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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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
446e8bcf-95fc-48e2-923f-2a887bc05c3d | learning-to-aggregate-and-personalize-3d-face | 2106.07852 | null | https://arxiv.org/abs/2106.07852v1 | https://arxiv.org/pdf/2106.07852v1.pdf | Learning to Aggregate and Personalize 3D Face from In-the-Wild Photo Collection | Non-parametric face modeling aims to reconstruct 3D face only from images without shape assumptions. While plausible facial details are predicted, the models tend to over-depend on local color appearance and suffer from ambiguous noise. To address such problem, this paper presents a novel Learning to Aggregate and Pers... | ['Feiyue Huang', 'Jilin Li', 'Chengjie Wang', 'Jian Yang', 'Yan Yan', 'Ying Tai', 'Renwang Chen', 'Yanhao Ge', 'Zhenyu Zhang'] | 2021-06-15 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Zhang_Learning_To_Aggregate_and_Personalize_3D_Face_From_In-the-Wild_Photo_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Zhang_Learning_To_Aggregate_and_Personalize_3D_Face_From_In-the-Wild_Photo_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-face-modeling'] | ['computer-vision'] | [ 2.67170101e-01 3.14561188e-01 -1.48187904e-02 -8.40005815e-01
-6.43618226e-01 -3.64769965e-01 3.70326221e-01 -8.19929898e-01
2.35879660e-01 3.12856287e-01 7.38725364e-02 4.22630787e-01
-1.57578230e-01 -4.69666004e-01 -9.01891589e-01 -8.43195677e-01
3.36676866e-01 7.09929466e-01 -3.54463547e-01 1.92990139... | [12.839277267456055, -0.06349680572748184] |
9192013f-e268-4dba-91b1-cd705d567a26 | open-domain-targeted-sentiment-analysis-via | 1906.03820 | null | https://arxiv.org/abs/1906.03820v1 | https://arxiv.org/pdf/1906.03820v1.pdf | Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and Classification | Open-domain targeted sentiment analysis aims to detect opinion targets along with their sentiment polarities from a sentence. Prior work typically formulates this task as a sequence tagging problem. However, such formulation suffers from problems such as huge search space and sentiment inconsistency. To address these p... | ['Minghao Hu', 'Yiwei Lv', 'Zhen Huang', 'Yuxing Peng', 'Dongsheng Li'] | 2019-06-10 | open-domain-targeted-sentiment-analysis-via-1 | https://aclanthology.org/P19-1051 | https://aclanthology.org/P19-1051.pdf | acl-2019-7 | ['aspect-term-extraction-and-sentiment'] | ['natural-language-processing'] | [ 5.26133180e-01 -1.35965168e-01 -5.46790302e-01 -6.88245773e-01
-1.12098432e+00 -1.14133799e+00 5.35963953e-01 1.50374845e-01
-2.53387809e-01 7.23444581e-01 5.97773075e-01 -2.59070456e-01
5.47227621e-01 -5.06966233e-01 -3.18034142e-01 -7.37926722e-01
3.91216904e-01 9.38440785e-02 4.03645277e-01 -3.83952349... | [11.46235179901123, 6.657419681549072] |
69e3e715-5efd-4032-9738-73e4c986da93 | language-acquisition-do-children-and-language | 2306.03586 | null | https://arxiv.org/abs/2306.03586v1 | https://arxiv.org/pdf/2306.03586v1.pdf | Language acquisition: do children and language models follow similar learning stages? | During language acquisition, children follow a typical sequence of learning stages, whereby they first learn to categorize phonemes before they develop their lexicon and eventually master increasingly complex syntactic structures. However, the computational principles that lead to this learning trajectory remain largel... | ['Jean-Rémi King', 'Yair Lakretz', 'Linnea Evanson'] | 2023-06-06 | null | null | null | null | ['language-acquisition'] | ['natural-language-processing'] | [ 5.87567762e-02 2.72721499e-01 -2.14209780e-02 -4.75040495e-01
-1.20271556e-01 -1.04817283e+00 6.15234673e-01 7.83572018e-01
-6.31495714e-01 2.36402661e-01 1.99266687e-01 -3.53392094e-01
-1.48594985e-02 -9.74674821e-01 -8.52846146e-01 -2.81061023e-01
-2.68389016e-01 7.35912383e-01 2.72998095e-01 -3.93236101... | [10.434433937072754, 8.948539733886719] |
80549c49-2450-4b43-9817-10070ac45663 | dadagp-a-dataset-of-tokenized-guitarpro-songs | 2107.14653 | null | https://arxiv.org/abs/2107.14653v1 | https://arxiv.org/pdf/2107.14653v1.pdf | DadaGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models | Originating in the Renaissance and burgeoning in the digital era, tablatures are a commonly used music notation system which provides explicit representations of instrument fingerings rather than pitches. GuitarPro has established itself as a widely used tablature format and software enabling musicians to edit and shar... | ['Yi-Hsuan Yang', 'Mathieu Barthet', 'Zack Zukowski', 'CJ Carr', 'Adarsh Kumar', 'Pedro Sarmento'] | 2021-07-30 | null | null | null | null | ['music-generation', 'genre-classification', 'music-generation'] | ['audio', 'computer-vision', 'music'] | [ 3.50648791e-01 -1.09562710e-01 2.19303906e-01 -7.02853426e-02
-6.66563928e-01 -1.18575037e+00 5.39295793e-01 -3.08097631e-01
-4.36401367e-02 7.52238095e-01 2.82604277e-01 4.03180867e-02
-5.94794393e-01 -8.74537289e-01 -5.85691452e-01 -3.45711976e-01
9.92617831e-02 8.12959731e-01 -1.33823484e-01 -5.10185421... | [16.04753303527832, 5.5158820152282715] |
c0be35b0-d28c-4f05-aa9f-becc4daf52c2 | sample-level-multi-view-graph-clustering | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Tan_Sample-Level_Multi-View_Graph_Clustering_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Tan_Sample-Level_Multi-View_Graph_Clustering_CVPR_2023_paper.pdf | Sample-Level Multi-View Graph Clustering | Multi-view clustering have hitherto been studied due to their effectiveness in dealing with heterogeneous data. Despite the empirical success made by recent works, there still exists several severe challenges. Particularly, previous multi-view clustering algorithms seldom consider the topological structure in data,... | ['Jiancheng Lv', 'Wentao Feng', 'Shudong Huang', 'Yixi Liu', 'Yuze Tan'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['graph-clustering'] | ['graphs'] | [-3.94810885e-01 -3.33701909e-01 -2.89339304e-01 -3.35964918e-01
-4.47368205e-01 -5.73437274e-01 3.84885401e-01 -1.99091192e-02
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-6.26008511e-01 -5.21520853e-01 -2.80507565e-01 -9.94509816e-01
2.76793480e-01 4.16822791e-01 2.42174432e-01 1.45058567... | [8.235664367675781, 4.627853870391846] |
e977b3a1-47a5-4242-8343-5fa49eb1d60d | rotogbml-towards-out-of-distribution | 2303.06679 | null | https://arxiv.org/abs/2303.06679v1 | https://arxiv.org/pdf/2303.06679v1.pdf | RotoGBML: Towards Out-of-Distribution Generalization for Gradient-Based Meta-Learning | Gradient-based meta-learning (GBML) algorithms are able to fast adapt to new tasks by transferring the learned meta-knowledge, while assuming that all tasks come from the same distribution (in-distribution, ID). However, in the real world, they often suffer from an out-of-distribution (OOD) generalization problem, wher... | ['Wenbin Li', 'Donglin Wang', 'Zhitao Wang', 'Zifeng Zhuang', 'Min Zhang'] | 2023-03-12 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [-8.35638419e-02 -4.52012509e-01 -2.34906465e-01 -3.41840237e-01
-2.83557147e-01 -4.08715397e-01 6.94017947e-01 -5.90105243e-02
-5.33406377e-01 7.35387802e-01 2.57998496e-01 1.57450289e-01
-3.35088640e-01 -6.94654644e-01 -8.16051185e-01 -9.15654898e-01
1.59581155e-01 1.47194251e-01 4.85697001e-01 -1.62765458... | [9.86711311340332, 2.8006880283355713] |
3ebe6939-87b8-4daa-9cc4-e1d0c2ae666b | towards-reading-hidden-emotions-a-comparative | 1511.00423 | null | http://arxiv.org/abs/1511.00423v2 | http://arxiv.org/pdf/1511.00423v2.pdf | Towards Reading Hidden Emotions: A comparative Study of Spontaneous Micro-expression Spotting and Recognition Methods | Micro-expressions (MEs) are rapid, involuntary facial expressions which
reveal emotions that people do not intend to show. Studying MEs is valuable as
recognizing them has many important applications, particularly in forensic
science and psychotherapy. However, analyzing spontaneous MEs is very
challenging due to their... | ['Matti Pietikäinen', 'Xiaopeng Hong', 'Xiaobai Li', 'Antti Moilanen', 'Xiaohua Huang', 'Tomas Pfister', 'Guoying Zhao'] | 2015-11-02 | null | null | null | null | ['micro-expression-spotting'] | ['computer-vision'] | [ 2.30289862e-01 -2.87589610e-01 -1.59659341e-01 -3.90699834e-01
-7.45815337e-01 -4.50855017e-01 6.03268445e-01 -4.46213305e-01
-5.17947197e-01 4.41859841e-01 -7.77509063e-02 3.81241500e-01
1.27006114e-01 -1.13008417e-01 -2.13019654e-01 -9.83683169e-01
-3.14506918e-01 4.03757542e-02 -1.27285328e-02 -1.36821032... | [13.619081497192383, 1.8372533321380615] |
c322636e-f9ee-4c0a-8968-ce60864cf2e5 | email-spam-detection-using-hierarchical | 2204.07390 | null | https://arxiv.org/abs/2204.07390v2 | https://arxiv.org/pdf/2204.07390v2.pdf | Email Spam Detection Using Hierarchical Attention Hybrid Deep Learning Method | Email is one of the most widely used ways to communicate, with millions of people and businesses relying on it to communicate and share knowledge and information on a daily basis. Nevertheless, the rise in email users has occurred a dramatic increase in spam emails in recent years. Processing and managing emails proper... | ['Seyhmus Yilmaz', 'Sultan Zavrak'] | 2022-04-15 | null | null | null | null | ['spam-detection'] | ['natural-language-processing'] | [ 1.41100332e-01 -3.98414850e-01 9.40401256e-02 -3.83574665e-01
-7.46983960e-02 -2.43679747e-01 6.92477167e-01 2.20723450e-01
-7.38929451e-01 5.92799544e-01 1.99638203e-01 -3.93641293e-01
-1.02340572e-01 -8.53565991e-01 -1.19291335e-01 -4.00699437e-01
3.93391281e-01 6.36155605e-02 4.30871904e-01 -3.59545588... | [7.868724822998047, 9.967967987060547] |
d6eef7d6-7281-4818-a017-63f6666cbdd3 | face-generation-from-textual-features-using | 2301.09123 | null | https://arxiv.org/abs/2301.09123v1 | https://arxiv.org/pdf/2301.09123v1.pdf | Face Generation from Textual Features using Conditionally Trained Inputs to Generative Adversarial Networks | Generative Networks have proved to be extremely effective in image restoration and reconstruction in the past few years. Generating faces from textual descriptions is one such application where the power of generative algorithms can be used. The task of generating faces can be useful for a number of applications such a... | ['Mihir Tale', 'Aniket Ghorpade', 'Tejas Pradhan', 'Sandeep Shinde'] | 2023-01-22 | null | null | null | null | ['face-generation'] | ['computer-vision'] | [ 4.89540398e-01 3.62149328e-01 2.11426795e-01 -6.16027713e-01
-4.96504933e-01 -4.66355562e-01 1.21814954e+00 -4.72841829e-01
2.31598574e-03 9.18017209e-01 3.54131281e-01 6.32357076e-02
4.80167456e-02 -1.22163677e+00 -5.13068259e-01 -7.86825299e-01
8.13716799e-02 5.75217366e-01 -3.80474091e-01 -3.87229711... | [12.071979522705078, -0.18816308677196503] |
0409fd13-5e27-4dfb-a65d-44330632c1ce | uln-towards-underspecified-vision-and | 2210.10020 | null | https://arxiv.org/abs/2210.10020v1 | https://arxiv.org/pdf/2210.10020v1.pdf | ULN: Towards Underspecified Vision-and-Language Navigation | Vision-and-Language Navigation (VLN) is a task to guide an embodied agent moving to a target position using language instructions. Despite the significant performance improvement, the wide use of fine-grained instructions fails to characterize more practical linguistic variations in reality. To fill in this gap, we int... | ['William Yang Wang', 'Yujie Lu', 'Tsu-Jui Fu', 'Weixi Feng'] | 2022-10-18 | null | null | null | null | ['vision-and-language-navigation'] | ['robots'] | [-1.39652580e-01 -3.10631320e-02 -3.04183006e-01 -3.14280987e-01
-6.15636945e-01 -5.67610443e-01 9.05324042e-01 -2.38047749e-01
-8.79923344e-01 7.62750626e-01 2.10384861e-01 -4.97602373e-01
1.23016261e-01 -6.55302107e-01 -9.27433908e-01 -5.36509812e-01
-1.57909542e-01 5.07340193e-01 2.01709256e-01 -4.15525347... | [4.482208251953125, 0.5112665295600891] |
6265eb40-f7a6-4d33-9a99-72a166c8471a | neural-syntactic-generative-models-with-exact | null | null | https://aclanthology.org/N18-1086 | https://aclanthology.org/N18-1086.pdf | Neural Syntactic Generative Models with Exact Marginalization | We present neural syntactic generative models with exact marginalization that support both dependency parsing and language modeling. Exact marginalization is made tractable through dynamic programming over shift-reduce parsing and minimal RNN-based feature sets. Our algorithms complement previous approaches by supporti... | ['Jan Buys', 'Phil Blunsom'] | 2018-06-01 | null | null | null | naacl-2018-6 | ['transition-based-dependency-parsing'] | ['natural-language-processing'] | [-1.69030670e-02 7.00236022e-01 -4.23072547e-01 -8.90833437e-01
-1.21072114e+00 -6.47655666e-01 6.23073936e-01 -2.57135928e-01
-3.91482711e-01 6.31917059e-01 6.04247093e-01 -9.33174789e-01
1.86670467e-01 -9.84027267e-01 -9.03958261e-01 -4.23910230e-01
-1.76646158e-01 7.99178660e-01 -1.34152621e-01 -4.18693721... | [10.373902320861816, 9.586186408996582] |
e3e86539-6e4c-478e-89e1-dcc7eeeff6f0 | infoverse-a-universal-framework-for-dataset | 2305.19344 | null | https://arxiv.org/abs/2305.19344v2 | https://arxiv.org/pdf/2305.19344v2.pdf | infoVerse: A Universal Framework for Dataset Characterization with Multidimensional Meta-information | The success of NLP systems often relies on the availability of large, high-quality datasets. However, not all samples in these datasets are equally valuable for learning, as some may be redundant or noisy. Several methods for characterizing datasets based on model-driven meta-information (e.g., model's confidence) have... | ['Dongyeop Kang', 'Jinwoo Shin', 'Karin de Langis', 'Yekyung Kim', 'Jaehyung Kim'] | 2023-05-30 | null | null | null | null | ['active-learning', 'active-learning'] | ['methodology', 'natural-language-processing'] | [ 1.53983697e-01 9.57677960e-02 -7.35673964e-01 -6.78824246e-01
-1.17983222e+00 -7.36030340e-01 4.05070424e-01 5.25633574e-01
-2.28166953e-01 8.44670236e-01 3.78043264e-01 2.82610923e-01
-6.44187212e-01 -6.40148699e-01 -3.22080970e-01 -7.64911890e-01
1.34343445e-01 6.57458782e-01 1.39576018e-01 2.15808496... | [9.863653182983398, 3.7503573894500732] |
1ed4596f-d0a2-4ccf-a582-754d307609ba | temperate-fish-detection-and-classification-a | 2005.07518 | null | https://arxiv.org/abs/2005.07518v1 | https://arxiv.org/pdf/2005.07518v1.pdf | Temperate Fish Detection and Classification: a Deep Learning based Approach | A wide range of applications in marine ecology extensively uses underwater cameras. Still, to efficiently process the vast amount of data generated, we need to develop tools that can automatically detect and recognize species captured on film. Classifying fish species from videos and images in natural environments can ... | ['Tonje Knutsen Sørdalen', 'Arne Wiklund', 'Kristian Muri Knausgård', 'Morten Goodwin', 'Alf Ring Kleiven', 'Kim Halvorsen', 'Lei Jiao'] | 2020-05-14 | null | null | null | null | ['fish-detection'] | ['computer-vision'] | [ 1.10733375e-01 -2.81939447e-01 6.41250849e-01 -4.52213705e-01
-1.17040299e-01 -4.98821169e-01 3.65768731e-01 7.52265975e-02
-1.24062443e+00 2.94393241e-01 -2.04181001e-01 3.02061677e-01
1.74114108e-01 -9.58248794e-01 -9.05299544e-01 -8.81973445e-01
-4.46316242e-01 -1.34008691e-01 5.76241553e-01 -1.02780655... | [8.485209465026855, -1.2191493511199951] |
9e6104a9-dce9-4a8a-85ee-6fd73f76f3cc | symbolic-brittleness-in-sequence-models-on | 2109.13986 | null | https://arxiv.org/abs/2109.13986v2 | https://arxiv.org/pdf/2109.13986v2.pdf | Symbolic Brittleness in Sequence Models: on Systematic Generalization in Symbolic Mathematics | Neural sequence models trained with maximum likelihood estimation have led to breakthroughs in many tasks, where success is defined by the gap between training and test performance. However, their ability to achieve stronger forms of generalization remains unclear. We consider the problem of symbolic mathematical integ... | ['Yejin Choi', 'Jize Cao', 'Peter West', 'Sean Welleck'] | 2021-09-28 | null | null | null | null | ['systematic-generalization'] | ['reasoning'] | [ 6.60193741e-01 -2.68262535e-01 -1.73186332e-01 -3.11700255e-01
-9.67274666e-01 -9.91499662e-01 4.63514090e-01 7.55568668e-02
-3.77376109e-01 9.30134535e-01 -3.63804668e-01 -7.41221309e-01
-4.45486754e-01 -4.99521524e-01 -8.15676272e-01 -5.69625556e-01
-2.90661037e-01 5.21978080e-01 3.33284527e-01 -1.41681671... | [9.607229232788086, 7.186089038848877] |
b6ae23ec-46a0-4874-b74a-f5ff1b523efe | using-deep-learning-method-for-classification | 1703.02182 | null | http://arxiv.org/abs/1703.02182v2 | http://arxiv.org/pdf/1703.02182v2.pdf | Using Deep Learning Method for Classification: A Proposed Algorithm for the ISIC 2017 Skin Lesion Classification Challenge | Skin cancer, the most common human malignancy, is primarily diagnosed
visually by physicians [1]. Classification with an automated method like CNN
[2, 3] shows potential for challenging tasks [1]. By now, the deep
convolutional neural networks are on par with human dermatologist [1]. This
abstract is dedicated on devel... | ['Wenhao Zhang', 'Liangcai Gao', 'Runtao Liu'] | 2017-03-07 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 4.61440563e-01 1.96310699e-01 -2.66915172e-01 -2.79864162e-01
-7.61424959e-01 -3.24024409e-01 5.06843865e-01 2.34438464e-01
-4.26233917e-01 6.34209931e-01 -1.30810171e-01 -3.13397706e-01
8.79993141e-02 -7.95023203e-01 -3.89930099e-01 -7.32639909e-01
2.62297422e-01 -4.79299063e-03 4.19346541e-01 2.80645519... | [15.582022666931152, -3.0884318351745605] |
6059ab7c-2b77-4db7-9028-826399f8f46d | learned-multiphysics-inversion-with | 2304.05592 | null | https://arxiv.org/abs/2304.05592v1 | https://arxiv.org/pdf/2304.05592v1.pdf | Learned multiphysics inversion with differentiable programming and machine learning | We present the Seismic Laboratory for Imaging and Modeling/Monitoring (SLIM) open-source software framework for computational geophysics and, more generally, inverse problems involving the wave-equation (e.g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiph... | ['Felix J. Herrmann', 'Gerard J. Gorman', 'Olav Møyner', 'Philipp A. Witte', 'Gabrio Rizzuti', 'Ali Siahkoohi', 'Thomas J. Grady II', 'Rafael Orozco', 'Ziyi Yin', 'Mathias Louboutin'] | 2023-04-12 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [ 2.36756831e-01 -2.59579383e-02 4.64102060e-01 -1.64188564e-01
-1.20426309e+00 -1.93780363e-02 1.95455700e-01 -1.02225788e-01
-4.77195531e-01 5.36388278e-01 4.33224469e-01 -6.61285996e-01
-3.25494140e-01 -9.16759133e-01 -4.56251860e-01 -1.02325404e+00
-6.30649626e-01 4.47320879e-01 2.88092434e-01 -1.91942990... | [6.7980523109436035, 2.598499298095703] |
f0f944f8-b225-4724-9d20-1bfa931e5b76 | lit-zero-shot-transfer-with-locked-image-text | 2111.07991 | null | https://arxiv.org/abs/2111.07991v3 | https://arxiv.org/pdf/2111.07991v3.pdf | LiT: Zero-Shot Transfer with Locked-image text Tuning | This paper presents contrastive-tuning, a simple method employing contrastive training to align image and text models while still taking advantage of their pre-training. In our empirical study we find that locked pre-trained image models with unlocked text models work best. We call this instance of contrastive-tuning "... | ['Lucas Beyer', 'Alexander Kolesnikov', 'Daniel Keysers', 'Andreas Steiner', 'Basil Mustafa', 'Xiao Wang', 'Xiaohua Zhai'] | 2021-11-15 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Zhai_LiT_Zero-Shot_Transfer_With_Locked-Image_Text_Tuning_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Zhai_LiT_Zero-Shot_Transfer_With_Locked-Image_Text_Tuning_CVPR_2022_paper.pdf | cvpr-2022-1 | ['zero-shot-transfer-image-classification'] | ['computer-vision'] | [ 5.02516329e-01 1.88949868e-01 -1.62706554e-01 -5.62518895e-01
-9.04812157e-01 -4.11604553e-01 9.95531559e-01 -4.88124132e-01
-6.62954450e-01 3.42598975e-01 -8.33586231e-02 -1.56567007e-01
1.61604747e-01 -4.79444146e-01 -1.17035079e+00 -5.49367189e-01
4.63900059e-01 7.69317925e-01 2.86348969e-01 -2.34459460... | [10.059002876281738, 1.960604190826416] |
59f32fa5-20b6-4513-84be-6b5efd60a61d | style-based-point-generator-with-adversarial | 2103.02535 | null | https://arxiv.org/abs/2103.02535v3 | https://arxiv.org/pdf/2103.02535v3.pdf | Style-based Point Generator with Adversarial Rendering for Point Cloud Completion | In this paper, we proposed a novel Style-based Point Generator with Adversarial Rendering (SpareNet) for point cloud completion. Firstly, we present the channel-attentive EdgeConv to fully exploit the local structures as well as the global shape in point features. Secondly, we observe that the concatenation manner used... | ['Fang Wen', 'Dong Chen', 'Hao Yang', 'Bo Zhang', 'Chuxin Wang', 'Chulin Xie'] | 2021-03-03 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Xie_Style-Based_Point_Generator_With_Adversarial_Rendering_for_Point_Cloud_Completion_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Xie_Style-Based_Point_Generator_With_Adversarial_Rendering_for_Point_Cloud_Completion_CVPR_2021_paper.pdf | cvpr-2021-1 | ['point-cloud-completion'] | ['computer-vision'] | [ 1.84909448e-01 2.13487566e-01 1.83658630e-01 -2.31330171e-01
-5.23940384e-01 -8.03442597e-01 6.21445000e-01 -5.51542878e-01
7.06662089e-02 5.55876374e-01 -2.35572122e-02 -1.62644818e-01
3.07685435e-01 -1.12303817e+00 -9.89461243e-01 -4.82246876e-01
1.76865816e-01 1.13843873e-01 8.19030926e-02 -6.56809092... | [9.18867301940918, -3.332256555557251] |
e73b603a-6296-4f95-a49e-1d485f30b5ce | wgansing | null | null | https://arxiv.org/pdf/1903.10729.pdf | https://arxiv.org/pdf/1903.10729.pdf | WGANSing | We present a deep neural network based singing
voice synthesizer, inspired by the Deep Convolutions Generative
Adversarial Networks (DCGAN) architecture and optimized using the Wasserstein-GAN algorithm. We use vocoder parameters
for acoustic modelling, to separate the influence of pitch and
timbre. This facilitate... | ['Merlijn Blaauw', 'Pritish Chandna'] | 2019-03-01 | null | null | null | interspeech-2019-3 | ['acoustic-modelling'] | ['speech'] | [-8.93023517e-03 1.37581035e-01 3.59728396e-01 -4.02750224e-02
-8.39062691e-01 -6.85155630e-01 5.67388475e-01 -4.92822468e-01
-1.83289587e-01 7.34917939e-01 3.92367691e-01 7.12208450e-02
-9.24191438e-03 -7.15545833e-01 -7.64385223e-01 -9.90940630e-01
-1.28762856e-01 1.03461571e-01 -1.25120223e-01 -3.62861484... | [15.522953033447266, 5.975251197814941] |
cc32fb5c-1a39-46d8-9c59-34970700c7e0 | 3d-convolutional-networks-for-action | 2204.08460 | null | https://arxiv.org/abs/2204.08460v1 | https://arxiv.org/pdf/2204.08460v1.pdf | 3D Convolutional Networks for Action Recognition: Application to Sport Gesture Recognition | 3D convolutional networks is a good means to perform tasks such as video segmentation into coherent spatio-temporal chunks and classification of them with regard to a target taxonomy. In the chapter we are interested in the classification of continuous video takes with repeatable actions, such as strokes of table tenni... | ['J Morlier', 'A Zemmari', 'R Péteri', 'J Benois-Pineau', 'Pierre-Etienne Martin'] | 2022-04-13 | null | null | null | null | ['gesture-recognition'] | ['computer-vision'] | [ 3.32083851e-02 -4.42393333e-01 -4.11578357e-01 -9.63453948e-02
3.74358326e-01 -7.19582498e-01 6.79194331e-01 4.42029051e-02
-7.43256629e-01 4.45480376e-01 -7.26282969e-02 -2.86092103e-01
-2.63660967e-01 -7.15176761e-01 -6.32347643e-01 -4.46343899e-01
-7.12501645e-01 3.20315659e-01 8.57779741e-01 -2.78367043... | [8.460123062133789, 0.05564970523118973] |
4dfa597c-95fa-4f1d-9793-cd96caaebf95 | counterfactual-cycle-consistent-learning-for | 2203.16586 | null | https://arxiv.org/abs/2203.16586v1 | https://arxiv.org/pdf/2203.16586v1.pdf | Counterfactual Cycle-Consistent Learning for Instruction Following and Generation in Vision-Language Navigation | Since the rise of vision-language navigation (VLN), great progress has been made in instruction following -- building a follower to navigate environments under the guidance of instructions. However, far less attention has been paid to the inverse task: instruction generation -- learning a speaker~to generate grounded d... | ['Wenguan Wang', 'Luc van Gool', 'Jianbing Shen', 'Wei Liang', 'Hanqing Wang'] | 2022-03-30 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Wang_Counterfactual_Cycle-Consistent_Learning_for_Instruction_Following_and_Generation_in_Vision-Language_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_Counterfactual_Cycle-Consistent_Learning_for_Instruction_Following_and_Generation_in_Vision-Language_CVPR_2022_paper.pdf | cvpr-2022-1 | ['vision-language-navigation'] | ['computer-vision'] | [ 5.43812633e-01 4.04510438e-01 -1.61542282e-01 -6.61549032e-01
-6.41692042e-01 -5.66640854e-01 1.00210619e+00 -2.39514709e-01
-5.65095305e-01 8.10551107e-01 5.85566640e-01 -5.59282243e-01
1.49085432e-01 -7.99458444e-01 -1.09993529e+00 -7.77319968e-01
4.63474207e-02 8.57005417e-01 2.56300539e-01 -5.58674514... | [4.445479869842529, 0.6023882627487183] |
176cbc53-264c-4607-8043-c6894dc21591 | a-comparison-of-approaches-for-imbalanced | 2205.01600 | null | https://arxiv.org/abs/2205.01600v1 | https://arxiv.org/pdf/2205.01600v1.pdf | A Comparison of Approaches for Imbalanced Classification Problems in the Context of Retrieving Relevant Documents for an Analysis | One of the first steps in many text-based social science studies is to retrieve documents that are relevant for the analysis from large corpora of otherwise irrelevant documents. The conventional approach in social science to address this retrieval task is to apply a set of keywords and to consider those documents to b... | ['Sandra Wankmüller'] | 2022-05-03 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [ 3.10152858e-01 2.21305370e-01 -6.32837951e-01 -8.49038176e-03
-1.12728417e+00 -6.77158058e-01 1.21783721e+00 1.09094656e+00
-9.33322251e-01 8.76979172e-01 3.95908296e-01 -3.59656632e-01
-5.99811494e-01 -8.60567033e-01 -4.67993498e-01 -5.13418138e-01
1.27092302e-01 6.92326903e-01 3.79295737e-01 -2.90144116... | [10.422343254089355, 7.242920398712158] |
bce590d9-d66e-4583-b531-b2b0db0d580a | isometricmt-neural-machine-translation-for | 2112.08682 | null | https://arxiv.org/abs/2112.08682v3 | https://arxiv.org/pdf/2112.08682v3.pdf | Isometric MT: Neural Machine Translation for Automatic Dubbing | Automatic dubbing (AD) is among the machine translation (MT) use cases where translations should match a given length to allow for synchronicity between source and target speech. For neural MT, generating translations of length close to the source length (e.g. within +-10% in character count), while preserving quality ... | ['Marcello Federico', 'Prashant Mathur', 'Yogesh Virkar', 'Surafel M. Lakew'] | 2021-12-16 | null | null | null | null | ['self-learning'] | ['natural-language-processing'] | [ 5.74247599e-01 2.70878851e-01 -1.48500353e-01 -2.80061007e-01
-1.35135591e+00 -9.63703811e-01 8.19074214e-01 1.77912802e-01
-5.04621744e-01 9.33855534e-01 2.57541955e-01 -4.94021088e-01
3.13428164e-01 -6.02439940e-01 -9.46568608e-01 -3.75696927e-01
3.34316164e-01 8.05777133e-01 2.12899014e-01 -4.66974497... | [11.658409118652344, 10.229499816894531] |
d6e143c9-1a48-4eed-b756-720e0b803994 | asymptotic-decoupling-of-population-growth | 2209.14683 | null | https://arxiv.org/abs/2209.14683v1 | https://arxiv.org/pdf/2209.14683v1.pdf | Asymptotic decoupling of population growth rate and cell size distribution | The rate at which individual bacterial cells grow depends on the concentrations of cellular components such as ribosomes and proteins. These concentrations continuously fluctuate over time and are inherited from mother to daughter cells leading to correlations between the growth rates of cells across generations. Divis... | ['Farshid Jafarpour', 'Yaïr Hein'] | 2022-09-29 | null | null | null | null | ['culture'] | ['speech'] | [-6.53873086e-02 -2.05122486e-01 -8.58108774e-02 8.30340028e-01
3.98345232e-01 -6.55190825e-01 5.90011001e-01 4.58088368e-01
-4.08810139e-01 1.24538553e+00 -7.86511898e-02 -3.84000540e-02
2.29406022e-02 -9.25169289e-01 -5.78168988e-01 -1.46763527e+00
-7.47033432e-02 7.22539544e-01 2.71833986e-01 -2.17602819... | [5.776303768157959, 4.258798122406006] |
d40df8c2-3839-48b7-9bec-e50ad9c5d617 | bess-balanced-entity-sampling-and-sharing-for | 2211.12281 | null | https://arxiv.org/abs/2211.12281v1 | https://arxiv.org/pdf/2211.12281v1.pdf | BESS: Balanced Entity Sampling and Sharing for Large-Scale Knowledge Graph Completion | We present the award-winning submission to the WikiKG90Mv2 track of OGB-LSC@NeurIPS 2022. The task is link-prediction on the large-scale knowledge graph WikiKG90Mv2, consisting of 90M+ nodes and 600M+ edges. Our solution uses a diverse ensemble of $85$ Knowledge Graph Embedding models combining five different scoring f... | ['Carlo Luschi', 'Blazej Banaszewski', 'Andrew Fitzgibbon', 'Thorin Farnsworth', 'Zhenying Liu', 'Jerome Maloberti', 'Douglas Orr', 'Harry Mellor', 'Daniel Justus', 'Alberto Cattaneo'] | 2022-11-22 | null | null | null | null | ['knowledge-graph-embedding'] | ['graphs'] | [-5.64076960e-01 8.68033171e-01 -3.19088519e-01 -3.07790458e-01
-4.85427260e-01 -3.80922973e-01 5.77442229e-01 3.99816722e-01
-6.51036024e-01 1.07502365e+00 1.16361283e-01 -3.94777238e-01
-6.93060160e-01 -8.85084569e-01 -1.09069657e+00 -3.25113356e-01
-6.62502825e-01 1.00585294e+00 4.07929182e-01 -2.07334429... | [8.7723970413208, 7.9221930503845215] |
17bceb9a-ca6f-4d56-87d5-b5bfcced785a | towards-fast-adaptation-of-pretrained | 2206.02082 | null | https://arxiv.org/abs/2206.02082v4 | https://arxiv.org/pdf/2206.02082v4.pdf | Towards Fast Adaptation of Pretrained Contrastive Models for Multi-channel Video-Language Retrieval | Multi-channel video-language retrieval require models to understand information from different channels (e.g. video$+$question, video$+$speech) to correctly link a video with a textual response or query. Fortunately, contrastive multimodal models are shown to be highly effective at aligning entities in images/videos an... | ['Shih-Fu Chang', 'Heng Ji', 'Mike Zheng Shou', 'Manling Li', 'Shiyuan Huang', 'Simran Tiwari', 'Xudong Lin'] | 2022-06-05 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Lin_Towards_Fast_Adaptation_of_Pretrained_Contrastive_Models_for_Multi-Channel_Video-Language_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Lin_Towards_Fast_Adaptation_of_Pretrained_Contrastive_Models_for_Multi-Channel_Video-Language_CVPR_2023_paper.pdf | cvpr-2023-1 | ['video-question-answering'] | ['computer-vision'] | [ 1.81262344e-01 -5.70052743e-01 -3.51216465e-01 -3.17132264e-01
-1.19168532e+00 -7.78962493e-01 9.44797337e-01 9.70386900e-04
-7.35898376e-01 4.13571000e-01 2.17814147e-01 -9.31364764e-03
-1.93522293e-02 -4.06763881e-01 -9.07150388e-01 -6.23868167e-01
5.46831153e-02 3.79003942e-01 5.36620170e-02 -1.84898734... | [10.46341323852539, 1.0554085969924927] |
80e4d7cf-fcf0-46e2-8a33-b340bb20c933 | adjacency-pair-recognition-in-wikipedia | null | null | https://aclanthology.org/Y14-1055 | https://aclanthology.org/Y14-1055.pdf | Adjacency Pair Recognition in Wikipedia Discussions using Lexical Pairs | null | ['Iryna Gurevych', 'Emily Jamison'] | 2014-12-01 | adjacency-pair-recognition-in-wikipedia-1 | https://aclanthology.org/Y14-1055 | https://aclanthology.org/Y14-1055.pdf | paclic-2014-12 | ['dialogue-act-classification'] | ['natural-language-processing'] | [-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01
-8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01
-2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00
-3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01
-9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444... | [-7.417853355407715, 3.6773390769958496] |
1f230910-946b-497e-b5e0-36a99207f3dd | nr-dfernet-noise-robust-network-for-dynamic | 2206.04975 | null | https://arxiv.org/abs/2206.04975v1 | https://arxiv.org/pdf/2206.04975v1.pdf | NR-DFERNet: Noise-Robust Network for Dynamic Facial Expression Recognition | Dynamic facial expression recognition (DFER) in the wild is an extremely challenging task, due to a large number of noisy frames in the video sequences. Previous works focus on extracting more discriminative features, but ignore distinguishing the key frames from the noisy frames. To tackle this problem, we propose a n... | ['Feng Zhao', 'Zhaoqing Zhu', 'Mingzhe Sui', 'Hanting Li'] | 2022-06-10 | null | null | null | null | ['facial-expression-recognition'] | ['computer-vision'] | [ 2.67597318e-01 -6.72601104e-01 3.97044197e-02 -6.22245908e-01
-5.89273751e-01 -1.44154325e-01 3.49851489e-01 -6.03455603e-01
-5.79189658e-01 4.06022102e-01 3.94633114e-01 1.80583939e-01
3.84287387e-02 -3.52493852e-01 -4.65881556e-01 -1.18008912e+00
1.83045790e-02 -6.18319154e-01 4.26678568e-01 -3.24165225... | [13.641763687133789, 1.653706431388855] |
23dbec8c-c186-4809-a945-d0d48aa6eb65 | decker-double-check-with-heterogeneous | 2305.05921 | null | https://arxiv.org/abs/2305.05921v2 | https://arxiv.org/pdf/2305.05921v2.pdf | Decker: Double Check with Heterogeneous Knowledge for Commonsense Fact Verification | Commonsense fact verification, as a challenging branch of commonsense question-answering (QA), aims to verify through facts whether a given commonsense claim is correct or not. Answering commonsense questions necessitates a combination of knowledge from various levels. However, existing studies primarily rest on graspi... | ['Hai Zhao', 'Zhuosheng Zhang', 'Anni Zou'] | 2023-05-10 | null | null | null | null | ['fact-verification'] | ['natural-language-processing'] | [ 2.79253185e-01 5.86050093e-01 -4.19504613e-01 -3.52027491e-02
-8.38267624e-01 -8.51880252e-01 8.54426324e-01 3.99543911e-01
2.72707820e-01 9.66083586e-01 6.36928201e-01 -7.37016380e-01
-4.57485139e-01 -1.12781048e+00 -6.49383903e-01 -1.28529943e-03
3.94069612e-01 3.56402338e-01 3.99621934e-01 -7.00539172... | [9.94754695892334, 8.107573509216309] |
73b2d325-80c6-4c95-8fa6-e890b4bba42d | recup-fl-reconciling-utility-and-privacy-in | 2304.05135 | null | https://arxiv.org/abs/2304.05135v1 | https://arxiv.org/pdf/2304.05135v1.pdf | RecUP-FL: Reconciling Utility and Privacy in Federated Learning via User-configurable Privacy Defense | Federated learning (FL) provides a variety of privacy advantages by allowing clients to collaboratively train a model without sharing their private data. However, recent studies have shown that private information can still be leaked through shared gradients. To further minimize the risk of privacy leakage, existing de... | ['Jian Liu', 'Jiaxin Zhang', 'Luyang Liu', 'Zhuohang Li', 'Syed Irfan Ali Meerza', 'Yue Cui'] | 2023-04-11 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [ 1.87576637e-01 -2.01130323e-02 -2.29622215e-01 -6.52469575e-01
-9.91521776e-01 -1.36552393e+00 3.18417728e-01 1.93960441e-03
-4.20418054e-01 5.86724281e-01 -1.19681410e-01 -5.29791534e-01
-1.54487388e-02 -1.21051264e+00 -8.03993344e-01 -8.95944953e-01
-1.33148776e-02 1.90514117e-01 -4.75187153e-02 -7.50346631... | [5.8958282470703125, 6.8592329025268555] |
213afeea-7395-402c-b11d-08d9e6b23497 | emulating-reader-behaviors-for-fake-news | 2306.15231 | null | https://arxiv.org/abs/2306.15231v1 | https://arxiv.org/pdf/2306.15231v1.pdf | Emulating Reader Behaviors for Fake News Detection | The wide dissemination of fake news has affected our lives in many aspects, making fake news detection important and attracting increasing attention. Existing approaches make substantial contributions in this field by modeling news from a single-modal or multi-modal perspective. However, these modal-based methods can r... | ['Jia Wang', 'Yinqiu Huang', 'Zehua Zhao', 'Kai Shu', 'Min Gao', 'Junwei Yin'] | 2023-06-27 | null | null | null | null | ['fake-news-detection'] | ['natural-language-processing'] | [-1.41553823e-02 -2.53787726e-01 -3.00354868e-01 -2.23050028e-01
-6.33499146e-01 -5.23490071e-01 9.18505609e-01 1.45627916e-01
-4.04214263e-02 3.22724849e-01 4.12370265e-01 -7.61839449e-02
2.58750588e-01 -1.02825069e+00 -5.63363731e-01 -5.21743238e-01
4.88209754e-01 4.80934829e-02 5.30241072e-01 -4.10033137... | [8.137410163879395, 10.268625259399414] |
146d326e-bc23-4f90-8fbd-1e9e8cc2545a | traffic-data-imputation-using-deep | 2002.04406 | null | https://arxiv.org/abs/2002.04406v1 | https://arxiv.org/pdf/2002.04406v1.pdf | Traffic Data Imputation using Deep Convolutional Neural Networks | We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information. Using a convolutional encoder-decoder based architecture, we show that a well trained neural network can learn spatio-temporal traffic speed dynamics from time-space diagrams. We demonstrate this for... | ['Monica Menendez', 'Saif Eddin Jabari', 'Ouafa Benkraouda', 'Hwasoo Yeo', 'Bilal Thonnam Thodi'] | 2020-01-21 | null | null | null | null | ['traffic-data-imputation'] | ['time-series'] | [-3.36192906e-01 -3.09110343e-01 -3.58099550e-01 -1.89446479e-01
-9.17772293e-01 -7.43802562e-02 6.24037147e-01 -1.87396973e-01
-8.16815421e-02 8.79878640e-01 -7.91890025e-02 -1.08350945e+00
-2.13246986e-01 -9.60424006e-01 -8.44296992e-01 -6.66777849e-01
-8.01082492e-01 3.84537548e-01 2.90935844e-01 -3.44096363... | [6.089039325714111, 1.4084235429763794] |
e3c7647d-ec3f-44bf-9848-742c042b6832 | on-the-robustness-of-arabic-speech-dialect | 2306.03789 | null | https://arxiv.org/abs/2306.03789v1 | https://arxiv.org/pdf/2306.03789v1.pdf | On the Robustness of Arabic Speech Dialect Identification | Arabic dialect identification (ADI) tools are an important part of the large-scale data collection pipelines necessary for training speech recognition models. As these pipelines require application of ADI tools to potentially out-of-domain data, we aim to investigate how vulnerable the tools may be to this domain shift... | ['Muhammad Abdul-Mageed', 'AbdelRahim Elmadany', 'Peter Sullivan'] | 2023-06-01 | null | null | null | null | ['dialect-identification'] | ['natural-language-processing'] | [ 2.20091537e-01 -1.00900143e-01 1.65895373e-01 -7.44338512e-01
-8.31628799e-01 -9.88301218e-01 8.98418069e-01 -3.41716371e-02
-5.52008390e-01 2.77082324e-01 3.37955147e-01 -7.01379955e-01
-2.24772282e-02 -4.10488009e-01 -5.93597472e-01 -1.19829491e-01
-1.63477585e-02 8.55365098e-01 2.05616549e-01 -5.50743163... | [14.17331314086914, 6.66780948638916] |
444e1efe-3483-43bc-ab4b-f6642f2185de | probing-toxic-content-in-large-pre-trained | null | null | https://aclanthology.org/2021.acl-long.329/ | https://aclanthology.org/2021.acl-long.329.pdf | Probing Toxic Content in Large Pre-Trained Language Models | Large pre-trained language models (PTLMs) have been shown to carry biases towards different social groups which leads to the reproduction of stereotypical and toxic content by major NLP systems. We propose a method based on logistic regression classifiers to probe English, French, and Arabic PTLMs and quantify the pote... | ['Dit-yan Yeung', 'Yangqiu Song', 'Tianqing Fang', 'Xinran Zhao', 'Nedjma Ousidhoum'] | 2021-08-01 | null | https://aclanthology.org/2021.acl-long.329 | https://aclanthology.org/2021.acl-long.329.pdf | acl-2021-5 | ['probing-language-models'] | ['natural-language-processing'] | [ 6.08757257e-01 1.53869167e-01 3.81290652e-02 -2.49676913e-01
-5.19600809e-01 -8.45023155e-01 1.13526571e+00 7.75718749e-01
-4.37593579e-01 9.02980566e-01 7.05633521e-01 -5.74378192e-01
1.29459575e-01 -8.16767275e-01 -8.84123325e-01 -6.61867261e-01
-2.04785541e-01 8.92959610e-02 -5.54463826e-02 -1.87299266... | [8.885666847229004, 10.585638999938965] |
6b7ffc90-0a37-47ee-95c6-a6730db9b791 | super-resolution-with-deep-convolutional | 1511.05666 | null | http://arxiv.org/abs/1511.05666v4 | http://arxiv.org/pdf/1511.05666v4.pdf | Super-Resolution with Deep Convolutional Sufficient Statistics | Inverse problems in image and audio, and super-resolution in particular, can
be seen as high-dimensional structured prediction problems, where the goal is
to characterize the conditional distribution of a high-resolution output given
its low-resolution corrupted observation. When the scaling ratio is small,
point estim... | ['Yann Lecun', 'Joan Bruna', 'Pablo Sprechmann'] | 2015-11-18 | null | null | null | null | ['bandwidth-extension', 'bandwidth-extension'] | ['audio', 'speech'] | [ 3.93136770e-01 2.54554093e-01 1.90418720e-01 -6.00932427e-02
-1.01100981e+00 -2.27541119e-01 6.39936149e-01 -5.22798240e-01
-3.12510371e-01 8.95496964e-01 2.79607654e-01 4.90287125e-01
-3.94414425e-01 -9.04472113e-01 -9.05285358e-01 -1.18266320e+00
-1.99590996e-02 4.67872560e-01 2.36676678e-01 -1.47881791... | [11.575672149658203, -2.3190441131591797] |
4fdf9b5a-bf0f-4c09-9338-8169dc8d55ec | drug-repurposing-for-sars-cov-2-a-molecular | 2201.00287 | null | https://arxiv.org/abs/2201.00287v3 | https://arxiv.org/pdf/2201.00287v3.pdf | Drug repurposing for SARS-COV-2: A high-throughput molecular docking, molecular dynamics, machine learning, & ab-initio study | A molecule of dimension 125nm has caused around 479 Million human infections (80M for the USA) & 6.1 Million human deaths (977,000 for the USA) worldwide and slashed the global economy by US$ 8.5 Trillion over two years. The only other events in recent history that caused comparative human life loss through direct usag... | ['Dibakar Datta', 'Jatin Kashyap'] | 2022-01-02 | null | null | null | null | ['molecular-docking'] | ['medical'] | [ 1.00692518e-01 -1.61623627e-01 9.30093154e-02 1.54175535e-01
-3.56443673e-01 -5.22268593e-01 4.12238151e-01 3.52754265e-01
-6.33863211e-01 1.51917195e+00 3.65840532e-02 -5.57138026e-01
-1.55133948e-01 -7.11693704e-01 -5.43496549e-01 -8.51979733e-01
-2.06688240e-01 6.71990931e-01 2.25551948e-01 -3.86120945... | [4.679758071899414, 5.181245803833008] |
ec2abd0c-11af-4e36-91fa-771e4cff424c | speckle2void-deep-self-supervised-sar | 2007.02075 | null | https://arxiv.org/abs/2007.02075v1 | https://arxiv.org/pdf/2007.02075v1.pdf | Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks | Information extraction from synthetic aperture radar (SAR) images is heavily impaired by speckle noise, hence despeckling is a crucial preliminary step in scene analysis algorithms. The recent success of deep learning envisions a new generation of despeckling techniques that could outperform classical model-based metho... | ['Andrea Bordone Molini', 'Giulia Fracastoro', 'Enrico Magli', 'Diego Valsesia'] | 2020-07-04 | null | null | null | null | ['sar-image-despeckling'] | ['computer-vision'] | [ 7.47049510e-01 -3.02401632e-01 5.79414487e-01 -4.91927683e-01
-9.78966653e-01 -5.18913209e-01 8.80216837e-01 -2.96467513e-01
-5.80867350e-01 8.06387722e-01 1.27382845e-01 -8.35416093e-02
-5.15633345e-01 -6.77730799e-01 -5.55463910e-01 -1.09083629e+00
1.11505955e-01 4.99428272e-01 -2.55785435e-02 -1.84980795... | [10.439704895019531, -2.179399251937866] |
cffe35a7-2be6-4743-bbda-d123dff9020a | sql-rank-a-listwise-approach-to-collaborative | 1803.00114 | null | http://arxiv.org/abs/1803.00114v3 | http://arxiv.org/pdf/1803.00114v3.pdf | SQL-Rank: A Listwise Approach to Collaborative Ranking | In this paper, we propose a listwise approach for constructing user-specific
rankings in recommendation systems in a collaborative fashion. We contrast the
listwise approach to previous pointwise and pairwise approaches, which are
based on treating either each rating or each pairwise comparison as an
independent instan... | ['Cho-Jui Hsieh', 'Liwei Wu', 'James Sharpnack'] | 2018-02-28 | sql-rank-a-listwise-approach-to-collaborative-1 | https://icml.cc/Conferences/2018/Schedule?showEvent=2098 | http://proceedings.mlr.press/v80/wu18c/wu18c.pdf | icml-2018-7 | ['collaborative-ranking'] | ['graphs'] | [ 1.61245987e-01 -1.64287284e-01 -4.29676533e-01 -5.59531033e-01
-9.86264586e-01 -9.59618866e-01 4.69457954e-01 1.00048982e-01
-1.21427052e-01 7.26304770e-01 6.69310749e-01 -5.26959062e-01
-1.10302007e+00 -8.32483768e-01 -7.70093381e-01 -3.84265184e-01
-4.60221827e-01 7.89881110e-01 -4.75511001e-03 -3.05655450... | [9.799064636230469, 5.585465431213379] |
40968b32-417f-482e-8a8a-0886f35b0c3d | robust-time-series-denoising-with-learnable | 2206.06126 | null | https://arxiv.org/abs/2206.06126v4 | https://arxiv.org/pdf/2206.06126v4.pdf | Robust Time Series Denoising with Learnable Wavelet Packet Transform | Signal denoising is a key preprocessing step for many applications, as the performance of a learning task is closely related to the quality of the input data. In this paper, we apply a signal processing based deep neural network architecture, a learnable extension of the wavelet packet transform. As main advantages, th... | ['Olga Fink', 'Gaetan Frusque'] | 2022-06-13 | null | null | null | null | ['time-series-denoising'] | ['time-series'] | [ 2.63786316e-01 -3.28782141e-01 3.69555742e-01 -3.75816703e-01
-7.62597919e-01 -3.34630698e-01 2.57432222e-01 1.09377749e-01
-6.75727367e-01 5.89063644e-01 -5.19691519e-02 -1.08336531e-01
-4.81531262e-01 -7.84349084e-01 -6.67989552e-01 -1.14233100e+00
-3.36373866e-01 -6.92349821e-02 2.16724411e-01 -6.15450144... | [11.574498176574707, -2.281494140625] |
f95acf5c-faf6-4138-8737-5c23dd167dfa | survey-on-3d-face-reconstruction-from | 2011.05740 | null | https://arxiv.org/abs/2011.05740v2 | https://arxiv.org/pdf/2011.05740v2.pdf | Survey on 3D face reconstruction from uncalibrated images | Recently, a lot of attention has been focused on the incorporation of 3D data into face analysis and its applications. Despite providing a more accurate representation of the face, 3D facial images are more complex to acquire than 2D pictures. As a consequence, great effort has been invested in developing systems that ... | ['Federico M. Sukno', 'Gemma Piella', 'Araceli Morales'] | 2020-11-11 | null | null | null | null | ['face-reconstruction'] | ['computer-vision'] | [ 3.81405987e-02 8.95055160e-02 -7.07962885e-02 -4.54569817e-01
-3.78559649e-01 -6.53469115e-02 4.97072250e-01 -5.74300647e-01
-2.38818854e-01 2.27586120e-01 -1.53563023e-01 3.68310101e-02
-1.61966711e-01 -5.17371476e-01 -6.31219864e-01 -7.91554093e-01
1.71397343e-01 4.88479614e-01 -4.04724032e-01 1.12461671... | [13.186300277709961, 0.2591889798641205] |
7c72e053-fb65-442a-8edc-fb3697938669 | how-to-efficiently-adapt-large-segmentation | 2306.13731 | null | https://arxiv.org/abs/2306.13731v1 | https://arxiv.org/pdf/2306.13731v1.pdf | How to Efficiently Adapt Large Segmentation Model(SAM) to Medical Images | The emerging scale segmentation model, Segment Anything (SAM), exhibits impressive capabilities in zero-shot segmentation for natural images. However, when applied to medical images, SAM suffers from noticeable performance drop. To make SAM a real ``foundation model" for the computer vision community, it is critical to... | ['Yiyu Shi', 'Xiaowei Xu', 'Xinrong Hu'] | 2023-06-23 | null | null | null | null | ['zero-shot-segmentation', 'self-supervised-learning', 'medical-image-segmentation'] | ['computer-vision', 'computer-vision', 'medical'] | [ 3.93454432e-01 3.33453417e-01 -3.94557983e-01 -4.63391870e-01
-7.92180777e-01 -4.48111236e-01 3.45832333e-02 -1.26208499e-01
-5.96482337e-01 4.86499697e-01 -1.52244627e-01 -2.42329895e-01
4.12898123e-01 -7.11766899e-01 -7.08748698e-01 -4.72832829e-01
5.61902642e-01 5.17006159e-01 7.35944808e-01 1.21324160... | [14.588747024536133, -2.2403433322906494] |
f0c50828-5195-4a7e-8f00-0bfa342d8803 | animatediff-animate-your-personalized-text-to | 2307.04725 | null | https://arxiv.org/abs/2307.04725v1 | https://arxiv.org/pdf/2307.04725v1.pdf | AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning | With the advance of text-to-image models (e.g., Stable Diffusion) and corresponding personalization techniques such as DreamBooth and LoRA, everyone can manifest their imagination into high-quality images at an affordable cost. Subsequently, there is a great demand for image animation techniques to further combine gene... | ['Bo Dai', 'Dahua Lin', 'Yu Qiao', 'Yaohui Wang', 'Anyi Rao', 'Ceyuan Yang', 'Yuwei Guo'] | 2023-07-10 | null | null | null | null | ['image-animation'] | ['computer-vision'] | [ 4.09625694e-02 -9.75490063e-02 -1.46940663e-01 -3.09286058e-01
-6.05134428e-01 -4.52749044e-01 5.94847083e-01 -7.00851619e-01
-1.55354455e-01 5.00058413e-01 2.46838689e-01 6.24754541e-02
2.66805232e-01 -5.84142804e-01 -6.69046044e-01 -7.83901632e-01
3.10351625e-02 2.44217694e-01 2.78153062e-01 -2.22949445... | [10.918158531188965, -0.654175877571106] |
f64bee1d-805e-4dbf-ae04-928e476c4ebe | distilling-efficient-language-specific-models | 2306.01709 | null | https://arxiv.org/abs/2306.01709v1 | https://arxiv.org/pdf/2306.01709v1.pdf | Distilling Efficient Language-Specific Models for Cross-Lingual Transfer | Massively multilingual Transformers (MMTs), such as mBERT and XLM-R, are widely used for cross-lingual transfer learning. While these are pretrained to represent hundreds of languages, end users of NLP systems are often interested only in individual languages. For such purposes, the MMTs' language coverage makes them u... | ['Ivan Vulić', 'Anna Korhonen', 'Edoardo Maria Ponti', 'Alan Ansell'] | 2023-06-02 | null | null | null | null | ['zero-shot-cross-lingual-transfer', 'cross-lingual-transfer', 'xlm-r'] | ['natural-language-processing', 'natural-language-processing', 'natural-language-processing'] | [-5.62344640e-02 -1.39040962e-01 -5.88618219e-01 -3.22466612e-01
-1.47639954e+00 -8.90007615e-01 7.41622865e-01 -4.24636230e-02
-6.08127892e-01 9.80106175e-01 1.78491652e-01 -8.20098281e-01
6.31018281e-01 -7.40036249e-01 -1.12495661e+00 -5.07761657e-01
3.58773172e-01 8.11265111e-01 -1.69604659e-01 -4.45706874... | [11.111538887023926, 9.942839622497559] |
de34bfa6-77d8-4b98-a5d2-e8d499693062 | abd-net-attention-based-decomposition-network | 2108.04221 | null | https://arxiv.org/abs/2108.04221v1 | https://arxiv.org/pdf/2108.04221v1.pdf | ABD-Net: Attention Based Decomposition Network for 3D Point Cloud Decomposition | In this paper, we propose Attention Based Decomposition Network (ABD-Net), for point cloud decomposition into basic geometric shapes namely, plane, sphere, cone and cylinder. We show improved performance of 3D object classification using attention features based on primitive shapes in point clouds. Point clouds, being ... | ['Uma Mudenagudi', 'Ramesh Ashok Tabib', 'Akshaykumar Gunari', 'Shashidhar V Kudari', 'Siddharth Katageri'] | 2021-07-09 | null | null | null | null | ['3d-object-classification'] | ['computer-vision'] | [-6.29521012e-01 -2.04586044e-01 1.97191387e-01 -2.37192035e-01
-5.55833161e-01 -4.56270397e-01 3.44116956e-01 2.84192294e-01
1.83653280e-01 9.65627357e-02 -3.95853259e-02 -2.14508250e-02
-5.36828935e-01 -8.58313978e-01 -1.13073623e+00 -4.39555675e-01
-4.83800590e-01 6.97865784e-01 -8.83477032e-02 -4.96545881... | [7.939449787139893, -3.6262593269348145] |
75bf0183-e52c-43d9-937a-711d34e1cc59 | talla-at-semeval-2017-task-3-identifying | null | null | https://aclanthology.org/S17-2062 | https://aclanthology.org/S17-2062.pdf | Talla at SemEval-2017 Task 3: Identifying Similar Questions Through Paraphrase Detection | This paper describes our approach to the SemEval-2017 shared task of determining question-question similarity in a community question-answering setting (Task 3B). We extracted both syntactic and semantic similarity features between candidate questions, performed pairwise-preference learning to optimize for ranking orde... | ['Daniel Shank', 'Byron Galbraith', 'Bhanu Pratap'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['question-similarity'] | ['natural-language-processing'] | [ 2.06032261e-01 3.40532690e-01 1.37638405e-01 -8.52542460e-01
-1.76391757e+00 -8.55807841e-01 4.94129598e-01 6.39213622e-01
-7.97325850e-01 5.67580104e-01 4.00140494e-01 -4.72616404e-01
-3.51165265e-01 -4.25368071e-01 -6.12386644e-01 4.61960807e-02
7.17967153e-02 9.43703473e-01 7.00844646e-01 -1.01704389... | [11.338483810424805, 8.039427757263184] |
86f47216-3c9c-4209-922b-1b6cfc5c4f62 | unsupervised-feature-learning-based-on-deep | 1607.03681 | null | http://arxiv.org/abs/1607.03681v2 | http://arxiv.org/pdf/1607.03681v2.pdf | Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging | Environmental audio tagging aims to predict only the presence or absence of
certain acoustic events in the interested acoustic scene. In this paper we make
contributions to audio tagging in two parts, respectively, acoustic modeling
and feature learning. We propose to use a shrinking deep neural network (DNN)
framework... | ['Philip J. B. Jackson', 'Wenwu Wang', 'Qiang Huang', 'Peter Foster', 'Yong Xu', 'Siddharth Sigtia', 'Mark D. Plumbley'] | 2016-07-13 | null | null | null | null | ['audio-tagging'] | ['audio'] | [ 3.22422236e-01 -1.44126695e-02 3.59844714e-01 -5.73793530e-01
-1.24433851e+00 -3.27289402e-01 2.19612658e-01 4.90642302e-02
-6.57129347e-01 2.61402398e-01 3.11970413e-01 7.52155408e-02
1.12024575e-01 -3.55628312e-01 -5.84869027e-01 -9.06982362e-01
-1.44268140e-01 -1.48696214e-01 2.09615171e-01 1.47913262... | [15.196418762207031, 5.217362880706787] |
9b9634d3-06e3-4b66-8abb-4a3141ab3215 | canonical-capsules-unsupervised-capsules-in | 2012.04718 | null | https://arxiv.org/abs/2012.04718v2 | https://arxiv.org/pdf/2012.04718v2.pdf | Canonical Capsules: Self-Supervised Capsules in Canonical Pose | We propose a self-supervised capsule architecture for 3D point clouds. We compute capsule decompositions of objects through permutation-equivariant attention, and self-supervise the process by training with pairs of randomly rotated objects. Our key idea is to aggregate the attention masks into semantic keypoints, and ... | ['Kwang Moo Yi', 'Geoffrey Hinton', 'Soroosh Yazdani', 'Sara Sabour', 'Boyang Deng', 'Andrea Tagliasacchi', 'Weiwei Sun'] | 2020-12-08 | canonical-capsules-self-supervised-capsules | http://proceedings.neurips.cc/paper/2021/hash/d1ee59e20ad01cedc15f5118a7626099-Abstract.html | http://proceedings.neurips.cc/paper/2021/file/d1ee59e20ad01cedc15f5118a7626099-Paper.pdf | neurips-2021-12 | ['3d-point-cloud-reconstruction', 'point-cloud-reconstruction'] | ['computer-vision', 'computer-vision'] | [-1.19809039e-01 1.84192464e-01 -1.24471687e-01 -3.61236751e-01
-5.78941643e-01 -8.22134674e-01 6.55458629e-01 1.24300368e-01
-3.89442546e-03 -4.73558493e-02 2.34309271e-01 -5.07235974e-02
-2.25321457e-01 -6.44843340e-01 -1.02934873e+00 -5.28116465e-01
-2.50562221e-01 1.00148118e+00 -2.57003933e-01 2.99428850... | [8.262853622436523, -3.29272723197937] |
0cc5985c-86bc-427c-82de-8d46fd4dcc45 | fully-convolutional-networks-for-semantic-1 | 1411.4038 | null | http://arxiv.org/abs/1411.4038v2 | http://arxiv.org/pdf/1411.4038v2.pdf | Fully Convolutional Networks for Semantic Segmentation | Convolutional networks are powerful visual models that yield hierarchies of
features. We show that convolutional networks by themselves, trained
end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic
segmentation. Our key insight is to build "fully convolutional" networks that
take input of arbitrary siz... | ['Jonathan Long', 'Evan Shelhamer', 'Trevor Darrell'] | 2014-11-14 | fully-convolutional-networks-for-semantic-2 | http://openaccess.thecvf.com/content_cvpr_2015/html/Long_Fully_Convolutional_Networks_2015_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf | cvpr-2015-6 | ['thermal-image-segmentation', 'multi-tissue-nucleus-segmentation'] | ['computer-vision', 'medical'] | [ 2.93513089e-01 2.16641054e-01 -2.55202115e-01 -5.26256561e-01
-5.85217834e-01 -6.82370782e-01 3.45577031e-01 -3.50141346e-01
-4.80130136e-01 5.79754353e-01 1.67773720e-02 -3.72928083e-01
3.69427741e-01 -9.21768785e-01 -1.10432124e+00 -2.98900813e-01
-1.50893982e-02 5.10412872e-01 6.26920760e-01 -1.23496756... | [9.510059356689453, 0.19289232790470123] |
59d08dac-299a-4c37-85b4-6d32bfc3f907 | uninl-aligning-representation-learning-with | 2210.10722 | null | https://arxiv.org/abs/2210.10722v1 | https://arxiv.org/pdf/2210.10722v1.pdf | UniNL: Aligning Representation Learning with Scoring Function for OOD Detection via Unified Neighborhood Learning | Detecting out-of-domain (OOD) intents from user queries is essential for avoiding wrong operations in task-oriented dialogue systems. The key challenge is how to distinguish in-domain (IND) and OOD intents. Previous methods ignore the alignment between representation learning and scoring function, limiting the OOD dete... | ['Weiran Xu', 'Wei Wu', 'Jingang Wang', 'Yanan Wu', 'Keqing He', 'Pei Wang', 'Yutao Mou'] | 2022-10-19 | null | null | null | null | ['task-oriented-dialogue-systems'] | ['natural-language-processing'] | [ 2.62613234e-04 -2.09089983e-02 -4.51473087e-01 -7.22515702e-01
-6.83360219e-01 -6.20508254e-01 8.42500865e-01 2.96430439e-01
-2.78285623e-01 1.99830055e-01 7.47820854e-01 -4.64012504e-01
-9.06338468e-02 -4.27930176e-01 1.39374271e-01 -1.08098544e-01
3.93869206e-02 3.79799545e-01 1.77263618e-01 -5.01888871... | [12.350739479064941, 7.532840251922607] |
5e3d0511-bafe-4d07-a824-f4727a3eaf85 | multi-order-networks-for-action-unit | 2202.00446 | null | https://arxiv.org/abs/2202.00446v2 | https://arxiv.org/pdf/2202.00446v2.pdf | Multi-Order Networks for Action Unit Detection | Action Units (AU) are muscular activations used to describe facial expressions. Therefore accurate AU recognition unlocks unbiaised face representation which can improve face-based affective computing applications. From a learning standpoint AU detection is a multi-task problem with strong inter-task dependencies. To s... | ['Kevin Bailly', 'Arnaud Dapogny', 'Gauthier Tallec'] | 2022-02-01 | null | null | null | null | ['action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision'] | [ 5.45538843e-01 -6.57444298e-02 -2.99274355e-01 -4.58286196e-01
-8.19239438e-01 -4.04313445e-01 6.03481412e-01 -3.13634187e-01
-5.45670331e-01 4.98070955e-01 3.72311622e-02 4.23231333e-01
-1.27686903e-01 -1.87172383e-01 -7.16155291e-01 -6.91634536e-01
-1.39427915e-01 4.57201868e-01 -3.03822696e-01 -3.21304142... | [13.603974342346191, 1.769471526145935] |
bca43abc-9d1e-475b-b8d3-f032665a562f | semi-blind-source-separation-using | 2207.01556 | null | https://arxiv.org/abs/2207.01556v1 | https://arxiv.org/pdf/2207.01556v1.pdf | Semi-blind source separation using convolutive transfer function for nonlinear acoustic echo cancellation | The recently proposed semi-blind source separation (SBSS) method for nonlinear acoustic echo cancellation (NAEC) outperforms adaptive NAEC in attenuating the nonlinear acoustic echo. However, the multiplicative transfer function (MTF) approximation makes it unsuitable for real-time applications especially in highly rev... | ['Jing Lu', 'Changbao Zhu', 'Yuxiang Hu', 'Kai Chen', 'Lele Liao', 'Guoliang Cheng'] | 2022-07-04 | null | null | null | null | ['acoustic-echo-cancellation', 'acoustic-echo-cancellation'] | ['medical', 'speech'] | [ 1.85860768e-01 -7.73774743e-01 7.95030653e-01 -1.88485421e-02
-7.56925404e-01 -5.14642835e-01 2.86790669e-01 -3.04499328e-01
-6.43492818e-01 5.96108913e-01 6.06275678e-01 -3.86858165e-01
-4.74293053e-01 -6.51772767e-02 -4.16473955e-01 -1.13357234e+00
-1.96169332e-01 -2.07423776e-01 -1.37118828e-02 -2.58452922... | [15.06945514678955, 5.788017272949219] |
d50aa94a-8cf1-460f-b9dc-f258ba1aab03 | human-body-pose-estimation-for-gait | 2305.13765 | null | https://arxiv.org/abs/2305.13765v1 | https://arxiv.org/pdf/2305.13765v1.pdf | Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models | Person identification is a problem that has received substantial attention, particularly in security domains. Gait recognition is one of the most convenient approaches enabling person identification at a distance without the need of high-quality images. There are several review studies addressing person identification ... | ['Abir Hussain', 'Dhiya Al-Jumeily', 'Wasiq Khan', 'Luke K. Topham'] | 2023-05-23 | null | null | null | null | ['gait-recognition', 'person-identification', 'gait-identification'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-1.38225228e-01 -6.19222939e-01 -3.17522496e-01 -2.94775158e-01
-2.64153510e-01 -3.69023740e-01 2.27250963e-01 -6.55662790e-02
-7.25142002e-01 6.22021496e-01 2.62817740e-01 3.92224789e-01
1.50171798e-02 -5.32967210e-01 7.53159225e-02 -6.43181205e-01
-3.18754256e-01 3.72102082e-01 -2.30797991e-01 -1.50138512... | [14.259079933166504, 1.3886311054229736] |
5ade1573-3bd9-46de-bba0-aeadd2fe90bb | sampling-frequency-independent-audio-source | 2105.04079 | null | https://arxiv.org/abs/2105.04079v1 | https://arxiv.org/pdf/2105.04079v1.pdf | Sampling-Frequency-Independent Audio Source Separation Using Convolution Layer Based on Impulse Invariant Method | Audio source separation is often used as preprocessing of various applications, and one of its ultimate goals is to construct a single versatile model capable of dealing with the varieties of audio signals. Since sampling frequency, one of the audio signal varieties, is usually application specific, the preceding audio... | ['Hiroshi Saruwatari', 'Yuma Koizumi', 'Kohei Yatabe', 'Tomohiko Nakamura', 'Koichi Saito'] | 2021-05-10 | null | null | null | null | ['audio-source-separation', 'music-source-separation'] | ['audio', 'music'] | [ 3.99728656e-01 -4.55546796e-01 -9.64217037e-02 -1.84196867e-02
-5.76648116e-01 -6.16026163e-01 2.19507143e-01 -1.59439430e-01
-1.12451769e-01 4.55188483e-01 5.69487400e-02 -6.23521134e-02
-2.75333911e-01 -6.30731404e-01 -4.41041917e-01 -6.58881664e-01
-7.83101097e-02 7.08713308e-02 1.68484077e-01 -3.68284732... | [15.388863563537598, 5.615823268890381] |
af709b41-4e9c-4fd4-a54e-8074ca3ee593 | towards-efficient-coarse-to-fine-networks-for | null | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/7035_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123750035.pdf | Towards Efficient Coarse-to-Fine Networks for Action and Gesture Recognition | State-of-the-art approaches to video-based action and gesture recognition often employ two key concepts: First, they employ multistream processing; second, they use an ensemble of convolutional networks. We improve and extend both aspects. First, we systematically yield enhanced receptive fields for complementary featu... | ['Peng Dai', 'Juwei Lu', 'Wei Li', 'Niamul Quader'] | null | null | null | null | eccv-2020-8 | ['3d-human-action-recognition'] | ['computer-vision'] | [ 3.62366974e-01 -5.49388766e-01 -2.82512128e-01 -2.98237175e-01
-6.82285190e-01 -5.57814121e-01 8.68693888e-01 -2.09881306e-01
-9.35266912e-01 5.11905789e-01 4.54096794e-01 2.45403741e-02
-2.06999376e-01 -6.16647720e-01 -4.70724821e-01 -7.01368034e-01
-1.70230046e-01 1.60915628e-01 4.92991835e-01 -4.23773289... | [8.322864532470703, 0.42315468192100525] |
12b2ad67-1681-470f-b9be-24e7b23c0c29 | diffusion-models-in-nlp-a-survey-1 | 2305.14671 | null | https://arxiv.org/abs/2305.14671v2 | https://arxiv.org/pdf/2305.14671v2.pdf | A Survey of Diffusion Models in Natural Language Processing | This survey paper provides a comprehensive review of the use of diffusion models in natural language processing (NLP). Diffusion models are a class of mathematical models that aim to capture the diffusion of information or signals across a network or manifold. In NLP, diffusion models have been used in a variety of app... | ['Dongyeop Kang', 'Zae Myung Kim', 'Hao Zou'] | 2023-05-24 | null | null | null | null | ['sentiment-analysis'] | ['natural-language-processing'] | [-2.99601611e-02 1.29051894e-01 -4.62856531e-01 -1.23848192e-01
-4.98452276e-01 -6.65764570e-01 1.32412934e+00 1.48978084e-01
-1.12576865e-01 3.16011548e-01 7.67369926e-01 -3.71966302e-01
-2.26303101e-01 -1.19949126e+00 -1.96457535e-01 -5.59357584e-01
-1.89728752e-01 6.30704284e-01 -2.25249138e-02 -4.84569579... | [11.240300178527832, -0.06144842505455017] |
21ae0dfe-03ad-4baa-ac8b-8a61585713c4 | comparison-of-probabilistic-deep-learning | 2303.12707 | null | https://arxiv.org/abs/2303.12707v1 | https://arxiv.org/pdf/2303.12707v1.pdf | Comparison of Probabilistic Deep Learning Methods for Autism Detection | Autism Spectrum Disorder (ASD) is one neuro developmental disorder that is now widespread in the world. ASD persists throughout the life of an individual, impacting the way they behave and communicate, resulting to notable deficits consisting of social life retardation, repeated behavioural traits and a restriction in ... | ['Kinyua Gikunda', 'Golda Moni', 'Kenneth Chesoli', 'Godfrin Ismail'] | 2023-03-09 | null | null | null | null | ['probabilistic-deep-learning'] | ['computer-vision'] | [-1.42767103e-02 4.81777899e-02 1.34727150e-01 -2.45578825e-01
3.14444564e-02 -2.79451042e-01 3.24860543e-01 6.17894650e-01
-5.38026094e-01 5.40476024e-01 7.25905672e-02 1.23864867e-01
-5.89609444e-01 -3.06223780e-01 -1.18910283e-01 -4.91536528e-01
-1.18132077e-01 1.03918302e+00 3.98509145e-01 -3.23151499... | [12.71854305267334, 3.0315351486206055] |
e240963c-2c3f-40bf-a387-2abc7d642e1c | few-shot-learning-with-global-class | 1908.05257 | null | https://arxiv.org/abs/1908.05257v1 | https://arxiv.org/pdf/1908.05257v1.pdf | Few-Shot Learning with Global Class Representations | In this paper, we propose to tackle the challenging few-shot learning (FSL) problem by learning global class representations using both base and novel class training samples. In each training episode, an episodic class mean computed from a support set is registered with the global representation via a registration modu... | ['Li-Wei Wang', 'Tao Xiang', 'Aoxue Li', 'Weiran Huang', 'Tiange Luo'] | 2019-08-14 | few-shot-learning-with-global-class-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Li_Few-Shot_Learning_With_Global_Class_Representations_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Li_Few-Shot_Learning_With_Global_Class_Representations_ICCV_2019_paper.pdf | iccv-2019-10 | ['generalized-few-shot-classification'] | ['computer-vision'] | [ 6.11295879e-01 1.82572097e-01 -4.79463518e-01 -3.99266243e-01
-1.23963225e+00 -3.73413324e-01 7.63351798e-01 3.23563069e-01
-3.74989450e-01 8.46636653e-01 -2.55721271e-01 4.93246973e-01
-3.90765816e-01 -1.19117820e+00 -5.18172443e-01 -8.18350434e-01
2.54192412e-01 7.42735684e-01 3.94376338e-01 -1.93923190... | [9.924860000610352, 3.2262895107269287] |
92a832e5-bb61-4bdc-a39d-e9889fff5df6 | stratified-sampling-for-extreme-multi-label | 2103.03494 | null | https://arxiv.org/abs/2103.03494v1 | https://arxiv.org/pdf/2103.03494v1.pdf | Stratified Sampling for Extreme Multi-Label Data | Extreme multi-label classification (XML) is becoming increasingly relevant in the era of big data. Yet, there is no method for effectively generating stratified partitions of XML datasets. Instead, researchers typically rely on provided test-train splits that, 1) aren't always representative of the entire dataset, and ... | ['Lan Du', 'Maximillian Merrillees'] | 2021-03-05 | null | null | null | null | ['extreme-multi-label-classification'] | ['methodology'] | [ 4.77056563e-01 -8.15867037e-02 -2.28216618e-01 -7.49783337e-01
-1.28820467e+00 -9.06533182e-01 2.64373958e-01 5.16395509e-01
-3.41825545e-01 1.06707501e+00 1.04994975e-01 -5.21620512e-01
-4.03111130e-01 -7.01436281e-01 -4.48534131e-01 -5.43677747e-01
8.64230692e-02 9.64690804e-01 2.85718851e-02 3.55002135... | [8.994812965393066, 4.608782768249512] |
5b530e9a-0105-4b79-a729-1ad93e6308e4 | multi-mask-aggregators-for-graph-neural | null | null | https://openreview.net/forum?id=hZ3b8CskgC | https://openreview.net/pdf?id=hZ3b8CskgC | Multi-Mask Aggregators for Graph Neural Networks | One of the most critical operations in graph neural networks (GNNs) is the aggregation operation, which aims to extract information from neighbors of the target node. Several convolution methods have been proposed such as standard graph convolution (GCN), graph attention (GAT), and message passing (MPNN). In this study... | ['Ahmet Sureyya Rifaioglu', 'Ahmet Sarıgün'] | 2022-11-24 | null | null | null | learning-on-graphs-conference-2022-11 | ['graph-regression'] | ['graphs'] | [ 6.58654645e-02 4.12195444e-01 -1.82716995e-01 -3.94498944e-01
-1.07284412e-01 -2.82971989e-02 7.46617019e-01 3.96027386e-01
-4.17011887e-01 7.27526486e-01 6.36762530e-02 -4.93356556e-01
1.26590086e-02 -1.14380527e+00 -6.52841687e-01 -7.77666271e-01
-5.55375457e-01 2.60342360e-01 1.81332961e-01 -8.47383812... | [6.994881629943848, 6.23036527633667] |
27b3cda7-47d8-45ee-9e62-9ba6be8c0978 | iot-device-identification-based-on-network-1 | 2303.12800 | null | https://arxiv.org/abs/2303.12800v1 | https://arxiv.org/pdf/2303.12800v1.pdf | IoT Device Identification Based on Network Communication Analysis Using Deep Learning | Attack vectors for adversaries have increased in organizations because of the growing use of less secure IoT devices. The risk of attacks on an organization's network has also increased due to the bring your own device (BYOD) policy which permits employees to bring IoT devices onto the premises and attach them to the o... | ['Yuval Elovici', 'Jaidip Kotak'] | 2023-03-02 | null | null | null | null | ['feature-engineering'] | ['methodology'] | [ 4.05768335e-01 9.92627349e-03 -2.70246953e-01 1.78584069e-01
1.98491126e-01 -9.18804824e-01 4.78361279e-01 2.27300674e-01
-4.02649999e-01 3.48712921e-01 -3.52423519e-01 -1.01546526e+00
-1.90325305e-01 -1.35918796e+00 -3.83022308e-01 -4.03689682e-01
4.44602698e-01 3.49089086e-01 1.97145045e-01 3.27135846... | [5.165846824645996, 7.162745952606201] |
06f71786-3c47-4ce5-8b9c-325bd6f3666a | removing-human-bottlenecks-in-bird | 2305.02097 | null | https://arxiv.org/abs/2305.02097v1 | https://arxiv.org/pdf/2305.02097v1.pdf | Removing Human Bottlenecks in Bird Classification Using Camera Trap Images and Deep Learning | Birds are important indicators for monitoring both biodiversity and habitat health; they also play a crucial role in ecosystem management. Decline in bird populations can result in reduced eco-system services, including seed dispersal, pollination and pest control. Accurate and long-term monitoring of birds to identify... | ['Amira Nuseibeh', 'Jens Mudde', 'Naomi Matthews', 'Chris Sutherland', 'Philip Stephens', 'Naomi Davies Walsh', 'Steven N Longmore', 'Serge Wich', 'Paul Fergus', 'Carl Chalmers'] | 2023-05-03 | null | null | null | null | ['specificity'] | ['natural-language-processing'] | [ 2.37569049e-01 -7.68340647e-01 7.48259872e-02 -5.59860468e-02
5.73143251e-02 -7.73526728e-01 3.69047552e-01 6.80090725e-01
-1.12830436e+00 6.75671220e-01 -2.11491078e-01 -2.57781625e-01
-8.98287296e-02 -1.32305181e+00 -4.04244840e-01 -8.62135231e-01
-7.27373362e-01 -5.31120673e-02 4.55490410e-01 3.03479675... | [8.679686546325684, -1.0805284976959229] |
d4d4fa1f-faf0-42fa-be59-43b725ac81e6 | multi-branch-with-attention-network-for-hand | 2108.02234 | null | https://arxiv.org/abs/2108.02234v5 | https://arxiv.org/pdf/2108.02234v5.pdf | Multi-Branch with Attention Network for Hand-Based Person Recognition | In this paper, we propose a novel hand-based person recognition method for the purpose of criminal investigations since the hand image is often the only available information in cases of serious crime such as sexual abuse. Our proposed method, Multi-Branch with Attention Network (MBA-Net), incorporates both channel and... | ['Sue Black', 'Plamen Angelov', 'Hossein Rahmani', 'Bryan Williams', 'Nathanael L. Baisa'] | 2021-08-04 | null | null | null | null | ['person-recognition'] | ['computer-vision'] | [ 1.47637725e-01 -5.48532188e-01 -1.62361190e-01 -3.65707487e-01
-4.36948180e-01 -4.15826321e-01 3.32192272e-01 -3.32238406e-01
-6.86186969e-01 6.14511907e-01 2.77153701e-01 -1.78855434e-01
-1.15716852e-01 -7.11058795e-01 -3.49773914e-01 -8.48111987e-01
2.63068199e-01 2.77898014e-01 -1.87665131e-02 -2.03980710... | [13.862713813781738, 0.9302264451980591] |
0af69b7d-b1ec-4088-ac23-a02ca16e437c | active-learning-based-non-intrusive-model | 2204.08523 | null | https://arxiv.org/abs/2204.08523v1 | https://arxiv.org/pdf/2204.08523v1.pdf | Active-learning-based non-intrusive Model Order Reduction | The Model Order Reduction (MOR) technique can provide compact numerical models for fast simulation. Different from the intrusive MOR methods, the non-intrusive MOR does not require access to the Full Order Models (FOMs), especially system matrices. Since the non-intrusive MOR methods strongly rely on the snapshots of t... | ['Juan Manuel Lorenzi', 'Hans Joachim Bungartz', 'Dirk Hartmann', 'Qinyu Zhuang'] | 2022-04-08 | null | null | null | null | ['gpr', 'gpr'] | ['computer-vision', 'miscellaneous'] | [ 2.09432140e-01 6.59902319e-02 2.17644811e-01 3.28381777e-01
-7.58079052e-01 -2.67628789e-01 7.41460800e-01 2.67576098e-01
-3.46359789e-01 7.62547255e-01 -6.35330141e-01 -3.96638215e-01
-5.65885663e-01 -7.25560427e-01 -4.71801221e-01 -1.03088593e+00
-1.83320969e-01 8.14969122e-01 2.16983631e-01 -2.68417001... | [6.584064483642578, 2.9979074001312256] |
33e17664-6bb3-413b-930d-248ff11e5703 | pseudo-bag-mixup-augmentation-for-multiple | 2306.16180 | null | https://arxiv.org/abs/2306.16180v2 | https://arxiv.org/pdf/2306.16180v2.pdf | Pseudo-Bag Mixup Augmentation for Multiple Instance Learning-Based Whole Slide Image Classification | Given the special situation of modeling gigapixel images, multiple instance learning (MIL) has become one of the most important frameworks for Whole Slide Image (WSI) classification. In current practice, most MIL networks often face two unavoidable problems in training: i) insufficient WSI data, and ii) the sample memo... | ['Feng Ye', 'Xinyu Zhang', 'Luping Ji', 'Pei Liu'] | 2023-06-28 | null | null | null | null | ['classification-1', 'multiple-instance-learning', 'memorization'] | ['methodology', 'methodology', 'natural-language-processing'] | [ 2.95025259e-01 -3.59169059e-02 -5.90992510e-01 -1.72005609e-01
-9.13353264e-01 -8.82872939e-02 3.37279826e-01 9.78649706e-02
-4.58056062e-01 8.75386477e-01 -3.16955119e-01 -2.53213763e-01
-2.24621370e-01 -7.57602513e-01 -7.00886250e-01 -1.06751800e+00
4.21601117e-01 3.22981060e-01 3.07282507e-01 -1.74712792... | [15.098479270935059, -2.783194065093994] |
c9a61058-1309-4af9-a3cb-5a4f69b4b845 | learning-feature-engineering-for | null | null | https://dl.acm.org/citation.cfm?id=3172240 | https://www.ijcai.org/proceedings/2017/0352.pdf | Learning Feature Engineering for Classification | Feature engineering is the task of improving predictive modelling performance on a dataset by transforming its feature space. Existing approaches to automate this process rely on either transformed feature space exploration through evaluation-guided search, or explicit expansion of datasets with all transformed feature... | ['Udayan Khurana', 'Horst Samulowitz', 'Deepak Turaga', 'Fatemeh Nargesian', 'Elias B. Khalil'] | 2017-01-01 | null | null | null | ijcai-2017-2017-1 | ['automated-feature-engineering'] | ['methodology'] | [ 4.40094262e-01 -1.42135277e-01 -1.72355533e-01 -7.40671992e-01
-7.04635978e-01 -4.52736676e-01 5.99792182e-01 2.60415852e-01
-2.63329178e-01 6.05873406e-01 -9.71177444e-02 -5.36681652e-01
-6.11815691e-01 -9.99583185e-01 -5.34124792e-01 -3.82671356e-01
-1.60619274e-01 2.74195135e-01 -1.92767248e-01 -1.60064064... | [8.240287780761719, 4.658243656158447] |
c9aad2e7-d2b3-4cc9-a2fc-82547c87696a | bullying10k-a-neuromorphic-dataset-towards | 2306.11546 | null | https://arxiv.org/abs/2306.11546v1 | https://arxiv.org/pdf/2306.11546v1.pdf | Bullying10K: A Neuromorphic Dataset towards Privacy-Preserving Bullying Recognition | The prevalence of violence in daily life poses significant threats to individuals' physical and mental well-being. Using surveillance cameras in public spaces has proven effective in proactively deterring and preventing such incidents. However, concerns regarding privacy invasion have emerged due to their widespread de... | ['Yi Zeng', 'Guobin Shen', 'Dongcheng Zhao', 'Yang Li', 'Yiting Dong'] | 2023-06-20 | null | null | null | null | ['pose-estimation', 'action-recognition-in-videos', 'action-localization', 'action-recognition'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 4.54224437e-01 -1.45417973e-01 -1.42549276e-01 -4.43360239e-01
-5.89385748e-01 -7.74721265e-01 4.98285949e-01 -8.40030611e-02
-1.06709611e+00 6.63594067e-01 3.77498120e-01 3.20717722e-01
1.85814977e-01 -4.53991175e-01 -4.31854665e-01 -7.52979875e-01
-1.36035889e-01 -2.84989148e-01 1.04550883e-01 2.43010968... | [7.920560836791992, 1.1313912868499756] |
12dc49c5-ef4b-4e02-a863-dd3ed17cfade | clutter-detection-and-removal-in-3d-scenes | 2304.03763 | null | https://arxiv.org/abs/2304.03763v1 | https://arxiv.org/pdf/2304.03763v1.pdf | Clutter Detection and Removal in 3D Scenes with View-Consistent Inpainting | Removing clutter from scenes is essential in many applications, ranging from privacy-concerned content filtering to data augmentation. In this work, we present an automatic system that removes clutter from 3D scenes and inpaints with coherent geometry and texture. We propose techniques for its two key components: 3D se... | ['Szymon Rusinkiewicz', 'Thomas Funkhouser', 'Fangyin Wei'] | 2023-04-07 | null | null | null | null | ['3d-inpainting'] | ['computer-vision'] | [ 5.21994531e-01 -8.16795230e-02 2.25876644e-01 -3.53170931e-01
-9.23093915e-01 -7.66699195e-01 2.38907009e-01 1.31148651e-01
-1.41250074e-01 4.43316132e-01 4.27593989e-03 5.80099374e-02
5.67847714e-02 -6.16312265e-01 -9.53494668e-01 -3.97310227e-01
1.14770994e-01 6.29972100e-01 7.56776035e-01 1.41339347... | [8.5687255859375, -2.9931559562683105] |
35e91125-037e-4e73-b594-62e544c91bf1 | non-contact-transmittance | 1503.06775 | null | http://arxiv.org/abs/1503.06775v1 | http://arxiv.org/pdf/1503.06775v1.pdf | Non-contact transmittance photoplethysmographic imaging (PPGI) for long-distance cardiovascular monitoring | Photoplethysmography (PPG) devices are widely used for monitoring
cardiovascular function. However, these devices require skin contact, which
restrict their use to at-rest short-term monitoring using single-point
measurements. Photoplethysmographic imaging (PPGI) has been recently proposed
as a non-contact monitoring a... | ['Kaylen J. Pfisterer', 'Farnoud Kazemzadeh', 'Alexander Wong', 'Robert Amelard', 'Christian Scharfenberger', 'Bill S. Lin', 'David A. Clausi'] | 2015-03-23 | null | null | null | null | ['photoplethysmography-ppg'] | ['medical'] | [ 3.70002866e-01 -1.36079237e-01 3.60366404e-01 -2.65082270e-01
-2.90730178e-01 -4.89377260e-01 9.06341895e-02 -3.21058482e-02
-3.93070787e-01 7.85310924e-01 -1.92765296e-01 -8.06558877e-02
1.20486386e-01 -6.73965454e-01 -1.70387134e-01 -8.58580828e-01
-3.16940874e-01 -1.13808244e-01 7.34213665e-02 2.26082340... | [13.927047729492188, 2.9264533519744873] |
5805ea8c-bffc-4aac-a9d2-dee52a88a637 | global-object-proposals-for-improving-multi | null | null | https://ieeexplore.ieee.org/document/9533883 | https://ieeexplore.ieee.org/document/9533883 | Global Object Proposals for Improving Multi-Sentence Video Descriptions | There has been significant progress in image captioning in recent years. The generation of video descriptions is still in its early stages; this is due to the complex nature of videos in comparison to images. Generating paragraph descriptions of a video is even more challenging. Amongst the main issues are temporal obj... | ['Pushpak Bhattacharyya', 'Sriparna Saha', 'Chandresh S. Kanani'] | 2021-07-18 | null | null | null | international-joint-conference-on-neural-3 | ['dense-video-captioning'] | ['computer-vision'] | [ 3.70098323e-01 1.81586713e-01 -7.06801517e-03 -4.16705638e-01
-8.30179214e-01 -3.71108502e-01 1.02570963e+00 2.94871688e-01
-4.22906071e-01 1.05013609e+00 4.57880527e-01 2.97351688e-01
1.42563611e-01 -4.97469425e-01 -7.52696693e-01 -4.43659037e-01
8.59196484e-02 6.76202714e-01 9.18389857e-01 -1.20006185... | [10.58262825012207, 0.6626916527748108] |
557f61d0-0758-4d19-8f55-bb62f2399338 | event-driven-vision-and-control-for-uavs-on-a | 2108.03694 | null | https://arxiv.org/abs/2108.03694v2 | https://arxiv.org/pdf/2108.03694v2.pdf | Event-driven Vision and Control for UAVs on a Neuromorphic Chip | Event-based vision sensors achieve up to three orders of magnitude better speed vs. power consumption trade off in high-speed control of UAVs compared to conventional image sensors. Event-based cameras produce a sparse stream of events that can be processed more efficiently and with a lower latency than images, enablin... | ['Yulia Sandamirskaya', 'Davide Scaramuzza', 'Celine Nauer', 'Alpha Renner', 'Antonio Vitale'] | 2021-08-08 | null | null | null | null | ['event-based-vision', 'drone-controller'] | ['computer-vision', 'robots'] | [ 3.55286032e-01 -3.34520400e-01 5.78551769e-01 -9.19412822e-02
2.67362148e-01 -8.04210484e-01 4.36385781e-01 2.76176780e-01
-7.73529351e-01 2.97286570e-01 -4.72122103e-01 1.41178831e-01
1.04702704e-01 -8.40280890e-01 -1.06608748e+00 -4.80612844e-01
-3.20969447e-02 7.64214247e-02 9.69277978e-01 -7.43756145... | [8.200605392456055, 2.257495164871216] |
793628c1-38a9-45ca-9244-7708fbae5c0f | a-learning-based-adaptive-compliance-method | 2303.15262 | null | https://arxiv.org/abs/2303.15262v1 | https://arxiv.org/pdf/2303.15262v1.pdf | A Learning-based Adaptive Compliance Method for Symmetric Bi-manual Manipulation | Symmetric bi-manual manipulation is essential for various on-orbit operations due to its potent load capacity. As a result, there exists an emerging research interest in the problem of achieving high operation accuracy while enhancing adaptability and compliance. However, previous works relied on an inefficient algorit... | ['Tao Zhang', 'Wenke Ma', 'Xiang Zheng', 'Shengjie Wang', 'Yuxue Cao'] | 2023-03-27 | null | null | null | null | ['motion-planning'] | ['robots'] | [-3.03754389e-01 1.75351366e-01 -3.20601404e-01 7.36408234e-02
-2.56230444e-01 -5.22752643e-01 2.69731790e-01 -7.39760473e-02
-4.34502661e-01 5.38346469e-01 1.14653431e-01 -1.72186896e-01
-7.04424798e-01 -5.70442557e-01 -6.12174630e-01 -7.39513576e-01
-1.37480900e-01 5.16586125e-01 -1.12604192e-02 -8.71447206... | [4.703604221343994, 1.232393741607666] |
b54ec177-7c38-4e7c-ae5c-fdb875e4ef07 | neural-waveshaping-synthesis | 2107.05050 | null | https://arxiv.org/abs/2107.05050v2 | https://arxiv.org/pdf/2107.05050v2.pdf | Neural Waveshaping Synthesis | We present the Neural Waveshaping Unit (NEWT): a novel, lightweight, fully causal approach to neural audio synthesis which operates directly in the waveform domain, with an accompanying optimisation (FastNEWT) for efficient CPU inference. The NEWT uses time-distributed multilayer perceptrons with periodic activations t... | ['György Fazekas', 'Charalampos Saitis', 'Ben Hayes'] | 2021-07-11 | null | null | null | null | ['audio-generation'] | ['audio'] | [ 4.90938395e-01 -2.35561773e-01 5.43938398e-01 -1.04172945e-01
-1.03297341e+00 -7.44364440e-01 4.70735580e-01 -4.54262853e-01
-1.72114104e-01 5.63492477e-01 3.32176834e-01 -6.81543574e-02
-3.94472599e-01 -6.63776338e-01 -9.21826184e-01 -6.75641119e-01
-3.36404264e-01 4.72600996e-01 -5.21918572e-02 -4.33920294... | [15.668808937072754, 5.825524806976318] |
6d7c8ab0-5652-42cf-97e9-6b9824f53a9e | sharing-models-or-coresets-a-study-based-on | 2007.02977 | null | https://arxiv.org/abs/2007.02977v1 | https://arxiv.org/pdf/2007.02977v1.pdf | Sharing Models or Coresets: A Study based on Membership Inference Attack | Distributed machine learning generally aims at training a global model based on distributed data without collecting all the data to a centralized location, where two different approaches have been proposed: collecting and aggregating local models (federated learning) and collecting and training over representative data... | ['Shiqiang Wang', 'Kevin S. Chan', 'Ting He', 'Hanlin Lu', 'Changchang Liu'] | 2020-07-06 | null | null | null | null | ['membership-inference-attack'] | ['computer-vision'] | [-2.72738524e-02 3.53323609e-01 -5.35142660e-01 -4.77709234e-01
-1.15322864e+00 -8.42226982e-01 6.75171435e-01 7.23354101e-01
-3.17129374e-01 5.32541692e-01 8.40018764e-02 -5.61124444e-01
-3.48794460e-01 -8.24079454e-01 -6.96372628e-01 -7.54095018e-01
-2.06882149e-01 6.41084254e-01 3.86808775e-02 4.84267592... | [5.897747993469238, 6.698664665222168] |
f49f7bd6-3d35-4a42-826c-bf8882679ef6 | cd-ctfm-a-lightweight-cnn-transformer-network | 2306.07186 | null | https://arxiv.org/abs/2306.07186v1 | https://arxiv.org/pdf/2306.07186v1.pdf | CD-CTFM: A Lightweight CNN-Transformer Network for Remote Sensing Cloud Detection Fusing Multiscale Features | Clouds in remote sensing images inevitably affect information extraction, which hinder the following analysis of satellite images. Hence, cloud detection is a necessary preprocessing procedure. However, the existing methods have numerous calculations and parameters. In this letter, a lightweight CNN-Transformer network... | ['Li Zhang', 'Xubing Yang', 'Wenxuan Ge'] | 2023-06-12 | null | null | null | null | ['cloud-detection'] | ['computer-vision'] | [-4.83100042e-02 -7.17555761e-01 3.17412883e-01 -3.07280511e-01
-5.29452562e-01 -7.47388527e-02 3.01745683e-01 -1.65819749e-01
-4.06174302e-01 2.73034394e-01 1.22219801e-01 -2.70091027e-01
5.70593923e-02 -1.01909029e+00 -5.38067877e-01 -9.26275253e-01
1.15308397e-01 -4.81706619e-01 2.83336014e-01 -6.03844114... | [9.916974067687988, -1.7792588472366333] |
a85fdee3-d94a-4be0-9604-334a737d8819 | lasr-learning-articulated-shape | 2105.02976 | null | https://arxiv.org/abs/2105.02976v1 | https://arxiv.org/pdf/2105.02976v1.pdf | LASR: Learning Articulated Shape Reconstruction from a Monocular Video | Remarkable progress has been made in 3D reconstruction of rigid structures from a video or a collection of images. However, it is still challenging to reconstruct nonrigid structures from RGB inputs, due to its under-constrained nature. While template-based approaches, such as parametric shape models, have achieved gre... | ['Ce Liu', 'William T. Freeman', 'Deva Ramanan', 'Huiwen Chang', 'Forrester Cole', 'Daniel Vlasic', 'Varun Jampani', 'Deqing Sun', 'Gengshan Yang'] | 2021-05-06 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Yang_LASR_Learning_Articulated_Shape_Reconstruction_From_a_Monocular_Video_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Yang_LASR_Learning_Articulated_Shape_Reconstruction_From_a_Monocular_Video_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-shape-reconstruction-from-videos'] | ['computer-vision'] | [ 1.07454076e-01 -9.37862471e-02 1.62881404e-01 -1.95516571e-01
-3.85230511e-01 -8.51243019e-01 5.08647621e-01 -5.87088346e-01
-4.87990156e-02 5.42892873e-01 4.46369722e-02 -2.25033741e-02
2.06618220e-01 -3.92140418e-01 -7.65121818e-01 -6.50698900e-01
3.37273091e-01 6.66613519e-01 4.02918637e-01 2.89628476... | [8.790433883666992, -2.7721621990203857] |
3e8aebdc-07c3-4a8c-b51c-273ccd3125d5 | sea-a-scalable-entity-alignment-system | 2304.07065 | null | https://arxiv.org/abs/2304.07065v1 | https://arxiv.org/pdf/2304.07065v1.pdf | SEA: A Scalable Entity Alignment System | Entity alignment (EA) aims to find equivalent entities in different knowledge graphs (KGs). State-of-the-art EA approaches generally use Graph Neural Networks (GNNs) to encode entities. However, most of them train the models and evaluate the results in a fullbatch fashion, which prohibits EA from being scalable on larg... | ['Ziheng Wei', 'Yunjun Gao', 'Lu Chen', 'Tianyi Li', 'Junyang Wu'] | 2023-04-14 | null | null | null | null | ['entity-alignment', 'entity-alignment'] | ['knowledge-base', 'natural-language-processing'] | [-2.45028317e-01 -1.11379102e-01 -1.26035705e-01 -2.14232877e-01
-3.53532255e-01 -6.94312036e-01 8.23687389e-02 4.71061289e-01
-7.64613867e-01 4.97218132e-01 -3.94516408e-01 -6.93508744e-01
-1.80731788e-02 -1.28025079e+00 -6.53963387e-01 -3.27763468e-01
-2.33295962e-01 9.36472416e-01 2.62688726e-01 -4.54112560... | [8.764131546020508, 7.963877201080322] |
f0e51e0a-38b2-4d84-92a0-bb26e0e98a1b | kornia-an-open-source-differentiable-computer | 1910.02190 | null | https://arxiv.org/abs/1910.02190v2 | https://arxiv.org/pdf/1910.02190v2.pdf | Kornia: an Open Source Differentiable Computer Vision Library for PyTorch | This work presents Kornia -- an open source computer vision library which consists of a set of differentiable routines and modules to solve generic computer vision problems. The package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and comp... | ['Daniel Ponsa', 'Edgar Riba', 'Dmytro Mishkin', 'Ethan Rublee', 'Gary Bradski'] | 2019-10-05 | null | null | null | null | ['image-smoothing', 'image-stitching', 'image-morphing', 'image-matching', 'image-cropping'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [-3.15771341e-01 -1.66534573e-01 5.95337033e-01 -3.17980289e-01
5.46689928e-02 -4.38804716e-01 8.18985283e-01 -3.29844713e-01
-6.85967922e-01 1.59901276e-01 -3.58108371e-01 -2.82012731e-01
2.56369561e-02 -7.26121902e-01 -4.98780936e-01 -6.99117720e-01
-3.92240435e-02 3.65654200e-01 2.92707533e-01 -3.55667591... | [8.961140632629395, -2.0382916927337646] |
71642c5b-1832-4dc0-b747-7582892ce4b5 | a-14uj-decision-keyword-spotting-accelerator | 2205.04665 | null | https://arxiv.org/abs/2205.04665v1 | https://arxiv.org/pdf/2205.04665v1.pdf | A 14uJ/Decision Keyword Spotting Accelerator with In-SRAM-Computing and On Chip Learning for Customization | Keyword spotting has gained popularity as a natural way to interact with consumer devices in recent years. However, because of its always-on nature and the variety of speech, it necessitates a low-power design as well as user customization. This paper describes a low-power, energy-efficient keyword spotting accelerator... | ['Shyh Jye Jou', 'Tian-Sheuan Chang', 'Yu-Hsiang Chiang'] | 2022-05-10 | null | null | null | null | ['keyword-spotting'] | ['speech'] | [ 1.35375774e-02 -4.06870514e-01 -5.35496712e-01 -2.64593095e-01
-5.30394852e-01 -1.75839394e-01 -1.22782238e-01 3.26686293e-01
-6.28354311e-01 5.30431986e-01 -1.46219060e-01 -5.27054369e-01
1.80367202e-01 -7.28884816e-01 -5.36168218e-01 -4.52212781e-01
2.08534732e-01 4.55945842e-02 5.02569497e-01 -2.58771107... | [8.438650131225586, 2.802136182785034] |
f25ab1ac-dfce-4153-9d96-589e8641afdb | uncovering-probabilistic-implications-in | 1906.07389 | null | https://arxiv.org/abs/1906.07389v1 | https://arxiv.org/pdf/1906.07389v1.pdf | Uncovering Probabilistic Implications in Typological Knowledge Bases | The study of linguistic typology is rooted in the implications we find between linguistic features, such as the fact that languages with object-verb word ordering tend to have post-positions. Uncovering such implications typically amounts to time-consuming manual processing by trained and experienced linguists, which p... | ['Isabelle Augenstein', 'Johannes Bjerva', 'Yova Kementchedjhieva', 'Ryan Cotterell'] | 2019-06-18 | uncovering-probabilistic-implications-in-1 | https://aclanthology.org/P19-1382 | https://aclanthology.org/P19-1382.pdf | acl-2019-7 | ['knowledge-base-population'] | ['natural-language-processing'] | [-4.68110479e-02 6.93356916e-02 -6.55030787e-01 -1.69119164e-01
-5.33415079e-01 -8.19456697e-01 5.52339315e-01 6.49899006e-01
-4.34030831e-01 8.45400095e-01 7.05733657e-01 -7.47830153e-01
-1.84366018e-01 -6.94936991e-01 -5.99294782e-01 -3.35467964e-01
-1.58834949e-01 4.45902914e-01 4.46381241e-01 -5.73488176... | [10.370855331420898, 9.332405090332031] |
067afe7d-399b-40ca-b023-b98195d65ab1 | variance-reduction-for-policy-gradient | 2206.06827 | null | https://arxiv.org/abs/2206.06827v2 | https://arxiv.org/pdf/2206.06827v2.pdf | Variance Reduction for Policy-Gradient Methods via Empirical Variance Minimization | Policy-gradient methods in Reinforcement Learning(RL) are very universal and widely applied in practice but their performance suffers from the high variance of the gradient estimate. Several procedures were proposed to reduce it including actor-critic(AC) and advantage actor-critic(A2C) methods. Recently the approaches... | ['Denis Belomestny', 'Alexander Golubev', 'Maxim Kaledin'] | 2022-06-14 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-1.88845143e-01 3.64536852e-01 -2.17056006e-01 3.75396758e-02
-8.36310685e-01 -3.93982649e-01 7.00100303e-01 2.52157718e-01
-8.59279871e-01 1.29233968e+00 -8.47355947e-02 -2.54822135e-01
-3.48416656e-01 -4.63207573e-01 -8.02242756e-01 -1.02713037e+00
-4.52023223e-02 4.23039079e-01 1.68613702e-01 -5.91711998... | [4.316885948181152, 2.398952007293701] |
ae01c1cf-bef1-4c9c-935c-7404d3987dec | a-simple-and-effective-baseline-for | 2306.14708 | null | https://arxiv.org/abs/2306.14708v2 | https://arxiv.org/pdf/2306.14708v2.pdf | A Simple and Effective Baseline for Attentional Generative Adversarial Networks | Synthesising a text-to-image model of high-quality images by guiding the generative model through the Text description is an innovative and challenging task. In recent years, AttnGAN based on the Attention mechanism to guide GAN training has been proposed, SD-GAN, which adopts a self-distillation technique to improve t... | ['Xi Yang', 'Xiaobo Jin', 'Haochen Xue', 'Qinkai Yu', 'Chong Zhang', 'Mingyu Jin'] | 2023-06-26 | null | null | null | null | ['image-generation'] | ['computer-vision'] | [ 2.46508524e-01 4.08419192e-01 2.07949668e-01 -1.54618129e-01
-5.42741656e-01 -2.86566794e-01 6.85527921e-01 -6.99262500e-01
1.52380213e-01 8.46577287e-01 3.85662764e-01 -4.99841012e-02
2.79403299e-01 -9.44299102e-01 -6.65829659e-01 -9.63529944e-01
6.03075683e-01 2.07570285e-01 9.47251078e-03 -2.48463392... | [11.556195259094238, -0.6275332570075989] |
34e91057-e4d7-4638-96bb-25f7779b0e7a | zero-shot-text-to-image-generation | 2102.12092 | null | https://arxiv.org/abs/2102.12092v2 | https://arxiv.org/pdf/2102.12092v2.pdf | Zero-Shot Text-to-Image Generation | Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset. These assumptions might involve complex architectures, auxiliary losses, or side information such as object part labels or segmentation masks supplied during training. We describe a simple approach... | ['Ilya Sutskever', 'Mark Chen', 'Alec Radford', 'Chelsea Voss', 'Scott Gray', 'Gabriel Goh', 'Mikhail Pavlov', 'Aditya Ramesh'] | 2021-02-24 | null | null | null | null | ['zero-shot-text-to-image-generation'] | ['natural-language-processing'] | [ 5.10667682e-01 5.20125866e-01 -2.34405234e-01 -7.14775324e-01
-9.05129373e-01 -4.47345465e-01 1.21292782e+00 -4.36715931e-02
-5.33389628e-01 7.73073494e-01 -2.18037795e-02 -1.42985046e-01
3.17001581e-01 -7.19507813e-01 -8.28086615e-01 -4.49846148e-01
3.62369716e-01 1.00804639e+00 3.71510923e-01 -1.01900041... | [10.99173355102539, 0.13344649970531464] |
f1ff04f9-9a7b-4bd9-874c-23e5827b6e1a | discriminative-models-can-still-outperform | null | null | https://openreview.net/forum?id=uZaiKvl7C5p | https://openreview.net/pdf?id=uZaiKvl7C5p | Discriminative Models Can Still Outperform Generative Models in Aspect Based Sentiment Analysis | Aspect-based Sentiment Analysis (ABSA) helps to explain customers' opinions towards products and services. In the past, ABSA models were discriminative, but more recently generative models have been used to generate aspects and polarities directly from text. In contrast, discriminative models commonly first select asp... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['aspect-based-sentiment-analysis'] | ['natural-language-processing'] | [-1.78237036e-01 6.75371066e-02 -3.78101587e-01 -9.06745255e-01
-1.06281352e+00 -1.02371955e+00 8.95292282e-01 5.14684990e-02
-1.51984468e-01 4.22722220e-01 5.70312142e-01 -3.85183156e-01
1.41801447e-01 -8.79508138e-01 -3.98845434e-01 -4.30337638e-01
5.56186497e-01 8.67332935e-01 -2.77342469e-01 -5.98619640... | [11.431407928466797, 6.721214294433594] |
a2363b9a-486e-452b-a471-bc6154ebb907 | 70-years-of-machine-learning-in-geoscience-in | 2006.13311 | null | https://arxiv.org/abs/2006.13311v3 | https://arxiv.org/pdf/2006.13311v3.pdf | 70 years of machine learning in geoscience in review | This review gives an overview of the development of machine learning in geoscience. A thorough analysis of the co-developments of machine learning applications throughout the last 70 years relates the recent enthusiasm for machine learning to developments in geoscience. I explore the shift of kriging towards a mainstre... | ['Jesper Sören Dramsch'] | 2020-06-16 | null | null | null | null | ['geophysics'] | ['miscellaneous'] | [ 4.21113847e-03 1.52996451e-01 7.89627805e-02 -6.32795691e-02
-1.81334108e-01 -3.94111067e-01 7.45760083e-01 -1.95629336e-02
-9.10826698e-02 6.23283505e-01 5.07826395e-02 -1.26250267e+00
-1.90845668e-01 -1.25918484e+00 -7.03878105e-01 -1.13037908e+00
-4.53381270e-01 1.12136364e-01 -5.40663600e-01 -5.71433723... | [6.717464923858643, 2.936596632003784] |
4dcbf9b8-31f4-4fd4-b2bd-92d88c157e67 | subject-specific-lesion-generation-and-pseudo | 2208.02135 | null | https://arxiv.org/abs/2208.02135v1 | https://arxiv.org/pdf/2208.02135v1.pdf | Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Images | Understanding the intensity characteristics of brain lesions is key for defining image-based biomarkers in neurological studies and for predicting disease burden and outcome. In this work, we present a novel foreground-based generative method for modelling the local lesion characteristics that can both generate synthet... | ['Wenjia Bai', 'Paul M. Matthews', 'Mengyun Qiao', 'Berke Doga Basaran'] | 2022-08-03 | null | null | null | null | ['brain-image-segmentation'] | ['medical'] | [ 8.40543568e-01 1.53347269e-01 -3.75262834e-02 -4.95537847e-01
-6.39616191e-01 -1.74400255e-01 7.30504215e-01 -2.55212545e-01
-3.43545794e-01 6.18936121e-01 -5.15856873e-03 -3.32478911e-01
3.07788312e-01 -6.82573199e-01 -5.00289738e-01 -9.08421874e-01
-9.65840891e-02 7.63349891e-01 3.18353623e-01 1.43490836... | [14.197196006774902, -2.1612725257873535] |
a8389267-eeae-4d37-b107-8ecfaeb9f590 | crad-clustering-with-robust-autocuts-and | 1904.04020 | null | http://arxiv.org/abs/1904.04020v1 | http://arxiv.org/pdf/1904.04020v1.pdf | CRAD: Clustering with Robust Autocuts and Depth | We develop a new density-based clustering algorithm named CRAD which is based
on a new neighbor searching function with a robust data depth as the
dissimilarity measure. Our experiments prove that the new CRAD is highly
competitive at detecting clusters with varying densities, compared with the
existing algorithms such... | ['Yulia R. Gel', 'Xin Huang'] | 2019-04-08 | null | null | null | null | ['time-series-clustering'] | ['time-series'] | [-5.73232174e-01 -8.29551697e-01 4.14325185e-02 -2.16700062e-01
-7.23937035e-01 -5.21200001e-01 4.23971236e-01 -9.14606545e-03
-3.46413046e-01 4.62572753e-01 -1.41146645e-01 -2.13432342e-01
-6.98986888e-01 -7.97854304e-01 -2.57209390e-01 -1.25822914e+00
-5.39314985e-01 8.72519255e-01 5.48548043e-01 2.69901663... | [7.491646766662598, 4.459718227386475] |
16a0e863-9ab6-49cf-a44e-adb153731412 | user-response-and-sentiment-prediction-for | 2111.08808 | null | https://arxiv.org/abs/2111.08808v2 | https://arxiv.org/pdf/2111.08808v2.pdf | User Response and Sentiment Prediction for Automatic Dialogue Evaluation | Automatic evaluation is beneficial for open-domain dialog system development. However, standard word-overlap metrics (BLEU, ROUGE) do not correlate well with human judgements of open-domain dialog systems. In this work we propose to use the sentiment of the next user utterance for turn or dialog level evaluation. Speci... | ['Dilek Hakkani-Tur', 'Yang Liu', 'Alexandros Papangelis', 'Behnam Hedayatnia', 'Sarik Ghazarian'] | 2021-11-16 | null | null | null | null | ['dialogue-evaluation', 'open-domain-dialog'] | ['natural-language-processing', 'natural-language-processing'] | [-1.18345894e-01 6.58326328e-01 9.63281989e-02 -8.84324610e-01
-6.31269991e-01 -1.01717329e+00 9.01955903e-01 1.28271669e-01
-4.65095460e-01 1.11907160e+00 6.94637001e-01 -3.92612636e-01
3.16393137e-01 -5.33838749e-01 2.56424159e-01 3.23254019e-02
3.90345007e-01 9.40080225e-01 3.21761847e-01 -1.10415471... | [12.874625205993652, 8.006439208984375] |
d7c29acb-7a61-4f2f-8001-eb2553348f49 | the-impact-of-twitter-sentiments-on-stock | 2302.07244 | null | https://arxiv.org/abs/2302.07244v1 | https://arxiv.org/pdf/2302.07244v1.pdf | The Impact of Twitter Sentiments on Stock Market Trends | The Web is a vast virtual space where people can share their opinions, impacting all aspects of life and having implications for marketing and communication. The most up-to-date and comprehensive information can be found on social media because of how widespread and straightforward it is to post a message. Proportionat... | ['Adel Karshenas', 'Niloufar Saeedi', 'Ali Seraj', 'Melvin Mokhtari'] | 2023-02-14 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [-7.76395202e-01 -4.02617246e-01 -9.86220956e-01 -7.87417442e-02
-2.36471429e-01 -7.58817315e-01 9.51298594e-01 7.35098124e-01
-6.41166925e-01 7.72230804e-01 4.79488730e-01 -5.18071234e-01
3.31081510e-01 -1.27896070e+00 -5.71252644e-01 -3.28555346e-01
6.22076243e-02 1.46049321e-01 3.42736036e-01 -7.07526267... | [4.525813102722168, 4.417088508605957] |
7472b945-38ef-4f0a-be31-5fd4ed557be3 | improving-knowledge-aware-recommendation-with | 2208.10061 | null | https://arxiv.org/abs/2208.10061v1 | https://arxiv.org/pdf/2208.10061v1.pdf | Improving Knowledge-aware Recommendation with Multi-level Interactive Contrastive Learning | Incorporating Knowledge Graphs (KG) into recommeder system has attracted considerable attention. Recently, the technical trend of Knowledge-aware Recommendation (KGR) is to develop end-to-end models based on graph neural networks (GNNs). However, the extremely sparse user-item interactions significantly degrade the per... | ['Dangyang Chen', 'Rui Fang', 'Feida Zhu', 'Xian-Ling Mao', 'Ziyang Wang', 'Wei Wei', 'Ding Zou'] | 2022-08-22 | null | null | null | null | ['knowledge-aware-recommendation'] | ['miscellaneous'] | [ 1.77720760e-03 3.15462232e-01 -6.39740407e-01 -1.59371451e-01
-1.43733919e-01 -1.12043321e-01 2.72026539e-01 3.28236222e-01
1.33751526e-01 4.75669146e-01 2.69184530e-01 -1.65573493e-01
-6.43604934e-01 -1.10546875e+00 -8.14388752e-01 -6.44137740e-01
-5.13949633e-01 1.69001833e-01 1.61192700e-01 -3.96459758... | [10.232565879821777, 5.608561992645264] |
59f462ce-ab42-41cf-a654-e585a11b6668 | finding-regions-of-counterfactual | 2301.11113 | null | https://arxiv.org/abs/2301.11113v2 | https://arxiv.org/pdf/2301.11113v2.pdf | Finding Regions of Counterfactual Explanations via Robust Optimization | Counterfactual explanations play an important role in detecting bias and improving the explainability of data-driven classification models. A counterfactual explanation (CE) is a minimal perturbed data point for which the decision of the model changes. Most of the existing methods can only provide one CE, which may not... | ['Dick den Hertog', 'Ş. Ilker Birbil', 'Rob Goedhart', 'Tabea E. Röber', 'Jannis Kurtz', 'Donato Maragno'] | 2023-01-26 | null | null | null | null | ['counterfactual-explanation'] | ['miscellaneous'] | [ 3.36625427e-01 2.95087188e-01 -7.45922327e-01 -4.03265834e-01
-8.80048275e-01 -5.04463732e-01 6.02939844e-01 2.77663711e-02
-1.77727133e-01 1.30209267e+00 9.75238383e-02 -7.46257007e-01
-4.42568362e-01 -5.65400481e-01 -7.79385567e-01 -7.96243370e-01
-1.60394281e-01 2.87894309e-01 -3.17564279e-01 1.26972005... | [8.611066818237305, 5.508005619049072] |
0a3230f9-5695-4678-8abb-5a82085de59c | a-cross-modal-image-fusion-theory-guided-by | 1912.08577 | null | https://arxiv.org/abs/1912.08577v4 | https://arxiv.org/pdf/1912.08577v4.pdf | A Cross-Modal Image Fusion Method Guided by Human Visual Characteristics | The characteristics of feature selection, nonlinear combination and multi-task auxiliary learning mechanism of the human visual perception system play an important role in real-world scenarios, but the research of image fusion theory based on the characteristics of human visual perception is less. Inspired by the chara... | ['Jiaqi Yang', 'Yanning Zhang', 'Xinbo Zhao', 'Aiqing Fang'] | 2019-12-18 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 1.41083255e-01 -6.93158329e-01 2.68474728e-01 -2.04262912e-01
-3.15669060e-01 7.05511868e-02 2.80403078e-01 -2.23797917e-01
-7.40404308e-01 3.84797007e-01 9.12133083e-02 -3.81417349e-02
-4.04558688e-01 -5.13847113e-01 -5.57565689e-01 -1.11165571e+00
3.79448861e-01 -4.60554302e-01 1.96201913e-02 -4.72044349... | [10.51905632019043, -1.816798448562622] |
37e92fde-849f-4ff9-88f1-3f2ef5c86143 | multi-view-pointnet-for-3d-scene | 1909.13603 | null | https://arxiv.org/abs/1909.13603v1 | https://arxiv.org/pdf/1909.13603v1.pdf | Multi-view PointNet for 3D Scene Understanding | Fusion of 2D images and 3D point clouds is important because information from dense images can enhance sparse point clouds. However, fusion is challenging because 2D and 3D data live in different spaces. In this work, we propose MVPNet (Multi-View PointNet), where we aggregate 2D multi-view image features into 3D point... | ['Jiayuan Gu', 'Hao Su', 'Maximilian Jaritz'] | 2019-09-30 | null | null | null | null | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 8.45143422e-02 -2.01172009e-03 -2.39990726e-02 -4.84006584e-01
-9.62075174e-01 -7.80532002e-01 6.56707883e-01 4.47148606e-02
-2.57795285e-02 1.15685081e-02 1.54448181e-01 -1.01790264e-01
-4.83290851e-02 -7.83971310e-01 -8.63461494e-01 -3.65807414e-01
1.43455669e-01 1.05448234e+00 5.58884442e-01 -1.63126260... | [8.221948623657227, -3.1837456226348877] |
b4121718-2187-4c33-a57b-956c83ffca5f | lad-rcnn-a-powerful-tool-for-livestock-face | 2210.17146 | null | https://arxiv.org/abs/2210.17146v2 | https://arxiv.org/pdf/2210.17146v2.pdf | LAD-RCNN:A Powerful Tool for Livestock Face Detection and Normalization | With the demand for standardized large-scale livestock farming and the development of artificial intelligence technology, a lot of research in area of animal face recognition were carried on pigs, cattle, sheep and other livestock. Face recognition consists of three sub-task: face detection, face normalizing and face i... | ['Xunping Jiang', 'Shiping Yang', 'Han Yang', 'Xu Wang', 'Junrui Liu', 'Guiqiong Liu', 'Ling Sun'] | 2022-10-31 | null | null | null | null | ['face-detection', 'face-identification'] | ['computer-vision', 'computer-vision'] | [-1.14741497e-01 -4.26819205e-01 1.16295464e-01 -7.15975583e-01
3.66353929e-01 -3.44814241e-01 2.05153860e-02 -5.63922226e-01
-3.17103118e-01 1.34784833e-01 -4.30842400e-01 -2.06228465e-01
2.91755378e-01 -8.99443328e-01 -6.60211921e-01 -7.81183779e-01
-2.09901109e-01 1.85991392e-01 -1.61413580e-01 -1.92654505... | [13.287932395935059, 0.8514235615730286] |
115b9ae8-7f2d-4efe-87aa-a91bde915a4f | low-light-enhancement-method-based-on | 2208.09330 | null | https://arxiv.org/abs/2208.09330v2 | https://arxiv.org/pdf/2208.09330v2.pdf | Low-light Enhancement Method Based on Attention Map Net | Low-light image enhancement is a crucial preprocessing task for some complex vision tasks. Target detection, image segmentation, and image recognition outcomes are all directly impacted by the impact of image enhancement. However, the majority of the currently used image enhancement techniques do not produce satisfacto... | ['Xinwei Xu', 'Taiji Lan', 'Xucheng Xue', 'Mengfei Wu'] | 2022-08-19 | null | null | null | null | ['low-light-image-enhancement'] | ['computer-vision'] | [ 5.38995445e-01 -1.80384740e-01 9.79219452e-02 -2.62849957e-01
-1.07914284e-01 -1.89013600e-01 6.25857949e-01 -4.35320199e-01
-5.19456089e-01 7.94795513e-01 -4.46526567e-03 -2.67591596e-01
2.53220379e-01 -9.06104505e-01 -5.49533010e-01 -8.68718863e-01
4.05420303e-01 -7.78110027e-01 5.77242553e-01 -2.49443918... | [10.941632270812988, -2.328233480453491] |
b8b3f1c8-c8d0-4e03-9e4c-d66c546e5838 | improving-application-performance-with-biased | 2107.07642 | null | https://arxiv.org/abs/2107.07642v1 | https://arxiv.org/pdf/2107.07642v1.pdf | Improving application performance with biased distributions of quantum states | We consider the properties of a specific distribution of mixed quantum states of arbitrary dimension that can be biased towards a specific mean purity. In particular, we analyze mixtures of Haar-random pure states with Dirichlet-distributed coefficients. We analytically derive the concentration parameters required to m... | ['Brian T. Kirby', 'Ryan T. Glasser', 'Thomas A. Searles', 'Daniel E. Jones', 'Joseph M. Lukens', 'Sanjaya Lohani'] | 2021-07-15 | null | null | null | null | ['quantum-state-tomography'] | ['medical'] | [ 4.10702899e-02 -9.44004487e-03 6.94709793e-02 -2.74847150e-01
-1.16711330e+00 -6.99861705e-01 6.29368007e-01 -2.64041454e-01
-5.04905283e-01 1.22932589e+00 -1.47190943e-01 -4.97765452e-01
-1.24743871e-01 -9.48080420e-01 -2.57219315e-01 -1.26210821e+00
4.59378548e-02 1.03663385e+00 -5.25923446e-02 -7.07297772... | [5.620548248291016, 4.901681900024414] |
d81d84cf-a896-4c0c-828d-6887c1975d64 | alephbert-a-hebrew-large-pre-trained-language | 2104.04052 | null | https://arxiv.org/abs/2104.04052v1 | https://arxiv.org/pdf/2104.04052v1.pdf | AlephBERT:A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With | Large Pre-trained Language Models (PLMs) have become ubiquitous in the development of language understanding technology and lie at the heart of many artificial intelligence advances. While advances reported for English using PLMs are unprecedented, reported advances using PLMs in Hebrew are few and far between. The pro... | ['Reut Tsarfaty', 'Refael Shaked Greenfeld', 'Idan Brusilovsky', 'Dan Bareket', 'Elron Bandel', 'Amit Seker'] | 2021-04-08 | null | null | null | null | ['morphological-tagging'] | ['natural-language-processing'] | [ 1.96576044e-02 1.45099312e-01 -4.14006889e-01 -5.86977839e-01
-8.54414523e-01 -8.89454842e-01 7.43118048e-01 4.34240580e-01
-1.09198034e+00 8.52492452e-01 2.86999762e-01 -3.65599424e-01
2.20849551e-02 -4.01448816e-01 -3.99826586e-01 -2.61714458e-01
3.04641336e-01 9.46801007e-01 2.75502473e-01 -3.77306670... | [10.305660247802734, 9.753811836242676] |
3f40da53-3334-4cd3-b172-a46f4d93c521 | the-role-of-general-intelligence-in | 2104.13468 | null | https://arxiv.org/abs/2104.13468v1 | https://arxiv.org/pdf/2104.13468v1.pdf | The Role of General Intelligence in Mathematical Reasoning | Objects are a centerpiece of the mathematical realm and our interaction with and reasoning about it, just as they are of the physical one (if not more). And humans' mathematical reasoning must ultimately be grounded in our general intelligence. Yet in contemporary cognitive science and A.I., the physical and mathematic... | ['Aviv Keren'] | 2021-04-27 | null | null | null | null | ['mathematical-reasoning'] | ['natural-language-processing'] | [ 1.89987585e-01 4.97083277e-01 1.34708375e-01 -1.57439366e-01
2.36905977e-01 -7.73872256e-01 9.73952293e-01 3.72329295e-01
-3.24110061e-01 3.52540851e-01 2.12947026e-01 -7.83033431e-01
-7.71682203e-01 -1.41664922e+00 -5.74895203e-01 -3.46206486e-01
-1.58622429e-01 5.31880736e-01 2.48894632e-01 -5.52281499... | [9.317983627319336, 7.0498433113098145] |
9dd02695-7e54-4131-8603-69110a391239 | negation-in-cognitive-reasoning | 2012.12641 | null | https://arxiv.org/abs/2012.12641v3 | https://arxiv.org/pdf/2012.12641v3.pdf | Negation in Cognitive Reasoning | Negation is both an operation in formal logic and in natural language by which a proposition is replaced by one stating the opposite, as by the addition of "not" or another negation cue. Treating negation in an adequate way is required for cognitive reasoning, which aims at modeling the human ability to draw meaningful... | ['Frieder Stolzenburg', 'Sophie Siebert', 'Claudia Schon'] | 2020-12-23 | null | null | null | null | ['formal-logic'] | ['reasoning'] | [ 3.51886421e-01 8.41375351e-01 -8.74042064e-02 -4.29847300e-01
-1.74019858e-01 -8.17811906e-01 7.06204295e-01 5.35274804e-01
-3.31876904e-01 1.25667250e+00 3.07655893e-03 -8.80365133e-01
-3.77640605e-01 -1.47956061e+00 -6.18631840e-01 -1.55474886e-01
3.86160821e-01 4.82445598e-01 6.04448140e-01 -6.12082720... | [9.061949729919434, 7.09018611907959] |
da44c7c3-9a46-48f9-9dcc-084ad1c47869 | hyperformer-learning-expressive-sparse | 2305.17386 | null | https://arxiv.org/abs/2305.17386v1 | https://arxiv.org/pdf/2305.17386v1.pdf | HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer | Learning expressive representations for high-dimensional yet sparse features has been a longstanding problem in information retrieval. Though recent deep learning methods can partially solve the problem, they often fail to handle the numerous sparse features, particularly those tail feature values with infrequent occur... | ['Derek Zhiyuan Cheng', 'Huan Liu', 'Ed H. Chi', 'Lichan Hong', 'Ruoxi Wang', 'Ting Chen', 'Bryan Perrozi', 'Albert Jiongqian Liang', 'Kaize Ding'] | 2023-05-27 | null | null | null | null | ['information-retrieval'] | ['natural-language-processing'] | [ 1.70602664e-01 1.63794279e-01 -6.20515585e-01 -4.79667217e-01
-5.94267309e-01 -2.61870176e-01 5.08734882e-01 3.08986545e-01
3.16952467e-01 6.62160575e-01 5.66177964e-01 3.73917162e-01
-6.52344048e-01 -9.29015636e-01 -5.84596395e-01 -7.19491005e-01
-3.29401910e-01 4.86377746e-01 -3.64550591e-01 -1.65382761... | [7.222139835357666, 6.304267406463623] |
412b578b-69f4-4899-be2b-d3566cd85315 | detector-free-weakly-supervised-grounding-by | 2104.09829 | null | https://arxiv.org/abs/2104.09829v1 | https://arxiv.org/pdf/2104.09829v1.pdf | Detector-Free Weakly Supervised Grounding by Separation | Nowadays, there is an abundance of data involving images and surrounding free-form text weakly corresponding to those images. Weakly Supervised phrase-Grounding (WSG) deals with the task of using this data to learn to localize (or to ground) arbitrary text phrases in images without any additional annotations. However, ... | ['Leonid Karlinsky', 'Rogerio Feris', 'Raja Giryes', 'Shimon Ullman', 'Kate Saenko', 'Alex Bronstein', 'Chun-Fu Chen', 'Rameswar Panda', 'Prasanna Sattigeri', 'Hila Barak Levi', 'Hilde Kuehne', 'Eli Schwartz', 'Guy Lev', 'Joseph Shtok', 'Amit Alfassy', 'Sivan Doveh', 'Assaf Arbelle'] | 2021-04-20 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Arbelle_Detector-Free_Weakly_Supervised_Grounding_by_Separation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Arbelle_Detector-Free_Weakly_Supervised_Grounding_by_Separation_ICCV_2021_paper.pdf | iccv-2021-1 | ['phrase-grounding'] | ['natural-language-processing'] | [ 4.46344167e-01 3.36850464e-01 -7.94502795e-02 -7.44500458e-02
-1.27211833e+00 -7.38585472e-01 6.17235005e-01 1.97635680e-01
-4.95086014e-01 3.02128762e-01 -1.85852051e-01 -2.04848617e-01
2.06372589e-01 -6.03870213e-01 -1.09731698e+00 -1.02559471e+00
2.32000619e-01 7.70755172e-01 6.72149837e-01 -1.69447169... | [10.08019733428955, 1.057013750076294] |
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