paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
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99ed8fcd-8fb3-47e5-b57e-167a3950ccdd | weakly-supervised-estimation-of-shadow | 1811.08164 | null | https://arxiv.org/abs/1811.08164v3 | https://arxiv.org/pdf/1811.08164v3.pdf | Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging | Detecting acoustic shadows in ultrasound images is important in many clinical and engineering applications. Real-time feedback of acoustic shadows can guide sonographers to a standardized diagnostic viewing plane with minimal artifacts and can provide additional information for other automatic image analysis algorithms... | ['Bernhard Kainz', 'Ozan Oktay', 'Nicolas Toussaint', 'Matthew Sinclair', 'Jo Schlemper', 'Alberto Gomez', 'Veronika Zimmer', 'Daniel Rueckert', 'Qingjie Meng', 'Martin Rajchl', 'Julia Schnabel', 'Jacqueline Matthew', 'Benjamin Hou', 'James Housden'] | 2018-11-20 | null | null | null | null | ['shadow-confidence-maps-in-ultrasound-imaging'] | ['medical'] | [ 6.95193350e-01 5.91000974e-01 3.01970303e-01 -8.02613258e-01
-1.15911603e+00 -5.03551245e-01 1.60133243e-01 4.97916788e-01
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-3.70084727e-03 7.78233290e-01 9.55140829e-01 3.28939736... | [14.353878021240234, -2.2300291061401367] |
cfac85fc-23f3-47b2-b8f4-139bd91059e3 | multi-label-image-classification-with-1 | 2107.11626 | null | https://arxiv.org/abs/2107.11626v1 | https://arxiv.org/pdf/2107.11626v1.pdf | Multi-Label Image Classification with Contrastive Learning | Recently, as an effective way of learning latent representations, contrastive learning has been increasingly popular and successful in various domains. The success of constrastive learning in single-label classifications motivates us to leverage this learning framework to enhance distinctiveness for better performance ... | ['Jianfei Cai', 'Dinh Phung', 'Ethan Zhao', 'Son D. Dao'] | 2021-07-24 | null | null | null | null | ['multi-label-image-classification'] | ['computer-vision'] | [ 6.54421687e-01 -3.36272180e-01 -6.12248898e-01 -4.89669442e-01
-1.06872880e+00 -5.86192012e-01 6.62459970e-01 3.19570869e-01
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2.83149719e-01 1.32657677e-01 -1.40681639e-01 6.20165318... | [9.643478393554688, 4.1897735595703125] |
73b66015-c60a-4a31-ace3-67d23ef3413c | hybrid-y-net-architecture-for-singing-voice | 2303.02599 | null | https://arxiv.org/abs/2303.02599v1 | https://arxiv.org/pdf/2303.02599v1.pdf | Hybrid Y-Net Architecture for Singing Voice Separation | This research paper presents a novel deep learning-based neural network architecture, named Y-Net, for achieving music source separation. The proposed architecture performs end-to-end hybrid source separation by extracting features from both spectrogram and waveform domains. Inspired by the U-Net architecture, Y-Net pr... | ['Pantaleon Perera', 'Janaka Wijayakulasooriya', 'Udula Ranasinghe', 'Pamudu Ranasinghe', 'Rashen Fernando'] | 2023-03-05 | null | null | null | null | ['music-source-separation'] | ['music'] | [ 1.40293986e-01 -3.77088487e-01 2.25713849e-02 5.03318645e-02
-1.13291872e+00 -5.12307942e-01 1.17503591e-02 -4.43633884e-01
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-1.52111396e-01 -4.01503623e-01 -5.64259529e-01 4.99588512... | [15.525400161743164, 5.5669755935668945] |
f9fc5753-7876-4ff7-b512-ad7797300ef8 | monte-carlo-siamese-policy-on-actor-for | 2004.03879 | null | https://arxiv.org/abs/2004.03879v1 | https://arxiv.org/pdf/2004.03879v1.pdf | Monte-Carlo Siamese Policy on Actor for Satellite Image Super Resolution | In the past few years supervised and adversarial learning have been widely adopted in various complex computer vision tasks. It seems natural to wonder whether another branch of artificial intelligence, commonly known as Reinforcement Learning (RL) can benefit such complex vision tasks. In this study, we explore the pl... | ['Saumyaa Shah', 'S Manthira Moorthi', 'Debajyoti Dhar', 'Litu Rout'] | 2020-04-08 | null | null | null | null | ['satellite-image-super-resolution'] | ['computer-vision'] | [ 6.81355596e-01 3.79519731e-01 -1.08542189e-01 -1.25293300e-01
-8.70912492e-01 -4.52218562e-01 9.07342911e-01 -4.15646076e-01
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-5.08006275e-01 -9.92789567e-01 -5.94977558e-01 -8.37308288e-01
-1.38165697e-01 1.04352303e-01 -1.27112061e-01 -4.42148596... | [10.22626781463623, -1.7780508995056152] |
60d8c679-e9c7-434c-b9fb-a8ba1d304904 | self-supervised-log-parsing | 2003.07905 | null | https://arxiv.org/abs/2003.07905v1 | https://arxiv.org/pdf/2003.07905v1.pdf | Self-Supervised Log Parsing | Logs are extensively used during the development and maintenance of software systems. They collect runtime events and allow tracking of code execution, which enables a variety of critical tasks such as troubleshooting and fault detection. However, large-scale software systems generate massive volumes of semi-structured... | ['Sasho Nedelkoski', 'Jorge Cardoso', 'Jasmin Bogatinovski', 'Odej Kao', 'Alexander Acker'] | 2020-03-17 | null | null | null | null | ['log-parsing'] | ['computer-code'] | [ 7.65588656e-02 -8.52448121e-02 -1.85101137e-01 -3.59473377e-01
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-3.97092462e-01 3.29266518e-01 5.24143457e-01 1.13723397... | [7.4693803787231445, 2.7137584686279297] |
b583a500-6bc7-42a6-ac05-7551315e769e | duck-rumour-detection-on-social-media-by-1 | null | null | https://aclanthology.org/2022.naacl-main.364 | https://aclanthology.org/2022.naacl-main.364.pdf | DUCK: Rumour Detection on Social Media by Modelling User and Comment Propagation Networks | Social media rumours, a form of misinformation, can mislead the public and cause significant economic and social disruption. Motivated by the observation that the user network — which captures \textit{who} engage with a story — and the comment network — which captures \textit{how} they react to it — provide complementa... | ['Jey Han Lau', 'Xiuzhen Zhang', 'Lin Tian'] | null | null | null | null | naacl-2022-7 | ['rumour-detection'] | ['natural-language-processing'] | [-3.06876779e-01 4.21661586e-01 -4.75719839e-01 6.54444769e-02
-2.07414657e-01 -5.75244486e-01 8.81906450e-01 5.05257845e-01
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-3.51831138e-01 3.78934294e-01 1.57361820e-01 -5.16626954... | [8.18432331085205, 10.142637252807617] |
4538a3e4-dfd3-421c-9f79-a9154dd798b1 | dense-relational-captioning-triple-stream | 1903.05942 | null | https://arxiv.org/abs/1903.05942v4 | https://arxiv.org/pdf/1903.05942v4.pdf | Dense Relational Captioning: Triple-Stream Networks for Relationship-Based Captioning | Our goal in this work is to train an image captioning model that generates more dense and informative captions. We introduce "relational captioning," a novel image captioning task which aims to generate multiple captions with respect to relational information between objects in an image. Relational captioning is a fram... | ['Tae-Hyun Oh', 'Jinsoo Choi', 'Dong-Jin Kim', 'In So Kweon'] | 2019-03-14 | dense-relational-captioning-triple-stream-1 | http://openaccess.thecvf.com/content_CVPR_2019/html/Kim_Dense_Relational_Captioning_Triple-Stream_Networks_for_Relationship-Based_Captioning_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Kim_Dense_Relational_Captioning_Triple-Stream_Networks_for_Relationship-Based_Captioning_CVPR_2019_paper.pdf | cvpr-2019-6 | ['relational-captioning', 'relational-captioning'] | ['computer-vision', 'natural-language-processing'] | [ 8.29858243e-01 5.36795020e-01 -2.90478915e-01 -6.24933779e-01
-1.15265584e+00 -3.13103646e-01 9.36920881e-01 -8.46873820e-02
-8.45136940e-02 6.76443696e-01 8.35020363e-01 -2.15781897e-01
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4.37354803e-01 5.56446731e-01 1.23775594e-01 -2.12376803... | [10.8507080078125, 0.9968134164810181] |
fe038ce2-24de-45ae-bb11-b57465f9f228 | imagen-video-high-definition-video-generation | 2210.02303 | null | https://arxiv.org/abs/2210.02303v1 | https://arxiv.org/pdf/2210.02303v1.pdf | Imagen Video: High Definition Video Generation with Diffusion Models | We present Imagen Video, a text-conditional video generation system based on a cascade of video diffusion models. Given a text prompt, Imagen Video generates high definition videos using a base video generation model and a sequence of interleaved spatial and temporal video super-resolution models. We describe how we sc... | ['Tim Salimans', 'David J. Fleet', 'Mohammad Norouzi', 'Ben Poole', 'Diederik P. Kingma', 'Alexey Gritsenko', 'Ruiqi Gao', 'Jay Whang', 'Chitwan Saharia', 'William Chan', 'Jonathan Ho'] | 2022-10-05 | null | null | null | null | ['video-super-resolution', 'video-generation'] | ['computer-vision', 'computer-vision'] | [ 3.62321913e-01 -1.82374746e-01 -7.71213248e-02 -3.45140658e-02
-7.12680936e-01 -7.32154429e-01 1.09586585e+00 -6.88441813e-01
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1.01036951e-01 3.18716288e-01 2.36018017e-01 -2.01169908... | [10.916481971740723, -0.6400829553604126] |
e7d410a5-16cf-48b2-b98e-6c571e27baf9 | a-theory-of-human-like-few-shot-learning | 2301.01047 | null | https://arxiv.org/abs/2301.01047v1 | https://arxiv.org/pdf/2301.01047v1.pdf | A Theory of Human-Like Few-Shot Learning | We aim to bridge the gap between our common-sense few-sample human learning and large-data machine learning. We derive a theory of human-like few-shot learning from von-Neuman-Landauer's principle. modelling human learning is difficult as how people learn varies from one to another. Under commonly accepted definitions,... | ['Ming Li', 'Dongbo Bu', 'Rui Wang', 'Zhiying Jiang'] | 2023-01-03 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [-4.13765386e-03 2.47580320e-01 2.68909894e-02 -2.96923429e-01
-4.64732170e-01 -8.05878788e-02 1.02208912e+00 -1.06672712e-01
-6.39573455e-01 6.83368564e-01 3.23284686e-01 -5.80904931e-02
9.12037771e-03 -1.23276663e+00 -7.75205612e-01 -5.44529855e-01
2.24983364e-01 8.15858960e-01 2.44017392e-01 -3.63734514... | [5.982165813446045, 4.667880058288574] |
5a2800dd-ee09-4960-8c38-0663a4946ddd | examining-the-presence-of-gender-bias-in | 1902.00496 | null | http://arxiv.org/abs/1902.00496v1 | http://arxiv.org/pdf/1902.00496v1.pdf | Examining the Presence of Gender Bias in Customer Reviews Using Word Embedding | Humans have entered the age of algorithms. Each minute, algorithms shape
countless preferences from suggesting a product to a potential life partner. In
the marketplace algorithms are trained to learn consumer preferences from
customer reviews because user-generated reviews are considered the voice of
customers and a v... | ['S. Rathee', 'H. Mishra', 'A. Mishra'] | 2019-02-01 | null | null | null | null | ['product-recommendation'] | ['miscellaneous'] | [ 7.63724372e-02 5.70355296e-01 -1.02163279e+00 -9.19676661e-01
1.01016209e-01 -5.19968212e-01 8.14803302e-01 4.82723296e-01
-6.81002021e-01 6.88218415e-01 6.71756387e-01 -7.48699486e-01
5.00948429e-02 -8.86508584e-01 -6.57454252e-01 -3.50804150e-01
3.55975181e-01 7.11936593e-01 -5.81741154e-01 -4.95317310... | [9.31798267364502, 10.135830879211426] |
47caf140-c3d8-448b-b2b4-72c8c429caf5 | lemma-bootstrapping-high-level-mathematical | 2211.08671 | null | https://arxiv.org/abs/2211.08671v1 | https://arxiv.org/pdf/2211.08671v1.pdf | LEMMA: Bootstrapping High-Level Mathematical Reasoning with Learned Symbolic Abstractions | Humans tame the complexity of mathematical reasoning by developing hierarchies of abstractions. With proper abstractions, solutions to hard problems can be expressed concisely, thus making them more likely to be found. In this paper, we propose Learning Mathematical Abstractions (LEMMA): an algorithm that implements th... | ['Armando Solar-Lezama', 'Noah Goodman', 'Omar Costilla-Reyes', 'Gabriel Poesia', 'Zhening Li'] | 2022-11-16 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 6.37065545e-02 6.08924031e-01 1.25868738e-01 -8.43285471e-02
-3.70979220e-01 -7.06127346e-01 3.77381235e-01 4.96514350e-01
-4.35317516e-01 1.30689180e+00 -5.71033806e-02 -4.78859276e-01
-2.56848335e-01 -1.19709122e+00 -7.38260806e-01 -3.50932419e-01
-3.35372597e-01 8.20005834e-01 1.74119800e-01 -5.63802063... | [9.068699836730957, 7.113010883331299] |
34818fba-c19f-4755-8dc2-216799359035 | message-based-web-service-composition | 1401.3470 | null | http://arxiv.org/abs/1401.3470v1 | http://arxiv.org/pdf/1401.3470v1.pdf | Message-Based Web Service Composition, Integrity Constraints, and Planning under Uncertainty: A New Connection | Thanks to recent advances, AI Planning has become the underlying technique
for several applications. Figuring prominently among these is automated Web
Service Composition (WSC) at the "capability" level, where services are
described in terms of preconditions and effects over ontological concepts. A
key issue in address... | ['Piergiorgio Bertoli', 'Jörg Hoffmann', 'Marco Pistore', 'Malte Helmert'] | 2014-01-15 | null | null | null | null | ['service-composition'] | ['miscellaneous'] | [ 2.27484450e-01 8.45668197e-01 -7.67410174e-03 -3.40358615e-01
-4.89200324e-01 -8.27333868e-01 1.14969957e+00 3.49554986e-01
-2.09477156e-01 6.01089656e-01 4.45193797e-01 -3.28313738e-01
-4.94572908e-01 -1.10193729e+00 -6.74481094e-01 -5.49177468e-01
-4.65868294e-01 7.14881897e-01 8.79058540e-01 -8.16352606... | [8.647599220275879, 6.834794044494629] |
4059848f-988c-47d2-b5ac-d12c19265ae3 | cogalex-vi-shared-task-transrelation-a-robust | null | null | https://aclanthology.org/2020.cogalex-1.7 | https://aclanthology.org/2020.cogalex-1.7.pdf | CogALex-VI Shared Task: Transrelation - A Robust Multilingual Language Model for Multilingual Relation Identification | We describe our submission to the CogALex-VI shared task on the identification of multilingual paradigmatic relations building on XLM-RoBERTa (XLM-R), a robustly optimized and multilingual BERT model. In spite of several experiments with data augmentation, data addition and ensemble methods with a Siamese Triple Net, T... | ['Dagmar Gromann', 'Barbara Heinisch', 'Christian Lang', 'Lennart Wachowiak'] | 2020-12-12 | null | null | null | null | ['multilingual-text-classification', 'hypernym-discovery'] | ['miscellaneous', 'natural-language-processing'] | [-4.43313122e-01 3.45290542e-01 -3.98572683e-01 -4.39669251e-01
-7.84414649e-01 -5.17642021e-01 1.06903410e+00 1.36677459e-01
-5.81280053e-01 1.05361676e+00 3.31992149e-01 -7.00046718e-01
-7.41616607e-01 -1.83683604e-01 -6.56576514e-01 -4.01632264e-02
-4.42924976e-01 1.42627573e+00 -6.31056651e-02 -8.44534636... | [9.975334167480469, 9.23398208618164] |
dd2cafd0-6e2d-4f4f-9b5b-6408ffa49e2b | mgrr-net-multi-level-graph-relational | 2204.01349 | null | https://arxiv.org/abs/2204.01349v3 | https://arxiv.org/pdf/2204.01349v3.pdf | MGRR-Net: Multi-level Graph Relational Reasoning Network for Facial Action Units Detection | The Facial Action Coding System (FACS) encodes the action units (AUs) in facial images, which has attracted extensive research attention due to its wide use in facial expression analysis. Many methods that perform well on automatic facial action unit (AU) detection primarily focus on modeling various types of AU relati... | ['Hu Han', 'Xiao Liu', 'Songpei Xu', 'Joemon M. Jose', 'Xuri Ge'] | 2022-04-04 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 1.86111376e-01 -1.34865552e-01 -3.87254149e-01 -4.82155502e-01
-5.40818155e-01 8.06331038e-02 3.63662302e-01 -2.35367984e-01
-1.44948196e-02 2.25033000e-01 3.02611083e-01 4.21833396e-01
-7.57311657e-02 -8.05430830e-01 -4.46436495e-01 -1.06052053e+00
-2.93668747e-01 -2.43616506e-01 -7.65244290e-02 -3.92735422... | [13.645766258239746, 1.6398398876190186] |
e7c23473-00f5-4fce-aa8a-c6420dcd1bc4 | globally-gated-deep-linear-networks | 2210.17449 | null | https://arxiv.org/abs/2210.17449v2 | https://arxiv.org/pdf/2210.17449v2.pdf | Globally Gated Deep Linear Networks | Recently proposed Gated Linear Networks present a tractable nonlinear network architecture, and exhibit interesting capabilities such as learning with local error signals and reduced forgetting in sequential learning. In this work, we introduce a novel gating architecture, named Globally Gated Deep Linear Networks (GGD... | ['Haim Sompolinsky', 'Qianyi Li'] | 2022-10-31 | null | null | null | null | ['l2-regularization'] | ['methodology'] | [ 6.27025217e-02 9.23568159e-02 -1.58743426e-01 -2.81032354e-01
6.46097288e-02 -4.67130512e-01 6.01151586e-01 5.08804582e-02
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-5.70600510e-01 4.56799686e-01 6.27774537e-01 -5.38016915... | [7.989327907562256, 3.4949376583099365] |
dc25f760-49cd-4d43-904b-e9427b7de091 | televit-teleconnection-driven-transformers | 2306.10940 | null | https://arxiv.org/abs/2306.10940v1 | https://arxiv.org/pdf/2306.10940v1.pdf | TeleViT: Teleconnection-driven Transformers Improve Subseasonal to Seasonal Wildfire Forecasting | Wildfires are increasingly exacerbated as a result of climate change, necessitating advanced proactive measures for effective mitigation. It is important to forecast wildfires weeks and months in advance to plan forest fuel management, resource procurement and allocation. To achieve such accurate long-term forecasts at... | ['Ioannis Papoutsis', 'Gustau Camps-Valls', 'Dimitrios Michail', 'Spyros Kondylatos', 'Nikolaos Ioannis Bountos', 'Ioannis Prapas'] | 2023-06-19 | null | null | null | null | ['management'] | ['miscellaneous'] | [-2.74922311e-01 -6.91807866e-01 3.01752985e-02 -8.62145126e-02
-8.09678510e-02 -7.57828653e-01 8.90743434e-01 2.22035855e-01
-2.69791722e-01 8.98029268e-01 3.46647382e-01 -8.37301075e-01
-3.42985690e-01 -1.24466693e+00 -3.91165465e-01 -6.43389225e-01
-7.86624372e-01 4.28748310e-01 4.05909419e-02 -7.07759738... | [9.497491836547852, -1.5511375665664673] |
9efd0593-8b92-47f1-bcb6-d49c64a365f6 | gpt-re-in-context-learning-for-relation | 2305.02105 | null | https://arxiv.org/abs/2305.02105v1 | https://arxiv.org/pdf/2305.02105v1.pdf | GPT-RE: In-context Learning for Relation Extraction using Large Language Models | In spite of the potential for ground-breaking achievements offered by large language models (LLMs) (e.g., GPT-3), they still lag significantly behind fully-supervised baselines (e.g., fine-tuned BERT) in relation extraction (RE). This is due to the two major shortcomings of LLMs in RE: (1) low relevance regarding entit... | ['Sadao Kurohashi', 'Jiwei Li', 'Haiyue Song', 'Qianying Liu', 'Zhuoyuan Mao', 'Fei Cheng', 'Zhen Wan'] | 2023-05-03 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [ 1.03804981e-02 3.59187603e-01 -8.60366225e-01 -2.01456010e-01
-1.10980690e+00 -5.86814284e-01 9.72740650e-01 1.65407866e-01
-5.36234558e-01 9.96301949e-01 3.75929445e-01 -4.30488557e-01
-3.22089583e-01 -7.40653157e-01 -9.16257322e-01 -5.94448596e-02
2.73432918e-02 6.91915751e-01 1.89726666e-01 -2.97168344... | [9.598231315612793, 8.59192943572998] |
79f5f1c1-43cc-494c-a9b3-4a65f3612a90 | efficient-neural-network-based-classification | 2305.07639 | null | https://arxiv.org/abs/2305.07639v1 | https://arxiv.org/pdf/2305.07639v1.pdf | Efficient Neural Network based Classification and Outlier Detection for Image Moderation using Compressed Sensing and Group Testing | Popular social media platforms employ neural network based image moderation engines to classify images uploaded on them as having potentially objectionable content. Such moderation engines must answer a large number of queries with heavy computational cost, even though the actual number of images with objectionable con... | ['Ajit Rajwade', 'Sanyam Saxena', 'Sabyasachi Ghosh'] | 2023-05-12 | null | null | null | null | ['outlier-detection'] | ['methodology'] | [ 5.01362324e-01 1.95508882e-01 -7.42042214e-02 -1.95822313e-01
-9.15963531e-01 -3.76179606e-01 1.07222989e-01 1.24715209e-01
-5.98636150e-01 3.28651965e-01 -2.93255687e-01 -2.99289227e-01
-2.02926956e-02 -9.74955976e-01 -1.29994595e+00 -6.79665565e-01
-5.48024833e-01 2.56162852e-01 3.51660401e-02 9.87562835... | [11.49918270111084, 0.8434935808181763] |
850722ec-18b4-4421-bc8f-dc19fe1e57d3 | local-low-rank-approximation-with-superpixel | null | null | https://ieeexplore.ieee.org/document/9861684 | https://ieeexplore.ieee.org/document/9861684 | Local Low-Rank Approximation With Superpixel-Guided Locality Preserving Graph for Hyperspectral Image Classification | Given the detrimental effect of spectral variations in a hyperspectral image (HSI), this article investigates to recover its discriminative representation to improve the classification performance. We propose a new method, namely local low-rank approximation with superpixel-guided locality preserving graph (LLRA-SLPG),... | ['and Weijia Zhang', 'Yuheng Jia', 'Yu Zhang', 'Shujun Yang'] | 2022-08-18 | null | null | null | journal-2022-8 | ['superpixels'] | ['computer-vision'] | [ 4.62737203e-01 -9.59105268e-02 -1.23621598e-01 6.65722200e-06
-6.32414639e-01 -3.21860224e-01 1.83883533e-01 -1.39035851e-01
8.28730837e-02 6.48934186e-01 1.01168536e-01 1.55966818e-01
-3.68004292e-01 -8.18887889e-01 -5.13033152e-01 -1.33417511e+00
1.34187117e-01 -3.11418205e-01 3.04234087e-01 2.53646821... | [10.142478942871094, -1.8961827754974365] |
0d51b08a-4528-410f-af47-6ad3885584af | leaf-only-sam-a-segment-anything-pipeline-for | 2305.09418 | null | https://arxiv.org/abs/2305.09418v2 | https://arxiv.org/pdf/2305.09418v2.pdf | Leaf Only SAM: A Segment Anything Pipeline for Zero-Shot Automated Leaf Segmentation | Segment Anything Model (SAM) is a new foundation model that can be used as a zero-shot object segmentation method with the use of either guide prompts such as bounding boxes, polygons, or points. Alternatively, additional post processing steps can be used to identify objects of interest after segmenting everything in a... | ['Avril Britten', 'Fraser MacFarlane', 'Dominic Williams'] | 2023-05-16 | null | null | null | null | ['plant-phenotyping'] | ['computer-vision'] | [ 3.18999797e-01 3.52218717e-01 -1.81875840e-01 -3.92673314e-01
-5.83751142e-01 -9.90671873e-01 2.36750215e-01 5.41795671e-01
-1.69553742e-01 2.79459506e-01 -6.43661499e-01 -6.53517365e-01
-1.61964938e-01 -1.03990769e+00 -5.95533967e-01 -5.01761854e-01
3.00114304e-01 6.73367739e-01 8.15439880e-01 -7.59903267... | [9.112325668334961, -1.5309500694274902] |
ff20f41d-ed57-4101-b34e-8b07b6e859a2 | unsupervised-long-term-person-re | 2202.03087 | null | https://arxiv.org/abs/2202.03087v2 | https://arxiv.org/pdf/2202.03087v2.pdf | Unsupervised Long-Term Person Re-Identification with Clothes Change | We investigate unsupervised person re-identification (Re-ID) with clothes change, a new challenging problem with more practical usability and scalability to real-world deployment. Most existing re-id methods artificially assume the clothes of every single person to be stationary across space and time. This condition is... | ['Jun Guo', 'Xiatian Zhu', 'Peng Xu', 'Mingkun Li'] | 2022-02-07 | null | null | null | null | ['unsupervised-long-term-person-re', 'unsupervised-person-re-identification'] | ['computer-vision', 'computer-vision'] | [ 4.02373858e-02 -3.40263575e-01 2.77910918e-01 -4.71006393e-01
6.39062598e-02 -8.56271803e-01 6.29165053e-01 7.09181204e-02
-5.35878897e-01 4.25832182e-01 -3.38180810e-02 4.13259357e-01
-1.71980157e-01 -5.31389654e-01 -4.70351160e-01 -6.85252547e-01
1.45189658e-01 8.05408120e-01 2.68002562e-02 -9.79948565... | [14.707045555114746, 1.0508508682250977] |
a9dfa86c-eff0-4e08-a101-b8ee30ed0bd3 | a-review-for-tone-mapping-operators-on-wide | 2101.03003 | null | https://arxiv.org/abs/2101.03003v1 | https://arxiv.org/pdf/2101.03003v1.pdf | A review for Tone-mapping Operators on Wide Dynamic Range Image | The dynamic range of our normal life can exceeds 120 dB, however, the smart-phone cameras and the conventional digital cameras can only capture a dynamic range of 90 dB, which sometimes leads to loss of details for the recorded image. Now, some professional hardware applications and image fusion algorithms have been de... | ['Ziyi Liu'] | 2021-01-08 | null | null | null | null | ['tone-mapping'] | ['computer-vision'] | [ 4.64922667e-01 -5.99491656e-01 -3.31522077e-02 -1.74090415e-01
-1.87743083e-01 -2.18046397e-01 8.47717747e-02 -6.28515661e-01
-2.07204610e-01 4.89564329e-01 -1.28308877e-01 -4.78869110e-01
1.26968279e-01 -8.45471621e-01 -1.55818075e-01 -7.81658649e-01
3.79836142e-01 -4.18772310e-01 4.77755547e-01 -3.87515336... | [10.858039855957031, -2.440796136856079] |
b7b292e8-f46a-40f9-9d05-045d138a214b | mapping-quantum-circuits-to-ibm-qx | 1907.02026 | null | https://arxiv.org/abs/1907.02026v1 | https://arxiv.org/pdf/1907.02026v1.pdf | Mapping Quantum Circuits to IBM QX Architectures Using the Minimal Number of SWAP and H Operations | The recent progress in the physical realization of quantum computers (the first publicly available ones--IBM's QX architectures--have been launched in 2017) has motivated research on automatic methods that aid users in running quantum circuits on them. Here, certain physical constraints given by the architectures which... | ['Alwin Zulehner', 'Lukas Burgholzer', 'Robert Wille'] | 2019-07-03 | null | null | null | null | ['quantum-circuit-mapping'] | ['methodology'] | [ 2.39628389e-01 2.58148819e-01 8.56880099e-02 -3.30266178e-01
-6.46285713e-01 -7.85453379e-01 2.95283586e-01 1.00131594e-01
-2.19035074e-01 9.95427489e-01 -4.18642730e-01 -9.22024667e-01
-2.67521888e-01 -9.83462214e-01 -8.15124512e-01 -6.24490917e-01
-3.29507678e-03 6.71322763e-01 8.00644010e-02 -6.09427392... | [5.640480041503906, 4.907327175140381] |
4fa3cee0-73db-4369-a139-c52e1d0a4949 | fine-grained-adversarial-semi-supervised | 2110.05848 | null | https://arxiv.org/abs/2110.05848v1 | https://arxiv.org/pdf/2110.05848v1.pdf | Fine-Grained Adversarial Semi-supervised Learning | In this paper we exploit Semi-Supervised Learning (SSL) to increase the amount of training data to improve the performance of Fine-Grained Visual Categorization (FGVC). This problem has not been investigated in the past in spite of prohibitive annotation costs that FGVC requires. Our approach leverages unlabeled data w... | ['Alberto del Bimbo', 'Francesco Turchini', 'Federico Pernici', 'Daniele Mugnai'] | 2021-10-12 | null | null | null | null | ['fine-grained-visual-categorization'] | ['computer-vision'] | [ 1.25166580e-01 1.78078830e-01 -8.60646218e-02 -5.80983639e-01
-5.75156868e-01 -1.01445293e+00 8.98845911e-01 -8.46537650e-02
-5.49864888e-01 9.82464731e-01 -1.05074167e-01 2.60757543e-02
-1.61854140e-02 -7.19956875e-01 -8.63200068e-01 -6.59607232e-01
7.37663656e-02 2.64873683e-01 4.04752672e-01 3.39372287... | [9.618657112121582, 2.0863378047943115] |
3a1eb399-4ca5-487e-a674-afc66af0e18a | semi-supervised-semantic-segmentation-with-9 | 2304.11539 | null | https://arxiv.org/abs/2304.11539v1 | https://arxiv.org/pdf/2304.11539v1.pdf | Semi-Supervised Semantic Segmentation With Region Relevance | Semi-supervised semantic segmentation aims to learn from a small amount of labeled data and plenty of unlabeled ones for the segmentation task. The most common approach is to generate pseudo-labels for unlabeled images to augment the training data. However, the noisy pseudo-labels will lead to cumulative classification... | ['Yazhou Yao', 'Qiong Wang', 'Tao Chen', 'Rui Chen'] | 2023-04-23 | null | null | null | null | ['semi-supervised-semantic-segmentation', 'pseudo-label'] | ['computer-vision', 'miscellaneous'] | [ 3.84021550e-01 2.66849458e-01 -3.74505758e-01 -9.05006230e-01
-1.04643512e+00 -6.05302095e-01 3.09643596e-01 -3.28442529e-02
-5.43079674e-01 8.26357961e-01 -3.01476657e-01 -8.15855414e-02
2.35598207e-01 -4.33460027e-01 -7.38147855e-01 -7.66414165e-01
4.96601760e-01 2.57102311e-01 4.19191211e-01 2.29019061... | [9.55556869506836, 1.1112116575241089] |
bb31ff74-7e3d-4f55-a42e-d6b2659ba843 | brain-morphometry-estimation-from-hours-to | null | null | https://doi.org/10.3389/fneur.2020.00244 | https://www.frontiersin.org/articles/10.3389/fneur.2020.00244/pdf | Brain Morphometry Estimation: From Hours to Seconds Using Deep Learning | Motivation: Brain morphometry from magnetic resonance imaging (MRI) is a promising neuroimaging biomarker for the non-invasive diagnosis and monitoring of neurodegenerative and neurological disorders. Current tools for brain morphometry often come with a high computational burden, making them hard to use in clinical ro... | ['Christian Rummel', 'Mauricio Reyes', 'Roland Wiest', 'Yannick Suter', 'Michael Rebsamen'] | 2020-04-08 | null | null | null | null | ['brain-morphometry'] | ['medical'] | [-1.63162693e-01 1.51275799e-01 9.07504484e-02 -6.43821120e-01
-7.71241069e-01 -8.58465880e-02 1.60662025e-01 1.50960237e-01
-7.73051083e-01 9.37083066e-01 2.77676344e-01 -1.97481155e-01
-5.28251082e-02 -7.11783409e-01 -5.94745576e-01 -6.31829023e-01
-8.45983624e-01 9.23097908e-01 -4.05930588e-03 1.35629028... | [14.10041332244873, -2.1630358695983887] |
c6f7c219-85eb-49eb-94ef-93a9f067ca4d | reflection-separation-via-multi-bounce | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2055_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580766.pdf | Reflection Separation via Multi-bounce Polarization State Tracing | Reflection removal from photographs is an important task in computational photography, but also for computer vision tasks that involve imaging through windows and similar settings. Traditionally, the problem is approached as a single reflection removal problem under very controlled scenarios. In this paper we aim to ge... | ['Simeng Qiu', 'Rui Li', 'Wolfgang Heidrich', 'Guangming Zang'] | null | null | null | null | eccv-2020-8 | ['reflection-removal'] | ['computer-vision'] | [ 1.16804421e+00 5.13389856e-02 4.35320318e-01 -2.89729089e-01
-6.99104309e-01 -3.24503541e-01 4.95390505e-01 -5.17236412e-01
-2.15662658e-01 5.55742502e-01 -1.22773640e-01 -3.54588926e-01
-1.88344046e-01 -6.59108102e-01 -8.25269163e-01 -1.30550110e+00
5.43328226e-01 3.46719146e-01 1.86892543e-02 -1.64499998... | [10.060291290283203, -2.835787773132324] |
18d48913-7b54-4934-9d52-bb0db77a9e35 | improved-diffusion-based-image-colorization | 2304.11105 | null | https://arxiv.org/abs/2304.11105v1 | https://arxiv.org/pdf/2304.11105v1.pdf | Improved Diffusion-based Image Colorization via Piggybacked Models | Image colorization has been attracting the research interests of the community for decades. However, existing methods still struggle to provide satisfactory colorized results given grayscale images due to a lack of human-like global understanding of colors. Recently, large-scale Text-to-Image (T2I) models have been exp... | ['Tien-Tsin Wong', 'Chengze Li', 'Minshan Xie', 'Jinbo Xing', 'Hanyuan Liu'] | 2023-04-21 | null | null | null | null | ['colorization'] | ['computer-vision'] | [ 3.63837987e-01 -2.21130297e-01 6.76372573e-02 -4.15012807e-01
-4.75356460e-01 -6.37562156e-01 4.86807555e-01 -3.00177872e-01
-1.61409661e-01 2.14896724e-01 6.43686578e-02 -1.89563856e-01
3.74937356e-01 -8.13662469e-01 -6.55388951e-01 -6.72152817e-01
5.94388068e-01 1.16577022e-01 3.41450483e-01 -2.96456516... | [11.394251823425293, -1.0477451086044312] |
6c027f9c-70c7-476b-95e3-c234c0d82def | hyper-parameter-sweep-on-alphazero-general | 1903.08129 | null | http://arxiv.org/abs/1903.08129v1 | http://arxiv.org/pdf/1903.08129v1.pdf | Hyper-Parameter Sweep on AlphaZero General | Since AlphaGo and AlphaGo Zero have achieved breakground successes in the
game of Go, the programs have been generalized to solve other tasks.
Subsequently, AlphaZero was developed to play Go, Chess and Shogi. In the
literature, the algorithms are explained well. However, AlphaZero contains many
parameters, and for nei... | ['Hui Wang', 'Aske Plaat', 'Mike Preuss', 'Michael Emmerich'] | 2019-03-19 | null | null | null | null | ['game-of-go'] | ['playing-games'] | [-2.19618633e-01 -2.02027261e-01 -1.31589115e-01 -1.77627087e-01
-5.92676342e-01 -5.87545812e-01 2.41630594e-03 -6.66057542e-02
-7.70701051e-01 8.51797760e-01 -4.62293446e-01 -5.04105389e-01
-6.50663793e-01 -8.23000789e-01 -5.13340890e-01 -7.37312853e-01
-4.41523314e-01 4.91978556e-01 4.62706774e-01 -6.75094903... | [3.4925832748413086, 1.4561840295791626] |
d098753c-1775-47d5-ae7a-d1bd20bb2eba | robust-gyroscope-aided-camera-self | 1805.12506 | null | http://arxiv.org/abs/1805.12506v1 | http://arxiv.org/pdf/1805.12506v1.pdf | Robust Gyroscope-Aided Camera Self-Calibration | Camera calibration for estimating the intrinsic parameters and lens
distortion is a prerequisite for various monocular vision applications
including feature tracking and video stabilization. This application paper
proposes a model for estimating the parameters on the fly by fusing gyroscope
and camera data, both readil... | ['Santiago Cortés Reina', 'Juho Kannala', 'Arno Solin'] | 2018-05-31 | null | null | null | null | ['video-stabilization'] | ['computer-vision'] | [ 2.83090919e-02 -2.96630055e-01 -1.53416008e-01 -1.41228884e-01
4.42440845e-02 -8.98851573e-01 6.47939146e-01 -7.12516665e-01
-3.95969868e-01 5.33668399e-01 -3.72505695e-01 -1.16213925e-01
2.22531825e-01 2.75213625e-02 -9.30814207e-01 -4.35419261e-01
3.18003625e-01 -9.09468308e-02 1.38767898e-01 4.01998878... | [7.929126262664795, -2.1915507316589355] |
f2b9c1fb-4f67-48b5-837d-1adb32ddc82f | f3net-fusion-feedback-and-focus-for-salient | 1911.11445 | null | https://arxiv.org/abs/1911.11445v1 | https://arxiv.org/pdf/1911.11445v1.pdf | F3Net: Fusion, Feedback and Focus for Salient Object Detection | Most of existing salient object detection models have achieved great progress by aggregating multi-level features extracted from convolutional neural networks. However, because of the different receptive fields of different convolutional layers, there exists big differences between features generated by these layers. C... | ['Jun Wei', 'Shuhui Wang', 'Qingming Huang'] | 2019-11-26 | null | null | null | null | ['dichotomous-image-segmentation'] | ['computer-vision'] | [ 2.75677294e-01 9.55072418e-02 -1.61835536e-01 -4.56864327e-01
-3.97306323e-01 3.57834734e-02 3.84485930e-01 2.10534304e-01
-4.04244810e-01 6.29829764e-01 4.05978829e-01 1.48885831e-01
8.89273584e-02 -8.80847514e-01 -7.21607268e-01 -7.80869007e-01
9.67095345e-02 -5.14409184e-01 8.32509875e-01 -3.63701195... | [9.687987327575684, -0.46443894505500793] |
da54b901-10ca-4fca-8b44-4fac210cf39b | knowledge-aware-audio-grounded-generative | 2307.01764 | null | https://arxiv.org/abs/2307.01764v1 | https://arxiv.org/pdf/2307.01764v1.pdf | Knowledge-Aware Audio-Grounded Generative Slot Filling for Limited Annotated Data | Manually annotating fine-grained slot-value labels for task-oriented dialogue (ToD) systems is an expensive and time-consuming endeavour. This motivates research into slot-filling methods that operate with limited amounts of labelled data. Moreover, the majority of current work on ToD is based solely on text as the inp... | ['Philip C. Woodland', 'Paweł Budzianowski', 'Ivan Vulić', 'Chao Zhang', 'Guangzhi Sun'] | 2023-07-04 | null | null | null | null | ['zero-shot-learning', 'text-generation', 'zero-shot-slot-filling', 'slot-filling', 'speech-recognition', 'automatic-speech-recognition'] | ['methodology', 'natural-language-processing', 'natural-language-processing', 'natural-language-processing', 'speech', 'speech'] | [ 4.15320128e-01 5.82299113e-01 -1.21831350e-01 -2.57705957e-01
-1.48681068e+00 -4.07437146e-01 6.74404860e-01 -3.84837613e-02
-3.00110281e-01 9.02478814e-01 5.90742230e-01 -5.65316021e-01
4.79173847e-02 -6.23008847e-01 -3.26993972e-01 -4.85969305e-01
3.41374874e-01 1.08116651e+00 2.87227571e-01 -6.38893187... | [13.095648765563965, 7.5842204093933105] |
6b0780f0-3046-464c-85d7-65a63a451a9c | posediffusion-solving-pose-estimation-via | 2306.15667 | null | https://arxiv.org/abs/2306.15667v2 | https://arxiv.org/pdf/2306.15667v2.pdf | PoseDiffusion: Solving Pose Estimation via Diffusion-aided Bundle Adjustment | Camera pose estimation is a long-standing computer vision problem that to date often relies on classical methods, such as handcrafted keypoint matching, RANSAC and bundle adjustment. In this paper, we propose to formulate the Structure from Motion (SfM) problem inside a probabilistic diffusion framework, modelling the ... | ['David Novotny', 'Christian Rupprecht', 'Jianyuan Wang'] | 2023-06-27 | null | null | null | null | ['pose-estimation'] | ['computer-vision'] | [-5.17994873e-02 -2.14710116e-01 3.72573249e-02 -3.58096868e-01
-8.42568040e-01 -1.00858819e+00 9.11737084e-01 -3.74227166e-01
-4.82657939e-01 2.39562064e-01 2.46153578e-01 -1.68172881e-01
-1.55735105e-01 -3.15739930e-01 -9.44805622e-01 -5.35692692e-01
3.10329676e-01 7.07595885e-01 2.26964846e-01 -8.46080855... | [8.118128776550293, -2.402538776397705] |
65e6fd8d-5014-4a77-b659-8963c0de8b0b | gpinn-physics-informed-neural-network-with | 2306.09792 | null | https://arxiv.org/abs/2306.09792v1 | https://arxiv.org/pdf/2306.09792v1.pdf | GPINN: Physics-informed Neural Network with Graph Embedding | This work proposes a Physics-informed Neural Network framework with Graph Embedding (GPINN) to perform PINN in graph, i.e. topological space instead of traditional Euclidean space, for improved problem-solving efficiency. The method integrates topological data into the neural network's computations, which significantly... | ['Haolin Li', 'Yuyang Miao'] | 2023-06-16 | null | null | null | null | ['graph-embedding'] | ['graphs'] | [ 4.46442355e-05 2.43525848e-01 1.30235747e-01 -1.84457172e-02
4.98393178e-01 -2.94328511e-01 4.79889423e-01 -6.27664328e-02
-2.58289218e-01 5.35795867e-01 6.22121170e-02 -3.97456914e-01
-8.22419584e-01 -1.17465937e+00 -6.52413487e-01 -6.84716225e-01
-5.78217208e-01 1.86249197e-01 -1.44887641e-01 -3.79347801... | [6.915254592895508, 6.128830909729004] |
0830284d-f351-452c-bae4-fea3d6b18555 | domain-adaptive-multiple-instance-learning | 2304.03537 | null | https://arxiv.org/abs/2304.03537v1 | https://arxiv.org/pdf/2304.03537v1.pdf | Domain Adaptive Multiple Instance Learning for Instance-level Prediction of Pathological Images | Pathological image analysis is an important process for detecting abnormalities such as cancer from cell images. However, since the image size is generally very large, the cost of providing detailed annotations is high, which makes it difficult to apply machine learning techniques. One way to improve the performance of... | ['Tatsuya Harada', 'Masaru Kitsuregawa', 'Masanobu Kitagawa', 'Masashi Fukayama', 'Tetsuo Ushiku', 'Akihiko Yoshizawa', 'Hiroyuki Abe', 'Yusuke Mukuta', 'Yusuke Kurose', 'Shusuke Takahama'] | 2023-04-07 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 5.55845618e-01 -1.75243653e-02 -2.41835624e-01 -4.03212398e-01
-1.03632224e+00 -3.71745795e-01 2.06428871e-01 6.61019742e-01
-5.70905387e-01 8.16117823e-01 -2.62270898e-01 -1.30324081e-01
8.52629095e-02 -7.54520893e-01 -4.90600556e-01 -8.54989231e-01
5.50534844e-01 4.33592558e-01 6.16311550e-01 3.82458955... | [15.018019676208496, -2.82610821723938] |
527f0b85-8aea-4a04-a76e-4768ff4e1020 | guiding-generative-language-models-for-data | 2111.09064 | null | https://arxiv.org/abs/2111.09064v2 | https://arxiv.org/pdf/2111.09064v2.pdf | Guiding Generative Language Models for Data Augmentation in Few-Shot Text Classification | Data augmentation techniques are widely used for enhancing the performance of machine learning models by tackling class imbalance issues and data sparsity. State-of-the-art generative language models have been shown to provide significant gains across different NLP tasks. However, their applicability to data augmentati... | ['Alun Preece', 'Hélène de Ribaupierre', 'Jose Camacho-Collados', 'Asahi Ushio', 'Aleksandra Edwards'] | 2021-11-17 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 4.93416220e-01 4.00851935e-01 -6.60672426e-01 -4.94701296e-01
-8.87013912e-01 -3.13976884e-01 9.34357524e-01 5.47406733e-01
-4.43792224e-01 8.80632758e-01 2.73313642e-01 -1.69654891e-01
-1.00872830e-01 -8.09071720e-01 -2.93382734e-01 -7.13899612e-01
5.67936972e-02 1.06553733e+00 9.31351185e-02 -2.07183927... | [10.838948249816895, 8.22099781036377] |
73ca19a0-ebf3-414a-93cf-80e71dbe5c5e | long-horizon-video-prediction-using-a-dynamic | 2212.14376 | null | https://arxiv.org/abs/2212.14376v2 | https://arxiv.org/pdf/2212.14376v2.pdf | Long-horizon video prediction using a dynamic latent hierarchy | The task of video prediction and generation is known to be notoriously difficult, with the research in this area largely limited to short-term predictions. Though plagued with noise and stochasticity, videos consist of features that are organised in a spatiotemporal hierarchy, different features possessing different te... | ['Zafeirios Fountas', 'Qinghai Guo', 'Alexey Zakharov'] | 2022-12-29 | null | null | null | null | ['video-prediction'] | ['computer-vision'] | [ 9.84411538e-02 -2.60421224e-02 -4.32279646e-01 -9.54866633e-02
-1.86608687e-01 -6.76509798e-01 9.05839980e-01 -7.79453516e-02
2.21945956e-01 5.19234180e-01 9.47422683e-01 -1.75519530e-02
-2.82791197e-01 -5.37842393e-01 -8.69933844e-01 -9.00430381e-01
-7.40400910e-01 4.81224805e-01 3.29146862e-01 -2.78558675... | [8.553470611572266, 0.6566185355186462] |
a839368f-64f6-4f63-8d2c-d1d5e275dd9d | a-novel-stereo-matching-pipeline-with | 2204.04865 | null | https://arxiv.org/abs/2204.04865v2 | https://arxiv.org/pdf/2204.04865v2.pdf | A novel stereo matching pipeline with robustness and unfixed disparity search range | Stereo matching is an essential basis for various applications, but most stereo matching methods have poor generalization performance and require a fixed disparity search range. Moreover, current stereo matching methods focus on the scenes that only have positive disparities, but ignore the scenes that contain both pos... | ['Feng Liu', 'Jiazhi Liu'] | 2022-04-11 | null | null | null | null | ['stereo-matching-1'] | ['computer-vision'] | [ 1.55564278e-01 -6.07568443e-01 -6.88736662e-02 -3.06660026e-01
-1.33253813e-01 -4.11996722e-01 5.74991167e-01 -3.86635274e-01
-1.34553894e-01 4.95417207e-01 2.59037077e-01 -3.66543353e-01
2.70896733e-01 -9.50287700e-01 -3.76522660e-01 -4.89782214e-01
4.19089586e-01 3.60571891e-02 9.05270278e-01 -3.37451220... | [9.042820930480957, -2.4330644607543945] |
353b467c-d1cb-4bfc-87b1-4d94c65f5def | crime-prediction-with-graph-neural-networks | 2111.14733 | null | https://arxiv.org/abs/2111.14733v2 | https://arxiv.org/pdf/2111.14733v2.pdf | Crime Prediction with Graph Neural Networks and Multivariate Normal Distributions | Existing approaches to the crime prediction problem are unsuccessful in expressing the details since they assign the probability values to large regions. This paper introduces a new architecture with the graph convolutional networks (GCN) and multivariate Gaussian distributions to perform high-resolution forecasting th... | ['Suleyman Serdar Kozat', 'Selim Furkan Tekin'] | 2021-11-29 | null | null | null | null | ['crime-prediction'] | ['miscellaneous'] | [-2.05393881e-01 2.43650705e-01 -1.64205730e-01 -3.48955780e-01
-6.25428915e-01 -1.73343852e-01 6.84724391e-01 1.42422058e-02
-7.10644852e-03 7.97690451e-01 6.46601319e-01 -3.53537709e-01
-2.89079808e-02 -1.34845829e+00 -9.09342051e-01 -4.34863478e-01
-5.50184727e-01 4.72413540e-01 3.30285937e-01 -1.33307651... | [6.685215473175049, 2.1044960021972656] |
9891d49b-8691-49f6-acf0-0e0b2da6f83e | forecasting-pandemic-tax-revenues-in-a-small | 2112.15431 | null | https://arxiv.org/abs/2112.15431v1 | https://arxiv.org/pdf/2112.15431v1.pdf | Forecasting pandemic tax revenues in a small, open economy | Tax analysis and forecasting of revenues are of paramount importance to ensure fiscal policy's viability and sustainability. However, the measures taken to contain the spread of the recent pandemic pose an unprecedented challenge to established models and approaches. This paper proposes a model to forecast tax revenues... | ['Fabio Ashtar Telarico'] | 2021-12-22 | null | null | null | null | ['econometrics'] | ['miscellaneous'] | [-4.55605596e-01 3.49969923e-01 -7.70559072e-01 4.93388139e-02
-3.07746530e-01 -4.61512625e-01 1.20016658e+00 3.78419280e-01
-5.24954736e-01 1.11821902e+00 7.17438400e-01 -1.06707907e+00
-2.52791405e-01 -8.50633979e-01 8.05051008e-04 -5.95158100e-01
-5.27879968e-02 4.63418096e-01 -2.85256952e-01 -6.18400633... | [5.734038352966309, 4.043146133422852] |
4986934a-d8ff-4313-b590-9130293bb5a1 | rethinking-cnn-based-pansharpening-guided | 2006.16644 | null | https://arxiv.org/abs/2006.16644v1 | https://arxiv.org/pdf/2006.16644v1.pdf | Rethinking CNN-Based Pansharpening: Guided Colorization of Panchromatic Images via GANs | Convolutional Neural Networks (CNN)-based approaches have shown promising results in pansharpening of satellite images in recent years. However, they still exhibit limitations in producing high-quality pansharpening outputs. To that end, we propose a new self-supervised learning framework, where we treat pansharpening ... | ['Gozde Unal', 'Ugur Alganci', 'Furkan Ozcelik', 'Elif Sertel'] | 2020-06-30 | null | null | null | null | ['pansharpening'] | ['computer-vision'] | [ 7.23170638e-01 -2.49683559e-01 4.48172214e-03 -6.31866008e-02
-6.85143769e-01 -8.72768164e-01 5.63139498e-01 -5.15129685e-01
-4.33360934e-01 8.69111657e-01 -8.34591389e-02 -1.98854864e-01
-1.12569734e-01 -1.33965802e+00 -8.67026925e-01 -1.02820098e+00
3.93330753e-01 -2.46649608e-01 1.45932958e-01 -6.33074403... | [10.20046329498291, -1.9765450954437256] |
4221eb8f-3f34-415c-8003-baaf47f8004c | study-and-observation-of-the-variations-of | 1809.06188 | null | http://arxiv.org/abs/1809.06188v3 | http://arxiv.org/pdf/1809.06188v3.pdf | Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Neural Network Algorithm | In recent days, Artificial Neural Network (ANN) can be applied to a vast
majority of fields including business, medicine, engineering, etc. The most
popular areas where ANN is employed nowadays are pattern and sequence
recognition, novelty detection, character recognition, regression analysis,
speech recognition, image... | ['Mohammad Mahmudur Rahman Khan', 'Md. Abu Bakr Siddique', 'Zahidun Ashrafi', 'Rezoana Bente Arif'] | 2018-09-17 | null | null | null | null | ['stock-market-prediction'] | ['time-series'] | [ 2.92681038e-01 -4.21976060e-01 -9.40979868e-02 -7.13907406e-02
5.17996788e-01 -8.77717361e-02 3.14693362e-01 3.92371804e-01
-5.55131912e-01 7.77996540e-01 -4.25912887e-01 -1.85797527e-01
-4.99618828e-01 -6.15231216e-01 -1.99277326e-01 -7.01590955e-01
-6.06523156e-02 -1.48054855e-02 2.15376258e-01 -1.67280406... | [8.277992248535156, 3.044825792312622] |
f5aa3b85-3ab9-4bcf-95e0-9b01fb13f1de | towards-holistic-surgical-scene-understanding | 2212.04582 | null | https://arxiv.org/abs/2212.04582v3 | https://arxiv.org/pdf/2212.04582v3.pdf | Towards Holistic Surgical Scene Understanding | Most benchmarks for studying surgical interventions focus on a specific challenge instead of leveraging the intrinsic complementarity among different tasks. In this work, we present a new experimental framework towards holistic surgical scene understanding. First, we introduce the Phase, Step, Instrument, and Atomic Vi... | ['Pablo Arbeláez', 'Nicolás Fernández', 'Juan Caicedo', 'Jessica Santander', 'Mathilde Verlyk', 'Nicolás Ayobi', 'Isabela Hernández', 'Paola Ruiz Puentes', 'Natalia Valderrama'] | 2022-12-08 | null | null | null | null | ['atomic-action-recognition'] | ['computer-vision'] | [ 5.46743274e-01 5.54214180e-01 -8.69334698e-01 -1.81946948e-01
-1.29030538e+00 -7.84790695e-01 6.83936715e-01 2.52555907e-01
-1.68532372e-01 9.93079618e-02 9.24824774e-01 -4.60151792e-01
-2.35359907e-01 -2.21293673e-01 -6.27618015e-01 -6.95759833e-01
-1.39590114e-01 1.50154248e-01 -1.18659832e-01 -1.76145211... | [14.074535369873047, -3.382964611053467] |
80ce7398-b99a-4538-a431-1cddf97c9d39 | supervised-anomaly-detection-based-on-deep | 1904.06034 | null | http://arxiv.org/abs/1904.06034v1 | http://arxiv.org/pdf/1904.06034v1.pdf | Supervised Anomaly Detection based on Deep Autoregressive Density Estimators | We propose a supervised anomaly detection method based on neural density
estimators, where the negative log likelihood is used for the anomaly score.
Density estimators have been widely used for unsupervised anomaly detection. By
the recent advance of deep learning, the density estimation performance has
been greatly i... | ['Tomoharu Iwata', 'Yuki Yamanaka'] | 2019-04-12 | null | null | null | null | ['supervised-anomaly-detection'] | ['computer-vision'] | [-3.32521200e-01 -1.41998425e-01 -1.44744068e-01 -6.35672033e-01
-3.18118304e-01 1.37211353e-01 3.54435354e-01 9.31011140e-02
-2.34063447e-01 5.51571071e-01 -4.72865105e-02 -1.46757141e-01
5.48463501e-03 -9.69227374e-01 -2.76372552e-01 -9.32225764e-01
-1.63562790e-01 4.32723522e-01 2.17561632e-01 3.44433606... | [7.607344627380371, 2.404714584350586] |
f2c38523-aee1-424e-807a-89c5f7cbbeab | mvpnet-multi-view-point-regression-networks | 1811.09410 | null | http://arxiv.org/abs/1811.09410v1 | http://arxiv.org/pdf/1811.09410v1.pdf | MVPNet: Multi-View Point Regression Networks for 3D Object Reconstruction from A Single Image | In this paper, we address the problem of reconstructing an object's surface
from a single image using generative networks. First, we represent a 3D surface
with an aggregation of dense point clouds from multiple views. Each point cloud
is embedded in a regular 2D grid aligned on an image plane of a viewpoint,
making th... | ['Yan Lu', 'Jinglu Wang', 'Bo Sun'] | 2018-11-23 | null | null | null | null | ['3d-object-reconstruction', '3d-object-reconstruction-from-a-single-image'] | ['computer-vision', 'computer-vision'] | [ 8.59057009e-02 3.66709709e-01 3.19785714e-01 -4.91166264e-01
-7.44177878e-01 -5.41342676e-01 6.55017436e-01 -5.21923602e-01
1.61702946e-01 4.31056440e-01 -1.73630133e-01 1.16379812e-01
2.52990425e-01 -1.23337424e+00 -1.54153299e+00 -4.61684495e-01
1.52153909e-01 1.12594032e+00 -1.55712262e-01 -1.03933424... | [8.593944549560547, -3.491241931915283] |
07c288e8-ae89-47bf-9e86-5d8aabc11162 | online-hybrid-ctc-attention-end-to-end | 2307.02351 | null | https://arxiv.org/abs/2307.02351v1 | https://arxiv.org/pdf/2307.02351v1.pdf | Online Hybrid CTC/Attention End-to-End Automatic Speech Recognition Architecture | Recently, there has been increasing progress in end-to-end automatic speech recognition (ASR) architecture, which transcribes speech to text without any pre-trained alignments. One popular end-to-end approach is the hybrid Connectionist Temporal Classification (CTC) and attention (CTC/attention) based ASR architecture.... | ['Yonghong Yan', 'Pengyuan Zhang', 'Gaofeng Cheng', 'Haoran Miao'] | 2023-07-05 | null | null | null | null | ['speech-recognition', 'automatic-speech-recognition'] | ['speech', 'speech'] | [ 2.12226123e-01 -7.48948231e-02 -1.28001407e-01 -3.05691749e-01
-1.26949215e+00 -2.97029316e-01 4.47676718e-01 -2.78948337e-01
-5.40575385e-01 4.05443907e-01 4.32692498e-01 -9.41752076e-01
3.53330165e-01 -2.93338802e-02 -6.12212360e-01 -5.49575269e-01
2.71661401e-01 4.93166685e-01 2.53228068e-01 -3.10256541... | [14.489850997924805, 6.827888011932373] |
a40c618b-600a-4568-8948-14a6e37a4419 | semantic-graph-parsing-with-recurrent-neural | 1910.00051 | null | https://arxiv.org/abs/1910.00051v2 | https://arxiv.org/pdf/1910.00051v2.pdf | Semantic Graph Parsing with Recurrent Neural Network DAG Grammars | Semantic parses are directed acyclic graphs (DAGs), so semantic parsing should be modeled as graph prediction. But predicting graphs presents difficult technical challenges, so it is simpler and more common to predict the linearized graphs found in semantic parsing datasets using well-understood sequence models. The co... | ['Sorcha Gilroy', 'Federico Fancellu', 'Adam Lopez', 'Mirella Lapata'] | 2019-09-30 | semantic-graph-parsing-with-recurrent-neural-1 | https://aclanthology.org/D19-1278 | https://aclanthology.org/D19-1278.pdf | ijcnlp-2019-11 | ['drs-parsing'] | ['natural-language-processing'] | [ 3.20662707e-01 8.60984147e-01 -1.97480112e-01 -4.39896584e-01
-5.51727295e-01 -9.43709254e-01 1.37664810e-01 1.98688731e-01
3.07534300e-02 8.62392366e-01 3.14458728e-01 -9.30004537e-01
1.11288972e-01 -1.16031754e+00 -8.15490603e-01 -2.33264551e-01
-3.28994125e-01 7.71557152e-01 5.23400068e-01 -3.15557897... | [10.334688186645508, 9.457886695861816] |
f7820324-39f6-4ea7-a414-6df7c6fbe9d3 | hybrid-dynamic-contrast-and-probability | 2109.14157 | null | https://arxiv.org/abs/2109.14157v1 | https://arxiv.org/pdf/2109.14157v1.pdf | Hybrid Dynamic Contrast and Probability Distillation for Unsupervised Person Re-Id | Unsupervised person re-identification (Re-Id) has attracted increasing attention due to its practical application in the read-world video surveillance system. The traditional unsupervised Re-Id are mostly based on the method alternating between clustering and fine-tuning with the classification or metric learning objec... | ['Xinbo Gao', 'Nannan Wang', 'Jingyu Zhou', 'De Cheng'] | 2021-09-29 | null | null | null | null | ['unsupervised-person-re-identification'] | ['computer-vision'] | [-1.05898611e-01 -3.52794677e-01 -5.65897450e-02 -3.99940163e-01
-5.18692851e-01 -1.40632372e-02 5.72237730e-01 -2.82661226e-02
-5.24256229e-01 5.74330986e-01 4.73110341e-02 4.55388933e-01
-4.23610926e-01 -6.01041555e-01 -1.63731292e-01 -1.09326363e+00
7.31386542e-02 7.18787849e-01 2.67294168e-01 1.75945774... | [14.878098487854004, 1.1332310438156128] |
6776961c-2047-45e3-aa89-f610ff3775dc | applying-unsupervised-keyphrase-methods-on | 2303.08928 | null | https://arxiv.org/abs/2303.08928v1 | https://arxiv.org/pdf/2303.08928v1.pdf | Applying unsupervised keyphrase methods on concepts extracted from discharge sheets | Clinical notes containing valuable patient information are written by different health care providers with various scientific levels and writing styles. It might be helpful for clinicians and researchers to understand what information is essential when dealing with extensive electronic medical records. Entities recogni... | ['Maryam Lotfi Shahreza', 'Matthias Samwald', 'Nasser Ghadiri', 'Hoda Memarzadeh'] | 2023-03-15 | null | null | null | null | ['entity-linking'] | ['natural-language-processing'] | [ 4.30103242e-01 9.50690582e-02 -2.70424604e-01 -1.42184108e-01
-5.61411262e-01 -6.18580103e-01 2.16027424e-01 1.13780797e+00
-5.70825160e-01 9.51110423e-01 5.49948037e-01 -3.61296684e-01
-5.71826637e-01 -6.64884746e-01 8.32089335e-02 -6.08660758e-01
2.53380295e-02 5.46416938e-01 -1.87354758e-01 -1.01307645... | [8.488511085510254, 8.675358772277832] |
758960b2-9713-468d-8dcf-dd4e5df100a2 | exploration-of-interpretability-techniques | 2006.02570 | null | https://arxiv.org/abs/2006.02570v3 | https://arxiv.org/pdf/2006.02570v3.pdf | Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images | The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosis of infected patients. Medical imaging such as X-ray and Computed Tomography (CT) combined with the potential of ... | ['Andreas Nürnberger', 'Oliver Speck', 'Georg Rose', 'Petia Radeva', 'Nirja Desai', 'Rahul Mishra', 'Rupali Khatun', 'Valerie Krug', 'Chompunuch Sarasaen', 'Sebastian Stober', 'Soumick Chatterjee', 'Suhita Ghosh', 'Fatima Saad'] | 2020-06-03 | null | null | null | null | ['pneumonia-detection', 'interpretability-techniques-for-deep-learning'] | ['medical', 'miscellaneous'] | [-2.24239323e-02 2.29484871e-01 -1.76140529e-04 -1.40375242e-01
2.60712564e-01 -1.10965818e-01 2.23769531e-01 3.52105111e-01
-5.08728445e-01 7.24322915e-01 9.75561664e-02 -4.67215925e-01
-8.36324215e-01 -4.42363858e-01 -1.64094433e-01 -6.67091846e-01
-2.77945757e-01 8.39815378e-01 -1.64946079e-01 -1.46943703... | [15.53707504272461, -1.7382797002792358] |
daee3ff7-ea19-45f3-ab88-1d56abb543f9 | fast-submodular-function-maximization | 2305.08367 | null | https://arxiv.org/abs/2305.08367v1 | https://arxiv.org/pdf/2305.08367v1.pdf | Fast Submodular Function Maximization | Submodular functions have many real-world applications, such as document summarization, sensor placement, and image segmentation. For all these applications, the key building block is how to compute the maximum value of a submodular function efficiently. We consider both the online and offline versions of the problem: ... | ['Yitan Wang', 'Zhao Song', 'Lianke Qin'] | 2023-05-15 | null | null | null | null | ['document-summarization'] | ['natural-language-processing'] | [ 1.53614745e-01 1.54963523e-01 -7.18326628e-01 -9.00608152e-02
-5.74709177e-01 -9.88803208e-01 -4.20177639e-01 5.30126929e-01
-4.91574287e-01 6.31964564e-01 -3.08176368e-01 -2.80008465e-01
-3.50275010e-01 -8.69349897e-01 -6.64936900e-01 -8.30336571e-01
-2.98951685e-01 7.37641633e-01 5.30613005e-01 -2.95027550... | [6.552679538726807, 4.891228199005127] |
888c319a-9039-49c3-aa1f-5d7c69e6c950 | vit-ret-vision-and-recurrent-transformer | 2208.07929 | null | https://arxiv.org/abs/2208.07929v2 | https://arxiv.org/pdf/2208.07929v2.pdf | ViT-ReT: Vision and Recurrent Transformer Neural Networks for Human Activity Recognition in Videos | Human activity recognition is an emerging and important area in computer vision which seeks to determine the activity an individual or group of individuals are performing. The applications of this field ranges from generating highlight videos in sports, to intelligent surveillance and gesture recognition. Most activity... | ['Arslan Munir', 'Hayat Ullah', 'James Wensel'] | 2022-08-16 | null | null | null | null | ['gesture-recognition', 'activity-recognition-in-videos'] | ['computer-vision', 'computer-vision'] | [ 7.21551001e-01 -3.11266363e-01 -3.41266930e-01 -1.37559026e-01
-2.05715317e-02 -1.46637991e-01 7.33860672e-01 -2.62110084e-01
-4.45327014e-01 4.68568295e-01 7.27249503e-01 6.94237873e-02
-1.29813358e-01 -7.34943151e-01 -3.25170875e-01 -7.58954346e-01
-2.76984662e-01 8.02204087e-02 3.33385170e-01 1.31586427... | [8.034012794494629, 0.4710472524166107] |
7621c368-7870-4ddd-8a04-8504800cb765 | g-sto-sequential-main-shopping-intention | 2306.14314 | null | https://arxiv.org/abs/2306.14314v1 | https://arxiv.org/pdf/2306.14314v1.pdf | G-STO: Sequential Main Shopping Intention Detection via Graph-Regularized Stochastic Transformer | Sequential recommendation requires understanding the dynamic patterns of users' behaviors, contexts, and preferences from their historical interactions. Most existing works focus on modeling user-item interactions only from the item level, ignoring that they are driven by latent shopping intentions (e.g., ballpoint pen... | ['Chao Zhang', 'Jin Li', 'Ming Wang', 'Chaosheng Dong', 'Yan Zhao', 'Xin Shen', 'Yuchen Zhuang'] | 2023-06-25 | null | null | null | null | ['sequential-recommendation'] | ['miscellaneous'] | [-1.33235723e-01 -3.49339306e-01 -5.04937291e-01 -6.22423053e-01
-2.03532860e-01 -4.68895793e-01 4.36793983e-01 2.13691905e-01
-3.24003458e-01 -2.99192309e-01 6.33304358e-01 -4.58135426e-01
-1.51284337e-01 -9.58346307e-01 -7.52305567e-01 -3.77035290e-01
-4.74132597e-02 4.70101446e-01 1.79870334e-02 -3.87393028... | [10.154829978942871, 5.631142616271973] |
b423c813-9eaa-49bc-a7da-b9a0dbbd851f | face-hallucination-by-attentive-sequence | 1905.01509 | null | https://arxiv.org/abs/1905.01509v1 | https://arxiv.org/pdf/1905.01509v1.pdf | Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning | Face hallucination is a domain-specific super-resolution problem that aims to generate a high-resolution (HR) face image from a low-resolution~(LR) input. In contrast to the existing patch-wise super-resolution models that divide a face image into regular patches and independently apply LR to HR mapping to each patch, ... | ['Liang Lin', 'Qingxing Cao', 'Keze Wang', 'Yukai Shi', 'Guanbin Li'] | 2019-05-04 | null | null | null | null | ['face-hallucination'] | ['computer-vision'] | [ 3.87269169e-01 4.04624879e-01 1.21477336e-01 -4.30899084e-01
-9.74258721e-01 2.65514672e-01 2.84115344e-01 -8.24083030e-01
1.39668822e-01 7.26896465e-01 4.37447727e-01 5.74320734e-01
2.67363526e-02 -8.84093165e-01 -8.97361636e-01 -8.27941418e-01
-3.57652642e-02 1.39479041e-01 -1.57394677e-01 -3.65932524... | [12.759881973266602, -0.1190842017531395] |
85f6c84c-ad6d-4062-8284-de25b358bb8d | improving-the-quality-of-dental-crown-using-a | 2303.02426 | null | https://arxiv.org/abs/2303.02426v1 | https://arxiv.org/pdf/2303.02426v1.pdf | Improving the quality of dental crown using a Transformer-based method | Designing a synthetic crown is a time-consuming, inconsistent, and labor-intensive process. In this work, we present a fully automatic method that not only learns human design dental crowns, but also improves the consistency, functionality, and esthetic of the crowns. Following success in point cloud completion using t... | ['Francois Guibault', 'Farida Cheriet', 'Julia Keren', 'Ying Zhang', 'Ammar Alsheghri', 'Farnoosh Ghadiri', 'Golriz Hosseinimanesh'] | 2023-03-04 | null | null | null | null | ['point-cloud-completion'] | ['computer-vision'] | [ 2.68112391e-01 8.62722337e-01 2.11882040e-01 -3.06126624e-01
-7.30944335e-01 -1.27354041e-01 1.68388575e-01 5.45417853e-02
-2.84878388e-02 5.79117298e-01 8.35641176e-02 -4.86523099e-02
-1.83530390e-01 -1.06539154e+00 -9.84884262e-01 -5.23739934e-01
3.05078983e-01 6.65434182e-01 1.58663794e-01 -3.07097375... | [13.062817573547363, -1.6135456562042236] |
7f649d32-8ca9-4cf3-b28d-3a1273eb51cb | comparison-of-model-free-and-model-based | 2212.08801 | null | https://arxiv.org/abs/2212.08801v1 | https://arxiv.org/pdf/2212.08801v1.pdf | Comparison of Model-Free and Model-Based Learning-Informed Planning for PointGoal Navigation | In recent years several learning approaches to point goal navigation in previously unseen environments have been proposed. They vary in the representations of the environments, problem decomposition, and experimental evaluation. In this work, we compare the state-of-the-art Deep Reinforcement Learning based approaches ... | ['Jana Kosecka', 'Gregory J. Stein', 'Arnab Debnath', 'Yimeng Li'] | 2022-12-17 | null | null | null | null | ['problem-decomposition', 'pointgoal-navigation'] | ['miscellaneous', 'robots'] | [-3.27257290e-02 4.81354803e-01 -4.05319557e-02 -3.07954490e-01
-1.13601446e+00 -8.01835656e-01 6.03312254e-01 2.57298261e-01
-8.00935447e-01 1.18652153e+00 5.58560014e-01 -2.11312711e-01
-7.08590984e-01 -8.83493185e-01 -1.01885521e+00 -6.25214458e-01
-7.28564799e-01 1.09135616e+00 4.26014483e-01 -3.04500818... | [4.609020233154297, 0.7913588881492615] |
d9f2e112-e940-4ed7-a332-5cb6893e595c | a-framework-for-unified-real-time | 2302.11768 | null | https://arxiv.org/abs/2302.11768v1 | https://arxiv.org/pdf/2302.11768v1.pdf | A Framework for Unified Real-time Personalized and Non-Personalized Speech Enhancement | In this study, we present an approach to train a single speech enhancement network that can perform both personalized and non-personalized speech enhancement. This is achieved by incorporating a frame-wise conditioning input that specifies the type of enhancement output. To improve the quality of the enhanced output an... | ['Paris Smaragdis', 'Michael M. Goodwin', 'Jean-Marc Valin', 'Devansh Shah', 'Ritwik Giri', 'Zhepei Wang'] | 2023-02-23 | null | null | null | null | ['speech-enhancement'] | ['speech'] | [ 6.38195455e-01 2.97119707e-01 -1.08953185e-01 -5.59081435e-01
-1.05414283e+00 -3.08527291e-01 8.65826368e-01 -8.80415589e-02
-7.33432531e-01 5.29291213e-01 7.60680377e-01 -2.30129480e-01
1.04710020e-01 -5.05166054e-01 -7.87078798e-01 -7.39905715e-01
2.01806173e-01 -1.19350776e-02 1.74234167e-01 -3.41528147... | [14.895088195800781, 6.037303447723389] |
b6342512-ea33-4804-a77c-f676b42fd6a0 | auto-mvcnn-neural-architecture-search-for | 2012.05493 | null | https://arxiv.org/abs/2012.05493v1 | https://arxiv.org/pdf/2012.05493v1.pdf | Auto-MVCNN: Neural Architecture Search for Multi-view 3D Shape Recognition | In 3D shape recognition, multi-view based methods leverage human's perspective to analyze 3D shapes and have achieved significant outcomes. Most existing research works in deep learning adopt handcrafted networks as backbones due to their high capacity of feature extraction, and also benefit from ImageNet pretraining. ... | ['Jinxing Li', 'Hongren Wang', 'Zhaoqun Li'] | 2020-12-10 | null | null | null | null | ['3d-shape-recognition'] | ['computer-vision'] | [-3.77469927e-01 -5.19027770e-01 -1.40218228e-01 -4.34135139e-01
-6.46079183e-01 -5.82411408e-01 5.79105437e-01 -5.38498521e-01
-1.68072313e-01 -1.75396875e-02 1.98541597e-01 -8.62600133e-02
-3.84148568e-01 -7.52842307e-01 -5.73689222e-01 -7.08271861e-01
2.03295365e-01 6.87867582e-01 -1.60241619e-01 -1.72016144... | [8.17464542388916, -3.7847399711608887] |
b4d23413-f052-4484-84da-524bfbf3601b | hierarchical-reinforcement-learning-for-ris | 2301.02771 | null | https://arxiv.org/abs/2301.02771v1 | https://arxiv.org/pdf/2301.02771v1.pdf | Hierarchical Reinforcement Learning for RIS-Assisted Energy-Efficient RAN | Reconfigurable intelligent surface (RIS) is emerging as a promising technology to boost the energy efficiency (EE) of 5G beyond and 6G networks. Inspired by this potential, in this paper, we investigate the RIS-assisted energy-efficient radio access networks (RAN). In particular, we combine RIS with sleep control techn... | ['Melike Erol-Kantarci', 'Steve Furr', 'Raimundas Gaigalas', 'Majid Bavand', 'Medhat Elsayed', 'Long Kong', 'Hao Zhou'] | 2023-01-07 | null | null | null | null | ['hierarchical-reinforcement-learning'] | ['methodology'] | [-4.88390774e-02 4.03498769e-01 -7.31483757e-01 -2.62346901e-02
3.32139105e-01 -3.21456730e-01 -1.72644481e-01 -4.80828494e-01
8.50294977e-02 1.02684593e+00 -1.10443331e-01 -4.50838923e-01
-4.07635391e-01 -1.13828301e+00 7.46653304e-02 -1.24739039e+00
-3.23323309e-01 6.40743300e-02 3.15935254e-01 -1.73587471... | [5.943614482879639, 1.593382477760315] |
b0ed9f71-77df-48bb-a99c-b3fd545430af | lif-seg-lidar-and-camera-image-fusion-for-3d | 2108.07511 | null | https://arxiv.org/abs/2108.07511v1 | https://arxiv.org/pdf/2108.07511v1.pdf | LIF-Seg: LiDAR and Camera Image Fusion for 3D LiDAR Semantic Segmentation | Camera and 3D LiDAR sensors have become indispensable devices in modern autonomous driving vehicles, where the camera provides the fine-grained texture, color information in 2D space and LiDAR captures more precise and farther-away distance measurements of the surrounding environments. The complementary information fro... | ['Wenbing Tao', 'Hongsheng Li', 'Xiao Song', 'Xinge Zhu', 'Hui Zhou', 'Lin Zhao'] | 2021-08-17 | null | null | null | null | ['lidar-semantic-segmentation'] | ['computer-vision'] | [ 5.84793501e-02 -6.09643877e-01 -1.44663170e-01 -2.93466181e-01
-7.45543242e-01 -3.83836716e-01 5.66354036e-01 -5.82475662e-02
-5.79604149e-01 6.61046445e-01 -5.13644814e-01 -8.61017704e-02
-2.31895640e-01 -8.57723594e-01 -4.97362673e-01 -8.65446687e-01
4.45606560e-01 1.93313658e-01 6.79900944e-01 -2.75482178... | [8.210474967956543, -2.5262129306793213] |
33ebd1f9-f16f-4ca5-935e-2def70b399da | a-visual-attention-grounding-neural-model-for | 1808.08266 | null | http://arxiv.org/abs/1808.08266v2 | http://arxiv.org/pdf/1808.08266v2.pdf | A Visual Attention Grounding Neural Model for Multimodal Machine Translation | We introduce a novel multimodal machine translation model that utilizes
parallel visual and textual information. Our model jointly optimizes the
learning of a shared visual-language embedding and a translator. The model
leverages a visual attention grounding mechanism that links the visual
semantics with the correspond... | ['Mingyang Zhou', 'Yong Jae Lee', 'Runxiang Cheng', 'Zhou Yu'] | 2018-08-24 | a-visual-attention-grounding-neural-model-for-1 | https://aclanthology.org/D18-1400 | https://aclanthology.org/D18-1400.pdf | emnlp-2018-10 | ['multimodal-machine-translation'] | ['natural-language-processing'] | [-1.04367450e-01 -2.41488799e-01 -6.82138562e-01 -5.07919848e-01
-1.02397525e+00 -8.78912747e-01 8.22742283e-01 1.56896383e-01
-2.37374410e-01 1.07900605e-01 4.44086164e-01 -4.13555890e-01
5.65608561e-01 -2.41460174e-01 -1.01554775e+00 -2.60658503e-01
3.14496726e-01 5.82929373e-01 -3.40049803e-01 -4.09445643... | [11.271248817443848, 1.5446293354034424] |
c8685d12-e3d5-4164-824a-499a90a18527 | planning-with-spatial-temporal-abstraction | 2210.15751 | null | https://arxiv.org/abs/2210.15751v2 | https://arxiv.org/pdf/2210.15751v2.pdf | Planning with Spatial-Temporal Abstraction from Point Clouds for Deformable Object Manipulation | Effective planning of long-horizon deformable object manipulation requires suitable abstractions at both the spatial and temporal levels. Previous methods typically either focus on short-horizon tasks or make strong assumptions that full-state information is available, which prevents their use on deformable objects. In... | ['David Held', 'Chuang Gan', 'Yunzhu Li', 'Katerina Fragkiadaki', 'Zhiao Huang', 'Yunchu Zhang', 'Carl Qi', 'Xingyu Lin'] | 2022-10-27 | null | null | null | null | ['deformable-object-manipulation'] | ['robots'] | [ 2.18766361e-01 1.08973116e-01 -1.89383551e-01 5.64345680e-02
-2.05504388e-01 -8.06836009e-01 6.70495570e-01 1.61650866e-01
-7.76936710e-02 3.65263671e-01 3.01953673e-01 -1.15175977e-01
-6.05306089e-01 -8.96444738e-01 -7.48375177e-01 -5.85025847e-01
-3.84793848e-01 9.86178160e-01 6.37111843e-01 -2.11517990... | [4.6679534912109375, 0.6883776783943176] |
a4b755a9-fee5-46b5-b857-b7297a85f723 | direct-attacks-using-fake-images-in-iris | 2111.00178 | null | https://arxiv.org/abs/2111.00178v1 | https://arxiv.org/pdf/2111.00178v1.pdf | Direct attacks using fake images in iris verification | In this contribution, the vulnerabilities of iris-based recognition systems to direct attacks are studied. A database of fake iris images has been created from real iris of the BioSec baseline database. Iris images are printed using a commercial printer and then, presented at the iris sensor. We use for our experiments... | ['Javier Ortega-Garcia', 'Julian Fierrez', 'Javier Galbally', 'Fernando Alonso-Fernandez', 'Pedro Tome-Gonzalez', 'Virginia Ruiz-Albacete'] | 2021-10-30 | null | null | null | null | ['iris-segmentation'] | ['medical'] | [ 4.57525820e-01 1.91030636e-01 -5.92921078e-02 -3.45783770e-01
1.91331446e-01 -7.08021343e-01 6.43786132e-01 8.72033983e-02
-4.34769392e-01 6.00066066e-01 -3.26733977e-01 -5.98354697e-01
-2.58437127e-01 -7.39589632e-01 -4.67857033e-01 -5.17650127e-01
-1.73380718e-01 4.85421777e-01 -7.81211630e-02 1.11888265... | [3.7449095249176025, -3.6267409324645996] |
4cfa60ad-ebd1-4af0-8642-c2b7ae50fcdf | blind-image-quality-assessment-using-semi | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Tang_Blind_Image_Quality_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Tang_Blind_Image_Quality_2014_CVPR_paper.pdf | Blind Image Quality Assessment using Semi-supervised Rectifier Networks | It is often desirable to evaluate images quality with a perceptually relevant measure that does not require a reference image. Recent approaches to this problem use human provided quality scores with machine learning to learn a measure. The biggest hurdles to these efforts are: 1) the difficulty of generalizing across... | ['Neel Joshi', 'Huixuan Tang', 'Ashish Kapoor'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['image-quality-estimation', 'blind-image-quality-assessment'] | ['computer-vision', 'computer-vision'] | [ 1.26058906e-01 -4.27089125e-01 -1.26339048e-01 -6.38169169e-01
-1.32092059e+00 -7.98465669e-01 3.78424436e-01 3.22985388e-02
-6.96288466e-01 6.22833550e-01 4.39092755e-01 -2.37521723e-01
-3.14207464e-01 -3.95557761e-01 -4.55746561e-01 -4.59484994e-01
-3.86034660e-02 1.66057125e-01 8.79076496e-02 5.60767874... | [11.911709785461426, -1.8090288639068604] |
22c92638-49c9-4a4a-86d6-5e8c5a1db910 | information-theoretic-safe-exploration-with | 2212.04914 | null | https://arxiv.org/abs/2212.04914v1 | https://arxiv.org/pdf/2212.04914v1.pdf | Information-Theoretic Safe Exploration with Gaussian Processes | We consider a sequential decision making task where we are not allowed to evaluate parameters that violate an a priori unknown (safety) constraint. A common approach is to place a Gaussian process prior on the unknown constraint and allow evaluations only in regions that are safe with high probability. Most current met... | ['Jan Peters', 'Felix Berkenkamp', 'Julia Vinogradska', 'Carlos E. Luis', 'Alessandro G. Bottero'] | 2022-12-09 | null | null | null | null | ['safe-exploration'] | ['robots'] | [ 3.05675834e-01 2.85176277e-01 -3.47081423e-01 -1.23921834e-01
-8.41882229e-01 -8.84618044e-01 4.32451397e-01 5.49594939e-01
-7.85633981e-01 1.06795740e+00 -3.86079729e-01 -4.12880033e-01
-4.41050351e-01 -8.55112910e-01 -5.89934230e-01 -8.75830173e-01
1.14990652e-01 9.12351310e-01 5.99214673e-01 2.50592023... | [4.965944766998291, 3.0786631107330322] |
e86e741e-d9d5-4820-bb72-1598d1bb1d03 | 3rd-place-solution-for-pvuw2023-vss-track-a | 2306.02291 | null | https://arxiv.org/abs/2306.02291v2 | https://arxiv.org/pdf/2306.02291v2.pdf | 3rd Place Solution for PVUW2023 VSS Track: A Large Model for Semantic Segmentation on VSPW | In this paper, we introduce 3rd place solution for PVUW2023 VSS track. Semantic segmentation is a fundamental task in computer vision with numerous real-world applications. We have explored various image-level visual backbones and segmentation heads to tackle the problem of video semantic segmentation. Through our expe... | ['Huchuan Lu', 'Lu Zhang', 'Lihe Zhang', 'Zhenyu Chen', 'Jiawen Zhu', 'Xiaoqi Zhao', 'Ben Kang', 'Zeqi Hao', 'Shijie Chang'] | 2023-06-04 | null | null | null | null | ['video-semantic-segmentation'] | ['computer-vision'] | [ 3.14126700e-01 1.89836502e-01 -3.38727951e-01 -2.30360866e-01
-6.16897821e-01 -6.70893729e-01 2.97581702e-01 -6.32892072e-01
-3.92857462e-01 5.44277489e-01 -3.50704402e-01 -4.98186141e-01
3.70341778e-01 -4.25422817e-01 -7.33749270e-01 -5.30112088e-01
3.25973302e-01 3.26965988e-01 1.20543504e+00 1.67501830... | [9.168819427490234, -0.07967841625213623] |
07701bd1-81dd-495d-960d-66835094e7c2 | scene-to-patch-earth-observation-multiple | 2211.08247 | null | https://arxiv.org/abs/2211.08247v1 | https://arxiv.org/pdf/2211.08247v1.pdf | Scene-to-Patch Earth Observation: Multiple Instance Learning for Land Cover Classification | Land cover classification (LCC), and monitoring how land use changes over time, is an important process in climate change mitigation and adaptation. Existing approaches that use machine learning with Earth observation data for LCC rely on fully-annotated and segmented datasets. Creating these datasets requires a large ... | ['Sarvapali Ramchurn', 'Christine Evers', 'Ying-Jung Deweese', 'Joseph Early'] | 2022-11-15 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 4.19055045e-01 -8.77366811e-02 -4.87983167e-01 -5.08658111e-01
-9.25104141e-01 -8.18093657e-01 7.32117772e-01 5.07473409e-01
-2.76836365e-01 6.89273179e-01 1.76955327e-01 -1.03754675e+00
1.54277340e-01 -1.36398280e+00 -8.80892277e-01 -5.60217321e-01
-1.45137936e-01 1.36215284e-01 2.72438437e-01 -1.74516082... | [9.416248321533203, -1.4368367195129395] |
a2ae576b-6fb1-4f4a-92cd-b86c4ccf21f9 | object-counting-you-only-need-to-look-at-one | 2112.05993 | null | https://arxiv.org/abs/2112.05993v1 | https://arxiv.org/pdf/2112.05993v1.pdf | Object Counting: You Only Need to Look at One | This paper aims to tackle the challenging task of one-shot object counting. Given an image containing novel, previously unseen category objects, the goal of the task is to count all instances in the desired category with only one supporting bounding box example. To this end, we propose a counting model by which you onl... | ['Yabin Wang', 'Xiaopeng Hong', 'Hui Lin'] | 2021-12-11 | null | null | null | null | ['object-counting'] | ['computer-vision'] | [ 5.03892936e-02 -2.74311900e-01 -7.64360428e-02 -4.62352216e-01
-5.81919909e-01 -2.59101510e-01 6.51537836e-01 2.87315190e-01
-7.01725364e-01 4.49487925e-01 -1.69518396e-01 3.33348304e-01
-7.44821727e-02 -8.13239753e-01 -5.54112196e-01 -5.28496265e-01
-8.92064720e-02 5.55751622e-01 6.46042645e-01 1.76829785... | [9.005268096923828, 0.5458279252052307] |
d2ad3a21-a106-46dd-98fb-fc637004d409 | a-practical-guide-to-multi-objective | 2103.09568 | null | https://arxiv.org/abs/2103.09568v1 | https://arxiv.org/pdf/2103.09568v1.pdf | A Practical Guide to Multi-Objective Reinforcement Learning and Planning | Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via... | ['Diederik M. Roijers', 'Peter Vamplew', 'Marcello Restelli', 'Gabriel Ramos', 'Ann Nowé', 'Patrick Mannion', 'Athirai A. Irissappane', 'Enda Howley', 'Fredrik Heintz', 'Richard Dazeley', 'Luisa M. Zintgraf', 'Timothy Verstraeten', 'Mathieu Reymond', 'Matthew Macfarlane', 'Johan Källström', 'Eugenio Bargiacchi', 'Roxan... | 2021-03-17 | null | null | null | null | ['multi-objective-reinforcement-learning'] | ['methodology'] | [ 2.77788609e-01 2.40508318e-01 -6.24309063e-01 -2.79384911e-01
-7.73018241e-01 -4.41829085e-01 3.98009032e-01 4.49530244e-01
-7.83429563e-01 1.05841470e+00 2.78209358e-01 -6.40197575e-01
-6.97875917e-01 -6.39255822e-01 -7.43078589e-02 -6.19947016e-01
7.96187967e-02 8.78237009e-01 -2.19725780e-02 -4.25205201... | [4.220820426940918, 2.407278537750244] |
5690d413-0117-462b-a658-0cec69bae9a1 | scalable-resource-management-for-dynamic-mec | 2306.08938 | null | https://arxiv.org/abs/2306.08938v2 | https://arxiv.org/pdf/2306.08938v2.pdf | Scalable Resource Management for Dynamic MEC: An Unsupervised Link-Output Graph Neural Network Approach | Deep learning has been successfully adopted in mobile edge computing (MEC) to optimize task offloading and resource allocation. However, the dynamics of edge networks raise two challenges in neural network (NN)-based optimization methods: low scalability and high training costs. Although conventional node-output graph ... | ['Xuemin Shen', 'Tom Luan', 'Yilong Hui', 'Ruijin Sun', 'Wei Quan', 'Lianhao Fu', 'Nan Cheng', 'Xiucheng Wang'] | 2023-06-15 | null | null | null | null | ['edge-computing'] | ['time-series'] | [-1.93897069e-01 -4.19571340e-01 -6.04829073e-01 4.26110476e-02
1.17617466e-01 -2.04126179e-01 -3.16010565e-01 -3.99269789e-01
-3.30593020e-01 8.31188381e-01 -3.81188333e-01 -6.90248549e-01
-6.28151953e-01 -7.58782744e-01 -5.26976883e-01 -6.39009595e-01
-2.65861630e-01 6.19043589e-01 -4.58652340e-02 -4.10414450... | [6.08039665222168, 1.8661434650421143] |
fbe4d472-2b55-4542-9116-494cf827bd0c | statistical-relational-learning-and-neuro | 2306.13660 | null | https://arxiv.org/abs/2306.13660v1 | https://arxiv.org/pdf/2306.13660v1.pdf | Statistical relational learning and neuro-symbolic AI: what does first-order logic offer? | In this paper, our aim is to briefly survey and articulate the logical and philosophical foundations of using (first-order) logic to represent (probabilistic) knowledge in a non-technical fashion. Our motivation is three fold. First, for machine learning researchers unaware of why the research community cares about rel... | ['Vaishak Belle'] | 2023-06-08 | null | null | null | null | ['relational-reasoning'] | ['natural-language-processing'] | [ 1.32896185e-01 8.44606221e-01 -4.88286227e-01 -4.42520827e-01
-5.44354141e-01 -4.98262286e-01 8.15374911e-01 3.83967429e-01
-4.29530025e-01 7.88014174e-01 2.91211456e-01 -6.73203409e-01
-7.67072320e-01 -1.07417119e+00 -6.00190163e-01 -5.43129325e-01
-2.48604774e-01 4.96970832e-01 8.68206695e-02 -1.27480805... | [8.788926124572754, 6.643649101257324] |
bff7fd1a-abfd-4811-a55d-813cca52ca9f | contextual-response-interpretation-for | 2305.00577 | null | https://arxiv.org/abs/2305.00577v1 | https://arxiv.org/pdf/2305.00577v1.pdf | Contextual Response Interpretation for Automated Structured Interviews: A Case Study in Market Research | Structured interviews are used in many settings, importantly in market research on topics such as brand perception, customer habits, or preferences, which are critical to product development, marketing, and e-commerce at large. Such interviews generally consist of a series of questions that are asked to a participant. ... | ['Eugene Agichtein', 'Venugopal Vasudevan', 'Ankur Purwar', 'Kaustubh Dhole', 'Harshita Sahijwani'] | 2023-04-30 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [ 5.99646389e-01 5.47242939e-01 -1.67501807e-01 -8.80056262e-01
-7.38024056e-01 -1.12321925e+00 3.17719072e-01 4.22149211e-01
-2.82198966e-01 3.77644747e-01 6.33030415e-01 -6.99701726e-01
5.74410753e-03 -5.27500570e-01 -9.00944024e-02 -3.52051228e-01
3.84640664e-01 8.89376879e-01 -1.86882108e-01 -3.48678410... | [12.5521879196167, 7.778435707092285] |
091f4734-0bcd-4cd2-a395-2a55b0275dfa | clip4str-a-simple-baseline-for-scene-text-1 | 2305.14014 | null | https://arxiv.org/abs/2305.14014v1 | https://arxiv.org/pdf/2305.14014v1.pdf | CLIP4STR: A Simple Baseline for Scene Text Recognition with Pre-trained Vision-Language Model | Pre-trained vision-language models are the de-facto foundation models for various downstream tasks. However, this trend has not extended to the field of scene text recognition (STR), despite the potential of CLIP to serve as a powerful scene text reader. CLIP can robustly identify regular (horizontal) and irregular (ro... | ['Yi Yang', 'Linchao Zhu', 'Xiaohan Wang', 'Shuai Zhao'] | 2023-05-23 | clip4str-a-simple-baseline-for-scene-text | https://arxiv.org/abs/2305.14014 | https://arxiv.org/pdf/2305.14014.pdf | null | ['scene-text-recognition'] | ['computer-vision'] | [ 4.95509326e-01 -3.16798955e-01 -3.60089630e-01 -4.08161551e-01
-8.26357782e-01 -5.87906182e-01 8.80898178e-01 -6.49202019e-02
-9.01709348e-02 1.22611642e-01 6.28903210e-01 -4.06273752e-01
5.43330610e-01 -4.47730571e-01 -8.98956418e-01 -5.12366474e-01
5.10734797e-01 1.08485743e-01 4.52737093e-01 2.15188973... | [11.80916976928711, 2.0800909996032715] |
416e3545-9b9c-41fd-a654-f582ab1bc970 | overcoming-barriers-to-skill-injection-in | 2211.02098 | null | https://arxiv.org/abs/2211.02098v1 | https://arxiv.org/pdf/2211.02098v1.pdf | Overcoming Barriers to Skill Injection in Language Modeling: Case Study in Arithmetic | Through their transfer learning abilities, highly-parameterized large pre-trained language models have dominated the NLP landscape for a multitude of downstream language tasks. Though linguistically proficient, the inability of these models to incorporate the learning of non-linguistic entities (numerals and arithmetic... | ['Naren Ramakrishnan', 'Nikhil Muralidhar', 'Mandar Sharma'] | 2022-11-03 | null | null | null | null | ['mathematical-reasoning', 'arithmetic-reasoning'] | ['natural-language-processing', 'reasoning'] | [ 1.56436488e-01 5.98147213e-01 -8.98171142e-02 -2.91168362e-01
-5.47587872e-01 -8.37461531e-01 5.51496387e-01 5.51150739e-01
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-2.93556958e-01 -1.25058019e+00 -9.66067970e-01 8.36748779e-02
1.50714844e-01 4.03965741e-01 -2.92746335e-01 -4.75066692... | [9.574441909790039, 7.346804141998291] |
4fc9c0de-ffdb-47ac-9790-ddd888b7e4ef | struct-mdc-mesh-refined-unsupervised-depth | 2204.13877 | null | https://arxiv.org/abs/2204.13877v1 | https://arxiv.org/pdf/2204.13877v1.pdf | Struct-MDC: Mesh-Refined Unsupervised Depth Completion Leveraging Structural Regularities from Visual SLAM | Feature-based visual simultaneous localization and mapping (SLAM) methods only estimate the depth of extracted features, generating a sparse depth map. To solve this sparsity problem, depth completion tasks that estimate a dense depth from a sparse depth have gained significant importance in robotic applications like e... | ['Hyun Myung', 'Dong-Uk Seo', 'Hyunjun Lim', 'Jinwoo Jeon'] | 2022-04-29 | null | null | null | null | ['depth-completion'] | ['computer-vision'] | [-4.40366287e-03 -6.09992146e-02 -2.92394489e-01 -4.39374447e-01
-4.90149558e-01 -1.41846627e-01 5.79042017e-01 2.97950774e-01
-3.80140066e-01 8.11537921e-01 2.48619020e-01 2.77466804e-01
9.52168740e-03 -1.19422901e+00 -7.16702461e-01 -4.39863175e-01
-6.67629167e-02 6.68621361e-01 3.02633941e-01 -5.80329373... | [8.00857162475586, -2.379518747329712] |
7969ca55-7b3d-409f-a95a-841aef9133ca | analyzing-culture-specific-argument | null | null | https://aclanthology.org/2022.argmining-1.4 | https://aclanthology.org/2022.argmining-1.4.pdf | Analyzing Culture-Specific Argument Structures in Learner Essays | Language education has been shown to benefit from computational argumentation, for example, from methods that assess quality dimensions of language learners’ argumentative essays, such as their organization and argument strength. So far, however, little attention has been paid to cultural differences in learners’ argum... | ['Henning Wachsmuth', 'Garima Mudgal', 'Mei-Hua Chen', 'Wei-Fan Chen'] | null | null | null | null | argmining-acl-2022-10 | ['culture'] | ['speech'] | [-3.14814538e-01 4.33239520e-01 -2.98133135e-01 -3.32490414e-01
-3.64865661e-01 -9.04671788e-01 7.71580756e-01 8.71316433e-01
-4.61285412e-01 6.04380071e-01 9.47577834e-01 -7.23336875e-01
-3.55367690e-01 -8.30357492e-01 -4.60822403e-01 -1.97986037e-01
7.40289330e-01 1.35009056e-02 -1.83261618e-01 -7.37508595... | [11.213569641113281, 9.308149337768555] |
733e110d-8bfb-4e53-9e63-af47c919d383 | cyclic-guidance-for-weakly-supervised-joint | null | null | http://openaccess.thecvf.com/content_CVPR_2019/html/Shen_Cyclic_Guidance_for_Weakly_Supervised_Joint_Detection_and_Segmentation_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Shen_Cyclic_Guidance_for_Weakly_Supervised_Joint_Detection_and_Segmentation_CVPR_2019_paper.pdf | Cyclic Guidance for Weakly Supervised Joint Detection and Segmentation | Weakly supervised learning has attracted growing research attention due to the significant saving in annotation cost for tasks that require intra-image annotations, such as object detection and semantic segmentation. To this end, existing weakly supervised object detection and semantic segmentation approaches follow an... | [' Liujuan Cao', ' Yongjian Wu', ' Yan Wang', ' Rongrong Ji', 'Yunhang Shen'] | 2019-06-01 | null | null | null | cvpr-2019-6 | ['image-level-supervised-instance-segmentation'] | ['computer-vision'] | [ 4.45572078e-01 1.80094779e-01 -5.07556856e-01 -4.06562209e-01
-9.32714581e-01 -3.29888970e-01 3.17157030e-01 2.73732960e-01
-6.35519922e-01 5.64770341e-01 -4.34304178e-01 -5.81941344e-02
2.29803011e-01 -3.76854807e-01 -6.97004318e-01 -9.58034575e-01
2.22877458e-01 3.96145880e-01 9.74339902e-01 2.83939123... | [9.378549575805664, 1.0193181037902832] |
82933be2-1fac-44db-85e9-181c32cfeaf9 | don-t-guess-what-s-true-choose-what-s-optimal | 2302.10578 | null | https://arxiv.org/abs/2302.10578v1 | https://arxiv.org/pdf/2302.10578v1.pdf | Don't guess what's true: choose what's optimal. A probability transducer for machine-learning classifiers | In fields such as medicine and drug discovery, the ultimate goal of a classification is not to guess a class, but to choose the optimal course of action among a set of possible ones, usually not in one-one correspondence with the set of classes. This decision-theoretic problem requires sensible probabilities for the cl... | ['P. G. L. Porta Mana', 'A. S. Lundervold', 'K. Dyrland'] | 2023-02-21 | null | null | null | null | ['drug-discovery'] | ['medical'] | [ 4.51670676e-01 2.20186248e-01 -1.50527462e-01 -3.25851887e-01
-7.14333832e-01 -5.75873494e-01 5.08193493e-01 5.12851894e-01
-6.09524429e-01 9.61889923e-01 -4.11546052e-01 -5.74710250e-01
-4.90990847e-01 -1.03287578e+00 -3.62870514e-01 -1.08478653e+00
-6.32641688e-02 1.01568234e+00 1.46615461e-01 4.80169319... | [7.798662185668945, 4.5949273109436035] |
e24aaab6-e362-4392-8521-c336d0d118de | 4dcontrast-contrastive-learning-with-dynamic | 2112.02990 | null | https://arxiv.org/abs/2112.02990v2 | https://arxiv.org/pdf/2112.02990v2.pdf | 4DContrast: Contrastive Learning with Dynamic Correspondences for 3D Scene Understanding | We present a new approach to instill 4D dynamic object priors into learned 3D representations by unsupervised pre-training. We observe that dynamic movement of an object through an environment provides important cues about its objectness, and thus propose to imbue learned 3D representations with such dynamic understand... | ['Angela Dai', 'Matthias Nießner', 'Yujin Chen'] | 2021-12-06 | null | null | null | null | ['3d-instance-segmentation-1', 'unsupervised-pre-training'] | ['computer-vision', 'methodology'] | [ 4.29770142e-01 4.06344861e-01 -3.15515608e-01 -4.99734819e-01
-4.15534735e-01 -7.44286835e-01 7.55222440e-01 7.43849054e-02
-1.57973528e-01 -1.33594498e-01 2.55185604e-01 -3.15201998e-01
-1.30266219e-01 -7.52182782e-01 -1.13394880e+00 -4.02498424e-01
-5.30176274e-02 8.10279250e-01 4.15760487e-01 -1.41037479... | [8.25251579284668, -3.1235873699188232] |
1f8db83c-e42b-48de-882f-52407306e4a1 | mcbert-momentum-contrastive-learning-with | 2203.12940 | null | https://arxiv.org/abs/2203.12940v2 | https://arxiv.org/pdf/2203.12940v2.pdf | mcBERT: Momentum Contrastive Learning with BERT for Zero-Shot Slot Filling | Zero-shot slot filling has received considerable attention to cope with the problem of limited available data for the target domain. One of the important factors in zero-shot learning is to make the model learn generalized and reliable representations. For this purpose, we present mcBERT, which stands for momentum cont... | ['Jong-Hyeok Lee', 'WonKee Lee', 'Seong-Hwan Heo'] | 2022-03-24 | null | null | null | null | ['zero-shot-slot-filling', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing'] | [-1.5557232e-01 2.6068762e-01 -7.0135331e-01 -1.2167629e-01
-9.6118325e-01 3.4467480e-01 4.4146901e-01 -5.6625992e-02
-3.5994723e-01 9.4791132e-01 1.2667401e-01 -3.2693322e-03
1.2600592e-01 -7.9063487e-01 -6.2364137e-01 -5.2879447e-01
1.3011277e-02 7.1942413e-01 7.3973650e-01 -3.6941969e-01
-6.4280242e-02... | [9.994941711425781, 3.055532932281494] |
990543f5-1209-4318-a9b6-a332967a28f1 | back-to-the-future-sequential-alignment-of | 1909.03464 | null | https://arxiv.org/abs/1909.03464v3 | https://arxiv.org/pdf/1909.03464v3.pdf | Back to the Future -- Sequential Alignment of Text Representations | Language evolves over time in many ways relevant to natural language processing tasks. For example, recent occurrences of tokens 'BERT' and 'ELMO' in publications refer to neural network architectures rather than persons. This type of temporal signal is typically overlooked, but is important if one aims to deploy a mac... | ['Isabelle Augenstein', 'Wouter Kouw', 'Johannes Bjerva'] | 2019-09-08 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [ 4.80608381e-02 -1.40085727e-01 -2.48687401e-01 -4.83280092e-01
-3.63205224e-01 -6.58735394e-01 1.28024018e+00 7.65154898e-01
-7.59603977e-01 9.04560864e-01 3.70850980e-01 -2.66710877e-01
5.51896021e-02 -4.90151972e-01 -8.32714736e-01 -4.48788851e-01
-1.82923585e-01 5.94786286e-01 -1.28328381e-03 -3.51939172... | [10.071425437927246, 8.621700286865234] |
8d2aedbb-0c92-40f9-b7fb-ee34acecdd4e | rapid-model-comparison-by-amortizing-across | null | null | https://openreview.net/forum?id=rylGty24YB | https://openreview.net/pdf?id=rylGty24YB | Rapid Model Comparison by Amortizing Across Models | Comparing the inferences of diverse candidate models is an essential part of model checking and escaping local optima. To enable efficient comparison, we introduce an amortized variational inference framework that can perform fast and reliable posterior estimation across models of the same architecture. Our Any Paramet... | ['Michael C. Hughes', 'Lily H. Zhang'] | 2019-10-16 | null | null | null | pproximateinference-aabi-symposium-2019-12 | ['topic-models'] | ['natural-language-processing'] | [ 1.75740346e-02 2.40996331e-01 -3.85438472e-01 -6.11796796e-01
-1.44686151e+00 -7.47035980e-01 8.23246956e-01 -4.60300893e-02
-3.50059450e-01 8.50941718e-01 -1.45476624e-01 -5.15208542e-01
5.71773238e-02 -7.09917545e-01 -1.21056199e+00 -4.55179632e-01
1.97983697e-01 9.72961426e-01 1.22060217e-01 3.76315653... | [7.006649971008301, 3.9927642345428467] |
c5ea7765-84b9-4c80-9d5e-9b0f500b2839 | attentional-graph-convolutional-network-for | 2301.00145 | null | https://arxiv.org/abs/2301.00145v1 | https://arxiv.org/pdf/2301.00145v1.pdf | Attentional Graph Convolutional Network for Structure-aware Audio-Visual Scene Classification | Audio-Visual scene understanding is a challenging problem due to the unstructured spatial-temporal relations that exist in the audio signals and spatial layouts of different objects and various texture patterns in the visual images. Recently, many studies have focused on abstracting features from convolutional neural n... | ['Yangsheng Xu', 'Tin Lun Lam', 'Junjie Hu', 'Xiaonan Qi', 'Yuhongze Zhou', 'Liguang Zhou'] | 2022-12-31 | null | null | null | null | ['scene-classification', 'scene-recognition'] | ['computer-vision', 'computer-vision'] | [ 3.21293712e-01 -3.11543286e-01 4.29516971e-01 -3.06197912e-01
-5.89734554e-01 -2.95025826e-01 1.89999357e-01 3.32559764e-01
1.22101262e-01 1.97436646e-01 4.61542875e-01 7.92051926e-02
-2.41563335e-01 -4.61884052e-01 -7.34493136e-01 -7.21157134e-01
-2.83525676e-01 -3.72241288e-01 4.61961508e-01 6.64890632... | [14.881149291992188, 4.822327613830566] |
55828482-29ca-41cb-becd-08ee10a91712 | tabgenie-a-toolkit-for-table-to-text | 2302.14169 | null | https://arxiv.org/abs/2302.14169v1 | https://arxiv.org/pdf/2302.14169v1.pdf | TabGenie: A Toolkit for Table-to-Text Generation | Heterogenity of data-to-text generation datasets limits the research on data-to-text generation systems. We present TabGenie - a toolkit which enables researchers to explore, preprocess, and analyze a variety of data-to-text generation datasets through the unified framework of table-to-text generation. In TabGenie, all... | ['Ondřej Dušek', 'Ondřej Plátek', 'Ekaterina Garanina', 'Zdeněk Kasner'] | 2023-02-27 | null | null | null | null | ['table-to-text-generation', 'data-to-text-generation'] | ['natural-language-processing', 'natural-language-processing'] | [-2.16650143e-01 9.97086763e-02 2.34655831e-02 -4.46081132e-01
-1.01632965e+00 -1.06532204e+00 8.53824139e-01 4.46901351e-01
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1.40012115e-01 -1.30168283e+00 -3.98645371e-01 -3.16067398e-01
1.83031037e-01 7.92600632e-01 -8.68670922e-03 -2.47662827... | [11.428180694580078, 8.80521297454834] |
c57eed16-0107-43f4-8c09-1067ac8c5d09 | inferno-inferring-object-centric-3d-scene | null | null | https://openreview.net/forum?id=YVa8X_2I1b | https://openreview.net/pdf?id=YVa8X_2I1b | INFERNO: Inferring Object-Centric 3D Scene Representations without Supervision | We propose INFERNO, a method to infer object-centric representations of visual scenes without relying on annotations. Our method learns to decompose a scene into multiple objects, each object having a structured representation that disentangles its shape, appearance and 3D pose. To impose this structure we rely on rece... | ['Aaron Courville', 'Nicolas Ballas', 'Lluis Castrejon'] | 2021-09-29 | null | null | null | null | ['video-object-tracking'] | ['computer-vision'] | [ 4.60795820e-01 5.29449821e-01 1.67468309e-01 -6.56961918e-01
-5.33419847e-01 -8.80155742e-01 8.46048772e-01 -6.30720928e-02
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8.14708415e-03 -8.39542329e-01 -1.27907145e+00 -4.77709174e-01
8.10884312e-02 4.59371477e-01 -2.02187374e-01 2.05526035... | [9.079679489135742, -3.0657904148101807] |
4b327c39-1417-4e0b-82dc-8722d3b8689f | a-preliminary-study-on-pattern-reconstruction | 2302.12972 | null | https://arxiv.org/abs/2302.12972v1 | https://arxiv.org/pdf/2302.12972v1.pdf | A Preliminary Study on Pattern Reconstruction for Optimal Storage of Wearable Sensor Data | Efficient querying and retrieval of healthcare data is posing a critical challenge today with numerous connected devices continuously generating petabytes of images, text, and internet of things (IoT) sensor data. One approach to efficiently store the healthcare data is to extract the relevant and representative featur... | ['Farhana Zulkernine', 'Sazia Mahfuz'] | 2023-02-25 | null | null | null | null | ['human-activity-recognition', 'human-activity-recognition'] | ['computer-vision', 'time-series'] | [ 8.85504261e-02 1.11604430e-01 4.73712713e-01 -3.08245510e-01
-3.53421569e-01 2.67176963e-02 2.33424827e-01 6.39692008e-01
-6.59017980e-01 7.29560077e-01 4.92166072e-01 9.83367935e-02
-3.60845983e-01 -1.06408024e+00 -6.21733963e-01 -6.23660624e-01
-3.73662055e-01 2.08440512e-01 1.18623801e-01 -1.22647688... | [13.935821533203125, 3.2961573600769043] |
a67cf8b7-951f-42e1-ba14-381b074d91d6 | generalized-multiple-intent-conditioned-slot | 2305.11023 | null | https://arxiv.org/abs/2305.11023v1 | https://arxiv.org/pdf/2305.11023v1.pdf | Generalized Multiple Intent Conditioned Slot Filling | Natural language understanding includes the tasks of intent detection (identifying a user's objectives) and slot filling (extracting the entities relevant to those objectives). Prior slot filling methods assume that each intent type cannot occur more than once within a message, however this is often not a valid assumpt... | ['David Barber', 'Edward Challis', 'Cristi Cobzarenco', 'Marius Cobzarenco', 'Arthur Wilcke', 'Harshil Shah'] | 2023-05-18 | null | null | null | null | ['intent-detection', 'slot-filling'] | ['natural-language-processing', 'natural-language-processing'] | [ 4.21178132e-01 7.84998715e-01 -1.68886244e-01 -4.29138869e-01
-5.93432903e-01 -6.56939328e-01 9.69741642e-01 6.56101227e-01
-4.60058033e-01 1.06575084e+00 4.99337077e-01 -6.88854158e-01
-4.85405140e-02 -1.01402330e+00 -7.46350229e-01 3.86262089e-01
1.94915198e-02 1.11535013e+00 3.96516055e-01 -5.37735045... | [10.277863502502441, 8.533125877380371] |
49fb1133-1409-4de5-a9e4-0f3c02337cba | low-cost-on-device-partial-domain-adaptation | 2203.00772 | null | https://arxiv.org/abs/2203.00772v1 | https://arxiv.org/pdf/2203.00772v1.pdf | Low-Cost On-device Partial Domain Adaptation (LoCO-PDA): Enabling efficient CNN retraining on edge devices | With the increased deployment of Convolutional Neural Networks (CNNs) on edge devices, the uncertainty of the observed data distribution upon deployment has led researchers to to utilise large and extensive datasets such as ILSVRC'12 to train CNNs. Consequently, it is likely that the observed data distribution upon dep... | ['Christos-Savvas Bouganis', 'Aditya Rajagopal'] | 2022-03-01 | null | null | null | null | ['partial-domain-adaptation'] | ['methodology'] | [-1.54739782e-01 6.84228241e-02 -4.66599047e-01 -5.59127033e-01
-2.31931880e-01 -1.03618228e+00 -3.99526618e-02 -1.26898333e-01
-4.54367518e-01 7.91775823e-01 -1.87706485e-01 -9.25135434e-01
-2.16124505e-01 -7.57608712e-01 -9.52103734e-01 -3.51256251e-01
3.14443521e-02 3.80850941e-01 1.93983302e-01 2.26906836... | [8.162870407104492, 2.6087539196014404] |
d9415531-29d7-4904-ae47-1b9b1924de58 | autolv-automatic-lecture-video-generator | 2209.08795 | null | https://arxiv.org/abs/2209.08795v1 | https://arxiv.org/pdf/2209.08795v1.pdf | AutoLV: Automatic Lecture Video Generator | We propose an end-to-end lecture video generation system that can generate realistic and complete lecture videos directly from annotated slides, instructor's reference voice and instructor's reference portrait video. Our system is primarily composed of a speech synthesis module with few-shot speaker adaptation and an a... | ['Sanjay Jha', 'Yang song', 'Wenbin Wang'] | 2022-09-19 | null | null | null | null | ['talking-head-generation', 'video-generation'] | ['computer-vision', 'computer-vision'] | [-1.01535045e-01 1.87029153e-01 7.83238411e-02 -3.88661653e-01
-1.05266905e+00 -9.25620615e-01 5.07912934e-01 -4.96388376e-01
9.21654403e-02 8.44646633e-01 4.83328909e-01 -4.31728572e-01
3.17834139e-01 -4.87416804e-01 -6.50841415e-01 -6.65125847e-01
4.91313994e-01 -9.86189023e-02 3.35850894e-01 -3.82931292... | [14.766676902770996, 6.532095909118652] |
47217747-77cc-4f51-ad58-baf5b88cd603 | turning-a-blind-eye-explicit-removal-of | 1809.02169 | null | http://arxiv.org/abs/1809.02169v2 | http://arxiv.org/pdf/1809.02169v2.pdf | Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings | Neural networks achieve the state-of-the-art in image classification tasks.
However, they can encode spurious variations or biases that may be present in
the training data. For example, training an age predictor on a dataset that is
not balanced for gender can lead to gender biased predicitons (e.g. wrongly
predicting ... | ['Christoffer Nellaker', 'Andrew Zisserman', 'Mohsan Alvi'] | 2018-09-06 | null | null | null | null | ['facial-attribute-classification'] | ['computer-vision'] | [ 5.71870387e-01 3.83913130e-01 -1.00960672e-01 -9.39039946e-01
9.53274406e-03 -3.18434060e-01 5.91910720e-01 5.19445390e-02
-7.88758516e-01 9.56316233e-01 1.69900686e-01 -1.80394843e-01
-8.72113975e-04 -1.00290692e+00 -6.10934317e-01 -5.82021713e-01
-2.29652971e-01 5.07951558e-01 -4.75085497e-01 -7.40268901... | [13.100668907165527, 1.2901705503463745] |
68ba9ba8-2a88-4ccc-a70b-e93b6b2bc331 | exploring-simple-siamese-representation | 2011.10566 | null | https://arxiv.org/abs/2011.10566v1 | https://arxiv.org/pdf/2011.10566v1.pdf | Exploring Simple Siamese Representation Learning | Siamese networks have become a common structure in various recent models for unsupervised visual representation learning. These models maximize the similarity between two augmentations of one image, subject to certain conditions for avoiding collapsing solutions. In this paper, we report surprising empirical results th... | ['Kaiming He', 'Xinlei Chen'] | 2020-11-20 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Chen_Exploring_Simple_Siamese_Representation_Learning_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Chen_Exploring_Simple_Siamese_Representation_Learning_CVPR_2021_paper.pdf | cvpr-2021-1 | ['self-supervised-image-classification'] | ['computer-vision'] | [ 2.66314834e-01 3.12375903e-01 -4.98531342e-01 -5.88625669e-01
-4.83073860e-01 -4.25815821e-01 7.80456245e-01 -9.64278262e-03
-6.80370986e-01 6.06747270e-01 4.34948623e-01 -3.08849126e-01
-3.93384881e-02 -2.67457545e-01 -9.11825895e-01 -6.91519082e-01
-3.75918329e-01 4.36942011e-01 1.22767061e-01 -2.63338238... | [9.314146041870117, 2.7761447429656982] |
f7a7f622-af02-4b4b-a8a0-9bb26bfd0a91 | pidnet-an-efficient-network-for-dynamic | 2009.00312 | null | https://arxiv.org/abs/2009.00312v1 | https://arxiv.org/pdf/2009.00312v1.pdf | PIDNet: An Efficient Network for Dynamic Pedestrian Intrusion Detection | Vision-based dynamic pedestrian intrusion detection (PID), judging whether pedestrians intrude an area-of-interest (AoI) by a moving camera, is an important task in mobile surveillance. The dynamically changing AoIs and a number of pedestrians in video frames increase the difficulty and computational complexity of dete... | ['Jiming Chen', 'Jingchen Sun', 'Jiayuan Fan', 'Tao Chen', 'Shibo He'] | 2020-09-01 | null | null | null | null | ['feature-compression'] | ['computer-vision'] | [ 2.79058442e-02 -8.58448267e-01 7.30479658e-02 -9.74653885e-02
-3.08577474e-02 -2.37635657e-01 2.83543080e-01 -2.60203093e-01
-7.37529933e-01 3.92698318e-01 -3.99190575e-01 -3.17640632e-01
2.93701440e-01 -1.06455600e+00 -6.12638652e-01 -7.94772029e-01
2.10073479e-02 1.28423586e-01 1.05818081e+00 5.02499798... | [8.258193016052246, -0.7755528688430786] |
e42af24e-428a-4d42-8442-db4922921617 | an-efficient-style-virtual-try-on-network | 2105.13183 | null | https://arxiv.org/abs/2105.13183v2 | https://arxiv.org/pdf/2105.13183v2.pdf | An Efficient Style Virtual Try on Network for Clothing Business Industry | With the increasing development of garment manufacturing industry, the method of combining neural network with industry to reduce product redundancy has been paid more and more attention.In order to reduce garment redundancy and achieve personalized customization, more researchers have appeared in the field of virtual ... | ['Neal N. Xiong', 'Yukun Dong', 'Xixi Tao', 'Shanchen Pang'] | 2021-05-27 | null | null | null | null | ['human-parsing'] | ['computer-vision'] | [ 1.17862597e-01 -1.85217455e-01 3.70605802e-03 -2.27632523e-01
5.68759084e-01 -5.17427742e-01 -2.82270640e-01 -5.80547512e-01
-4.88088885e-03 2.20817983e-01 -3.83482464e-02 -1.74605269e-02
5.28634414e-02 -1.07564139e+00 -3.90949339e-01 -5.46974480e-01
5.90053856e-01 1.19887367e-01 4.17287529e-01 -6.17719412... | [11.577136993408203, -0.9808207750320435] |
5bb7e241-b864-49c7-96e6-a145b359cfdb | using-two-losses-and-two-datasets | 2212.07669 | null | https://arxiv.org/abs/2212.07669v1 | https://arxiv.org/pdf/2212.07669v1.pdf | Using Two Losses and Two Datasets Simultaneously to Improve TempoWiC Accuracy | WSD (Word Sense Disambiguation) is the task of identifying which sense of a word is meant in a sentence or other segment of text. Researchers have worked on this task (e.g. Pustejovsky, 2002) for years but it's still a challenging one even for SOTA (state-of-the-art) LMs (language models). The new dataset, TempoWiC int... | ['Sauleh Eetemadi', 'Motahhare Mirzaei', 'Mohammad Javad Pirhadi'] | 2022-12-15 | null | null | null | null | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 3.90979573e-02 -7.47953206e-02 -3.82624865e-01 -2.39580616e-01
-8.15675139e-01 -7.45125353e-01 8.57109606e-01 4.41224009e-01
-9.38375890e-01 9.01581824e-01 2.42322758e-01 -3.63172889e-01
2.21524388e-01 -5.35847008e-01 -3.10842693e-01 -3.96984547e-01
7.18626752e-02 3.15943688e-01 6.06228828e-01 -7.93421507... | [10.252880096435547, 9.058541297912598] |
bc35509c-e107-4a84-a4bf-6a0c6612d1ae | towards-zero-shot-relation-extraction-in-web | 2305.13805 | null | https://arxiv.org/abs/2305.13805v1 | https://arxiv.org/pdf/2305.13805v1.pdf | Towards Zero-shot Relation Extraction in Web Mining: A Multimodal Approach with Relative XML Path | The rapid growth of web pages and the increasing complexity of their structure poses a challenge for web mining models. Web mining models are required to understand the semi-structured web pages, particularly when little is known about the subject or template of a new page. Current methods migrate language models to th... | ['Jingbo Shang', 'Zilong Wang'] | 2023-05-23 | null | null | null | null | ['relation-extraction'] | ['natural-language-processing'] | [ 2.69171357e-01 2.29125097e-01 -9.28140938e-01 6.99048787e-02
-7.40872920e-01 -4.60978359e-01 5.75360358e-01 5.36731541e-01
2.49280054e-02 5.56534350e-01 3.39486182e-01 -6.86675906e-01
-3.53257835e-01 -1.26556480e+00 -8.98423254e-01 -8.37744549e-02
-6.17647827e-01 5.11156142e-01 5.95384002e-01 -2.44654436... | [9.537734985351562, 8.21106243133545] |
50e0ce7d-31fc-44f7-b72e-22439b9e9b90 | regularized-two-branch-proposal-networks-for | 2008.08257 | null | https://arxiv.org/abs/2008.08257v1 | https://arxiv.org/pdf/2008.08257v1.pdf | Regularized Two-Branch Proposal Networks for Weakly-Supervised Moment Retrieval in Videos | Video moment retrieval aims to localize the target moment in an video according to the given sentence. The weak-supervised setting only provides the video-level sentence annotations during training. Most existing weak-supervised methods apply a MIL-based framework to develop inter-sample confrontment, but ignore the in... | ['Zhijie Lin', 'Jieming Zhu', 'Zhou Zhao', 'Zhu Zhang', 'Xiuqiang He'] | 2020-08-19 | null | null | null | null | ['moment-retrieval'] | ['computer-vision'] | [ 1.44896641e-01 -1.55020609e-01 -5.62560976e-01 -6.16729736e-01
-1.14317930e+00 -3.47022325e-01 7.06368744e-01 -5.28684966e-02
-6.47786558e-01 4.00365561e-01 3.31800967e-01 6.54814467e-02
9.24737528e-02 -2.60025740e-01 -6.26421571e-01 -7.37486601e-01
1.26337647e-01 6.32818416e-02 2.96711415e-01 -3.37148495... | [9.978055953979492, 0.6155602335929871] |
b544afb6-e6a2-441e-9c72-f3fe52b61dc8 | efficient-attention-free-video-shift | 2208.11108 | null | https://arxiv.org/abs/2208.11108v1 | https://arxiv.org/pdf/2208.11108v1.pdf | Efficient Attention-free Video Shift Transformers | This paper tackles the problem of efficient video recognition. In this area, video transformers have recently dominated the efficiency (top-1 accuracy vs FLOPs) spectrum. At the same time, there have been some attempts in the image domain which challenge the necessity of the self-attention operation within the transfor... | ['Georgios Tzimiropoulos', 'Brais Martinez', 'Adrian Bulat'] | 2022-08-23 | null | null | null | null | ['video-recognition'] | ['computer-vision'] | [ 5.60101151e-01 -6.76622894e-03 -1.53694987e-01 -2.01523498e-01
-6.47579670e-01 -2.91006118e-01 5.70782840e-01 -2.77101696e-01
-6.81996226e-01 3.17489266e-01 2.32292473e-01 -4.03387338e-01
-1.58534855e-01 -5.93316436e-01 -1.14870071e+00 -7.48537362e-01
1.78430393e-01 2.47120902e-01 4.32876408e-01 -1.65961549... | [8.885666847229004, 0.4929862916469574] |
ac6be1ce-9719-4ee1-9bc5-3633867d2735 | context-aware-language-modeling-for-goal | null | null | https://openreview.net/forum?id=qZEAjNzBHv | https://openreview.net/pdf?id=qZEAjNzBHv | Context-Aware Language Modeling for Goal-Oriented Dialogue Systems | Goal-oriented dialogue systems has long faced the trade-off between fluent language generation and task-specific control. While supervised learning with large language models are capable of producing realistic responses, how to steer such responses towards completing a specific task without sacrificing language quality... | ['Anonymous'] | 2021-11-16 | null | null | null | acl-arr-november-2021-11 | ['goal-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 3.40401322e-01 6.71873212e-01 -3.61481369e-01 -5.59912264e-01
-8.31286311e-01 -5.71030319e-01 9.03562844e-01 -7.07592815e-02
-5.53823054e-01 9.77725863e-01 4.56144571e-01 -4.88608181e-01
3.40718254e-02 -4.31133986e-01 -1.79279819e-01 -4.76332664e-01
7.91339502e-02 9.12643731e-01 -4.55395430e-02 -7.32846737... | [13.05444622039795, 8.065900802612305] |
2050b5c4-8774-4031-ac2e-11ff36b4c744 | data-aided-active-user-detection-with-a-user | 2205.10780 | null | https://arxiv.org/abs/2205.10780v2 | https://arxiv.org/pdf/2205.10780v2.pdf | Data-aided Active User Detection with a User Activity Extraction Network for Grant-free SCMA Systems | In grant-free sparse code multiple access (GF-SCMA) system, active user detection (AUD) is a major performance bottleneck as it involves complex combinatorial problem, which makes joint design of contention resources for users and AUD at the receiver a crucial but a challenging problem. To this end, we propose autoenco... | ['Chung G. Kang', 'Ameha T. Abebe', 'Minsig Han'] | 2022-05-22 | null | null | null | null | ['activity-detection'] | ['computer-vision'] | [ 3.62053573e-01 -1.68522671e-01 -4.13385600e-01 -3.64948213e-02
-6.63403928e-01 -2.31031664e-02 6.21426105e-02 -3.02699059e-01
-4.30998981e-01 8.68290961e-01 1.45879285e-05 -9.06466961e-01
-1.61171183e-01 -4.11502510e-01 -1.18874975e-01 -1.12767589e+00
-7.96961486e-01 -1.62178241e-02 -2.31950060e-01 6.74531469... | [6.299469947814941, 1.4675555229187012] |
e377f564-08ed-457b-a574-723de867ce71 | muffliato-peer-to-peer-privacy-amplification | 2206.05091 | null | https://arxiv.org/abs/2206.05091v2 | https://arxiv.org/pdf/2206.05091v2.pdf | Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging | Decentralized optimization is increasingly popular in machine learning for its scalability and efficiency. Intuitively, it should also provide better privacy guarantees, as nodes only observe the messages sent by their neighbors in the network graph. But formalizing and quantifying this gain is challenging: existing re... | ['Laurent Massoulié', 'Aurélien Bellet', 'Mathieu Even', 'Edwige Cyffers'] | 2022-06-10 | null | null | null | null | ['graph-matching'] | ['graphs'] | [-2.46970102e-01 2.61843055e-01 -1.71468347e-01 -6.38686478e-01
-6.37016654e-01 -1.07222140e+00 2.59768903e-01 5.52014351e-01
-5.73116183e-01 6.09395683e-01 7.33874738e-02 -1.31843984e-01
-1.83075115e-01 -9.56241488e-01 -6.95172966e-01 -1.07264280e+00
-7.71484613e-01 2.47784674e-01 -2.65629172e-01 -6.20057061... | [5.925891399383545, 6.450963973999023] |
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