Jawook Gu', 'Jong Chul Ye', 'Junghyun Lee'] | 2020-07-07 | null | null | null | null | ['metal-artifact-reduction'] | ['medical'] | [ 2.06020564e-01 -1.37392789e-01 -2.50499584e-02 -1.90175399e-01
-7.67930329e-01 3.15275431e-01 2.18281567e-01 -1.27717137e-01
-2.96486109e-01 6.24624789e-01 3.23738873e-01 8.94722342e-02
-4.77374852e-01 -7.28866994e-01 -5.11767447e-01 -1.11642802e+00
2.13267237e-01 -5.64710982e-02 1.50908634e-01 -2.11180210... | [13.503172874450684, -2.549690008163452] |
325af689-e21f-4bb3-becd-86bdc1303c90 | super-resolution-radar-imaging-with-sparse | 2306.09839 | null | https://arxiv.org/abs/2306.09839v1 | https://arxiv.org/pdf/2306.09839v1.pdf | Super-Resolution Radar Imaging with Sparse Arrays Using a Deep Neural Network Trained with Enhanced Virtual Data | This paper introduces a method based on a deep neural network (DNN) that is perfectly capable of processing radar data from extremely thinned radar apertures. The proposed DNN processing can provide both aliasing-free radar imaging and super-resolution. The results are validated by measuring the detection performance o... | ['Martin Vossiek', 'Marcel Hoffmann', 'Christian Schuessler'] | 2023-06-16 | null | null | null | null | ['super-resolution'] | ['computer-vision'] | [ 5.78406513e-01 -2.62272090e-01 4.95773196e-01 -2.72860527e-01
-7.38872468e-01 -2.80889779e-01 7.05347061e-01 -6.39925241e-01
-4.36564952e-01 7.00819135e-01 6.28031343e-02 -3.14357996e-01
-7.43911982e-01 -7.45039999e-01 -4.35029477e-01 -1.05595040e+00
-5.19531429e-01 6.86995149e-01 -3.27967644e-01 -2.08102182... | [6.79036808013916, 1.0294370651245117] |
5a6ad24f-8d31-49ba-92c9-e92c6f26747f | point-teaching-weakly-semi-supervised-object | 2206.00274 | null | https://arxiv.org/abs/2206.00274v2 | https://arxiv.org/pdf/2206.00274v2.pdf | Point-Teaching: Weakly Semi-Supervised Object Detection with Point Annotations | Point annotations are considerably more time-efficient than bounding box annotations. However, how to use cheap point annotations to boost the performance of semi-supervised object detection remains largely unsolved. In this work, we present Point-Teaching, a weakly semi-supervised object detection framework to fully e... | ['Chunhua Shen', 'Zhibin Wang', 'Hao Li', 'Xinlong Wang', 'Qiang Zhou', 'Yongtao Ge'] | 2022-06-01 | null | null | null | null | ['semi-supervised-object-detection'] | ['computer-vision'] | [ 4.10789490e-01 2.10213333e-01 -2.73195952e-01 -4.66883302e-01
-1.32106137e+00 -5.40368557e-01 5.44719577e-01 2.37658292e-01
-3.30643862e-01 2.26862743e-01 -3.66217226e-01 -2.15438884e-02
2.30557114e-01 -5.42175651e-01 -1.23753691e+00 -4.28372890e-01
2.98656642e-01 5.16740143e-01 8.02814424e-01 -1.95675585... | [7.897679328918457, -2.7432332038879395] |
56ad3b82-3e92-42f2-a211-1b306ad72069 | fooling-thermal-infrared-detectors-in | 2304.10712 | null | https://arxiv.org/abs/2304.10712v3 | https://arxiv.org/pdf/2304.10712v3.pdf | Adversarial Infrared Blocks: A Black-box Attack to Thermal Infrared Detectors at Multiple Angles in Physical World | Infrared imaging systems have a vast array of potential applications in pedestrian detection and autonomous driving, and their safety performance is of great concern. However, few studies have explored the safety of infrared imaging systems in real-world settings. Previous research has used physical perturbations such ... | ['Xiaoqian Chen', 'Ling Tian', 'Wen Yao', 'Tingsong Jiang', 'Weiwen Shi', 'Chengyin Hu'] | 2023-04-21 | null | null | null | null | ['pedestrian-detection'] | ['computer-vision'] | [ 1.44040108e-01 -3.54553014e-01 5.70422895e-02 2.10947081e-01
-1.82541698e-01 -9.56274509e-01 2.01384321e-01 -5.93326211e-01
-4.99780953e-01 4.23743159e-01 -3.57383102e-01 -7.57934868e-01
3.42059314e-01 -7.61008382e-01 -6.54428601e-01 -9.57892299e-01
6.54554889e-02 -8.20060909e-01 4.82305288e-01 -4.09526289... | [5.34542989730835, 8.022085189819336] |
05b4d980-4ee4-482a-a877-c55b9f697b0b | crop-yield-prediction-integrating-genotype | 2006.13847 | null | https://arxiv.org/abs/2006.13847v1 | https://arxiv.org/pdf/2006.13847v1.pdf | Crop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning | Accurate prediction of crop yield supported by scientific and domain-relevant insights, can help improve agricultural breeding, provide monitoring across diverse climatic conditions and thereby protect against climatic challenges to crop production including erratic rainfall and temperature variations. We used historic... | ['Linjiang Wu', 'Tryambak Gangopadhyay', 'Johnathon Shook', 'Baskar Ganapathysubramanian', 'Asheesh K. Singh', 'Soumik Sarkar'] | 2020-06-24 | null | null | null | null | ['explainable-models', 'crop-yield-prediction', 'crop-yield-prediction'] | ['computer-vision', 'computer-vision', 'miscellaneous'] | [ 1.60182297e-01 -3.63368005e-01 -5.96279323e-01 -5.16957402e-01
7.63337910e-02 -9.06605184e-01 -4.85116383e-03 4.50928301e-01
3.08304250e-01 6.45293355e-01 1.45335376e-01 -9.49647844e-01
-5.57716966e-01 -1.19577861e+00 -6.55154347e-01 -6.42296433e-01
-6.10000134e-01 1.94812149e-01 -2.61029691e-01 -6.84901357... | [9.34849739074707, -1.6020731925964355] |
357ad2a0-25f1-4c2e-9784-8b02d5fe8459 | graph-convolutional-networks-from-the | 2208.09309 | null | https://arxiv.org/abs/2208.09309v1 | https://arxiv.org/pdf/2208.09309v1.pdf | Graph Convolutional Networks from the Perspective of Sheaves and the Neural Tangent Kernel | Graph convolutional networks are a popular class of deep neural network algorithms which have shown success in a number of relational learning tasks. Despite their success, graph convolutional networks exhibit a number of peculiar features, including a bias towards learning oversmoothed and homophilic functions, which ... | ['Thomas Gebhart'] | 2022-08-19 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [-6.15648814e-02 4.49255019e-01 -2.00189035e-02 -4.75915790e-01
4.94299054e-01 -6.87620282e-01 8.65009189e-01 2.59224355e-01
-4.36888449e-03 3.43065381e-01 3.29424202e-01 -4.80786055e-01
-4.71297860e-01 -9.92663026e-01 -7.83732057e-01 -8.08387637e-01
-6.56353831e-01 4.49264348e-01 3.54211956e-01 -5.66504180... | [6.8180251121521, 6.011591911315918] |
53fcc245-eea7-4ce5-ac4c-d082d142e60a | industrial-scene-text-detection-with-refined | 2110.12663 | null | https://arxiv.org/abs/2110.12663v2 | https://arxiv.org/pdf/2110.12663v2.pdf | Industrial Scene Text Detection with Refined Feature-attentive Network | Detecting the marking characters of industrial metal parts remains challenging due to low visual contrast, uneven illumination, corroded character structures, and cluttered background of metal part images. Affected by these factors, bounding boxes generated by most existing methods locate low-contrast text areas inaccu... | ['Xinping Guan', 'Kaijie Wu', 'Qi Feng', 'Jingzheng Tu', 'Changsheng Lu', 'Chaochen Gu', 'Tongkun Guan'] | 2021-10-25 | null | null | null | null | ['scene-text-detection'] | ['computer-vision'] | [ 3.26849848e-01 -4.82251585e-01 3.34621489e-01 -2.85387546e-01
-6.95502818e-01 -2.93229163e-01 3.08680862e-01 -1.62627399e-01
-1.54241428e-01 1.95495442e-01 1.58749804e-01 2.93320775e-01
-4.56801355e-02 -7.08965361e-01 -6.66104615e-01 -5.66593170e-01
6.65190458e-01 2.95925707e-01 7.92629421e-01 -4.00326610... | [12.072015762329102, 2.2017946243286133] |
c2605656-a29a-4f4e-98f2-cb565b4d213d | learned-image-compression-with-generalized | 2209.03353 | null | https://arxiv.org/abs/2209.03353v1 | https://arxiv.org/pdf/2209.03353v1.pdf | Learned Image Compression with Generalized Octave Convolution and Cross-Resolution Parameter Estimation | The application of the context-adaptive entropy model significantly improves the rate-distortion (R-D) performance, in which hyperpriors and autoregressive models are jointly utilized to effectively capture the spatial redundancy of the latent representations. However, the latent representations still contain some spat... | ['Feng Liang', 'Haisheng Fu'] | 2022-09-07 | null | null | null | null | ['ms-ssim'] | ['computer-vision'] | [ 3.04268390e-01 -4.15927470e-01 -1.96584091e-01 -1.13304876e-01
-7.26250768e-01 1.26529828e-01 1.68763623e-01 -1.28141955e-01
-3.68545353e-01 4.63536650e-01 4.27342266e-01 -6.07024995e-04
-2.01272324e-01 -9.13893878e-01 -5.39007306e-01 -9.73263860e-01
-1.57215431e-01 -4.85538751e-01 2.63628930e-01 -4.38612141... | [11.295381546020508, -1.7159520387649536] |
19bfd50d-ceb2-46d1-8413-fbb07eb8a4bb | conversational-semantic-role-labeling | 2104.04947 | null | https://arxiv.org/abs/2104.04947v1 | https://arxiv.org/pdf/2104.04947v1.pdf | Conversational Semantic Role Labeling | Semantic role labeling (SRL) aims to extract the arguments for each predicate in an input sentence. Traditional SRL can fail to analyze dialogues because it only works on every single sentence, while ellipsis and anaphora frequently occur in dialogues. To address this problem, we propose the conversational SRL task, wh... | ['Dong Yu', 'Linqi Song', 'Haisong Zhang', 'Linfeng Song', 'Han Wu', 'Kun Xu'] | 2021-04-11 | null | null | null | null | ['dialogue-understanding'] | ['natural-language-processing'] | [ 5.85125208e-01 8.36486697e-01 -1.86430305e-01 -5.80866277e-01
-8.21801901e-01 -9.47718143e-01 1.07849073e+00 3.82420272e-01
-3.39539796e-01 1.16167259e+00 1.12712419e+00 -2.94155031e-01
7.39225447e-02 -4.95379686e-01 3.77426371e-02 -2.11320817e-01
3.38082552e-01 7.63344169e-01 4.37733471e-01 -1.12052870... | [12.515206336975098, 8.091487884521484] |
6f6f2528-4105-4b0e-a78b-87992f41f273 | generalized-deep-3d-shape-prior-via-part | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Li_Generalized_Deep_3D_Shape_Prior_via_Part-Discretized_Diffusion_Process_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Li_Generalized_Deep_3D_Shape_Prior_via_Part-Discretized_Diffusion_Process_CVPR_2023_paper.pdf | Generalized Deep 3D Shape Prior via Part-Discretized Diffusion Process | We develop a generalized 3D shape generation prior model, tailored for multiple 3D tasks including unconditional shape generation, point cloud completion, and cross-modality shape generation, etc. On one hand, to precisely capture local fine detailed shape information, a vector quantized variational autoencoder (VQ... | ['Fuzhen Wang', 'Yutian Liu', 'Yilin Sun', 'Bingbing Ni', 'Xuanhong Chen', 'Yishun Dou', 'Yuhan Li'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['point-cloud-completion', '3d-shape-generation'] | ['computer-vision', 'computer-vision'] | [-1.25313312e-01 -2.43634842e-02 2.27856353e-01 -1.63396716e-01
-1.05612135e+00 -4.79800820e-01 8.63620043e-01 2.12063938e-02
3.14942986e-01 3.71546179e-01 3.32730561e-01 2.03118458e-01
-1.71557292e-01 -9.56009388e-01 -6.16770446e-01 -8.83459628e-01
4.11568433e-01 5.08649170e-01 -1.27644718e-01 -2.87522316... | [8.776833534240723, -3.650559186935425] |
27c2774a-f5f6-4092-91a3-c3e4ff3f9c94 | towards-domain-generalization-for-ecg-and-eeg | 2303.11338 | null | https://arxiv.org/abs/2303.11338v3 | https://arxiv.org/pdf/2303.11338v3.pdf | Towards Domain Generalization for ECG and EEG Classification: Algorithms and Benchmarks | Despite their immense success in numerous fields, machine and deep learning systems have not yet been able to firmly establish themselves in mission-critical applications in healthcare. One of the main reasons lies in the fact that when models are presented with previously unseen, Out-of-Distribution samples, their per... | ['Christos Diou', 'Aristotelis Ballas'] | 2023-03-20 | null | null | null | null | ['electrocardiography-ecg', 'eeg', 'eeg'] | ['methodology', 'methodology', 'time-series'] | [ 4.63860542e-01 1.64992698e-02 3.10113817e-01 -3.31189305e-01
-5.43257654e-01 -3.20768207e-01 4.49765205e-01 3.71791214e-01
-4.24931407e-01 8.93005013e-01 -1.28062889e-01 -1.53071627e-01
-5.82478166e-01 -3.65686208e-01 -6.66417301e-01 -7.52164245e-01
-5.60455322e-01 4.22654539e-01 -1.69560194e-01 -1.55796170... | [13.635369300842285, 3.315863847732544] |
bc9254e7-43dd-4a87-a2d1-915c5bf3ea7c | a-fast-and-accurate-iris-segmentation-method | 2201.06176 | null | https://arxiv.org/abs/2201.06176v1 | https://arxiv.org/pdf/2201.06176v1.pdf | A fast and accurate iris segmentation method using an LoG filter and its zero-crossings | This paper presents a hybrid approach to achieve iris localization based on a Laplacian of Gaussian (LoG) filter, region growing, and zero-crossings of the LoG filter. In the proposed method, an LoG filter with region growing is used to detect the pupil region. Subsequently, zero-crossings of the LoG filter are used to... | ['Yinan Kong', 'Donald G. bailey', 'Tariq M. Khan'] | 2022-01-17 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [-2.89105568e-02 -3.87819946e-01 9.74367112e-02 1.39650181e-01
-1.25854105e-01 -6.70271516e-01 3.07916701e-01 4.33034092e-01
-5.08943379e-01 5.94545364e-01 2.07929928e-02 -2.08074927e-01
-5.52221462e-02 -4.22687620e-01 -1.99776828e-01 -7.48418331e-01
1.80573672e-01 -1.21783353e-01 4.20169592e-01 2.32573584... | [3.7535364627838135, -3.625347137451172] |
68394ba8-38b6-4c0f-bf56-2ff729d819fa | msp-former-multi-scale-projection-transformer | 2207.05621 | null | https://arxiv.org/abs/2207.05621v3 | https://arxiv.org/pdf/2207.05621v3.pdf | MSP-Former: Multi-Scale Projection Transformer for Single Image Desnowing | Snow removal causes challenges due to its characteristic of complex degradations. To this end, targeted treatment of multi-scale snow degradations is critical for the network to learn effective snow removal. In order to handle the diverse scenes, we propose a multi-scale projection transformer (MSP-Former), which under... | ['Peng Chen', 'Jingxia Jiang', 'ErKang Chen', 'Taodong Liao', 'Yun Liu', 'Tian Ye', 'Sixiang Chen'] | 2022-07-12 | null | null | null | null | ['single-image-desnowing'] | ['computer-vision'] | [ 3.61595958e-01 -3.31832051e-01 2.12540835e-01 -4.15436238e-01
-6.42574728e-01 -1.42304793e-01 2.10095495e-01 -3.25481206e-01
3.34803089e-02 5.79537809e-01 5.96062541e-01 -2.92600170e-02
6.52978346e-02 -7.37862349e-01 -6.84792221e-01 -1.19384229e+00
3.41270357e-01 -8.94801989e-02 2.59682357e-01 -5.16262293... | [10.980676651000977, -3.1357266902923584] |
86ff698f-94e0-4e16-ad74-5b4f4e1edff8 | how-would-stance-detection-techniques-evolve | 2212.14548 | null | https://arxiv.org/abs/2212.14548v3 | https://arxiv.org/pdf/2212.14548v3.pdf | How would Stance Detection Techniques Evolve after the Launch of ChatGPT? | Stance detection refers to the task of extracting the standpoint (Favor, Against or Neither) towards a target in given texts. Such research gains increasing attention with the proliferation of social media contents. The conventional framework of handling stance detection is converting it into text classification tasks.... | ['Liwen Jing', 'Daijun Ding', 'BoWen Zhang'] | 2022-12-30 | null | null | null | null | ['stance-detection'] | ['natural-language-processing'] | [ 1.24182411e-01 6.08793795e-01 -6.96096182e-01 -4.03292120e-01
-5.08254051e-01 -3.53174537e-01 9.20802236e-01 3.31520945e-01
-2.16414407e-01 8.08366120e-01 6.12706602e-01 -6.05640352e-01
9.90191698e-02 -1.00339985e+00 -4.57433820e-01 -5.33625305e-01
2.56144673e-01 8.15866113e-01 1.80132896e-01 -7.64056146... | [8.836859703063965, 10.05069637298584] |
5205b6d4-cf1d-49e2-bc99-e7223e2c9959 | unbiased-multiple-instance-learning-for | 2303.12369 | null | https://arxiv.org/abs/2303.12369v1 | https://arxiv.org/pdf/2303.12369v1.pdf | Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detection | Weakly Supervised Video Anomaly Detection (WSVAD) is challenging because the binary anomaly label is only given on the video level, but the output requires snippet-level predictions. So, Multiple Instance Learning (MIL) is prevailing in WSVAD. However, MIL is notoriously known to suffer from many false alarms because t... | ['Hanwang Zhang', 'Zhen Cui', 'Bin Luo', 'Qianru Sun', 'Zhongqi Yue', 'Hui Lv'] | 2023-03-22 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lv_Unbiased_Multiple_Instance_Learning_for_Weakly_Supervised_Video_Anomaly_Detection_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lv_Unbiased_Multiple_Instance_Learning_for_Weakly_Supervised_Video_Anomaly_Detection_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-anomaly-detection', 'multiple-instance-learning'] | ['computer-vision', 'methodology'] | [ 1.47300839e-01 -2.58453429e-01 -3.82751882e-01 -3.52034837e-01
-7.51156211e-01 -2.65596777e-01 3.69936377e-01 4.66339067e-02
-1.57250002e-01 5.72560906e-01 -1.11098378e-03 -1.66160643e-01
-7.30116665e-02 -5.34074008e-01 -6.88061833e-01 -9.66509700e-01
-1.10204376e-01 4.03375179e-01 3.00058693e-01 1.47573441... | [7.817310333251953, 1.6572784185409546] |
1cf89cf0-5940-48dc-b012-746344a6a73f | graph-scaling-cut-with-l1-norm-for | 1709.02920 | null | http://arxiv.org/abs/1709.02920v1 | http://arxiv.org/pdf/1709.02920v1.pdf | Graph Scaling Cut with L1-Norm for Classification of Hyperspectral Images | In this paper, we propose an L1 normalized graph based dimensionality
reduction method for Hyperspectral images, called as L1-Scaling Cut (L1-SC).
The underlying idea of this method is to generate the optimal projection matrix
by retaining the original distribution of the data. Though L2-norm is generally
preferred for... | ['S. L. Happy', 'Ramanarayan Mohanty', 'Aurobinda Routray'] | 2017-09-09 | null | null | null | null | ['classification-of-hyperspectral-images'] | ['computer-vision'] | [ 4.50561315e-01 -1.59704193e-01 -7.49927759e-02 -1.47805765e-01
-3.20763290e-01 -3.97599161e-01 1.23898871e-01 -1.28989905e-01
-6.46790415e-02 4.74008292e-01 1.81636557e-01 -3.74926664e-02
-8.19436431e-01 -9.41453278e-01 -1.21770218e-01 -1.10494244e+00
2.10981011e-01 -2.12046415e-01 -2.66348153e-01 -3.43706235... | [10.020586013793945, -1.9113413095474243] |
5621ee5c-038e-4a1f-8d9e-6d5cda5a63f1 | a-robust-panel-extraction-method-for-manga | null | null | http://visal.cs.cityu.edu.hk/static/pubs/conf/mm14-panels.pdf | http://visal.cs.cityu.edu.hk/static/pubs/conf/mm14-panels.pdf | A Robust Panel Extraction Method for Manga | Automatically extracting frames/panels from digital comic pages is crucial for techniques that facilitate comic reading on mobile devices with limited display areas. However, automatic panel extraction for manga, i.e., Japanese comics, can be especially challenging, largely because of its complex panel layout design mi... | ['Rynson W. H. Lau', 'Ying Cao', 'Xufang Pang', 'and Antoni B. Chan'] | 2014-01-01 | null | null | null | video-image-and-sound-analysis-lab-visal-at | ['layout-design'] | ['computer-vision'] | [ 4.72370982e-01 -2.69219220e-01 -7.35968500e-02 2.99325645e-01
-5.75111568e-01 -1.05517447e+00 1.68700218e-01 -1.99196324e-01
3.74212027e-01 4.29687053e-01 6.62799701e-02 -6.44199133e-01
1.77315488e-01 -6.05355442e-01 -5.36820233e-01 -5.74835837e-01
4.74280357e-01 4.53968309e-02 6.31635427e-01 2.66246498... | [11.974807739257812, 2.2394771575927734] |
138b2400-b86b-44f3-bf1c-4e8e7e597cf6 | estimating-parameters-of-nonlinear-systems | 1604.04198 | null | http://arxiv.org/abs/1604.04198v4 | http://arxiv.org/pdf/1604.04198v4.pdf | Estimating parameters of nonlinear systems using the elitist particle filter based on evolutionary strategies | In this article, we present the elitist particle filter based on evolutionary
strategies (EPFES) as an efficient approach for nonlinear system
identification. The EPFES is derived from the frequently-employed state-space
model, where the relevant information of the nonlinear system is captured by an
unknown state vecto... | ['Christian Hofmann', 'Roland Maas', 'Walter Kellermann', 'Christian Huemmer'] | 2016-04-14 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 1.51434928e-01 -3.44539434e-01 5.40980339e-01 3.33878160e-01
-3.23348761e-01 -1.89129367e-01 7.10849941e-01 -1.42511025e-01
-6.11714244e-01 9.50387716e-01 -3.96445505e-02 5.41884080e-02
-7.47433364e-01 -6.34717107e-01 -5.14111221e-01 -1.13101315e+00
-1.86461881e-01 6.90367579e-01 2.51585931e-01 -4.59311336... | [6.511843681335449, 3.5623154640197754] |
338c3bfc-381c-4b49-88f2-7875d15f8c9a | presenting-multiagent-challenges-in-team | 2303.13660 | null | https://arxiv.org/abs/2303.13660v1 | https://arxiv.org/pdf/2303.13660v1.pdf | Presenting Multiagent Challenges in Team Sports Analytics | This paper draws correlations between several challenges and opportunities within the area of team sports analytics and key research areas within multiagent systems (MAS). We specifically consider invasion games, defined as sports where players invade the opposing team's territory and can interact anywhere on a playing... | ['Alexi Orchard', 'David Radke'] | 2023-03-23 | null | null | null | null | ['sports-analytics'] | ['computer-vision'] | [-1.92839354e-01 1.55391723e-01 -4.97265048e-02 1.60005942e-01
-1.23294123e-01 -7.13320434e-01 5.79230368e-01 4.71925080e-01
-4.04950708e-01 7.19716847e-01 -7.78606569e-04 -2.24963829e-01
-6.78929985e-01 -1.18869412e+00 -2.11802602e-01 -3.37493479e-01
-6.07864380e-01 8.69923592e-01 3.94578815e-01 -1.51007998... | [3.473937511444092, 1.4138745069503784] |
39498db8-45a5-4cf8-a52c-c018bf9459ab | cycledrums-automatic-drum-arrangement-for | 2104.00353 | null | https://arxiv.org/abs/2104.00353v2 | https://arxiv.org/pdf/2104.00353v2.pdf | CycleDRUMS: Automatic Drum Arrangement For Bass Lines Using CycleGAN | The two main research threads in computer-based music generation are: the construction of autonomous music-making systems, and the design of computer-based environments to assist musicians. In the symbolic domain, the key problem of automatically arranging a piece music was extensively studied, while relatively fewer s... | ['Fabrizio Silvestri', 'Fabio Petroni', 'Angela Fan', 'Cesare Campagnano', 'Lorenzo Lastilla', 'Giovanni Trappolini', 'Giorgio Barnabò'] | 2021-04-01 | null | null | null | null | ['music-generation', 'music-generation'] | ['audio', 'music'] | [ 6.78992748e-01 1.90812141e-01 4.83153194e-01 1.40317246e-01
-7.77400672e-01 -1.11019802e+00 7.53936529e-01 -4.74865526e-01
-2.31216744e-01 7.01926410e-01 -8.90732463e-03 7.80785307e-02
-5.97830638e-02 -9.37733710e-01 -9.06570196e-01 -7.89470792e-01
1.93431914e-01 4.57981020e-01 -2.03381525e-03 -3.98163974... | [15.843621253967285, 5.616282939910889] |
83176151-55ac-4ac9-a7af-3db1f7d4db21 | artificial-intelligence-for-emergency | 2306.10068 | null | https://arxiv.org/abs/2306.10068v1 | https://arxiv.org/pdf/2306.10068v1.pdf | Artificial Intelligence for Emergency Response | Emergency response management (ERM) is a challenge faced by communities across the globe. First responders must respond to various incidents, such as fires, traffic accidents, and medical emergencies. They must respond quickly to incidents to minimize the risk to human life. Consequently, considerable attention has bee... | ['Ayan Mukhopadhyay'] | 2023-06-15 | null | null | null | null | ['management'] | ['miscellaneous'] | [ 3.59545499e-01 -1.80813089e-01 8.00528377e-02 -3.06834340e-01
-7.15201795e-01 -1.60042509e-01 1.58141613e-01 7.61129677e-01
-7.87625790e-01 7.79988468e-01 6.24372840e-01 -5.63654304e-01
-6.87400103e-01 -1.01133895e+00 1.78515360e-01 -4.24813509e-01
-2.19156608e-01 5.47687769e-01 -1.57970384e-01 -5.28259158... | [6.147292137145996, 3.9482293128967285] |
deb29f8f-d1f7-4a83-bf83-84f22cc9e8dc | unsupervised-video-object-segmentation-via | 2209.03712 | null | https://arxiv.org/abs/2209.03712v1 | https://arxiv.org/pdf/2209.03712v1.pdf | Unsupervised Video Object Segmentation via Prototype Memory Network | Unsupervised video object segmentation aims to segment a target object in the video without a ground truth mask in the initial frame. This challenging task requires extracting features for the most salient common objects within a video sequence. This difficulty can be solved by using motion information such as optical ... | ['Sangyoun Lee', 'Chaewon Park', 'Seunghoon Lee', 'Suhwan Cho', 'Minhyeok Lee'] | 2022-09-08 | null | null | null | null | ['unsupervised-video-object-segmentation', 'self-learning'] | ['computer-vision', 'natural-language-processing'] | [ 2.16644153e-01 -1.28384471e-01 -4.46202129e-01 -1.09215751e-01
-3.11191022e-01 -3.45580786e-01 7.35923499e-02 6.61932305e-02
-7.33094931e-01 5.97848654e-01 -5.14624715e-02 3.41374278e-01
-2.92791636e-03 -5.78286469e-01 -6.50546789e-01 -6.76817000e-01
-9.12618730e-03 1.38468623e-01 9.57605302e-01 2.04445675... | [9.23890495300293, -0.22559285163879395] |
86862cf4-0d12-4b8d-a92f-ff39b1bb8cc2 | learning-local-recurrent-models-for-human | 2107.12847 | null | https://arxiv.org/abs/2107.12847v1 | https://arxiv.org/pdf/2107.12847v1.pdf | Learning Local Recurrent Models for Human Mesh Recovery | We consider the problem of estimating frame-level full human body meshes given a video of a person with natural motion dynamics. While much progress in this field has been in single image-based mesh estimation, there has been a recent uptick in efforts to infer mesh dynamics from video given its role in alleviating iss... | ['Ziyan Wu', 'Bir Bhanu', 'Terrence Chen', 'Ren Li', 'Srikrishna Karanam', 'Runze Li'] | 2021-07-27 | null | null | null | null | ['human-mesh-recovery'] | ['computer-vision'] | [ 2.05440387e-01 -3.26140895e-02 -6.20374605e-02 2.91606523e-02
-6.20178521e-01 -2.24200830e-01 3.23515981e-01 -3.42329323e-01
-1.53952911e-01 5.52792609e-01 4.45701122e-01 3.70208561e-01
1.18171431e-01 -5.74256003e-01 -9.32479858e-01 -6.28377199e-01
-2.61689931e-01 8.72923493e-01 3.36846739e-01 -2.02585533... | [7.279355049133301, -0.8428763151168823] |
a586d80d-dfd0-481c-85e9-ec3cdb8677c1 | j-score-a-robust-measure-of-clustering | 2109.01306 | null | https://arxiv.org/abs/2109.01306v1 | https://arxiv.org/pdf/2109.01306v1.pdf | J-Score: A Robust Measure of Clustering Accuracy | Background. Clustering analysis discovers hidden structures in a data set by partitioning them into disjoint clusters. Robust accuracy measures that evaluate the goodness of clustering results are critical for algorithm development and model diagnosis. Common problems of current clustering accuracy measures include ove... | ['Li Liu', 'Navid Ahmadinejad'] | 2021-09-03 | null | null | null | null | ['set-matching'] | ['computer-vision'] | [ 1.50220677e-01 -6.43180236e-02 -2.01156870e-01 -5.78957617e-01
-8.95766199e-01 -9.15592551e-01 5.41825473e-01 6.21512890e-01
-2.11341351e-01 5.50258100e-01 1.25738040e-01 -2.57818997e-01
-6.76516473e-01 -7.18309104e-01 -2.04432547e-01 -9.70485449e-01
-4.28970009e-01 8.58206451e-01 1.89922929e-01 4.17933196... | [7.607521057128906, 4.562572956085205] |
01af5efc-f2b8-44e0-96dc-8d5e2405da80 | causality-matters-in-medical-imaging | 1912.08142 | null | https://arxiv.org/abs/1912.08142v1 | https://arxiv.org/pdf/1912.08142v1.pdf | Causality matters in medical imaging | This article discusses how the language of causality can shed new light on the major challenges in machine learning for medical imaging: 1) data scarcity, which is the limited availability of high-quality annotations, and 2) data mismatch, whereby a trained algorithm may fail to generalize in clinical practice. Looking... | ['Ian Walker', 'Daniel C. Castro', 'Ben Glocker'] | 2019-12-17 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 9.65393603e-01 5.03916204e-01 -7.59095967e-01 -5.67932129e-01
-8.39678943e-01 -5.36060393e-01 5.87890565e-01 7.39501297e-01
-4.80904549e-01 7.34852314e-01 6.81754291e-01 -9.38839972e-01
-6.57523870e-01 -3.53255004e-01 -6.96944535e-01 -8.49041581e-01
-1.25326604e-01 3.83461118e-01 -1.23370513e-01 3.95816118... | [8.420768737792969, 5.708489894866943] |
66b40dfd-93c0-4a93-aacc-c68e0610191d | paraformer-parallel-attention-transformer-for | 2303.00941 | null | https://arxiv.org/abs/2303.00941v2 | https://arxiv.org/pdf/2303.00941v2.pdf | ParaFormer: Parallel Attention Transformer for Efficient Feature Matching | Heavy computation is a bottleneck limiting deep-learningbased feature matching algorithms to be applied in many realtime applications. However, existing lightweight networks optimized for Euclidean data cannot address classical feature matching tasks, since sparse keypoint based descriptors are expected to be matched. ... | ['Songlin Du', 'Bin Kang', 'Yaping Yan', 'Xiaoyong Lu'] | 2023-03-02 | null | null | null | null | ['homography-estimation'] | ['computer-vision'] | [-1.69763848e-01 -3.34030002e-01 -1.59272447e-01 -1.59178108e-01
-5.33572853e-01 -9.20881033e-02 3.54453027e-01 2.21595764e-01
-4.43880022e-01 1.34908438e-01 1.60446689e-01 1.26369596e-01
-3.06979120e-01 -9.48358536e-01 -8.52305114e-01 -4.76566285e-01
-1.69112891e-01 1.10215597e-01 3.45477283e-01 -1.97528511... | [8.129951477050781, -1.9023139476776123] |
da65999d-f01a-444c-aa91-626fae87333c | memrein-rein-the-domain-shift-for-cross | null | null | https://openreview.net/forum?id=fY2-WyfrXhU | https://openreview.net/pdf?id=fY2-WyfrXhU | MemREIN: Rein the Domain Shift for Cross-Domain Few-Shot Learning | Few-shot learning aims to enable models generalize to new categories (query instances) with only limited labeled samples (support instances) from each category. Metric-based mechanism is a promising direction which compares feature embeddings via different metrics. However, it always fail to generalize to unseen domain... | ['Yun Fu', 'Yulun Zhang', 'Can Qin', 'Yizhou Wang', 'Lichen Wang', 'Yi Xu'] | 2021-09-29 | null | null | null | null | ['cross-domain-few-shot', 'cross-domain-few-shot-learning'] | ['computer-vision', 'computer-vision'] | [ 2.15342283e-01 -3.18128854e-01 -3.47592682e-01 -6.33279800e-01
-6.12585902e-01 -3.57348174e-01 6.50233448e-01 2.88944453e-01
-5.31906068e-01 7.72103786e-01 -1.79556254e-02 1.17705412e-01
-2.14936942e-01 -8.57893765e-01 -3.30750823e-01 -7.99373269e-01
3.31800371e-01 1.56735420e-01 4.75460112e-01 -2.41858006... | [10.110654830932617, 3.0531911849975586] |
545f063c-6bf4-4bb6-8dca-ba04dbdbb5fb | repnet-weakly-supervised-training-of-an | 1902.09868 | null | http://arxiv.org/abs/1902.09868v2 | http://arxiv.org/pdf/1902.09868v2.pdf | RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation | This paper addresses the problem of 3D human pose estimation from single
images. While for a long time human skeletons were parameterized and fitted to
the observation by satisfying a reprojection error, nowadays researchers
directly use neural networks to infer the 3D pose from the observations.
However, most of these... | ['Bastian Wandt', 'Bodo Rosenhahn'] | 2019-02-26 | repnet-weakly-supervised-training-of-an-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Wandt_RepNet_Weakly_Supervised_Training_of_an_Adversarial_Reprojection_Network_for_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Wandt_RepNet_Weakly_Supervised_Training_of_an_Adversarial_Reprojection_Network_for_CVPR_2019_paper.pdf | cvpr-2019-6 | ['monocular-3d-human-pose-estimation', 'weakly-supervised-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 5.68184927e-02 2.17944220e-01 -3.29029374e-02 -4.11759913e-01
-4.80310291e-01 -4.40795898e-01 4.58417863e-01 -1.86389759e-01
-8.20707083e-01 6.25196397e-01 -1.22145362e-01 1.89108729e-01
9.02534425e-02 -8.10143709e-01 -1.26887476e+00 -4.77845997e-01
2.51657814e-01 9.60942328e-01 2.58588433e-01 -4.52216752... | [7.010972023010254, -1.0538285970687866] |
3ca6a3df-3d5c-4bbb-a22e-c48e35378ed9 | proximal-nested-sampling-with-data-driven | 2307.00056 | null | https://arxiv.org/abs/2307.00056v1 | https://arxiv.org/pdf/2307.00056v1.pdf | Proximal nested sampling with data-driven priors for physical scientists | Proximal nested sampling was introduced recently to open up Bayesian model selection for high-dimensional problems such as computational imaging. The framework is suitable for models with a log-convex likelihood, which are ubiquitous in the imaging sciences. The purpose of this article is two-fold. First, we review pro... | ['Marcelo Pereyra', 'Xiaohao Cai', 'Matthew A. Price', 'Tobías I. Liaudat', 'Jason D. McEwen'] | 2023-06-30 | null | null | null | null | ['model-selection'] | ['methodology'] | [ 4.13046122e-01 4.09224629e-01 -2.63015062e-01 -5.43249369e-01
-8.77170384e-01 -1.06811225e-01 4.87466812e-01 -2.35350356e-01
-5.42864203e-01 5.85733116e-01 1.59059808e-01 -4.25102383e-01
-6.56361938e-01 -5.32524884e-01 -7.56649852e-01 -8.84822011e-01
-2.99854338e-01 4.67698038e-01 1.23661481e-01 4.64353561... | [7.0455780029296875, 3.921231746673584] |
da20550c-2d77-46fb-b0c2-10ee32df700a | a-domain-independent-agent-architecture-for | 2306.06272 | null | https://arxiv.org/abs/2306.06272v1 | https://arxiv.org/pdf/2306.06272v1.pdf | A Domain-Independent Agent Architecture for Adaptive Operation in Evolving Open Worlds | Model-based reasoning agents are ill-equipped to act in novel situations in which their model of the environment no longer sufficiently represents the world. We propose HYDRA - a framework for designing model-based agents operating in mixed discrete-continuous worlds, that can autonomously detect when the environment h... | ['Johan de Kleer', 'Jacob Le', 'Sookyung Kim', 'Sachin Grover', 'Roni Stern', 'Wiktor Piotrowski', 'Shiwali Mohan'] | 2023-06-09 | null | null | null | null | ['visual-reasoning', 'visual-reasoning'] | ['computer-vision', 'reasoning'] | [-1.74296260e-01 3.54982674e-01 2.51187623e-01 -1.52144143e-02
-6.46797102e-03 -5.94914258e-01 1.01138687e+00 2.78450429e-01
-3.96951765e-01 6.45615876e-01 2.33329535e-02 -9.73666832e-02
-3.01368833e-01 -7.82519102e-01 -5.13002455e-01 -4.74245667e-01
-6.36350334e-01 1.16118717e+00 5.31751335e-01 -7.93366015... | [3.885042428970337, 1.581701636314392] |
b756eed6-baee-43f4-8b63-9d13b43df463 | user-embedding-for-scholarly-microblog | null | null | https://aclanthology.org/P16-2073 | https://aclanthology.org/P16-2073.pdf | User Embedding for Scholarly Microblog Recommendation | null | ['Yang Yu', 'Xinjie Zhou', 'Xiaojun Wan'] | 2016-08-01 | null | null | null | acl-2016-8 | ['collaborative-ranking'] | ['graphs'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.306517124176025, 3.714883327484131] |
2d524ac1-520d-40bd-bf6f-992b6dfef562 | meeting-the-needs-of-low-resource-languages | 2302.07912 | null | https://arxiv.org/abs/2302.07912v1 | https://arxiv.org/pdf/2302.07912v1.pdf | Meeting the Needs of Low-Resource Languages: The Value of Automatic Alignments via Pretrained Models | Large multilingual models have inspired a new class of word alignment methods, which work well for the model's pretraining languages. However, the languages most in need of automatic alignment are low-resource and, thus, not typically included in the pretraining data. In this work, we ask: How do modern aligners perfor... | ['Katharina Kann', 'Rolando Coto-Solano', 'Gustavo A. Giménez-Lugo', 'John E. Ortega', 'Luis Chiruzzo', 'Arturo Oncevay', 'Arya D. McCarthy', 'Abteen Ebrahimi'] | 2023-02-15 | null | null | null | null | ['word-alignment', 'part-of-speech-tagging'] | ['natural-language-processing', 'natural-language-processing'] | [-1.60356462e-01 1.52109578e-01 -2.93387055e-01 -6.39481664e-01
-1.27441335e+00 -9.60477531e-01 5.28645158e-01 1.15013950e-01
-8.61033618e-01 1.02413464e+00 3.03358287e-01 -6.41647398e-01
4.75878537e-01 -4.92208421e-01 -6.30512714e-01 -4.06898737e-01
2.38728568e-01 1.19251132e+00 6.64203018e-02 -6.09169185... | [10.644481658935547, 9.941319465637207] |
cf67a9c6-57d9-4da9-af4b-68698197e4f7 | towards-real-time-multi-object-tracking | 1909.12605 | null | https://arxiv.org/abs/1909.12605v2 | https://arxiv.org/pdf/1909.12605v2.pdf | Towards Real-Time Multi-Object Tracking | Modern multiple object tracking (MOT) systems usually follow the \emph{tracking-by-detection} paradigm. It has 1) a detection model for target localization and 2) an appearance embedding model for data association. Having the two models separately executed might lead to efficiency problems, as the running time is simpl... | ['Ya-Li Li', 'Liang Zheng', 'Shengjin Wang', 'Zhongdao Wang', 'Yixuan Liu'] | 2019-09-27 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1292_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123560103.pdf | eccv-2020-8 | ['real-time-multi-object-tracking'] | ['computer-vision'] | [-2.74825264e-02 -2.67126530e-01 -1.51496753e-01 7.18736500e-02
-6.98536158e-01 -5.68756104e-01 5.82485914e-01 -7.44736101e-03
-5.39628685e-01 2.59198248e-01 -4.60194647e-01 -2.77011126e-01
2.81074375e-01 -5.22005558e-01 -7.90020943e-01 -7.70916283e-01
-1.77454829e-01 6.07548654e-01 7.95839190e-01 6.29823208... | [6.352555751800537, -2.073401689529419] |
3e821fd0-d6d4-4c24-a8f3-df0a4b06ad98 | a-learning-system-for-motion-planning-of-free | 2207.02464 | null | https://arxiv.org/abs/2207.02464v1 | https://arxiv.org/pdf/2207.02464v1.pdf | A Learning System for Motion Planning of Free-Float Dual-Arm Space Manipulator towards Non-Cooperative Object | Recent years have seen the emergence of non-cooperative objects in space, like failed satellites and space junk. These objects are usually operated or collected by free-float dual-arm space manipulators. Thanks to eliminating the difficulties of modeling and manual parameter-tuning, reinforcement learning (RL) methods ... | ['Tao Zhang', 'Xiang Zheng', 'Yuxue Cao', 'Shengjie Wang'] | 2022-07-06 | null | null | null | null | ['trajectory-planning'] | ['robots'] | [-4.20567602e-01 2.15589236e-02 -2.23065794e-01 2.29143813e-01
-5.28186262e-01 -8.27713966e-01 3.45927864e-01 -3.79256338e-01
-2.94431269e-01 8.67818952e-01 -5.47660410e-01 -3.76683414e-01
-9.42172468e-01 -3.40308309e-01 -5.91003358e-01 -9.92219090e-01
-4.28625375e-01 8.54840577e-01 3.97449613e-01 -4.35223848... | [4.8237385749816895, 1.1756937503814697] |
1c6b35a8-a7cc-4ad7-8110-88683318460b | whats-missing-a-knowledge-gap-guided-approach | 1909.09253 | null | https://arxiv.org/abs/1909.09253v1 | https://arxiv.org/pdf/1909.09253v1.pdf | What's Missing: A Knowledge Gap Guided Approach for Multi-hop Question Answering | Multi-hop textual question answering requires combining information from multiple sentences. We focus on a natural setting where, unlike typical reading comprehension, only partial information is provided with each question. The model must retrieve and use additional knowledge to correctly answer the question. To tackl... | ['Peter Clark', 'Ashish Sabharwal', 'Tushar Khot'] | 2019-09-19 | whats-missing-a-knowledge-gap-guided-approach-1 | https://aclanthology.org/D19-1281 | https://aclanthology.org/D19-1281.pdf | ijcnlp-2019-11 | ['multi-hop-question-answering'] | ['knowledge-base'] | [ 3.19844872e-01 3.62192810e-01 -2.30642587e-01 -3.16810250e-01
-1.61767292e+00 -1.04265010e+00 3.34001809e-01 8.76180291e-01
-4.92943913e-01 8.59163344e-01 4.73480016e-01 -5.62213838e-01
-4.26319569e-01 -8.74253333e-01 -9.73166525e-01 1.63702205e-01
5.24926245e-01 6.24458253e-01 6.50601447e-01 -4.33721513... | [11.173632621765137, 7.977997303009033] |
14d6c172-fe3f-4924-8445-6211f197dc7d | multi-task-driven-feature-models-for-thermal | 1911.11384 | null | https://arxiv.org/abs/1911.11384v1 | https://arxiv.org/pdf/1911.11384v1.pdf | Multi-Task Driven Feature Models for Thermal Infrared Tracking | Existing deep Thermal InfraRed (TIR) trackers usually use the feature models of RGB trackers for representation. However, these feature models learned on RGB images are neither effective in representing TIR objects nor taking fine-grained TIR information into consideration. To this end, we develop a multi-task framewor... | ['Nana Fan', 'Wei Liu', 'Qiao Liu', 'Yonsheng Liang', 'Zhenyu He', 'Xin Li', 'Di Yuan'] | 2019-11-26 | null | null | null | null | ['thermal-infrared-object-tracking'] | ['computer-vision'] | [ 1.24964051e-01 -5.64788401e-01 -2.09360838e-01 -6.03567302e-01
-8.10205281e-01 -5.16517341e-01 3.60875249e-01 -8.51663172e-01
-2.39294931e-01 2.33110994e-01 -9.24783200e-02 -8.09183866e-02
-8.74142051e-02 -3.77714485e-01 -5.06043315e-01 -1.17191291e+00
6.70793712e-01 1.91026628e-01 3.28070045e-01 5.95152788... | [6.353494644165039, -2.2246108055114746] |
79a17966-e34a-44e8-a220-198f66ba394d | the-singing-voice-conversion-challenge-2023 | 2306.14422 | null | https://arxiv.org/abs/2306.14422v2 | https://arxiv.org/pdf/2306.14422v2.pdf | The Singing Voice Conversion Challenge 2023 | We present the latest iteration of the voice conversion challenge (VCC) series, a bi-annual scientific event aiming to compare and understand different voice conversion (VC) systems based on a common dataset. This year we shifted our focus to singing voice conversion (SVC), thus named the challenge the Singing Voice Co... | ['Tomoki Toda', 'Jiatong Shi', 'Songxiang Liu', 'Lester Phillip Violeta', 'Wen-Chin Huang'] | 2023-06-26 | null | null | null | null | ['voice-conversion', 'voice-conversion'] | ['audio', 'speech'] | [-1.09712325e-01 -2.83766001e-01 2.94414610e-01 4.27127033e-02
-1.43009245e+00 -1.03548753e+00 7.39036083e-01 -1.57562569e-01
-3.78944993e-01 6.94341600e-01 5.50240815e-01 -2.16141671e-01
2.29072765e-01 -4.38520908e-02 -3.79534841e-01 -2.76013225e-01
3.18603843e-01 5.05496681e-01 4.23116356e-01 -4.37956989... | [14.927760124206543, 6.438412189483643] |
f415371e-2e55-46f4-a009-b1a8a84f41ac | recognizing-unseen-objects-via-multimodal | 2306.08487 | null | https://arxiv.org/abs/2306.08487v2 | https://arxiv.org/pdf/2306.08487v2.pdf | Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph Propagation | Zero-Shot Learning (ZSL), which aims at automatically recognizing unseen objects, is a promising learning paradigm to understand new real-world knowledge for machines continuously. Recently, the Knowledge Graph (KG) has been proven as an effective scheme for handling the zero-shot task with large-scale and non-attribut... | ['Enhong Chen', 'Nicholas Jing Yuan', 'Baoxing Huai', 'Qi Liu', 'Zhefeng Wang', 'Hongke Zhao', 'Zhi Li', 'Likang Wu'] | 2023-06-14 | null | null | null | null | ['knowledge-graphs'] | ['knowledge-base'] | [-2.08485529e-01 1.92148820e-01 -2.77508825e-01 -4.05537009e-01
-4.92713869e-01 -3.73448223e-01 6.42327130e-01 3.47793341e-01
-2.14853093e-01 4.80952233e-01 3.42290729e-01 1.33205950e-01
-2.79275477e-01 -1.10173893e+00 -8.12712431e-01 -6.15014911e-01
2.83246607e-01 3.32973748e-01 2.92778403e-01 -3.59539330... | [10.191065788269043, 2.171614170074463] |
af700e02-4170-4869-8c48-37568a6a0b62 | aggressive-language-in-an-online-hacking | null | null | https://aclanthology.org/W18-5109 | https://aclanthology.org/W18-5109.pdf | Aggressive language in an online hacking forum | We probe the heterogeneity in levels of abusive language in different sections of the Internet, using an annotated corpus of Wikipedia page edit comments to train a binary classifier for abuse detection. Our test data come from the CrimeBB Corpus of hacking-related forum posts and we find that (a) forum interactions ar... | ['Sergio Pastrana', 'Paula Buttery', 'Andrew Caines', 'Alice Hutchings'] | 2018-10-01 | null | null | null | ws-2018-10 | ['abuse-detection'] | ['natural-language-processing'] | [-2.53488868e-01 1.21597379e-01 -6.08581677e-02 -2.49617308e-01
-2.73881465e-01 -1.17478251e+00 9.33799982e-01 5.44147372e-01
-3.45703483e-01 7.37727821e-01 8.13351989e-01 -5.55863798e-01
1.12766765e-01 -7.24210262e-01 -2.21301720e-01 -9.57923084e-02
-1.78617343e-01 1.11425683e-01 8.58623162e-02 -6.78363442... | [8.614115715026855, 10.410286903381348] |
57e3b7b1-6d2a-405d-969d-66487ea12018 | video-shadow-detection-via-spatio-temporal-1 | 2206.08801 | null | https://arxiv.org/abs/2206.08801v1 | https://arxiv.org/pdf/2206.08801v1.pdf | Video Shadow Detection via Spatio-Temporal Interpolation Consistency Training | It is challenging to annotate large-scale datasets for supervised video shadow detection methods. Using a model trained on labeled images to the video frames directly may lead to high generalization error and temporal inconsistent results. In this paper, we address these challenges by proposing a Spatio-Temporal Interp... | ['Chunxia Xiao', 'Yimin Yang', 'Xuanyu Zhou', 'Zipei Chen', 'Chengjiang Long', 'Sheng Liu', 'Yihong Cao', 'Xiao Lu'] | 2022-06-17 | video-shadow-detection-via-spatio-temporal | http://openaccess.thecvf.com//content/CVPR2022/html/Lu_Video_Shadow_Detection_via_Spatio-Temporal_Interpolation_Consistency_Training_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Lu_Video_Shadow_Detection_via_Spatio-Temporal_Interpolation_Consistency_Training_CVPR_2022_paper.pdf | cvpr-2022-1 | ['shadow-detection'] | ['computer-vision'] | [ 4.84252542e-01 -1.77000329e-01 -5.00242472e-01 -5.83661258e-01
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1.77022427e-01 -6.91813007e-02 8.37574422e-01 2.83410549... | [9.232215881347656, -0.06549634784460068] |
29ce3675-48a1-4b1e-8978-cae6a92919eb | denoising-enhanced-distantly-supervised | 2210.09599 | null | https://arxiv.org/abs/2210.09599v1 | https://arxiv.org/pdf/2210.09599v1.pdf | Denoising Enhanced Distantly Supervised Ultrafine Entity Typing | Recently, the task of distantly supervised (DS) ultra-fine entity typing has received significant attention. However, DS data is noisy and often suffers from missing or wrong labeling issues resulting in low precision and low recall. This paper proposes a novel ultra-fine entity typing model with denoising capability. ... | ['Ping Li', 'Hongliang Fei', 'Yue Zhang'] | 2022-10-18 | null | null | null | null | ['entity-typing'] | ['natural-language-processing'] | [ 7.26673156e-02 -3.25082615e-02 -9.02707353e-02 -8.14377964e-01
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6.88764155e-01 2.41596878e-01 -2.09295020e-01 1.71079755... | [9.569132804870605, 8.981311798095703] |
ee1b2750-e996-47d3-90e0-1231257aeaff | large-language-models-are-reasoners-with-self | 2212.09561 | null | https://arxiv.org/abs/2212.09561v4 | https://arxiv.org/pdf/2212.09561v4.pdf | Large Language Models are Better Reasoners with Self-Verification | Recently, with the chain of thought (CoT) prompting, large language models (LLMs), e.g., GPT-3, have shown strong reasoning ability in several natural language processing tasks such as arithmetic, commonsense, and logical reasoning. However, LLMs with CoT require multi-step prompting and multi-token prediction, which i... | ['Jun Zhao', 'Kang Liu', 'Bin Li', 'Fei Xia', 'Shizhu He', 'Minjun Zhu', 'Yixuan Weng'] | 2022-12-19 | null | null | null | null | ['arithmetic-reasoning', 'logical-reasoning', 'common-sense-reasoning'] | ['reasoning', 'reasoning', 'reasoning'] | [-1.25913352e-01 9.50934067e-02 1.27212614e-01 -4.88657296e-01
-3.29444975e-01 -3.98220479e-01 2.51717061e-01 5.47282934e-01
-2.43538395e-01 5.92272580e-01 1.19572736e-01 -6.65011644e-01
-1.18108906e-01 -1.11930120e+00 -5.31253636e-01 -1.60749242e-01
4.85863805e-01 3.16650182e-01 3.16974759e-01 -2.80566245... | [9.59062385559082, 7.500366687774658] |
d10e989c-6223-4690-b958-499d2fe6f13d | measuring-compositional-consistency-for-video | 2204.07190 | null | https://arxiv.org/abs/2204.07190v2 | https://arxiv.org/pdf/2204.07190v2.pdf | Measuring Compositional Consistency for Video Question Answering | Recent video question answering benchmarks indicate that state-of-the-art models struggle to answer compositional questions. However, it remains unclear which types of compositional reasoning cause models to mispredict. Furthermore, it is difficult to discern whether models arrive at answers using compositional reasoni... | ['Maneesh Agrawala', 'Ranjay Krishna', 'Madeleine Grunde-McLaughlin', 'Eva Prakash', 'Mustafa Omer Gul', 'Mona Gandhi'] | 2022-04-14 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Gandhi_Measuring_Compositional_Consistency_for_Video_Question_Answering_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Gandhi_Measuring_Compositional_Consistency_for_Video_Question_Answering_CVPR_2022_paper.pdf | cvpr-2022-1 | ['video-question-answering'] | ['computer-vision'] | [ 1.49678141e-01 5.45851946e-01 -5.42514026e-02 -5.53951979e-01
-1.09839284e+00 -8.38453531e-01 5.32835007e-01 2.72584409e-01
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-2.00966746e-01 -1.01366472e+00 -1.12434793e+00 2.20445380e-01
2.35734835e-01 8.45951319e-01 8.38020205e-01 -4.04596925... | [10.949237823486328, 7.879020690917969] |
adaf4f57-e406-47d9-8beb-2efed2239fbe | unifiedqa-crossing-format-boundaries-with-a | 2005.00700 | null | https://arxiv.org/abs/2005.00700v3 | https://arxiv.org/pdf/2005.00700v3.pdf | UnifiedQA: Crossing Format Boundaries With a Single QA System | Question answering (QA) tasks have been posed using a variety of formats, such as extractive span selection, multiple choice, etc. This has led to format-specialized models, and even to an implicit division in the QA community. We argue that such boundaries are artificial and perhaps unnecessary, given the reasoning ab... | ['Sewon Min', 'Ashish Sabharwal', 'Daniel Khashabi', 'Tushar Khot', 'Hannaneh Hajishirzi', 'Peter Clark', 'Oyvind Tafjord'] | 2020-05-02 | null | https://aclanthology.org/2020.findings-emnlp.171 | https://aclanthology.org/2020.findings-emnlp.171.pdf | findings-of-the-association-for-computational | ['multi-task-language-understanding'] | ['methodology'] | [ 1.74052507e-01 2.35788465e-01 1.79990903e-01 -5.95417738e-01
-1.61976099e+00 -1.11012983e+00 8.00209820e-01 2.63459623e-01
-3.00253898e-01 7.92155325e-01 5.88660002e-01 -8.01964521e-01
-2.99442738e-01 -8.07615519e-01 -6.99021459e-01 -1.33561537e-01
2.86704302e-01 1.11096668e+00 4.23170596e-01 -9.55576658... | [11.164799690246582, 8.06218433380127] |
bab49e6b-d9c3-4272-89ae-d92a99b3a222 | physics-informed-neural-networks-based-model | 2109.10793 | null | https://arxiv.org/abs/2109.10793v1 | https://arxiv.org/pdf/2109.10793v1.pdf | Physics-informed Neural Networks-based Model Predictive Control for Multi-link Manipulators | We discuss nonlinear model predictive control (NMPC) for multi-body dynamics via physics-informed machine learning methods. Physics-informed neural networks (PINNs) are a promising tool to approximate (partial) differential equations. PINNs are not suited for control tasks in their original form since they are not desi... | ['Benjamin Unger', 'Jörg Fehr', 'Jonas Kneifl', 'Jonas Nicodemus'] | 2021-09-22 | null | null | null | null | ['physics-informed-machine-learning'] | ['graphs'] | [ 1.46778226e-01 4.85997975e-01 -4.64902341e-01 5.25267899e-01
-2.40353554e-01 -3.36828172e-01 6.18275344e-01 7.38200918e-02
-5.27619958e-01 1.16268373e+00 -5.03438771e-01 -1.37035489e-01
-6.18712783e-01 -5.33247173e-01 -8.74617159e-01 -9.35555995e-01
-6.62084147e-02 7.73491919e-01 -7.82139599e-02 -5.38655639... | [5.429316997528076, 2.6298117637634277] |
057eaa32-558a-4714-9380-c2c7f2bb8c0f | self-improving-slam-in-dynamic-environments | 2210.08350 | null | https://arxiv.org/abs/2210.08350v3 | https://arxiv.org/pdf/2210.08350v3.pdf | Self-Improving SLAM in Dynamic Environments: Learning When to Mask | Visual SLAM - Simultaneous Localization and Mapping - in dynamic environments typically relies on identifying and masking image features on moving objects to prevent them from negatively affecting performance. Current approaches are suboptimal: they either fail to mask objects when needed or, on the contrary, mask obje... | ['Hervé Le Borgne', 'Mohamed Tamaazousti', 'Romain Dupont', 'Adrian Bojko'] | 2022-10-15 | null | null | null | null | ['simultaneous-localization-and-mapping'] | ['computer-vision'] | [ 2.74735570e-01 -9.43924710e-02 -5.11737093e-02 -5.22220135e-01
-5.45380831e-01 -7.53322363e-01 5.74108183e-01 -9.10953581e-02
-6.69814110e-01 6.36980534e-01 -1.30152896e-01 -1.81172431e-01
1.60279080e-01 -3.12980145e-01 -1.03718257e+00 -5.40986657e-01
-3.14391255e-01 6.54536247e-01 7.73819685e-01 -1.43618375... | [7.6886444091796875, -2.1451621055603027] |
a9805586-bc58-440f-863e-2d4dee449530 | meshstereo-a-global-stereo-model-with-mesh | null | null | http://openaccess.thecvf.com/content_iccv_2015/html/Zhang_MeshStereo_A_Global_ICCV_2015_paper.html | http://openaccess.thecvf.com/content_iccv_2015/papers/Zhang_MeshStereo_A_Global_ICCV_2015_paper.pdf | MeshStereo: A Global Stereo Model With Mesh Alignment Regularization for View Interpolation | We present a novel global stereo model designed for view interpolation. Unlike existing stereo models which only output a disparity map, our model is able to output a 3D triangular mesh, which can be directly used for view interpolation. To this aim, we partition the input stereo images into 2D triangles with shared ve... | ['Zhiwei Li', 'Yong Rui', 'Rui Cai', 'Hongyang Chao', 'Yanhua Cheng', 'Chi Zhang'] | 2015-12-01 | null | null | null | iccv-2015-12 | ['stereo-matching'] | ['computer-vision'] | [ 2.44945765e-01 1.71752065e-01 9.54616368e-02 -1.83380663e-01
-6.33773386e-01 -5.51782310e-01 5.61581790e-01 -6.75471872e-02
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4.03010428e-01 -9.04938817e-01 -9.86967325e-01 -5.33434153e-01
3.30222815e-01 6.56535625e-01 4.14270699e-01 -2.33795598... | [9.129422187805176, -3.0092766284942627] |
20d1eab9-f3c4-46e6-8a42-6bbe83883c5e | point-cloud-pre-training-by-mixing-and | 2109.00452 | null | https://arxiv.org/abs/2109.00452v3 | https://arxiv.org/pdf/2109.00452v3.pdf | Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes | The manual annotation for large-scale point clouds costs a lot of time and is usually unavailable in harsh real-world scenarios. Inspired by the great success of the pre-training and fine-tuning paradigm in both vision and language tasks, we argue that pre-training is one potential solution for obtaining a scalable mod... | ['Mingliang Xu', 'Xiaohan Wang', 'Yi Yang', 'Zhedong Zheng', 'Chao Sun'] | 2021-09-01 | null | null | null | null | ['point-cloud-pre-training', '3d-part-segmentation'] | ['computer-vision', 'computer-vision'] | [ 4.96961176e-02 1.93976149e-01 -8.83679166e-02 -3.83156359e-01
-8.74937654e-01 -7.30131507e-01 5.66436172e-01 -1.38051316e-01
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2.54878223e-01 6.51062787e-01 1.17265493e-01 1.57110114... | [8.146618843078613, -3.4138267040252686] |
6f36664f-9c76-403a-8fea-f98832cb4cf5 | cascaded-cross-attention-networks-for-data | 2305.06963 | null | https://arxiv.org/abs/2305.06963v1 | https://arxiv.org/pdf/2305.06963v1.pdf | Cascaded Cross-Attention Networks for Data-Efficient Whole-Slide Image Classification Using Transformers | Whole-Slide Imaging allows for the capturing and digitization of high-resolution images of histological specimen. An automated analysis of such images using deep learning models is therefore of high demand. The transformer architecture has been proposed as a possible candidate for effectively leveraging the high-resolu... | ['Daniel Truhn', 'Johannes Stegmaier', 'Christiane Kuhl', 'Sven Nebelung', 'Tianyu Han', 'Jakob Nikolas Kather', 'Firas Khader'] | 2023-05-11 | null | null | null | null | ['whole-slide-images'] | ['computer-vision'] | [ 2.15234786e-01 2.17737496e-01 -2.02694699e-01 4.16502077e-03
-1.41435456e+00 -3.92695904e-01 3.66505712e-01 5.14936328e-01
-7.33114779e-01 4.22965586e-01 -2.52508610e-01 -4.70611215e-01
-1.38461098e-01 -8.04274559e-01 -8.14226389e-01 -9.89744306e-01
-6.08331859e-02 3.67356300e-01 1.43535882e-01 2.14137673... | [15.052903175354004, -2.9066896438598633] |
b2c58b33-e6dd-4943-af43-58b3def94918 | ssn-stockwell-scattering-network-for-sar | 2304.11404 | null | https://arxiv.org/abs/2304.11404v1 | https://arxiv.org/pdf/2304.11404v1.pdf | SSN: Stockwell Scattering Network for SAR Image Change Detection | Recently, synthetic aperture radar (SAR) image change detection has become an interesting yet challenging direction due to the presence of speckle noise. Although both traditional and modern learning-driven methods attempted to overcome this challenge, deep convolutional neural networks (DCNNs)-based methods are still ... | ['Kim-Hui Yap', 'Yi Wang', 'Yanan Zhao', 'Gong Chen'] | 2023-04-22 | null | null | null | null | ['change-detection'] | ['computer-vision'] | [ 6.79737210e-01 -7.88672209e-01 5.64576387e-01 -4.81812537e-01
-4.90455359e-01 -2.09998235e-01 6.55244589e-01 -6.01706266e-01
-4.84728843e-01 8.58181596e-01 1.15747929e-01 -3.91985737e-02
-7.13173866e-01 -9.28803742e-01 -2.38174856e-01 -1.16694129e+00
-1.22007951e-01 -1.31625667e-01 3.51488322e-01 -6.00503385... | [10.457472801208496, -2.219019889831543] |
4a413d14-b6f4-4547-a08f-2624cd6f2aad | a-survey-on-multi-resident-activity | 2304.12304 | null | https://arxiv.org/abs/2304.12304v1 | https://arxiv.org/pdf/2304.12304v1.pdf | A Survey on Multi-Resident Activity Recognition in Smart Environments | Human activity recognition (HAR) is a rapidly growing field that utilizes smart devices, sensors, and algorithms to automatically classify and identify the actions of individuals within a given environment. These systems have a wide range of applications, including assisting with caring tasks, increasing security, and ... | ['Shingo Yamaguchi', 'Mohd Anuaruddin Bin Ahmadon', 'Raihani Mohamed', 'Norwati Mustapha', 'Thinagaran Perumal', 'Farhad MortezaPour Shiri'] | 2023-04-24 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 2.52094597e-01 -3.51545036e-01 -3.08341205e-01 -2.62687892e-01
-1.30282804e-01 -2.28394791e-01 2.03046918e-01 5.85839510e-01
-5.51618636e-01 7.93007851e-01 6.56704545e-01 1.22215338e-01
-2.83331722e-01 -7.15086460e-01 2.57541090e-02 -5.85403264e-01
-3.03712547e-01 4.35819291e-02 -3.84050459e-02 -6.02250267... | [7.259828090667725, 0.6421546936035156] |
6ba68499-bf4e-42ac-9fc7-c6958fae2071 | a-simple-and-effective-pruning-approach-for | 2306.11695 | null | https://arxiv.org/abs/2306.11695v1 | https://arxiv.org/pdf/2306.11695v1.pdf | A Simple and Effective Pruning Approach for Large Language Models | As their size increases, Large Languages Models (LLMs) are natural candidates for network pruning methods: approaches that drop a subset of network weights while striving to preserve performance. Existing methods, however, require either retraining, which is rarely affordable for billion-scale LLMs, or solving a weight... | ['J. Zico Kolter', 'Anna Bair', 'Zhuang Liu', 'MingJie Sun'] | 2023-06-20 | null | null | null | null | ['network-pruning'] | ['methodology'] | [ 1.41807005e-01 1.35206178e-01 -3.98779631e-01 -2.41372973e-01
-3.03050011e-01 -2.56283164e-01 4.35071647e-01 2.83557564e-01
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-9.56536084e-02 -8.11555624e-01 -7.51366496e-01 -2.81254470e-01
-2.54805293e-02 2.77706653e-01 3.49011511e-01 -2.06875741... | [8.653300285339355, 3.4666733741760254] |
b623e22a-0274-4d45-b75a-9689938e3bda | towards-generative-aspect-based-sentiment | null | null | https://aclanthology.org/2021.acl-short.64/ | https://aclanthology.org/2021.acl-short.64.pdf | Towards Generative Aspect-Based Sentiment Analysis | Aspect-based sentiment analysis (ABSA) has received increasing attention recently. Most existing work tackles ABSA in a discriminative manner, designing various task-specific classification networks for the prediction. Despite their effectiveness, these methods ignore the rich label semantics in ABSA problems and requi... | ['Wai Lam', 'Lidong Bing', 'Yang Deng', 'Xin Li', 'Wenxuan Zhang'] | 2021-08-01 | null | https://aclanthology.org/2021.acl-short.64 | https://aclanthology.org/2021.acl-short.64.pdf | acl-2021-5 | ['aspect-sentiment-triplet-extraction'] | ['natural-language-processing'] | [ 5.74449480e-01 1.20011710e-01 -1.66652530e-01 -6.56695724e-01
-8.66622388e-01 -4.71731067e-01 1.12225425e+00 -1.73288956e-01
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-5.38644753e-03 -8.84565890e-01 -6.23974442e-01 -7.00572193e-01
6.10493481e-01 4.44030076e-01 6.67648017e-02 -2.96641082... | [11.441903114318848, 6.62898063659668] |
c6d53f92-c3c9-401a-b284-e8d9d0b669d5 | polcovid-a-multicenter-multiclass-chest-x-ray | 2211.16359 | null | https://arxiv.org/abs/2211.16359v3 | https://arxiv.org/pdf/2211.16359v3.pdf | POLCOVID: a multicenter multiclass chest X-ray database (Poland, 2020-2021) | The outbreak of the SARS-CoV-2 pandemic has put healthcare systems worldwide to their limits, resulting in increased waiting time for diagnosis and required medical assistance. With chest radiographs (CXR) being one of the most common COVID-19 diagnosis methods, many artificial intelligence tools for image-based COVID-... | ['Damian Piotrowski', 'POLCOVID Study Group', 'Joanna Polanska', 'Andrzej Cieszanowski', 'Michal Marczyk', 'Edyta Szurowska', 'Barbara Gizycka', 'Gabriela Zapolska', 'Krzysztof Simon', 'Robert Flisiak', 'Malgorzata Pawlowska', 'Piotr Fiedor', 'Mateusz Nowak', 'Grzegorz Przybylski', 'Tadeusz Popiela', 'Jerzy Walecki', '... | 2022-11-29 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [ 3.20110977e-01 -3.48448634e-01 1.03625216e-01 -4.22196873e-02
-7.00229049e-01 -5.77571869e-01 3.87597352e-01 6.32343948e-01
-8.12374651e-01 5.18641710e-01 -8.28549713e-02 -4.73943293e-01
-4.75502759e-01 -5.93260348e-01 -9.50051621e-02 -7.31977999e-01
9.65343118e-02 1.34467125e+00 4.60055411e-01 4.45239544... | [15.488753318786621, -1.7832316160202026] |
d0ca7226-5a38-4598-967d-4bc483b697d2 | retroprime-a-diverse-plausible-and | null | null | https://www.sciencedirect.com/science/article/pii/S1385894721014303?via%3Dihub | https://reader.elsevier.com/reader/sd/pii/S1385894721014303?token=421B055EB4B5E4561A9153E3E126DF84836F5355C84111D07A19462C5226B2124333B7E075E50F93E7519F751ED5ADEA&originRegion=eu-west-1&originCreation=20220211095011 | RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions | Retrosynthesis prediction is a crucial task for organic synthesis. In this work, we propose a single-step template-free and Transformer-based method dubbed RetroPrime, integrating chemists’ retrosynthetic strategy of (1) decomposing a molecule into synthons then (2) generating reactants by attaching leaving groups. The... | ['Xiaojun Yaoa', 'Chang-Yu Hsieh', 'Benben Liao', 'Huanxiang Liu', 'Guangyong Chen', 'Jiezhong Qiu', 'Yuquan Li', 'Xiaorui Wang'] | 2021-09-15 | null | null | null | chemical-engineering-journal-2021-9 | ['retrosynthesis'] | ['medical'] | [ 3.60002935e-01 -2.25164443e-01 -3.62511307e-01 1.42579392e-01
-4.90309507e-01 -1.19307709e+00 7.76802659e-01 3.68348777e-01
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2.60235995e-01 -6.75062180e-01 -5.93419313e-01 -1.01092279e+00
1.47407383e-01 3.99009854e-01 3.73260677e-01 -3.06405038... | [4.488991737365723, 6.1105875968933105] |
e5b7bc6e-4329-45d9-a0ce-f11192a8334c | dual-illumination-estimation-for-robust | 1910.13688 | null | https://arxiv.org/abs/1910.13688v1 | https://arxiv.org/pdf/1910.13688v1.pdf | Dual Illumination Estimation for Robust Exposure Correction | Exposure correction is one of the fundamental tasks in image processing and computational photography. While various methods have been proposed, they either fail to produce visually pleasing results, or only work well for limited types of image (e.g., underexposed images). In this paper, we present a novel automatic ex... | ['Wei-Shi Zheng', 'Yongwei Nie', 'Qing Zhang'] | 2019-10-30 | null | null | null | null | ['multi-exposure-image-fusion'] | ['computer-vision'] | [ 1.01364839e+00 -2.94222623e-01 4.76776540e-01 -2.90856689e-01
-5.39140224e-01 -3.82673204e-01 4.17918950e-01 1.14538856e-02
-3.92614812e-01 4.91654277e-01 -3.78758870e-02 -1.56454608e-01
-1.84108883e-01 -7.00888634e-01 -5.97699642e-01 -7.89633155e-01
5.95158279e-01 -1.20929860e-01 4.17970419e-01 -7.62933418... | [10.85098648071289, -2.4802236557006836] |
d0a09d5e-0e59-4d57-9e8a-c0210aa0a6f3 | mining-unseen-classes-via-regional-objectness | 2211.06866 | null | https://arxiv.org/abs/2211.06866v3 | https://arxiv.org/pdf/2211.06866v3.pdf | Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation | Incremental or continual learning has been extensively studied for image classification tasks to alleviate catastrophic forgetting, a phenomenon that earlier learned knowledge is forgotten when learning new concepts. For class incremental semantic segmentation, such a phenomenon often becomes much worse due to the back... | ['Yunchao Wei', 'Jianbo Jiao', 'Zhiyuan Fang', 'Guangyu Gao', 'Zekang Zhang'] | 2022-11-13 | null | null | null | null | ['class-incremental-semantic-segmentation'] | ['computer-vision'] | [ 3.53392750e-01 1.75312102e-01 7.45828543e-03 -3.31257075e-01
-2.55360571e-03 -1.41470730e-01 2.61929721e-01 5.19182265e-01
-6.69110417e-01 9.45849359e-01 -3.97283852e-01 1.58425987e-01
6.12613000e-02 -9.29525435e-01 -8.04323554e-01 -1.10613513e+00
3.80963862e-01 4.56883013e-01 9.23824728e-01 1.13930134... | [9.40721321105957, 1.8321558237075806] |
fde6ac01-b8b9-4ccb-8091-ad6c9199cfcb | protein-secondary-structure-prediction-using | 1604.07176 | null | http://arxiv.org/abs/1604.07176v1 | http://arxiv.org/pdf/1604.07176v1.pdf | Protein Secondary Structure Prediction Using Cascaded Convolutional and Recurrent Neural Networks | Protein secondary structure prediction is an important problem in
bioinformatics. Inspired by the recent successes of deep neural networks, in
this paper, we propose an end-to-end deep network that predicts protein
secondary structures from integrated local and global contextual features. Our
deep architecture leverage... | ['Yizhou Yu', 'Zhen Li'] | 2016-04-25 | null | null | null | null | ['protein-secondary-structure-prediction'] | ['medical'] | [ 3.58033985e-01 -1.31084785e-01 -9.97886583e-02 -6.15887761e-01
-1.15500665e+00 -4.58751976e-01 1.51429027e-01 4.64121699e-01
-5.98625302e-01 1.12210608e+00 1.51584908e-01 -7.24592984e-01
1.52725399e-01 -4.13119406e-01 -1.01032352e+00 -9.62004602e-01
3.82561460e-02 1.02148734e-01 -6.54883357e-03 -1.69881731... | [4.7120137214660645, 5.635164737701416] |
0a28149e-2a86-4619-b6b9-a9f9dab9df29 | chemellia-an-ecosystem-for-atomistic | 2305.12010 | null | https://arxiv.org/abs/2305.12010v1 | https://arxiv.org/pdf/2305.12010v1.pdf | Chemellia: An Ecosystem for Atomistic Scientific Machine Learning | Chemellia is an open-source framework for atomistic machine learning in the Julia programming language. The framework takes advantage of Julia's high speed as well as the ability to share and reuse code and interfaces through the paradigm of multiple dispatch. Chemellia is designed to make use of existing interfaces an... | ['Rachel C. Kurchin', 'Venkatasubramanian Viswanathan', 'Dhairya Gandhi', 'Anant Thazhemadam'] | 2023-05-19 | null | null | null | null | ['property-prediction', 'feature-engineering'] | ['medical', 'methodology'] | [-1.88309163e-01 7.89932013e-02 -2.06820928e-02 -3.10417295e-01
-3.11576933e-01 -6.25052273e-01 6.12085402e-01 4.98982787e-01
-2.28536993e-01 6.75761700e-01 3.44929174e-02 -5.04128933e-01
-2.18685955e-01 -1.20077980e+00 -8.08366597e-01 -6.09037638e-01
-1.75818801e-01 2.75122732e-01 6.19062148e-02 -3.31342965... | [5.113016605377197, 5.551919937133789] |
547fd164-4637-465a-afd7-650a27774bb2 | dialogue-act-classification-with-context | 1904.02594 | null | https://arxiv.org/abs/1904.02594v2 | https://arxiv.org/pdf/1904.02594v2.pdf | Dialogue Act Classification with Context-Aware Self-Attention | Recent work in Dialogue Act classification has treated the task as a sequence labeling problem using hierarchical deep neural networks. We build on this prior work by leveraging the effectiveness of a context-aware self-attention mechanism coupled with a hierarchical recurrent neural network. We conduct extensive evalu... | ['Joel Tetreault', 'Vipul Raheja'] | 2019-04-04 | dialogue-act-classification-with-context-2 | https://aclanthology.org/N19-1373 | https://aclanthology.org/N19-1373.pdf | naacl-2019-6 | ['dialogue-act-classification'] | ['natural-language-processing'] | [ 5.34052908e-01 5.56463182e-01 -1.56135365e-01 -7.11526811e-01
-7.69077599e-01 -3.03949833e-01 1.13788700e+00 2.71953613e-01
-4.29921895e-01 6.85266852e-01 1.17796826e+00 -3.97750288e-01
4.76155251e-01 -5.48829079e-01 -1.08744518e-03 -2.55095124e-01
8.52967724e-02 8.75795186e-01 1.44873098e-01 -9.94772077... | [12.767010688781738, 7.818905830383301] |
e6f69356-b12b-4db3-b4ab-d26d66d97a5e | guaranteed-tensor-recovery-fused-low-rankness | 2302.02155 | null | https://arxiv.org/abs/2302.02155v1 | https://arxiv.org/pdf/2302.02155v1.pdf | Guaranteed Tensor Recovery Fused Low-rankness and Smoothness | The tensor data recovery task has thus attracted much research attention in recent years. Solving such an ill-posed problem generally requires to explore intrinsic prior structures underlying tensor data, and formulate them as certain forms of regularization terms for guiding a sound estimate of the restored tensor. Re... | ['Deyu Meng', 'Jianjun Wang', 'Wenjin Qin', 'Jiangjun Peng', 'Hailin Wang'] | 2023-02-04 | null | null | null | null | ['image-inpainting', 'low-rank-matrix-completion'] | ['computer-vision', 'methodology'] | [ 6.64407015e-02 -2.68759072e-01 -2.64220625e-01 -2.74222177e-02
-7.24458158e-01 -4.13282543e-01 2.86053926e-01 -3.76711816e-01
-5.77186374e-03 3.48137558e-01 3.61466020e-01 -2.02414095e-01
-3.79072279e-01 -1.89115047e-01 -6.54668093e-01 -1.09615278e+00
-1.05848968e-01 1.32666975e-01 -4.58867922e-02 -3.37060124... | [7.435087203979492, 4.467124938964844] |
ad10de8b-c45d-4bdb-a6c3-bfdde9b7a2d8 | generative-adversarial-transformers | 2103.01209 | null | https://arxiv.org/abs/2103.01209v4 | https://arxiv.org/pdf/2103.01209v4.pdf | Generative Adversarial Transformers | We introduce the GANformer, a novel and efficient type of transformer, and explore it for the task of visual generative modeling. The network employs a bipartite structure that enables long-range interactions across the image, while maintaining computation of linear efficiency, that can readily scale to high-resolution... | ['C. Lawrence Zitnick', 'Drew A. Hudson'] | 2021-03-01 | null | null | null | null | ['scene-generation'] | ['computer-vision'] | [ 4.51021165e-01 2.54000753e-01 -1.80043429e-01 -1.19183011e-01
-4.82941180e-01 -7.36535907e-01 9.66498852e-01 -4.69047189e-01
2.11227566e-01 6.73223495e-01 5.50461948e-01 -1.33725643e-01
-2.51660079e-01 -8.23098958e-01 -7.80594230e-01 -1.00145185e+00
-1.87873557e-01 3.07022035e-01 2.75414195e-02 -2.93846130... | [11.537423133850098, -0.4020812213420868] |
6764697f-1a9e-4a2b-87c3-daf1fec36d1d | pointinet-point-cloud-frame-interpolation | 2012.10066 | null | https://arxiv.org/abs/2012.10066v1 | https://arxiv.org/pdf/2012.10066v1.pdf | PointINet: Point Cloud Frame Interpolation Network | LiDAR point cloud streams are usually sparse in time dimension, which is limited by hardware performance. Generally, the frame rates of mechanical LiDAR sensors are 10 to 20 Hz, which is much lower than other commonly used sensors like cameras. To overcome the temporal limitations of LiDAR sensors, a novel task named P... | ['Alois Knoll', 'Yinlong Liu', 'Zhijun Li', 'Sanqing Qu', 'Guang Chen', 'Fan Lu'] | 2020-12-18 | null | null | null | null | ['3d-point-cloud-interpolation'] | ['computer-vision'] | [ 3.05965263e-02 -7.21323609e-01 -1.20725356e-01 -2.59341091e-01
-6.45127356e-01 -3.09622288e-01 4.38065827e-01 3.75163704e-02
-4.12925512e-01 6.56874299e-01 -3.27481866e-01 -1.81577057e-01
1.80836573e-01 -9.69640195e-01 -7.47719526e-01 -4.84899759e-01
2.49832973e-01 2.59907186e-01 4.40080166e-01 1.71438396... | [8.554668426513672, -2.168421506881714] |
66190c33-335d-450d-a52d-00ef4584952d | multi-task-regularization-based-on-infrequent | 2007.04660 | null | https://arxiv.org/abs/2007.04660v1 | https://arxiv.org/pdf/2007.04660v1.pdf | Multi-task Regularization Based on Infrequent Classes for Audio Captioning | Audio captioning is a multi-modal task, focusing on using natural language for describing the contents of general audio. Most audio captioning methods are based on deep neural networks, employing an encoder-decoder scheme and a dataset with audio clips and corresponding natural language descriptions (i.e. captions). A ... | ['Emre Çakır', 'Tuomas Virtanen', 'Konstantinos Drossos'] | 2020-07-09 | null | null | null | null | ['audio-captioning'] | ['audio'] | [ 4.79470909e-01 1.16337657e-01 4.63884063e-02 -3.58904123e-01
-1.53403318e+00 -4.78619099e-01 2.38828257e-01 4.11212921e-01
-2.87613273e-01 7.04640448e-01 6.71333551e-01 2.27700099e-01
3.35484475e-01 -4.28904831e-01 -1.14361000e+00 -6.57827795e-01
-5.54482937e-02 5.90187132e-01 1.75044928e-02 -1.54943675... | [15.267409324645996, 4.930999279022217] |
e30c7bee-8502-4bac-b253-6d0c2dd6d2ac | literature-review-on-vulnerability-detection | 2104.11230 | null | https://arxiv.org/abs/2104.11230v1 | https://arxiv.org/pdf/2104.11230v1.pdf | Literature review on vulnerability detection using NLP technology | Vulnerability detection has always been the most important task in the field of software security. With the development of technology, in the face of massive source code, automated analysis and detection of vulnerabilities has become a current research hotspot. For special text files such as source code, using some of ... | ['Jiajie Wu'] | 2021-04-23 | null | null | null | null | ['vulnerability-detection'] | ['miscellaneous'] | [-1.62249506e-01 -4.10094969e-02 -3.33046883e-01 -9.09178630e-02
-3.87616992e-01 -8.05598974e-01 2.60478854e-01 7.59923279e-01
-9.76822600e-02 1.69835776e-01 1.01543963e-01 -7.23261833e-01
-1.45910189e-01 -6.98889196e-01 -4.64542992e-02 -1.04378529e-01
-1.68769509e-01 -1.94897324e-01 4.93801326e-01 -3.79922062... | [7.034426689147949, 7.768433570861816] |
c0ed9ee6-c5e0-45b6-998d-35c1905191b2 | shoring-design-provable-conditional-high | 2107.01326 | null | https://arxiv.org/abs/2107.01326v1 | https://arxiv.org/pdf/2107.01326v1.pdf | SHORING: Design Provable Conditional High-Order Interaction Network via Symbolic Testing | Deep learning provides a promising way to extract effective representations from raw data in an end-to-end fashion and has proven its effectiveness in various domains such as computer vision, natural language processing, etc. However, in domains such as content/product recommendation and risk management, where sequence... | ['Yuan Qi', 'Tao Xiong', 'Leilei Shi', 'Shuai Chen', 'Weiqiang Wang', 'xiaofu Chang', 'Kai Xiao', 'Jinyu Xu', 'Ruofan Wu', 'Xing Fu', 'Hui Li'] | 2021-07-03 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 3.71487379e-01 1.27341613e-01 -4.06648368e-01 -4.60105419e-01
-5.40725708e-01 -6.83330715e-01 5.76551616e-01 1.84213027e-01
-6.48193955e-01 6.50923371e-01 -1.01164781e-01 -7.59155631e-01
-2.83987463e-01 -9.26462114e-01 -1.21631241e+00 -5.52508533e-01
-3.15324336e-01 4.76906568e-01 8.59211385e-02 -5.25652289... | [10.540862083435059, 8.0144624710083] |
d101f409-c278-492a-a957-ee867b82d38e | neural-residual-radiance-fields-for | 2304.04452 | null | https://arxiv.org/abs/2304.04452v2 | https://arxiv.org/pdf/2304.04452v2.pdf | Neural Residual Radiance Fields for Streamably Free-Viewpoint Videos | The success of the Neural Radiance Fields (NeRFs) for modeling and free-view rendering static objects has inspired numerous attempts on dynamic scenes. Current techniques that utilize neural rendering for facilitating free-view videos (FVVs) are restricted to either offline rendering or are capable of processing only b... | ['Minye Wu', 'Lan Xu', 'Tinne Tuytelaars', 'Jingyi Yu', 'Ziyu Wang', 'Qihan He', 'Qiang Hu', 'Liao Wang'] | 2023-04-10 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Wang_Neural_Residual_Radiance_Fields_for_Streamably_Free-Viewpoint_Videos_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_Neural_Residual_Radiance_Fields_for_Streamably_Free-Viewpoint_Videos_CVPR_2023_paper.pdf | cvpr-2023-1 | ['neural-rendering'] | ['computer-vision'] | [ 2.70427912e-01 -6.13367379e-01 -1.26618430e-01 -4.08705235e-01
-4.78815019e-01 -3.72313738e-01 4.53440905e-01 -5.36226869e-01
-1.38023123e-01 2.48106197e-01 3.82936925e-01 -3.48119676e-01
-3.98261510e-02 -9.09718812e-01 -8.94241333e-01 -7.09562719e-01
-2.70508051e-01 -4.88055348e-01 2.47343704e-01 -3.66575658... | [10.485280990600586, -2.105069875717163] |
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