paperID stringlengths 36 36 | pwc_id stringlengths 8 47 | arxiv_id stringlengths 6 16 ⌀ | nips_id float64 | url_abs stringlengths 18 329 | url_pdf stringlengths 18 742 | title stringlengths 8 325 | abstract stringlengths 1 7.27k ⌀ | authors stringlengths 2 7.06k | published stringlengths 10 10 ⌀ | conference stringlengths 12 47 ⌀ | conference_url_abs stringlengths 16 198 ⌀ | conference_url_pdf stringlengths 27 199 ⌀ | proceeding stringlengths 6 47 ⌀ | taskID stringlengths 7 1.44k | areaID stringclasses 688
values | embedding stringlengths 9.26k 12.5k | umap_embedding stringlengths 29 44 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
c90bfff7-3748-4b80-bc51-8480771e500f | recurrent-neural-networks-with-stochastic | 1902.0498 | null | http://arxiv.org/abs/1902.04980v1 | http://arxiv.org/pdf/1902.04980v1.pdf | Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection | In this paper, we adapt Recurrent Neural Networks with Stochastic Layers,
which are the state-of-the-art for generating text, music and speech, to the
problem of acoustic novelty detection. By integrating uncertainty into the
hidden states, this type of network is able to learn the distribution of
complex sequences. Be... | ['Fábio Frazão', 'Duong Nguyen', 'Stan Matwin', 'Ronan Fablet', 'Oliver S. Kirsebom'] | 2019-02-13 | null | null | null | null | ['acoustic-novelty-detection'] | ['audio'] | [ 8.86067748e-02 -5.96808232e-02 1.53739169e-01 -2.58314848e-01
-8.67591023e-01 -4.85033929e-01 5.77742994e-01 -7.91311264e-04
-4.94210631e-01 7.57452250e-01 8.89391303e-02 -1.83531553e-01
-3.48518267e-02 -7.61107802e-01 -9.34930384e-01 -5.70879698e-01
-2.81516284e-01 5.27775407e-01 4.31497008e-01 6.90920353... | [15.585981369018555, 5.709064483642578] |
bfe9263b-45e6-468c-9a75-25cf2db75b5d | from-point-forecasts-to-multivariate | 2204.10154 | null | https://arxiv.org/abs/2204.10154v1 | https://arxiv.org/pdf/2204.10154v1.pdf | From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting | Modeling price risks is crucial for economic decision making in energy markets. Besides the risk of a single price, the dependence structure of multiple prices is often relevant. We therefore propose a generic and easy-to-implement method for creating multivariate probabilistic forecasts based on univariate point forec... | ['Fabian Krüger', 'Fabian Kächele', 'Oliver Grothe'] | 2022-04-21 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [-2.84448445e-01 -1.73761532e-01 1.09539889e-01 -2.16637850e-01
-7.13954389e-01 -7.87860930e-01 7.80048072e-01 3.94551396e-01
-5.38402051e-03 1.08428192e+00 -1.31158322e-01 -6.50143266e-01
-4.68049526e-01 -1.16954362e+00 -5.35716891e-01 -7.81506777e-01
-4.99078929e-01 6.30511284e-01 -3.17954361e-01 1.31447881... | [6.072834491729736, 3.0294950008392334] |
37475279-b5f6-4ccf-85c0-94589d000021 | assisting-undergraduate-students-in-writing | null | null | https://aclanthology.org/2020.bea-1.11 | https://aclanthology.org/2020.bea-1.11.pdf | Assisting Undergraduate Students in Writing Spanish Methodology Sections | In undergraduate theses, a good methodology section should describe the series of steps that were followed in performing the research. To assist students in this task, we develop machine-learning models and an app that uses them to provide feedback while students write. We construct an annotated corpus that identifies ... | ['Aurelio Lopez-Lopez', "Samuel Gonz{\\'a}lez-L{\\'o}pez", 'Steven Bethard'] | 2020-07-01 | null | null | null | ws-2020-7 | ['logical-sequence'] | ['reasoning'] | [ 7.44302617e-03 2.16019168e-01 -4.83251333e-01 -6.76561475e-01
-9.56917226e-01 -8.38689446e-01 2.32534260e-01 8.50044847e-01
-3.40501517e-01 7.10074127e-01 1.32079631e-01 -1.06258547e+00
-2.51434267e-01 -9.94890451e-01 -9.97186542e-01 4.01007026e-01
4.30023581e-01 -9.41672921e-02 7.44002685e-02 -2.29670897... | [11.194523811340332, 9.215643882751465] |
614205f2-6a36-4bb3-b7d2-e1ea7a1881e4 | improving-prediction-confidence-in-learning | 2110.03123 | null | https://arxiv.org/abs/2110.03123v1 | https://arxiv.org/pdf/2110.03123v1.pdf | Improving Prediction Confidence in Learning-Enabled Autonomous Systems | Autonomous systems use extensively learning-enabled components such as deep neural networks (DNNs) for prediction and decision making. In this paper, we utilize a feedback loop between learning-enabled components used for classification and the sensors of an autonomous system in order to improve the confidence of the p... | ['Xenofon Koutsoukos', 'Dimitrios Boursinos'] | 2021-10-07 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 4.10771728e-01 4.98460770e-01 -4.19649146e-02 -7.44778633e-01
-2.53972590e-01 -2.47174188e-01 4.95717406e-01 1.52221069e-01
-4.88500834e-01 6.59943223e-01 -2.06308901e-01 -1.98123783e-01
-4.09571439e-01 -1.03674281e+00 -6.52220428e-01 -5.57878971e-01
-4.20709886e-02 8.26939404e-01 7.00600207e-01 -3.00246160... | [7.6927924156188965, -0.6782286167144775] |
e0f27df0-b1af-493a-9d1f-67ea07295a17 | ranking-with-fairness-constraints | 1704.0684 | null | https://arxiv.org/abs/1704.06840v4 | https://arxiv.org/pdf/1704.06840v4.pdf | Ranking with Fairness Constraints | Ranking algorithms are deployed widely to order a set of items in applications such as search engines, news feeds, and recommendation systems. Recent studies, however, have shown that, left unchecked, the output of ranking algorithms can result in decreased diversity in the type of content presented, promote stereotype... | ['Nisheeth K. Vishnoi', 'Damian Straszak', 'L. Elisa Celis'] | 2017-04-22 | null | null | null | null | ['hypergraph-matching'] | ['graphs'] | [ 2.53334790e-01 3.45940322e-01 -5.65161884e-01 -3.96363318e-01
-4.25832629e-01 -9.56395447e-01 2.12099090e-01 7.03881383e-01
-4.74309504e-01 6.51537120e-01 2.81462193e-01 -3.47546190e-01
-8.84514093e-01 -9.65642214e-01 -6.25593066e-01 -4.59481418e-01
-1.78564340e-01 8.79304409e-01 1.68862656e-01 -3.90308589... | [9.337355613708496, 5.5633745193481445] |
65ed2fb0-a294-4db2-843e-32ecda13271b | efficient-reinforcement-learning-for | 2204.07696 | null | https://arxiv.org/abs/2204.07696v1 | https://arxiv.org/pdf/2204.07696v1.pdf | Efficient Reinforcement Learning for Unsupervised Controlled Text Generation | Controlled text generation tasks such as unsupervised text style transfer have increasingly adopted the use of Reinforcement Learning (RL). A major challenge in applying RL to such tasks is the sparse reward, which is available only after the full text is generated. Sparse rewards, combined with a large action space ma... | ['Arjun Maheswaran', 'Akhilesh Sudhakar', 'Bhargav Upadhyay'] | 2022-04-16 | null | null | null | null | ['text-style-transfoer'] | ['natural-language-processing'] | [ 5.55254400e-01 4.16562021e-01 -1.75105378e-01 -1.62036002e-01
-1.21181822e+00 -5.87852716e-01 9.57755864e-01 -2.88838521e-02
-8.00102055e-01 1.33882177e+00 2.98756182e-01 -1.32830560e-01
3.60675365e-01 -6.47554338e-01 -5.74282646e-01 -4.28543180e-01
3.14346224e-01 7.87857711e-01 1.12870179e-01 -4.71303076... | [11.766336441040039, 9.081097602844238] |
dfd771c3-8be7-41af-972a-4cf181415df1 | learning-a-discriminative-prior-for-blind | 1803.03363 | null | http://arxiv.org/abs/1803.03363v2 | http://arxiv.org/pdf/1803.03363v2.pdf | Learning a Discriminative Prior for Blind Image Deblurring | We present an effective blind image deblurring method based on a data-driven
discriminative prior.Our work is motivated by the fact that a good image prior
should favor clear images over blurred images.In this work, we formulate the
image prior as a binary classifier which can be achieved by a deep
convolutional neural... | ['Ming-Hsuan Yang', 'Wei-Sheng Lai', 'Lerenhan Li', 'Nong Sang', 'Jinshan Pan', 'Changxin Gao'] | 2018-03-09 | learning-a-discriminative-prior-for-blind-1 | http://openaccess.thecvf.com/content_cvpr_2018/html/Li_Learning_a_Discriminative_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Li_Learning_a_Discriminative_CVPR_2018_paper.pdf | cvpr-2018-6 | ['blind-image-deblurring'] | ['computer-vision'] | [ 3.16565007e-01 -3.69030297e-01 -1.43058309e-02 -3.11869979e-01
-6.05644584e-01 -3.07688296e-01 5.28963447e-01 -6.56970501e-01
-2.89451480e-01 8.20508003e-01 2.61119336e-01 -1.06232494e-01
-2.74088569e-02 -3.41854990e-01 -6.68732345e-01 -1.11399448e+00
5.17985225e-01 6.99782930e-03 -1.08984850e-01 6.59308881... | [11.595486640930176, -2.666778564453125] |
d06f90c4-643c-4901-b240-6837cc2bf24a | learning-to-generalize-one-sample-at-a-time | 1910.03915 | null | https://arxiv.org/abs/1910.03915v3 | https://arxiv.org/pdf/1910.03915v3.pdf | Learning to Generalize One Sample at a Time with Self-Supervision | Although deep networks have significantly increased the performance of visual recognition methods, it is still challenging to achieve the robustness across visual domains that is necessary for real-world applications. To tackle this issue, research on domain adaptation and generalization has flourished over the last de... | ['Tatiana Tommasi', "Antonio D'Innocente", 'Barbara Caputo', 'Silvia Bucci'] | 2019-10-09 | null | null | null | null | ['auxiliary-learning'] | ['methodology'] | [ 6.32914245e-01 2.34697670e-01 -1.32084265e-01 -5.61789572e-01
-2.73530334e-01 -7.54051447e-01 7.23982453e-01 2.09565639e-01
-6.68520510e-01 8.71518433e-01 -1.83721960e-01 -1.33204699e-01
-1.25686571e-01 -5.94609320e-01 -6.23273849e-01 -7.53721118e-01
1.60211205e-01 4.32280362e-01 4.13554013e-01 -4.07276824... | [9.739410400390625, 2.5066866874694824] |
67c99aba-61e1-4306-b2ed-18ea84870da7 | inferring-dynamic-regulatory-interaction | 2306.07803 | null | https://arxiv.org/abs/2306.07803v1 | https://arxiv.org/pdf/2306.07803v1.pdf | Inferring dynamic regulatory interaction graphs from time series data with perturbations | Complex systems are characterized by intricate interactions between entities that evolve dynamically over time. Accurate inference of these dynamic relationships is crucial for understanding and predicting system behavior. In this paper, we propose Regulatory Temporal Interaction Network Inference (RiTINI) for inferrin... | ['Smita Krishnaswamy', 'Guy Wolf', 'Frederik Wenkel', 'Aarthi Venkat', 'Edward De Brouwer', 'Sumner Magruder', 'Dhananjay Bhaskar'] | 2023-06-13 | null | null | null | null | ['graph-attention', 'causal-inference', 'causal-inference'] | ['graphs', 'knowledge-base', 'miscellaneous'] | [ 1.39420167e-01 1.48789018e-01 -2.16936599e-02 1.33966118e-01
1.48457944e-01 -7.74469137e-01 7.85436630e-01 3.10296029e-01
3.51569086e-01 8.19698632e-01 1.33975878e-01 -6.19909525e-01
-6.26844704e-01 -8.66802275e-01 -7.31021166e-01 -5.74898183e-01
-1.00358737e+00 5.80664217e-01 5.10252714e-02 -3.10996264... | [7.098731994628906, 5.836288928985596] |
e9c241fd-34a6-438d-bbb6-1745cf8eff09 | on-the-efficacy-and-noise-robustness-of | 2305.1254 | null | https://arxiv.org/abs/2305.12540v2 | https://arxiv.org/pdf/2305.12540v2.pdf | On the Efficacy and Noise-Robustness of Jointly Learned Speech Emotion and Automatic Speech Recognition | New-age conversational agent systems perform both speech emotion recognition (SER) and automatic speech recognition (ASR) using two separate and often independent approaches for real-world application in noisy environments. In this paper, we investigate a joint ASR-SER multitask learning approach in a low-resource sett... | ['Aravind Ganapathiraju', 'Pushpak Jagtap', 'Malolan Chetlur', 'S. Pavankumar Dubagunta', 'Lokesh Bansal'] | 2023-05-21 | null | null | null | null | ['speech-emotion-recognition'] | ['speech'] | [-4.38454039e-02 -2.28871346e-01 5.37700534e-01 -3.64167720e-01
-1.71294761e+00 -3.81698251e-01 7.44458497e-01 -2.54751772e-01
-7.42610037e-01 9.08020496e-01 4.65745449e-01 -1.73035786e-02
2.87295878e-01 1.65173933e-01 -3.65375817e-01 -8.28798711e-01
5.87247871e-02 2.83552885e-01 -1.75436899e-01 -4.91953999... | [14.515018463134766, 6.301003456115723] |
1ab5ca29-91d5-44e0-9875-7cab38581cce | face-transformer-towards-high-fidelity-and | 2304.0253 | null | https://arxiv.org/abs/2304.02530v1 | https://arxiv.org/pdf/2304.02530v1.pdf | Face Transformer: Towards High Fidelity and Accurate Face Swapping | Face swapping aims to generate swapped images that fuse the identity of source faces and the attributes of target faces. Most existing works address this challenging task through 3D modelling or generation using generative adversarial networks (GANs), but 3D modelling suffers from limited reconstruction accuracy and GA... | ['Shijian Lu', 'Fangneng Zhan', 'Rongliang Wu', 'Kaiwen Cui'] | 2023-04-05 | null | null | null | null | ['face-swapping'] | ['computer-vision'] | [ 2.60850787e-01 3.49209338e-01 2.72635221e-01 -5.93873143e-01
-6.58988357e-01 -8.11611533e-01 4.61040586e-01 -9.88731205e-01
4.03077036e-01 7.34471679e-01 2.41085485e-01 4.78427708e-01
3.59551400e-01 -8.70272219e-01 -8.53064835e-01 -8.35357547e-01
2.98335880e-01 3.62414092e-01 -3.13164055e-01 -3.41386348... | [12.665267944335938, -0.11663150787353516] |
bdb092ef-6d75-439f-8f3e-a1190b149b4a | learning-semantic-neural-tree-for-human | 1912.09622 | null | https://arxiv.org/abs/1912.09622v1 | https://arxiv.org/pdf/1912.09622v1.pdf | Learning Semantic Neural Tree for Human Parsing | The majority of existing human parsing methods formulate the task as semantic segmentation, which regard each semantic category equally and fail to exploit the intrinsic physiological structure of human body, resulting in inaccurate results. In this paper, we design a novel semantic neural tree for human parsing, which... | ['Siwei Lyu', 'Longyin Wen', 'Libo Zhang', 'Ruyi Ji', 'Yanjun Wu', 'Dawei Du', 'Chen Zhao', 'Feiyue Huang'] | 2019-12-20 | null | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/1829_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123580205.pdf | eccv-2020-8 | ['human-parsing'] | ['computer-vision'] | [ 1.20119788e-01 4.33000684e-01 -8.09819847e-02 -6.65681422e-01
-4.51522648e-01 -1.91917270e-01 -2.29369551e-02 -1.38445422e-01
-2.45275602e-01 4.64247197e-01 3.78233463e-01 5.18417135e-02
4.76248026e-01 -7.04356432e-01 -6.80845320e-01 -5.28757572e-01
4.17985409e-01 1.80194959e-01 2.65097618e-01 -4.43241000... | [8.79185962677002, 0.13781601190567017] |
0a94be85-7523-429a-8fa9-19e61fae4ab1 | dual-progressive-transformations-for-weakly | 2209.15211 | null | https://arxiv.org/abs/2209.15211v1 | https://arxiv.org/pdf/2209.15211v1.pdf | Dual Progressive Transformations for Weakly Supervised Semantic Segmentation | Weakly supervised semantic segmentation (WSSS), which aims to mine the object regions by merely using class-level labels, is a challenging task in computer vision. The current state-of-the-art CNN-based methods usually adopt Class-Activation-Maps (CAMs) to highlight the potential areas of the object, however, they may ... | ['Qingyao Wu', 'Yukun Su', 'Dongjian Huo'] | 2022-09-30 | null | null | null | null | ['weakly-supervised-object-localization'] | ['computer-vision'] | [ 2.66093850e-01 2.39309952e-01 -1.95196316e-01 -5.49968004e-01
-7.49876320e-01 -3.19302976e-01 5.31432569e-01 -1.98348016e-01
-4.72566932e-01 5.64546943e-01 -2.02191964e-01 4.49016783e-03
1.32581234e-01 -6.77515328e-01 -8.90130222e-01 -8.18801820e-01
3.68555009e-01 2.07786605e-01 1.03316140e+00 -1.49886057... | [9.564949989318848, 0.6073523163795471] |
b05b2089-6995-43c4-a3ca-50b49399a9b1 | n2sky-neural-networks-as-services-in-the | 1401.2468 | null | http://arxiv.org/abs/1401.2468v1 | http://arxiv.org/pdf/1401.2468v1.pdf | N2Sky - Neural Networks as Services in the Clouds | We present the N2Sky system, which provides a framework for the exchange of
neural network specific knowledge, as neural network paradigms and objects, by
a virtual organization environment. It follows the sky computing paradigm
delivering ample resources by the usage of federated Clouds. N2Sky is a novel
Cloud-based n... | ['Erwin Mann', 'Erich Schikuta'] | 2014-01-10 | null | null | null | null | ['neural-network-simulation'] | ['computer-code'] | [-9.09303784e-01 -2.62377918e-01 2.38499045e-01 -2.44017079e-01
5.02270162e-01 -6.41859114e-01 7.74662197e-01 -2.90153861e-01
-2.75076866e-01 4.10673201e-01 -3.32266659e-01 -3.85174513e-01
-3.88360590e-01 -9.37590003e-01 -2.26338953e-01 -8.18205357e-01
7.40317479e-02 5.07976532e-01 2.19952151e-01 -4.94659215... | [8.173344612121582, 2.9691081047058105] |
3ed1e3b0-eb0d-48b3-af4a-540487b8a7ab | texrel-a-green-family-of-datasets-for | 2105.12804 | null | https://arxiv.org/abs/2105.12804v1 | https://arxiv.org/pdf/2105.12804v1.pdf | TexRel: a Green Family of Datasets for Emergent Communications on Relations | We propose a new dataset TexRel as a playground for the study of emergent communications, in particular for relations. By comparison with other relations datasets, TexRel provides rapid training and experimentation, whilst being sufficiently large to avoid overfitting in the context of emergent communications. By compa... | ['Hugh Perkins'] | 2021-05-26 | null | null | null | null | ['emergent-communications-on-relations'] | ['natural-language-processing'] | [ 1.54484034e-01 1.58337027e-01 4.69330281e-01 -1.89974383e-01
-3.19443017e-01 -6.43215716e-01 1.09902692e+00 3.25517058e-01
-3.01101565e-01 6.70184255e-01 5.00592887e-01 -4.76179391e-01
-5.99626720e-01 -8.53718221e-01 -6.58374965e-01 -4.98233885e-01
-7.14673996e-01 7.70102084e-01 1.39432430e-01 -4.75104779... | [9.49374008178711, 7.023814678192139] |
2bbf259e-8bf4-4e51-acd0-d297a3bbec15 | an-entity-driven-recursive-neural-network | 1704.04336 | null | http://arxiv.org/abs/1704.04336v1 | http://arxiv.org/pdf/1704.04336v1.pdf | An entity-driven recursive neural network model for chinese discourse coherence modeling | Chinese discourse coherence modeling remains a challenge taskin Natural
Language Processing field.Existing approaches mostlyfocus on the need for
feature engineering, whichadoptthe sophisticated features to capture the logic
or syntactic or semantic relationships acrosssentences within a text.In this
paper, we present ... | ['Shujing Du', 'Mingwen Wang', 'Maoxi Li', 'Fan Xu'] | 2017-04-14 | null | null | null | null | ['sentence-ordering', 'coherence-evaluation'] | ['natural-language-processing', 'natural-language-processing'] | [-3.48962754e-01 3.17360431e-01 -2.41007939e-01 -3.69677961e-01
-5.40364087e-01 -2.01663494e-01 9.24269199e-01 -4.42238599e-02
-4.60938662e-01 9.77426469e-01 1.17818213e+00 -3.69856320e-02
-4.17879261e-02 -7.54673958e-01 -3.40191334e-01 -5.26397824e-01
2.61223763e-01 2.79482782e-01 -9.17510390e-02 -5.65942883... | [11.373621940612793, 9.304925918579102] |
add9b772-08b2-4d15-8fa8-3b8a04459d2d | ccg-supertagging-as-top-down-tree-generation | null | null | https://aclanthology.org/2021.scil-1.34 | https://aclanthology.org/2021.scil-1.34.pdf | CCG Supertagging as Top-down Tree Generation | null | ['Vivek Srikumar', 'Nathan Schneider', 'Jakob Prange'] | null | null | null | null | scil-2021-2 | ['ccg-supertagging'] | ['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... | [-7.467878818511963, 3.631009578704834] |
eef77d1a-4516-4172-b0aa-8116c903eddf | visual-prompting-modifying-pixel-space-to | 2203.17274 | null | https://arxiv.org/abs/2203.17274v2 | https://arxiv.org/pdf/2203.17274v2.pdf | Exploring Visual Prompts for Adapting Large-Scale Models | We investigate the efficacy of visual prompting to adapt large-scale models in vision. Following the recent approach from prompt tuning and adversarial reprogramming, we learn a single image perturbation such that a frozen model prompted with this perturbation performs a new task. Through comprehensive experiments, we ... | ['Phillip Isola', 'Swami Sankaranarayanan', 'Ali Jahanian', 'Hyojin Bahng'] | 2022-03-31 | null | null | null | null | ['visual-prompting'] | ['computer-vision'] | [ 2.43313998e-01 2.21893042e-01 2.87243407e-02 -2.83865303e-01
-9.38994646e-01 -1.03682256e+00 6.56246185e-01 -3.92790169e-01
-4.01005656e-01 5.68260968e-01 2.80447960e-01 -3.03110629e-01
2.72350818e-01 -4.80497703e-02 -1.18385303e+00 -7.22166896e-01
3.82797509e-01 2.97423489e-02 1.80616945e-01 2.98517663... | [10.287446975708008, 2.0543127059936523] |
673d9487-0dd6-48de-9149-e499f96dd660 | calibrated-interpretation-confidence | 2211.07443 | null | https://arxiv.org/abs/2211.07443v6 | https://arxiv.org/pdf/2211.07443v6.pdf | Calibrated Interpretation: Confidence Estimation in Semantic Parsing | Sequence generation models are increasingly being used to translate natural language into programs, i.e. to perform executable semantic parsing. The fact that semantic parsing aims to predict programs that can lead to executed actions in the real world motivates developing safe systems. This in turn makes measuring cal... | ['Benjamin Van Durme', 'Elias Stengel-Eskin'] | 2022-11-14 | null | null | null | null | ['semantic-parsing'] | ['natural-language-processing'] | [ 3.23436052e-01 3.35125327e-01 -6.06222987e-01 -6.12432241e-01
-9.84284937e-01 -9.52023685e-01 6.30052686e-01 2.61157662e-01
-1.24928705e-01 4.96970445e-01 5.48990071e-01 -7.44339645e-01
3.26755643e-01 -1.04296482e+00 -1.09707654e+00 1.74265608e-01
2.40499347e-01 1.61902919e-01 4.27787781e-01 -1.34370923... | [8.053108215332031, 7.717579364776611] |
aa21c836-0395-4425-ae11-498ccecaa58c | multi-task-semantic-dependency-parsing-with | 1906.01239 | null | https://arxiv.org/abs/1906.01239v1 | https://arxiv.org/pdf/1906.01239v1.pdf | Multi-Task Semantic Dependency Parsing with Policy Gradient for Learning Easy-First Strategies | In Semantic Dependency Parsing (SDP), semantic relations form directed acyclic graphs, rather than trees. We propose a new iterative predicate selection (IPS) algorithm for SDP. Our IPS algorithm combines the graph-based and transition-based parsing approaches in order to handle multiple semantic head words. We train t... | ['Anders Søgaard', 'Shuhei Kurita'] | 2019-06-04 | multi-task-semantic-dependency-parsing-with-1 | https://aclanthology.org/P19-1232 | https://aclanthology.org/P19-1232.pdf | acl-2019-7 | ['semantic-dependency-parsing'] | ['natural-language-processing'] | [ 3.47752780e-01 6.91509724e-01 -7.05459774e-01 -6.71361089e-01
-8.64014208e-01 -7.41116047e-01 5.92920065e-01 3.19320083e-01
-4.53825742e-01 8.00524592e-01 2.67592639e-01 -7.14575648e-01
4.61595505e-02 -7.41716623e-01 -7.79813349e-01 -1.88928545e-01
-7.32639953e-02 1.10900080e+00 7.10981548e-01 -1.37969777... | [10.334651947021484, 9.544744491577148] |
5ab176e7-7867-4d9f-82bd-ac3a8c77d1bb | towards-part-based-understanding-of-rgb-d | 2012.02094 | null | https://arxiv.org/abs/2012.02094v1 | https://arxiv.org/pdf/2012.02094v1.pdf | Towards Part-Based Understanding of RGB-D Scans | Recent advances in 3D semantic scene understanding have shown impressive progress in 3D instance segmentation, enabling object-level reasoning about 3D scenes; however, a finer-grained understanding is required to enable interactions with objects and their functional understanding. Thus, we propose the task of part-bas... | ['Angela Dai', 'Evgeny Burnaev', 'Alexey Artemov', 'Denis Zorin', 'Emil Bogomolov', 'Vladislav Ishimtsev', 'Alexey Bokhovkin'] | 2020-12-03 | null | http://openaccess.thecvf.com//content/CVPR2021/html/Bokhovkin_Towards_Part-Based_Understanding_of_RGB-D_Scans_CVPR_2021_paper.html | http://openaccess.thecvf.com//content/CVPR2021/papers/Bokhovkin_Towards_Part-Based_Understanding_of_RGB-D_Scans_CVPR_2021_paper.pdf | cvpr-2021-1 | ['3d-instance-segmentation-1'] | ['computer-vision'] | [ 4.88268107e-01 6.55337393e-01 -3.17658260e-02 -6.33202493e-01
-3.48543316e-01 -8.13357830e-01 6.21579349e-01 1.98396608e-01
3.66705328e-01 -4.61553149e-02 2.43625805e-01 -1.48523197e-01
8.29547793e-02 -7.52782345e-01 -1.02884257e+00 1.93216689e-02
1.05009459e-01 1.01268172e+00 5.16294897e-01 5.30198440... | [8.346024513244629, -2.9343550205230713] |
16d2e85f-0b6a-492e-8792-7f1a6623ef75 | deep-alignment-network-a-convolutional-neural | 1706.01789 | null | http://arxiv.org/abs/1706.01789v2 | http://arxiv.org/pdf/1706.01789v2.pdf | Deep Alignment Network: A convolutional neural network for robust face alignment | In this paper, we propose Deep Alignment Network (DAN), a robust face
alignment method based on a deep neural network architecture. DAN consists of
multiple stages, where each stage improves the locations of the facial
landmarks estimated by the previous stage. Our method uses entire face images
at all stages, contrary... | ['Tomasz Trzcinski', 'Marek Kowalski', 'Jacek Naruniec'] | 2017-06-06 | null | null | null | null | ['robust-face-alignment'] | ['computer-vision'] | [-1.93060473e-01 2.82183617e-01 1.49916753e-01 -6.67648137e-01
-5.65712392e-01 -2.42304817e-01 7.04337001e-01 -5.12404554e-02
-5.80946803e-01 3.36054415e-01 1.90090805e-01 2.12768465e-01
2.52318531e-01 -3.83380353e-01 -7.14514375e-01 -4.83667731e-01
-7.75538236e-02 7.82182753e-01 4.65860330e-02 -2.58255690... | [13.485325813293457, 0.3320131301879883] |
66671a43-daa8-43ee-bc4e-09dfbf8c03c7 | language-expansion-in-text-based-games | 1805.07274 | null | http://arxiv.org/abs/1805.07274v1 | http://arxiv.org/pdf/1805.07274v1.pdf | Language Expansion In Text-Based Games | Text-based games are suitable test-beds for designing agents that can learn
by interaction with the environment in the form of natural language text. Very
recently, deep reinforcement learning based agents have been successfully
applied for playing text-based games. In this paper, we explore the possibility
of designin... | ['Balaraman Ravindran', 'Ghulam Ahmed Ansari', 'Sagar J P', 'Sarath Chandar'] | 2018-05-17 | null | null | null | null | ['text-based-games'] | ['playing-games'] | [-8.20470303e-02 2.13523850e-01 2.28330772e-02 9.42106619e-02
-9.95347276e-02 -6.73528135e-01 1.06704223e+00 -1.81014597e-01
-1.07140458e+00 9.77251112e-01 1.13764964e-01 -7.82872140e-01
-5.61625510e-02 -1.19917047e+00 -4.51690793e-01 -4.86338228e-01
8.32541753e-03 9.77573991e-01 4.20234352e-01 -1.15877581... | [3.803438663482666, 1.473789095878601] |
ffd7927f-507b-4eba-92ac-78eaecbe4b9a | continual-training-of-language-models-for-few | 2210.05549 | null | https://arxiv.org/abs/2210.05549v1 | https://arxiv.org/pdf/2210.05549v1.pdf | Continual Training of Language Models for Few-Shot Learning | Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications. Adapting or posttraining an LM using an unlabeled domain corpus can produce even better performance for end-tasks in the domain. This paper proposes the problem of continually extending an LM by incrementally p... | ['Bing Liu', 'Lei Shu', 'Hu Xu', 'Yijia Shao', 'Haowei Lin', 'Zixuan Ke'] | 2022-10-11 | null | null | null | null | ['continual-pretraining'] | ['methodology'] | [ 2.27812052e-01 3.14299196e-01 -2.59676814e-01 -3.36604387e-01
-1.02560437e+00 -7.73628235e-01 6.29022837e-01 -3.13852429e-01
-9.08716023e-01 1.27652407e+00 -1.49110392e-01 -6.06969059e-01
4.04080123e-01 -3.65305990e-01 -7.17241824e-01 -2.55593061e-01
6.78204447e-02 1.04981303e+00 6.46917284e-01 -9.79186371... | [10.664831161499023, 8.225811004638672] |
c8ca8d48-e1b4-4474-9440-67e73c1fc9da | strengths-and-weaknesses-of-3d-pose | 2306.06117 | null | https://arxiv.org/abs/2306.06117v1 | https://arxiv.org/pdf/2306.06117v1.pdf | Strengths and Weaknesses of 3D Pose Estimation and Inertial Motion Capture System for Movement Therapy | 3D pose estimation offers the opportunity for fast, non-invasive, and accurate motion analysis. This is of special interest also for clinical use. Currently, motion capture systems are used, as they offer robust and precise data acquisition, which is essential in the case of clinical applications. In this study, we inv... | ['Ted Preuß', 'Hannah Siebers', 'Shawan Mohammed'] | 2023-06-01 | null | null | null | null | ['pose-estimation', '3d-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-1.03740081e-01 -6.24096580e-02 -2.59704530e-01 2.93967783e-01
-4.43277746e-01 -3.58205378e-01 1.61060542e-01 1.80633277e-01
-9.01096761e-01 7.48512208e-01 1.09280668e-01 2.37939179e-01
-5.93492389e-01 -2.69438535e-01 -2.85468221e-01 -6.21025980e-01
-4.97868866e-01 5.39603055e-01 3.53567153e-01 -2.50334859... | [7.010500431060791, 0.1575242429971695] |
a78a5fd9-9bc6-4bee-b790-260176b259d3 | a-unified-prompt-guided-in-context-inpainting | 2305.11577 | null | https://arxiv.org/abs/2305.11577v1 | https://arxiv.org/pdf/2305.11577v1.pdf | A Unified Prompt-Guided In-Context Inpainting Framework for Reference-based Image Manipulations | Recent advancements in Text-to-Image (T2I) generative models have yielded impressive results in generating high-fidelity images based on consistent text prompts. However, there is a growing interest in exploring the potential of these models for more diverse reference-based image manipulation tasks that require spatial... | ['Yanwei Fu', 'Yunuo Cai', 'Yikai Wang', 'Qiaole Dong', 'Chenjie Cao'] | 2023-05-19 | null | null | null | null | ['image-manipulation', 'image-inpainting', 'novel-view-synthesis'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 5.36378264e-01 7.00151399e-02 5.25985435e-02 -1.64308727e-01
-7.99061358e-01 -4.79206979e-01 9.49311137e-01 -6.22786403e-01
1.98289216e-01 7.01364934e-01 4.47949678e-01 1.69484578e-02
-5.40973954e-02 -6.29324913e-01 -9.01220977e-01 -6.10686421e-01
4.24089283e-01 3.31026137e-01 2.95501165e-02 -2.89764255... | [11.401127815246582, -0.643374502658844] |
e32f48d9-3892-4177-a7fe-a249b9adbcc1 | 6-pack-category-level-6d-pose-tracker-with | 1910.1075 | null | https://arxiv.org/abs/1910.10750v1 | https://arxiv.org/pdf/1910.10750v1.pdf | 6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints | We present 6-PACK, a deep learning approach to category-level 6D object pose tracking on RGB-D data. Our method tracks in real-time novel object instances of known object categories such as bowls, laptops, and mugs. 6-PACK learns to compactly represent an object by a handful of 3D keypoints, based on which the interfra... | ['Li Fei-Fei', 'Roberto Martín-Martín', 'Silvio Savarese', 'Jun Lv', 'Danfei Xu', 'Cewu Lu', 'Yuke Zhu', 'Chen Wang'] | 2019-10-23 | null | null | null | null | ['6d-pose-estimation-using-rgbd'] | ['computer-vision'] | [-4.06006455e-01 -1.60638332e-01 -6.27618492e-01 -1.18707679e-01
-7.57227540e-01 -7.14425266e-01 4.49894428e-01 2.73768127e-01
-2.99996585e-01 1.89740703e-01 -2.92414457e-01 -9.13322717e-02
-1.10049598e-01 -3.29923809e-01 -1.27366161e+00 -1.13629051e-01
-5.35191476e-01 9.94721413e-01 5.05688250e-01 1.46058455... | [7.080930233001709, -2.383798122406006] |
d986230f-ea15-4c39-b314-59557dc7b2d4 | deterministic-parsing-using-pcfgs | null | null | https://aclanthology.org/E14-1036 | https://aclanthology.org/E14-1036.pdf | Deterministic Parsing using PCFGs | null | ['Mark-Jan Nederhof', 'Martin McCaffery'] | 2014-04-01 | null | null | null | eacl-2014-4 | ['transition-based-dependency-parsing'] | ['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... | [-7.414912700653076, 3.640712022781372] |
e4915ec2-59b1-4b86-8a4a-df4c7918f3a7 | u-time-a-fully-convolutional-network-for-time | 1910.11162 | null | https://arxiv.org/abs/1910.11162v1 | https://arxiv.org/pdf/1910.11162v1.pdf | U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging | Neural networks are becoming more and more popular for the analysis of physiological time-series. The most successful deep learning systems in this domain combine convolutional and recurrent layers to extract useful features to model temporal relations. Unfortunately, these recurrent models are difficult to tune and op... | ['Poul Jørgen Jennum', 'Michael Hejselbak Jensen', 'Mathias Perslev', 'Christian Igel', 'Sune Darkner'] | 2019-10-24 | u-time-a-fully-convolutional-network-for-time-1 | http://papers.nips.cc/paper/8692-u-time-a-fully-convolutional-network-for-time-series-segmentation-applied-to-sleep-staging | http://papers.nips.cc/paper/8692-u-time-a-fully-convolutional-network-for-time-series-segmentation-applied-to-sleep-staging.pdf | neurips-2019-12 | ['sleep-staging'] | ['medical'] | [ 2.55996913e-01 -3.41694981e-01 -9.99280438e-02 -5.61380982e-01
-4.16828096e-01 -4.88571614e-01 2.34606847e-01 7.84748495e-02
-6.14780247e-01 6.27670884e-01 5.18022701e-02 -4.22196060e-01
-3.31399262e-01 -2.92939097e-01 -4.51997876e-01 -6.16360724e-01
-4.85615671e-01 1.70946896e-01 1.13616362e-01 -1.47756889... | [13.32819652557373, 3.508892774581909] |
6d28bbec-cc7a-4a96-aaef-fff2331c5754 | uwaterloo-at-semeval-2017-task-8-detecting | null | null | https://aclanthology.org/S17-2080 | https://aclanthology.org/S17-2080.pdf | UWaterloo at SemEval-2017 Task 8: Detecting Stance towards Rumours with Topic Independent Features | This paper describes our system for subtask-A: SDQC for RumourEval, task-8 of SemEval 2017. Identifying rumours, especially for breaking news events as they unfold, is a challenging task due to the absence of sufficient information about the exact rumour stories circulating on social media. Determining the stance of Tw... | ['Olga Vechtomova', 'Hareesh Bahuleyan'] | 2017-08-01 | null | null | null | semeval-2017-8 | ['rumour-detection'] | ['natural-language-processing'] | [-3.55628371e-01 4.57774214e-02 -3.61891031e-01 -3.01928163e-01
-7.57040322e-01 -2.57466972e-01 1.24928892e+00 5.83539128e-01
-2.57478595e-01 8.71069431e-01 5.67780972e-01 -3.32878143e-01
3.19998384e-01 -5.97369790e-01 -5.40785313e-01 -3.03822339e-01
-1.05299674e-01 3.66789252e-01 4.62884635e-01 -6.94390774... | [8.22685718536377, 10.117347717285156] |
6c8c8a07-a249-4f34-bd9b-d3e12ab83f03 | semantic-enhanced-text-to-sql-parsing-via | 2208.03903 | null | https://arxiv.org/abs/2208.03903v1 | https://arxiv.org/pdf/2208.03903v1.pdf | Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graph | The generalizability to new databases is of vital importance to Text-to-SQL systems which aim to parse human utterances into SQL statements. Existing works achieve this goal by leveraging the exact matching method to identify the lexical matching between the question words and the schema items. However, these methods f... | ['Lijie Wen', 'Li Lin', 'Xuming Hu', 'Aiwei Liu'] | 2022-08-08 | null | null | null | null | ['text-to-sql'] | ['computer-code'] | [ 2.65088677e-01 2.94123054e-01 -2.37107366e-01 -8.54674101e-01
-8.17923248e-01 -6.06484354e-01 4.13779020e-01 5.42726696e-01
-1.94373041e-01 1.37238964e-01 2.57997423e-01 -4.67091233e-01
1.31409392e-01 -9.26643431e-01 -1.05289364e+00 2.23247018e-02
3.98102552e-01 4.73165274e-01 3.89260232e-01 -3.56942654... | [9.900267601013184, 7.922833442687988] |
30249d10-995c-4c00-bc39-6d64a4332e2a | anticipatory-thinking-challenges-in-open | 2306.13157 | null | https://arxiv.org/abs/2306.13157v1 | https://arxiv.org/pdf/2306.13157v1.pdf | Anticipatory Thinking Challenges in Open Worlds: Risk Management | Anticipatory thinking drives our ability to manage risk - identification and mitigation - in everyday life, from bringing an umbrella when it might rain to buying car insurance. As AI systems become part of everyday life, they too have begun to manage risk. Autonomous vehicles log millions of miles, StarCraft and Go ag... | ['Leilani H. Gilpin', 'Dustin Dannenhauer', 'Adam Amos-Binks'] | 2023-06-22 | null | null | null | null | ['adversarial-robustness', 'autonomous-vehicles', 'management', 'starcraft'] | ['adversarial', 'computer-vision', 'miscellaneous', 'playing-games'] | [-5.91709465e-03 5.50664425e-01 -3.33316848e-02 -1.58817589e-01
-5.45854211e-01 -1.03366554e+00 1.02945316e+00 1.51163191e-01
-6.43102825e-01 4.00432765e-01 2.97200292e-01 -6.80497885e-01
-2.29474068e-01 -1.11715233e+00 -7.31142044e-01 -2.25438341e-01
-6.36152983e-01 7.16796041e-01 1.69420332e-01 -8.95244956... | [4.538244247436523, 2.1346216201782227] |
76103265-dcc5-45ad-a5ee-d7f8682c3681 | recurrent-attention-networks-for-long-text | 2306.06843 | null | https://arxiv.org/abs/2306.06843v1 | https://arxiv.org/pdf/2306.06843v1.pdf | Recurrent Attention Networks for Long-text Modeling | Self-attention-based models have achieved remarkable progress in short-text mining. However, the quadratic computational complexities restrict their application in long text processing. Prior works have adopted the chunking strategy to divide long documents into chunks and stack a self-attention backbone with the recur... | ['Qing Li', 'Fu Lee Wang', 'Yingbin Zhao', 'Xing Lee', 'Haoran Xie', 'Xiaotian Luo', 'Zongxi Li', 'Xianming Li'] | 2023-06-12 | null | null | null | null | ['chunking'] | ['natural-language-processing'] | [ 2.89611042e-01 2.18424454e-01 -4.72855270e-01 -2.43426323e-01
-4.63705838e-01 9.54484008e-03 4.68983263e-01 3.51051211e-01
-5.27562141e-01 3.98097247e-01 4.58605409e-01 -3.61755192e-01
-4.56852354e-02 -8.65487397e-01 -3.35220337e-01 -6.52104080e-01
-5.00429608e-03 2.29697019e-01 2.42837772e-01 4.47558910... | [10.850839614868164, 7.695093154907227] |
1c483995-1f74-41da-88e4-62866e4e1c12 | physical-passive-patch-adversarial-attacks-on | 2207.05729 | null | https://arxiv.org/abs/2207.05729v2 | https://arxiv.org/pdf/2207.05729v2.pdf | Physical Passive Patch Adversarial Attacks on Visual Odometry Systems | Deep neural networks are known to be susceptible to adversarial perturbations -- small perturbations that alter the output of the network and exist under strict norm limitations. While such perturbations are usually discussed as tailored to a specific input, a universal perturbation can be constructed to alter the mode... | ['Matan Jacoby', 'Chaim Baskin', 'Alex M. Bronstein', 'Yaniv Nemcovsky'] | 2022-07-11 | null | null | null | null | ['drone-navigation'] | ['computer-vision'] | [ 1.09935075e-01 5.11211336e-01 2.52570435e-02 4.46193665e-02
-4.48441327e-01 -1.03746080e+00 5.70842743e-01 -1.92294195e-01
-4.58496541e-01 7.10348845e-01 -4.42843556e-01 -4.23513025e-01
2.23781615e-01 -8.29676867e-01 -1.49259245e+00 -8.71948361e-01
-2.02360898e-01 2.31537342e-01 3.87109399e-01 -6.41617477... | [5.436560153961182, 7.822946071624756] |
76f86bce-1521-426f-92aa-1a199d5bd4ec | ecg-heartbeat-classification-using-multimodal | 2107.09869 | null | https://arxiv.org/abs/2107.09869v1 | https://arxiv.org/pdf/2107.09869v1.pdf | ECG Heartbeat Classification Using Multimodal Fusion | Electrocardiogram (ECG) is an authoritative source to diagnose and counter critical cardiovascular syndromes such as arrhythmia and myocardial infarction (MI). Current machine learning techniques either depend on manually extracted features or large and complex deep learning networks which merely utilize the 1D ECG sig... | ['Naimul Khan', 'Ling Guan', 'Anika Tabassum', 'Zeeshan Ahmad'] | 2021-07-21 | null | null | null | null | ['heartbeat-classification'] | ['medical'] | [ 4.98179585e-01 -3.67355943e-01 2.37268880e-01 -2.75205046e-01
-9.03356731e-01 -4.55018252e-01 2.13220686e-01 3.56656492e-01
-3.38728309e-01 8.98741722e-01 -8.77171084e-02 -5.93575597e-01
-3.04063320e-01 -6.19305730e-01 -3.50310981e-01 -8.00896883e-01
-1.62355125e-01 2.31664598e-01 -5.00022769e-01 1.06773913... | [14.275814056396484, 3.2620701789855957] |
49e3bb73-b2ac-4631-8dea-0e77186117c8 | pmaa-a-progressive-multi-scale-attention | 2303.16565 | null | https://arxiv.org/abs/2303.16565v1 | https://arxiv.org/pdf/2303.16565v1.pdf | PMAA: A Progressive Multi-scale Attention Autoencoder Model for High-Performance Cloud Removal from Multi-temporal Satellite Imagery | Satellite imagery analysis plays a vital role in remote sensing, but the information loss caused by cloud cover seriously hinders its application. This study presents a high-performance cloud removal architecture called Progressive Multi-scale Attention Autoencoder (PMAA), which simultaneously leverages global and loca... | ['Yachao Cui', 'Pin Tao', 'Junliang Xing', 'Kai Li', 'Xuechao Zou'] | 2023-03-29 | null | null | null | null | ['cloud-removal', 'cloud-detection'] | ['computer-vision', 'computer-vision'] | [ 5.92694990e-03 -4.28164750e-01 2.47892320e-01 -1.98941585e-02
-5.68294048e-01 -3.41021478e-01 5.47710240e-01 -3.26817751e-01
-2.16329843e-01 5.46244144e-01 2.28102580e-02 -3.33688766e-01
-1.00328691e-01 -1.08362198e+00 -5.79458654e-01 -1.26899660e+00
-1.44119352e-01 -3.16897221e-02 1.01052625e-02 -2.42007405... | [9.894438743591309, -1.798379898071289] |
214c5eef-3cbd-4c10-be9e-ab65834b0bff | lightweight-attribute-localizing-models-for | 2306.09822 | null | https://arxiv.org/abs/2306.09822v1 | https://arxiv.org/pdf/2306.09822v1.pdf | Lightweight Attribute Localizing Models for Pedestrian Attribute Recognition | Pedestrian Attribute Recognition (PAR) deals with the problem of identifying features in a pedestrian image. It has found interesting applications in person retrieval, suspect re-identification and soft biometrics. In the past few years, several Deep Neural Networks (DNNs) have been designed to solve the task; however,... | ['Andrzej Cichocki', 'Ahmed Mohamed Khedr', 'Thar Baker', 'Zaher Al Aghbari', 'Imran Junejo', 'Naveed Ahmed', 'Salman Ahmadi-Asl', 'Anh Huy Phan', 'Konstantin Sobolev', 'Dimitrii Ermilov', 'Ashish Jha'] | 2023-06-16 | null | null | null | null | ['pedestrian-attribute-recognition', 'person-retrieval', 'neural-network-compression', 'neural-network-compression'] | ['computer-vision', 'computer-vision', 'methodology', 'miscellaneous'] | [ 3.95701051e-01 -5.07723272e-01 -1.97603360e-01 -4.54341859e-01
-2.35998183e-01 -7.22107068e-02 7.06724644e-01 3.66544873e-01
-5.15006959e-01 5.38858771e-01 1.66047230e-01 -9.58422720e-02
-9.94067565e-02 -9.18090880e-01 -5.58616519e-01 -9.89875853e-01
-1.22757077e-01 3.00470769e-01 -1.83807492e-01 -2.23176870... | [14.571295738220215, 1.0010404586791992] |
c34238c5-c275-4ba7-8c4d-acd5a6c92283 | extracting-fine-grained-knowledge-graphs-of-1 | null | null | https://aclanthology.org/2021.emnlp-main.381 | https://aclanthology.org/2021.emnlp-main.381.pdf | Extracting Fine-Grained Knowledge Graphs of Scientific Claims: Dataset and Transformer-Based Results | Recent transformer-based approaches demonstrate promising results on relational scientific information extraction. Existing datasets focus on high-level description of how research is carried out. Instead we focus on the subtleties of how experimental associations are presented by building SciClaim, a dataset of scient... | ['Scott Friedman', 'Ian Magnusson'] | null | null | null | null | emnlp-2021-11 | ['joint-entity-and-relation-extraction'] | ['natural-language-processing'] | [ 4.60477732e-02 5.09186745e-01 -1.07576263e+00 -1.66101336e-01
-5.38354993e-01 -8.80283773e-01 1.01614296e+00 1.20613277e+00
-2.08780274e-01 1.17427719e+00 7.09627926e-01 -7.74984896e-01
-5.83316147e-01 -9.92106915e-01 -9.07719672e-01 -1.19181857e-01
-8.17270875e-02 5.33125281e-01 1.42507747e-01 1.82779685... | [8.897939682006836, 8.455231666564941] |
97fe974f-798f-4e8b-89d2-4a5a1d1d3a08 | space-time-recurrent-memory-network | 2109.06474 | null | https://arxiv.org/abs/2109.06474v2 | https://arxiv.org/pdf/2109.06474v2.pdf | Space Time Recurrent Memory Network | Transformers have recently been popular for learning and inference in the spatial-temporal domain. However, their performance relies on storing and applying attention to the feature tensor of each frame in video. Hence, their space and time complexity increase linearly as the length of video grows, which could be very ... | ['Fuxin Li', 'Chanho Kim', 'Hung Nguyen'] | 2021-09-14 | space-time-recurrent-memory-network-1 | https://openreview.net/forum?id=TYqb6EXphrr | https://openreview.net/pdf?id=TYqb6EXphrr | null | ['video-prediction'] | ['computer-vision'] | [ 1.37615442e-01 -2.16553867e-01 -6.75870180e-01 -3.98344934e-01
-6.40281260e-01 -2.24124715e-01 2.14632571e-01 -2.93245852e-01
-4.62537467e-01 3.59940112e-01 -1.18583813e-01 -4.28232491e-01
5.16611971e-02 -6.38213277e-01 -1.14567864e+00 -5.64925790e-01
-2.11337522e-01 4.10138458e-01 7.38322198e-01 5.52873075... | [9.041831016540527, 0.2798403203487396] |
711cf9f2-ce4b-43b7-808e-5fa978977c7c | educational-game-on-cryptocurrency-investment | 2301.10541 | null | https://arxiv.org/abs/2301.10541v3 | https://arxiv.org/pdf/2301.10541v3.pdf | Educational Game on Cryptocurrency Investment: Using Microeconomic Decision Making to Understand Macroeconomics Principles | Gamification is an effective strategy for motivating and engaging users, which is grounded in business, marketing, and management by designing games in nongame contexts. Gamifying education, which consists of the design and study of educational games, is an emerging trend. However, the existing classroom games for unde... | ['Luyao Zhang', 'Jiasheng Zhu'] | 2023-01-25 | null | null | null | null | ['marketing'] | ['miscellaneous'] | [-8.53720129e-01 1.90883368e-01 -2.89475322e-01 1.80980518e-01
-1.68882355e-01 -4.67873335e-01 3.61180425e-01 1.73565850e-01
-3.15011531e-01 2.77163655e-01 2.71308422e-01 -1.29835427e+00
1.62583571e-02 -1.42982471e+00 -1.46457806e-01 -3.01633805e-01
2.06814170e-01 8.64116922e-02 -1.22357300e-02 -6.61896706... | [10.026567459106445, 7.061843395233154] |
1f177952-f1f9-4b82-a940-8f4b79a9ca8a | campari-camera-aware-decomposed-generative | 2103.17269 | null | https://arxiv.org/abs/2103.17269v1 | https://arxiv.org/pdf/2103.17269v1.pdf | CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields | Tremendous progress in deep generative models has led to photorealistic image synthesis. While achieving compelling results, most approaches operate in the two-dimensional image domain, ignoring the three-dimensional nature of our world. Several recent works therefore propose generative models which are 3D-aware, i.e.,... | ['Andreas Geiger', 'Michael Niemeyer'] | 2021-03-31 | null | null | null | null | ['3d-aware-image-synthesis'] | ['computer-vision'] | [ 3.46988201e-01 1.78994790e-01 9.80689302e-02 -2.16770500e-01
-5.46455920e-01 -1.00512302e+00 9.47954834e-01 -8.21891129e-01
1.67425182e-02 3.38825583e-01 2.59922177e-01 -4.54147309e-02
2.71800905e-01 -7.42376983e-01 -9.16237473e-01 -8.78529191e-01
5.69016516e-01 6.40011013e-01 -2.67947074e-02 1.59592390... | [9.26497745513916, -3.1114513874053955] |
612f3230-12c3-42c3-881c-753587dc90c5 | calibrated-feature-decomposition-for | 2111.13945 | null | https://arxiv.org/abs/2111.13945v1 | https://arxiv.org/pdf/2111.13945v1.pdf | Calibrated Feature Decomposition for Generalizable Person Re-Identification | Existing disentangled-based methods for generalizable person re-identification aim at directly disentangling person representations into domain-relevant interference and identity-relevant feature. However, they ignore that some crucial characteristics are stubbornly entwined in both the domain-relevant interference and... | ['Zheng-Jun Zha', 'Liang Li', 'Wei Wu', 'Jiawei Liu', 'Kecheng Zheng'] | 2021-11-27 | null | null | null | null | ['generalizable-person-re-identification'] | ['computer-vision'] | [-1.21324062e-02 -3.36439312e-01 -1.71488568e-01 -5.28453887e-01
-6.12947762e-01 -7.05634654e-01 6.64036632e-01 -1.54123427e-02
-4.56103027e-01 7.08022952e-01 4.70085740e-01 2.75728881e-01
-6.44760370e-01 -7.15004146e-01 -2.62876958e-01 -9.50103104e-01
1.65567487e-01 4.45990980e-01 -3.12525809e-01 -3.70438427... | [14.725126266479492, 1.0242007970809937] |
bd1444cf-86bc-4df3-9ead-3f30c0d18bc6 | abstractive-sentence-summarization-with-1 | 2108.05123 | null | https://arxiv.org/abs/2108.05123v3 | https://arxiv.org/pdf/2108.05123v3.pdf | ICAF: Iterative Contrastive Alignment Framework for Multimodal Abstractive Summarization | Integrating multimodal knowledge for abstractive summarization task is a work-in-progress research area, with present techniques inheriting fusion-then-generation paradigm. Due to semantic gaps between computer vision and natural language processing, current methods often treat multiple data points as separate objects ... | ['Lu Zheng', 'Qian Zhang', 'Jing Xiao', 'Youxin Chen', 'Chang Shu', 'Zijian Zhang'] | 2021-08-11 | null | null | null | null | ['abstractive-sentence-summarization'] | ['natural-language-processing'] | [ 4.84067053e-01 -1.64603442e-01 -1.91358954e-01 -3.22080761e-01
-1.02281582e+00 -3.21490884e-01 9.01742697e-01 3.19025815e-01
-4.16614950e-01 3.07945460e-01 7.18565345e-01 1.71567842e-01
-8.35417509e-02 -4.31786984e-01 -5.65233052e-01 -7.03720272e-01
3.49462926e-01 1.97025254e-01 5.94785847e-02 -1.54704511... | [10.702460289001465, 0.9827584028244019] |
d274ce51-31e2-4297-a908-1bbb9a6f99b9 | object-detection-based-handwriting | 2106.14989 | null | https://arxiv.org/abs/2106.14989v1 | https://arxiv.org/pdf/2106.14989v1.pdf | Object Detection Based Handwriting Localization | We present an object detection based approach to localize handwritten regions from documents, which initially aims to enhance the anonymization during the data transmission. The concatenated fusion of original and preprocessed images containing both printed texts and handwritten notes or signatures are fed into the con... | ['Suting Miao', 'Yucheng Hu', 'Yuli Wu'] | 2021-06-28 | null | null | null | null | ['handwriting-recognition'] | ['computer-vision'] | [ 5.28886497e-01 -4.09683771e-02 1.09841421e-01 -4.04725969e-01
-6.44208848e-01 -8.91353250e-01 5.32389998e-01 -1.73649695e-02
-6.08706057e-01 4.17253762e-01 -7.19342753e-02 -1.44052580e-01
1.66139379e-02 -7.03252792e-01 -8.74415576e-01 -9.65789020e-01
1.27761140e-01 3.86096001e-01 -1.50925234e-01 2.93634832... | [12.25903034210205, 1.804038166999817] |
eafc0539-1d5e-4b81-93a9-880e85d5d6a3 | approaching-small-molecule-prioritization-as | 1911.10241 | null | https://arxiv.org/abs/1911.10241v2 | https://arxiv.org/pdf/1911.10241v2.pdf | Cross-modal representation alignment of molecular structure and perturbation-induced transcriptional profiles | Modeling the relationship between chemical structure and molecular activity is a key goal in drug development. Many benchmark tasks have been proposed for molecular property prediction, but these tasks are generally aimed at specific, isolated biomedical properties. In this work, we propose a new cross-modal small mole... | ['Samuel G. Finlayson', 'Scott L. Lipnick', 'Matthew B. A. McDermott', 'Alex V. Pickering', 'Isaac S. Kohane'] | 2019-11-22 | null | null | null | null | ['cross-modal-information-retrieval'] | ['miscellaneous'] | [ 8.10486257e-01 -2.28093818e-01 -6.90935910e-01 -3.32583129e-01
-1.34862542e+00 -9.87503409e-01 5.63821256e-01 5.63534558e-01
-1.86752796e-01 1.20262814e+00 4.00191814e-01 -4.18777376e-01
-2.16243610e-01 -6.14423335e-01 -1.05675387e+00 -1.09856749e+00
-2.31935363e-03 5.18427551e-01 -4.64509279e-01 -8.44431669... | [5.106541633605957, 5.827070236206055] |
cb81b604-6795-495f-9972-40f94edb8a74 | threat-model-agnostic-adversarial-defense | 2207.08089 | null | https://arxiv.org/abs/2207.08089v1 | https://arxiv.org/pdf/2207.08089v1.pdf | Threat Model-Agnostic Adversarial Defense using Diffusion Models | Deep Neural Networks (DNNs) are highly sensitive to imperceptible malicious perturbations, known as adversarial attacks. Following the discovery of this vulnerability in real-world imaging and vision applications, the associated safety concerns have attracted vast research attention, and many defense techniques have be... | ['Michael Elad', 'Alex Bronstein', 'Bahjat Kawar', 'Roy Ganz', 'Tsachi Blau'] | 2022-07-17 | null | null | null | null | ['adversarial-defense'] | ['adversarial'] | [ 6.40732050e-01 1.97358206e-02 2.84228355e-01 -5.75402305e-02
-7.31289744e-01 -8.59571815e-01 9.36004639e-01 -1.97368428e-01
-4.51966554e-01 4.71952021e-01 -1.06938206e-01 -3.52832139e-01
-8.38048309e-02 -8.17674637e-01 -1.05415380e+00 -1.25109899e+00
-2.00154439e-01 -5.78166768e-02 1.84486896e-01 -3.02146465... | [5.572411060333252, 7.882796764373779] |
24a92b66-2d7b-4325-9b42-c108d4c620f2 | polyvoice-language-models-for-speech-to | 2306.02982 | null | https://arxiv.org/abs/2306.02982v2 | https://arxiv.org/pdf/2306.02982v2.pdf | PolyVoice: Language Models for Speech to Speech Translation | We propose PolyVoice, a language model-based framework for speech-to-speech translation (S2ST) system. Our framework consists of two language models: a translation language model and a speech synthesis language model. We use discretized speech units, which are generated in a fully unsupervised way, and thus our framewo... | ['Yuxuan Wang', 'Siyuan Feng', 'Tom Ko', 'Mingxuan Wang', 'Yuping Wang', 'Zejun Ma', 'Lu Lu', 'Xi Chen', 'Ye Bai', 'Fengpeng Yue', 'Tang Li', 'Qiao Tian', 'Xuxin Cheng', 'Kexin Wang', 'Yunlong Zhao', 'Chen Xu', 'Zhiying Huang', 'Qianqian Dong'] | 2023-06-05 | null | null | null | null | ['speech-to-speech-translation', 'speech-synthesis'] | ['speech', 'speech'] | [ 0.04373016 0.29986015 -0.17800465 -0.11767933 -1.2105147 -0.5518887
0.5374795 -0.41505408 -0.00967555 0.56001574 0.56226903 -0.747115
0.70962435 -0.6073567 -0.60213727 -0.4870477 0.48869896 0.30410632
0.07205168 -0.4697137 -0.31280223 0.2300678 -1.3078116 0.5446299
0.8909824 0.51023924 0.5058... | [14.686823844909668, 6.853146076202393] |
566c8554-5ef9-40ac-88a8-8a0f4a762c18 | forged-image-detection-using-sota-image | 2211.15196 | null | https://arxiv.org/abs/2211.15196v1 | https://arxiv.org/pdf/2211.15196v1.pdf | Forged Image Detection using SOTA Image Classification Deep Learning Methods for Image Forensics with Error Level Analysis | The advancement in the area of computer vision has been brought using deep learning mechanisms. Image Forensics is one of the major areas of computer vision application. Forgery of images is sub-category of image forensics and can be detected using Error Level Analysis. Using such images as an input, this can turn out ... | ['Pandharinath Ghonge', 'Nandan Kanvinde', 'Abhishek Gupta', 'Raunak Joshi'] | 2022-11-28 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [ 1.52382687e-01 -1.09449483e-01 1.55622795e-01 7.51548307e-03
-4.94139791e-01 -3.19928974e-01 7.19003201e-01 1.08227193e-01
-5.75296402e-01 7.22568333e-01 -3.91579002e-01 -8.01328540e-01
-1.34496495e-01 -8.96251082e-01 -8.43092382e-01 -5.87645173e-01
-6.08462729e-02 -1.80386811e-01 4.51646060e-01 -2.45760337... | [12.413532257080078, 1.0247831344604492] |
d8f32c8e-9359-483e-8730-88f7e8ab34eb | multi-scale-relational-graph-convolutional | 2212.08781 | null | https://arxiv.org/abs/2212.08781v1 | https://arxiv.org/pdf/2212.08781v1.pdf | Multi-Scale Relational Graph Convolutional Network for Multiple Instance Learning in Histopathology Images | Graph convolutional neural networks have shown significant potential in natural and histopathology images. However, their use has only been studied in a single magnification or multi-magnification with late fusion. In order to leverage the multi-magnification information and early fusion with graph convolutional networ... | ['Septimiu Salcudean', 'Ali Bashashati', 'Martin Gleave', 'Larry Goldenberg', 'Ladan Fazli', 'Roozbeh Bazargani'] | 2022-12-17 | null | null | null | null | ['multiple-instance-learning'] | ['methodology'] | [ 1.81114912e-01 4.57551211e-01 -6.02265485e-02 -3.91282290e-01
-8.08059752e-01 -2.98908800e-01 3.00672531e-01 7.33745515e-01
-2.17533067e-01 2.29106829e-01 1.25148043e-01 -3.89355779e-01
-2.88743913e-01 -1.14437664e+00 -6.07252955e-01 -6.94825709e-01
-5.88714361e-01 2.04978079e-01 3.04746568e-01 -2.24829942... | [15.140381813049316, -2.978750705718994] |
d37938db-83d4-40c8-87a6-1d7b62c6058b | extensible-motion-based-identification-of-xr | 2302.07517 | null | https://arxiv.org/abs/2302.07517v4 | https://arxiv.org/pdf/2302.07517v4.pdf | Extensible Motion-based Identification of XR Users using Non-Specific Motion Data | In this paper, we combine the strengths of distance-based and classification-based approaches for the task of identifying extended reality users by their movements. For this we explore an embedding-based model that leverages deep metric learning. We train the model on a dataset of users playing the VR game ``Half-Life:... | ['Christian Rack', 'Marc Erich Latoschik', 'Andreas Hotho', 'Tamara Fernando', 'Konstantin Kobs'] | 2023-02-15 | null | null | null | null | ['metric-learning', 'metric-learning'] | ['computer-vision', 'methodology'] | [-3.95713039e-02 -2.36156881e-01 -2.60919034e-01 -2.45557547e-01
-9.43063259e-01 -7.60435164e-01 3.66955966e-01 1.08802207e-01
-6.48668408e-01 3.39041501e-01 1.92118529e-02 -4.39542800e-01
-2.55674124e-01 -5.82808733e-01 -3.60702425e-01 -9.12168473e-02
-2.20811173e-01 2.63357997e-01 2.37700760e-01 -5.42019069... | [7.5521240234375, 0.6816166043281555] |
84cb7969-bc28-49c6-a8bd-a37cd674d260 | ensemble-clustering-based-on-evidence | null | null | https://www.sciencedirect.com/science/article/pii/S0031320319301281 | https://www.sciencedirect.com/science/article/pii/S0031320319301281 | Ensemble clustering based on evidence extracted from the co-association matrix | The evidence accumulation model is an approach for collecting the information of base partitions in a clustering ensemble method, and can be viewed as a kernel transformation from the original data space to a co-association matrix. However, cluster structure information may be partially lost in this transformation; hen... | [] | 2019-08-01 | null | null | null | null | ['spectral-graph-clustering', 'clustering-ensemble'] | ['graphs', 'graphs'] | [ 5.51315024e-02 -2.57763058e-01 6.25222083e-03 -5.20661734e-02
-5.89324795e-02 -3.16655248e-01 8.01566690e-02 4.38513458e-01
-3.38283569e-01 7.79930949e-01 -1.99231520e-01 9.29395482e-02
-6.44826412e-01 -9.42319930e-01 -2.61627376e-01 -1.33418036e+00
-8.18829611e-02 2.21285850e-01 2.32221708e-01 -2.62686256... | [7.6503167152404785, 4.557351112365723] |
4598ca7f-c667-474e-a1c7-c5e3c511d9cd | gnep-based-dynamic-segmentation-and-motion | 2307.02595 | null | https://arxiv.org/abs/2307.02595v2 | https://arxiv.org/pdf/2307.02595v2.pdf | GNEP Based Dynamic Segmentation and Motion Estimation for Neuromorphic Imaging | This paper explores the application of event-based cameras in the domains of image segmentation and motion estimation. These cameras offer a groundbreaking technology by capturing visual information as a continuous stream of asynchronous events, departing from the conventional frame-based image acquisition. We introduc... | ['David Sayre', 'Harbir Antil'] | 2023-07-05 | null | null | null | null | ['motion-estimation'] | ['computer-vision'] | [ 3.66687745e-01 -2.79517323e-01 -3.82739365e-01 -2.38956232e-02
-4.07907933e-01 -7.21319914e-01 6.35098755e-01 1.52199911e-02
-8.29191566e-01 5.73501825e-01 -1.36352956e-01 -2.40513429e-01
-3.08039576e-01 -4.97752696e-01 -5.50782442e-01 -5.84590733e-01
-3.46136838e-01 -1.56141967e-01 5.09440839e-01 1.97547689... | [8.687732696533203, -1.2013835906982422] |
4ab1129b-7bc0-4d42-9cb0-9708fa3171ec | zuo-zhuan-ancient-chinese-dataset-for-word | null | null | https://aclanthology.org/2022.naacl-srw.17 | https://aclanthology.org/2022.naacl-srw.17.pdf | Zuo Zhuan Ancient Chinese Dataset for Word Sense Disambiguation | Word Sense Disambiguation (WSD) is a core task in Natural Language Processing (NLP). Ancient Chinese has rarely been used in WSD tasks, however, as no public dataset for ancient Chinese WSD tasks exists. Creation of an ancient Chinese dataset is considered a significant challenge because determining the most appropriat... | ['Mamoru Komachi', 'Teruaki Oka', 'Hongfei Wang', 'Xiaomeng Pan'] | null | null | null | null | naacl-acl-2022-7 | ['word-sense-disambiguation'] | ['natural-language-processing'] | [ 5.04343547e-02 -4.67519820e-01 -4.34900261e-02 -3.37282985e-01
-9.17791069e-01 -7.92462707e-01 4.72547024e-01 3.64349395e-01
-1.05510843e+00 1.10894263e+00 3.92771274e-01 -3.48094374e-01
-1.79759711e-02 -7.94930458e-01 -8.49452689e-02 -4.73392695e-01
2.59538889e-01 5.87162912e-01 2.79509932e-01 -5.98334789... | [10.252280235290527, 9.412559509277344] |
e8f6d9d5-ad05-46d2-a4ba-3891ebf598fe | inconsistent-few-shot-relation-classification | 2110.08254 | null | https://arxiv.org/abs/2110.08254v1 | https://arxiv.org/pdf/2110.08254v1.pdf | Inconsistent Few-Shot Relation Classification via Cross-Attentional Prototype Networks with Contrastive Learning | Standard few-shot relation classification (RC) is designed to learn a robust classifier with only few labeled data for each class. However, previous works rarely investigate the effects of a different number of classes (i.e., $N$-way) and number of labeled data per class (i.e., $K$-shot) during training vs. testing. In... | ['Kam-Fai Wong', 'Gabriel Pui Cheong Fung', 'Jiarun Cao', 'Zhijing Jin', 'Hongru Wang'] | 2021-10-13 | null | null | null | null | ['few-shot-relation-classification', 'few-shot-relation-classification'] | ['methodology', 'natural-language-processing'] | [ 1.39929473e-01 1.21503528e-02 -5.71677268e-01 -5.64928114e-01
-8.66939247e-01 -4.62904423e-02 3.12620163e-01 3.53230909e-02
-4.05722171e-01 5.67925811e-01 -3.46260905e-01 -1.10431928e-02
-4.22457844e-01 -8.81216288e-01 -5.68083584e-01 -5.90128958e-01
1.19870445e-02 5.07578313e-01 3.30885500e-01 -1.94625914... | [9.995759010314941, 3.0285911560058594] |
b9466d4a-566e-4035-8b55-2de8613a3abd | taa-gcn-a-temporally-aware-adaptive-graph | 2305.08779 | null | https://arxiv.org/abs/2305.08779v1 | https://arxiv.org/pdf/2305.08779v1.pdf | TAA-GCN: A Temporally Aware Adaptive Graph Convolutional Network for Age Estimation | This paper proposes a novel age estimation algorithm, the Temporally-Aware Adaptive Graph Convolutional Network (TAA-GCN). Using a new representation based on graphs, the TAA-GCN utilizes skeletal, posture, clothing, and facial information to enrich the feature set associated with various ages. Such a novel graph repre... | ['Scott T. Acton', 'Peter Young', 'Matthew Korban'] | 2023-05-15 | null | null | null | null | ['age-estimation', 'age-estimation'] | ['computer-vision', 'miscellaneous'] | [-2.22562119e-01 -2.77496479e-03 7.30234459e-02 -3.91517907e-01
1.33464500e-01 7.87765682e-02 3.07706445e-01 -4.26953584e-02
-2.78324664e-01 4.07308429e-01 5.12715317e-02 2.72164285e-01
2.71785334e-02 -8.92644882e-01 -4.82702792e-01 -8.29952121e-01
-3.54139388e-01 2.02569261e-01 1.82525650e-01 -2.21364379... | [13.531078338623047, 0.8468642830848694] |
dbb64f8b-64e6-440e-9920-6957e1fbec65 | dynamic-persistent-homology-for-brain | 2201.00087 | null | https://arxiv.org/abs/2201.00087v2 | https://arxiv.org/pdf/2201.00087v2.pdf | Dynamic Topological Data Analysis for Brain Networks via Wasserstein Graph Clustering | We present the novel Wasserstein graph clustering for dynamically changing graphs. The Wasserstein clustering penalizes the topological discrepancy between graphs. The Wasserstein clustering is shown to outperform the widely used k-means clustering. The method applied in more accurate determination of the state spaces ... | ['H. Hill Goldsmith', 'Vince D. Calhoun', 'Ian C. Carroll', 'Shih-Gu Huang', 'Moo K. Chung'] | 2022-01-01 | null | null | null | null | ['graph-clustering'] | ['graphs'] | [-6.00359552e-02 2.19327092e-01 3.86352301e-01 -3.86656970e-01
8.32692236e-02 -5.31294882e-01 3.25943977e-01 1.50774911e-01
-2.93925673e-01 5.21022260e-01 -2.72419214e-01 -3.01855445e-01
-8.70006025e-01 -4.11088556e-01 -1.79308236e-01 -8.94158840e-01
-1.01944518e+00 5.32466054e-01 5.93540490e-01 -7.28250891... | [7.098659038543701, 5.251951694488525] |
5961faf0-20e6-49e7-8d6f-263858b960f9 | reducing-bias-in-modeling-real-world-password | 2010.12269 | null | https://arxiv.org/abs/2010.12269v5 | https://arxiv.org/pdf/2010.12269v5.pdf | Reducing Bias in Modeling Real-world Password Strength via Deep Learning and Dynamic Dictionaries | Password security hinges on an in-depth understanding of the techniques adopted by attackers. Unfortunately, real-world adversaries resort to pragmatic guessing strategies such as dictionary attacks that are inherently difficult to model in password security studies. In order to be representative of the actual threat, ... | ['Massimo Bernaschi', 'Giuseppe Ateniese', 'Marco Cianfriglia', 'Dario Pasquini'] | 2020-10-23 | null | null | null | null | ['security-studies'] | ['miscellaneous'] | [-4.07505482e-02 -4.49339122e-01 1.31253123e-01 1.02594392e-02
-5.30730844e-01 -1.30369627e+00 4.07649428e-01 3.59091640e-01
-5.69705784e-01 4.61324275e-01 -9.24657509e-02 -1.21657979e+00
3.23412754e-02 -8.65193427e-01 -4.83707041e-01 -2.67851919e-01
9.34544206e-02 3.98171842e-01 -1.65598281e-02 -6.38898492... | [5.834367752075195, 7.606562614440918] |
34eb5b05-1ed2-4752-83c2-635ca8daa96c | explainable-artificial-intelligence-4 | 2303.14615 | null | https://arxiv.org/abs/2303.14615v1 | https://arxiv.org/pdf/2303.14615v1.pdf | Explainable Artificial Intelligence Architecture for Melanoma Diagnosis Using Indicator Localization and Self-Supervised Learning | Melanoma is a prevalent lethal type of cancer that is treatable if diagnosed at early stages of development. Skin lesions are a typical indicator for diagnosing melanoma but they often led to delayed diagnosis due to high similarities of cancerous and benign lesions at early stages of melanoma. Deep learning (DL) can b... | ['Mohammad Rostami', 'Ruitong Sun'] | 2023-03-26 | null | null | null | null | ['melanoma-diagnosis'] | ['computer-vision'] | [ 1.76577494e-01 4.90653992e-01 -6.01167977e-01 -1.97986171e-01
-3.22002888e-01 -4.51855093e-01 4.90739256e-01 2.86725909e-01
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6.54889420e-02 -7.10454464e-01 -2.13331550e-01 -7.62264013e-01
2.48876378e-01 3.36330354e-01 -1.89783424e-01 -1.18119895... | [15.476926803588867, -2.778426170349121] |
2fd6e05a-4702-4530-a90e-8ce4279c4462 | increased-confidence-adversarial-examples-for | 2005.06023 | null | https://arxiv.org/abs/2005.06023v2 | https://arxiv.org/pdf/2005.06023v2.pdf | Increased-confidence adversarial examples for deep learning counter-forensics | Transferability of adversarial examples is a key issue to apply this kind of attacks against multimedia forensics (MMF) techniques based on Deep Learning (DL) in a real-life setting. Adversarial example transferability, in fact, would open the way to the deployment of successful counter forensics attacks also in cases ... | ['Rongrong Ni', 'Benedetta Tondi', 'Wenjie Li', 'Mauro Barni'] | 2020-05-12 | null | null | null | null | ['image-forensics'] | ['computer-vision'] | [-1.15785562e-01 -1.06481828e-01 2.26273075e-01 1.44166619e-01
-6.37574196e-01 -1.13457048e+00 7.69987762e-01 1.90974846e-01
-5.81486106e-01 5.09782732e-01 -2.96774626e-01 -7.82885492e-01
-3.32792327e-02 -1.04600322e+00 -1.00720835e+00 -6.97539449e-01
-2.37128973e-01 6.41791299e-02 5.01949668e-01 -3.61942291... | [5.623366832733154, 7.763838291168213] |
5462215e-9e05-4e7d-8da6-31161c115e16 | supervoice-text-independent-speaker | 2205.14496 | null | https://arxiv.org/abs/2205.14496v1 | https://arxiv.org/pdf/2205.14496v1.pdf | SuperVoice: Text-Independent Speaker Verification Using Ultrasound Energy in Human Speech | Voice-activated systems are integrated into a variety of desktop, mobile, and Internet-of-Things (IoT) devices. However, voice spoofing attacks, such as impersonation and replay attacks, in which malicious attackers synthesize the voice of a victim or simply replay it, have brought growing security concerns. Existing s... | ['Eric J. Hunter', 'Li Xiao', 'Ying Zhu', 'Nikolay Ivanov', 'Qiben Yan', 'Hanqing Guo'] | 2022-05-28 | null | null | null | null | ['text-independent-speaker-verification'] | ['speech'] | [ 1.19298734e-01 -3.30096215e-01 1.46682963e-01 -1.38619408e-01
-7.14152098e-01 -7.37382412e-01 3.30939472e-01 -1.88475236e-01
-2.11942479e-01 2.61883706e-01 3.33587676e-01 -5.91741979e-01
3.39252293e-01 -4.32975084e-01 -2.81468928e-01 -6.33034348e-01
-6.95661157e-02 -3.30962211e-01 1.57880858e-01 4.02276916... | [14.083671569824219, 5.888390064239502] |
0b450840-7b2e-4bc6-b197-7e6e1fc16892 | a-natural-language-corpus-of-common-grounding | 1907.03399 | null | https://arxiv.org/abs/1907.03399v1 | https://arxiv.org/pdf/1907.03399v1.pdf | A Natural Language Corpus of Common Grounding under Continuous and Partially-Observable Context | Common grounding is the process of creating, repairing and updating mutual understandings, which is a critical aspect of sophisticated human communication. However, traditional dialogue systems have limited capability of establishing common ground, and we also lack task formulations which introduce natural difficulty i... | ['Akiko Aizawa', 'Takuma Udagawa'] | 2019-07-08 | null | null | null | null | ['goal-oriented-dialog', 'dialogue-understanding'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.38576069e-01 6.98771894e-01 1.23794898e-01 -4.77627873e-01
-6.51802301e-01 -7.54887402e-01 1.11673617e+00 3.96615863e-01
-2.88034737e-01 8.96023333e-01 4.88033354e-01 -6.85581803e-01
1.64263561e-01 -5.20599008e-01 -4.24744010e-01 -1.74124211e-01
-2.88290903e-02 7.83282161e-01 2.65259564e-01 -9.09490943... | [12.640085220336914, 8.01456069946289] |
5a88431d-21c1-4059-ab7b-093da8c61ad8 | context-prediction-for-unsupervised-deep | 1901.08396 | null | https://arxiv.org/abs/1901.08396v2 | https://arxiv.org/pdf/1901.08396v2.pdf | Self-Supervised Deep Learning on Point Clouds by Reconstructing Space | Point clouds provide a flexible and natural representation usable in countless applications such as robotics or self-driving cars. Recently, deep neural networks operating on raw point cloud data have shown promising results on supervised learning tasks such as object classification and semantic segmentation. While mas... | ['Jonathan Sauder', 'Bjarne Sievers'] | 2019-01-24 | self-supervised-deep-learning-on-point-clouds | http://papers.nips.cc/paper/9455-self-supervised-deep-learning-on-point-clouds-by-reconstructing-space | http://papers.nips.cc/paper/9455-self-supervised-deep-learning-on-point-clouds-by-reconstructing-space.pdf | neurips-2019-12 | ['3d-point-cloud-linear-classification', 'point-cloud-pre-training', 'unsupervised-3d-point-cloud-linear-evaluation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.81747305e-01 4.14513618e-01 -3.08374375e-01 -8.41502130e-01
-6.04327440e-01 -6.00786448e-01 5.72487473e-01 1.76358879e-01
-5.30852199e-01 2.18742803e-01 -5.99735975e-01 -4.37073112e-01
7.81056583e-02 -8.29642355e-01 -1.32832754e+00 -3.05519998e-01
7.53846345e-03 1.34173810e+00 5.88199079e-01 8.78859237... | [8.05838394165039, -3.1912994384765625] |
a33e7066-7b23-41ca-aa85-77faf019e690 | extending-layered-models-to-3d-motion | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Dong_Lao_Extending_Layered_Models_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Dong_Lao_Extending_Layered_Models_ECCV_2018_paper.pdf | Extending Layered Models to 3D Motion | We consider the problem of inferring a layered representa-tion, its depth ordering and motion segmentation from a video in whichobjects may undergo 3D non-planar motion relative to the camera. Wegeneralize layered inference to the aforementioned case and correspond-ing self-occlusion phenomena. We accomplish this by in... | ['Dong Lao', 'Ganesh Sundaramoorthi'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['unsupervised-video-object-segmentation'] | ['computer-vision'] | [ 9.50861424e-02 5.44623911e-01 -2.78682888e-01 -2.12350085e-01
-5.83017111e-01 -5.19855618e-01 6.48291528e-01 -9.42371264e-02
-1.14848077e-01 5.32558918e-01 1.07766911e-01 -2.12372467e-01
-3.78159503e-03 -6.32472992e-01 -1.10115826e+00 -7.47091711e-01
-1.78043857e-01 7.16003656e-01 7.18517125e-01 2.59878546... | [8.85463809967041, -2.020390510559082] |
73471be7-7e14-48e2-83f0-548cb5bd529b | thermal-spread-functions-tsf-physics-guided | 2304.00696 | null | https://arxiv.org/abs/2304.00696v1 | https://arxiv.org/pdf/2304.00696v1.pdf | Thermal Spread Functions (TSF): Physics-guided Material Classification | Robust and non-destructive material classification is a challenging but crucial first-step in numerous vision applications. We propose a physics-guided material classification framework that relies on thermal properties of the object. Our key observation is that the rate of heating and cooling of an object depends on t... | ['Oliver Cossairt', 'Ashok Veeraraghavan', 'Aggelos Katsaggelos', 'Florian Willomitzer', 'Emma Alexander', 'Vishwanath Saragadam', 'Aniket Dashpute'] | 2023-04-03 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Dashpute_Thermal_Spread_Functions_TSF_Physics-Guided_Material_Classification_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Dashpute_Thermal_Spread_Functions_TSF_Physics-Guided_Material_Classification_CVPR_2023_paper.pdf | cvpr-2023-1 | ['material-classification'] | ['computer-vision'] | [ 5.70531309e-01 -5.86972713e-01 5.08503653e-02 -2.30885133e-01
-4.22400057e-01 -6.96468234e-01 7.56027877e-01 -4.50456627e-02
-4.64364588e-01 6.19775474e-01 -5.38548648e-01 -7.92265013e-02
7.68505186e-02 -8.19959104e-01 -4.81672466e-01 -1.59506428e+00
5.50543427e-01 7.28907704e-01 4.91503775e-01 4.26387429... | [9.81561279296875, -2.870887279510498] |
411be1d2-3d8c-41ef-bce0-9b90e5cbcc2f | semantic-relatedness-based-re-ranker-for-text | 1909.0795 | null | https://arxiv.org/abs/1909.07950v2 | https://arxiv.org/pdf/1909.07950v2.pdf | Semantic Relatedness Based Re-ranker for Text Spotting | Applications such as textual entailment, plagiarism detection or document clustering rely on the notion of semantic similarity, and are usually approached with dimension reduction techniques like LDA or with embedding-based neural approaches. We present a scenario where semantic similarity is not enough, and we devise ... | ['Lluís Padró', 'Francesc Moreno-Noguer', 'Ahmed Sabir'] | 2019-09-17 | semantic-relatedness-based-re-ranker-for-text-1 | https://aclanthology.org/D19-1346 | https://aclanthology.org/D19-1346.pdf | ijcnlp-2019-11 | ['text-spotting'] | ['computer-vision'] | [ 5.76168716e-01 -1.92130953e-01 1.78269729e-01 -5.75353742e-01
-6.92430973e-01 -4.71036077e-01 1.15321648e+00 7.82269835e-01
-4.77871418e-01 1.36308879e-01 5.03900468e-01 -1.74382731e-01
-5.65966070e-02 -5.00719547e-01 -4.86763597e-01 -5.38795769e-01
2.83200502e-01 4.23029780e-01 3.75975743e-02 -3.85552496... | [11.66940689086914, 2.2171733379364014] |
ac0d8b5a-02d7-47b2-97e8-5d335c70c10b | curved-text-detection-in-natural-scene-images | 1908.0999 | null | https://arxiv.org/abs/1908.09990v1 | https://arxiv.org/pdf/1908.09990v1.pdf | Curved Text Detection in Natural Scene Images with Semi- and Weakly-Supervised Learning | Detecting curved text in the wild is very challenging. Recently, most state-of-the-art methods are segmentation based and require pixel-level annotations. We propose a novel scheme to train an accurate text detector using only a small amount of pixel-level annotated data and a large amount of data annotated with rectan... | ['Weiping Wang', 'Xugong Qin', 'Yu Zhou', 'Dongbao Yang'] | 2019-08-27 | null | null | null | null | ['curved-text-detection'] | ['computer-vision'] | [ 2.48632088e-01 2.84718961e-01 -2.57624835e-01 -4.39711213e-01
-1.33316958e+00 -6.26787543e-01 3.68611693e-01 1.13734506e-01
-6.16511524e-01 4.61748719e-01 -1.98955774e-01 -1.98110685e-01
8.73394370e-01 -5.63812494e-01 -6.61089718e-01 -5.37562788e-01
5.24037480e-01 6.23955309e-01 1.26119924e+00 7.65071809... | [12.021356582641602, 2.2584116458892822] |
45952f9f-488f-46cd-9d7d-35d7fb93c745 | bbbd-bounding-box-based-detector-for | 2204.12841 | null | https://arxiv.org/abs/2204.12841v1 | https://arxiv.org/pdf/2204.12841v1.pdf | BBBD: Bounding Box Based Detector for Occlusion Detection and Order Recovery | Occlusion handling is one of the challenges of object detection and segmentation, and scene understanding. Because objects appear differently when they are occluded in varying degree, angle, and locations. Therefore, determining the existence of occlusion between objects and their order in a scene is a fundamental requ... | ['Zoltan Vamossy', 'Kaziwa Saleh'] | 2022-04-27 | null | null | null | null | ['occlusion-handling'] | ['computer-vision'] | [ 2.92359143e-01 -1.82877213e-01 -1.45110205e-01 -3.59833270e-01
-3.09871167e-01 -7.49800622e-01 2.02328399e-01 4.36303020e-01
-2.33317703e-01 1.39105886e-01 -1.87100321e-01 -2.44606152e-01
2.11711928e-01 -8.84057343e-01 -8.79782140e-01 -5.38657606e-01
1.34387612e-01 7.16157913e-01 7.19173312e-01 2.51233220... | [9.382972717285156, 0.46351781487464905] |
980f7494-7e00-42a7-91f7-f92e97edd52a | another-vertical-view-a-hierarchical-network | 2304.05106 | null | https://arxiv.org/abs/2304.05106v1 | https://arxiv.org/pdf/2304.05106v1.pdf | Another Vertical View: A Hierarchical Network for Heterogeneous Trajectory Prediction via Spectrums | With the fast development of AI-related techniques, the applications of trajectory prediction are no longer limited to easier scenes and trajectories. More and more heterogeneous trajectories with different representation forms, such as 2D or 3D coordinates, 2D or 3D bounding boxes, and even high-dimensional human skel... | ['Xinge You', 'Qinmu Peng', 'Beihao Xia', 'Conghao Wong'] | 2023-04-11 | null | null | null | null | ['trajectory-prediction'] | ['computer-vision'] | [-3.94080609e-01 -5.35551012e-01 -1.56986073e-01 -7.09981248e-02
-1.21485926e-01 -7.11082995e-01 7.60460794e-01 -1.79556519e-01
-1.88540310e-01 7.29770422e-01 2.80418515e-01 -9.04569179e-02
-2.52416819e-01 -9.85785007e-01 -6.50294185e-01 -8.76759410e-01
-5.27600050e-01 3.81382078e-01 5.27077615e-01 -5.96644521... | [6.294097900390625, 0.9958465099334717] |
ebede5ac-8e3b-4c4f-bf42-206b42e3157f | me-gcn-multi-dimensional-edge-embedded-graph | null | null | https://openreview.net/forum?id=JFEPWILzYU | https://openreview.net/pdf?id=JFEPWILzYU | ME-GCN: Multi-dimensional Edge-Embedded Graph Convolutional Networks for Semi-supervised Text Classification | Compared to sequential learning models, graph-based neural networks exhibit excellent ability in capturing global information and have been used for semi-supervised learning tasks, including citation network analysis or text classification. However, most GCNs are designed with the single-dimensional edge feature and ne... | ['Anonymous'] | 2021-10-16 | null | null | null | acl-arr-october-2021-10 | ['semi-supervised-text-classification-1'] | ['natural-language-processing'] | [ 3.35490666e-02 -1.46491593e-02 -2.26588592e-01 -1.06797032e-01
-1.46552250e-02 -2.53819853e-01 8.22754264e-01 4.85621303e-01
-1.60984159e-01 1.64405331e-01 2.54561931e-01 -4.43816632e-01
-1.92635670e-01 -1.15641820e+00 -2.65100867e-01 -5.73043048e-01
-3.75105143e-01 3.87556046e-01 1.75210118e-01 -1.84161633... | [9.975107192993164, 6.745555400848389] |
676b5bd6-fd7a-4076-8d4a-c97f8f53f4d6 | multi-target-domain-adaptation-via | 2103.1397 | null | https://arxiv.org/abs/2103.13970v1 | https://arxiv.org/pdf/2103.13970v1.pdf | Multi-Target Domain Adaptation via Unsupervised Domain Classification for Weather Invariant Object Detection | Object detection is an essential technique for autonomous driving. The performance of an object detector significantly degrades if the weather of the training images is different from that of test images. Domain adaptation can be used to address the domain shift problem so as to improve the robustness of an object dete... | ['Francis Ng', 'Jinlin Chen', 'Ting Sun'] | 2021-03-25 | null | null | null | null | ['robust-object-detection', 'multi-target-domain-adaptation'] | ['computer-vision', 'computer-vision'] | [ 1.30670100e-01 -6.39135003e-01 -8.95310342e-02 -6.16581976e-01
-1.15483634e-01 -6.13177538e-01 5.25134683e-01 -2.84812927e-01
-4.80445683e-01 6.45151019e-01 -2.40824163e-01 -1.79559842e-01
1.71304360e-01 -8.65828454e-01 -4.19796646e-01 -9.48288441e-01
4.31618005e-01 3.23633850e-01 1.05184937e+00 -4.22944605... | [9.870905876159668, 2.112945318222046] |
2144ddca-2bb1-45ef-a6cd-114c38325501 | tatum-level-drum-transcription-based-on-a | 2010.03749 | null | https://arxiv.org/abs/2010.03749v1 | https://arxiv.org/pdf/2010.03749v1.pdf | Tatum-Level Drum Transcription Based on a Convolutional Recurrent Neural Network with Language Model-Based Regularized Training | This paper describes a neural drum transcription method that detects from music signals the onset times of drums at the $\textit{tatum}$ level, where tatum times are assumed to be estimated in advance. In conventional studies on drum transcription, deep neural networks (DNNs) have often been used to take a music spectr... | ['Kazuyoshi Yoshii', 'Eita Nakamura', 'Ryo Nishikimi', 'Ryoto Ishizuka'] | 2020-10-08 | null | null | null | null | ['drum-transcription'] | ['music'] | [ 3.69023502e-01 -4.71247286e-01 4.53889742e-03 -1.07412621e-01
-9.85069215e-01 -5.16823590e-01 -5.85368499e-02 -2.28440925e-01
-3.64780456e-01 4.10696387e-01 2.14306921e-01 -2.61170473e-02
-1.71470329e-01 -5.39269388e-01 -6.52827382e-01 -7.81484663e-01
-4.24963199e-02 2.24341094e-01 -6.83398247e-02 -1.67566657... | [15.802563667297363, 5.485580921173096] |
e98ec003-d82e-442a-abbc-7345f1601f80 | updated-headline-generation-creating-updated | null | null | https://aclanthology.org/2022.acl-long.446 | https://aclanthology.org/2022.acl-long.446.pdf | Updated Headline Generation: Creating Updated Summaries for Evolving News Stories | We propose the task of updated headline generation, in which a system generates a headline for an updated article, considering both the previous article and headline. The system must identify the novel information in the article update, and modify the existing headline accordingly. We create data for this task using th... | ['Mark Dredze', 'Adrian Benton', 'Sheena Panthaplackel'] | null | null | null | null | acl-2022-5 | ['headline-generation'] | ['natural-language-processing'] | [ 7.21132159e-01 8.91955972e-01 -3.68868828e-01 -3.86989325e-01
-1.26666164e+00 -6.79019034e-01 1.03413582e+00 9.77203727e-01
-5.02108157e-01 1.42682397e+00 1.13217866e+00 -2.61559606e-01
1.49137020e-01 -4.39019382e-01 -1.01283026e+00 2.35153303e-01
2.78162301e-01 6.27124071e-01 2.50962555e-01 -4.74009365... | [12.372573852539062, 9.414962768554688] |
dd9e30da-3155-477a-ba52-ae4989820fdd | language-relatedness-and-lexical-closeness | null | null | https://aclanthology.org/2021.wat-1.26 | https://aclanthology.org/2021.wat-1.26.pdf | Language Relatedness and Lexical Closeness can help Improve Multilingual NMT: IITBombay@MultiIndicNMT WAT2021 | Multilingual Neural Machine Translation has achieved remarkable performance by training a single translation model for multiple languages. This paper describes our submission (Team ID: CFILT-IITB) for the MultiIndicMT: An Indic Language Multilingual Task at WAT 2021. We train multilingual NMT systems by sharing encoder... | ['Pushpak Bhattacharyya', 'Nikhil Saini', 'Jyotsana Khatri'] | null | null | null | null | acl-wat-2021-8 | ['transliteration'] | ['natural-language-processing'] | [-2.20730249e-02 -5.38941808e-02 -5.23174465e-01 -3.55761051e-01
-1.28967476e+00 -7.58919835e-01 5.76052070e-01 -3.05160910e-01
-6.29049778e-01 1.09056008e+00 3.39298040e-01 -1.04211199e+00
6.67002201e-01 -3.47249210e-01 -1.19270182e+00 -4.62685041e-02
2.93892503e-01 8.10865045e-01 -5.97692013e-01 -4.14393455... | [11.581209182739258, 10.40008544921875] |
d4d9752c-1baa-4720-ad70-0512daeb6885 | dmrnet-learning-discriminative-features-with | null | null | https://ieeexplore.ieee.org/document/9944858 | https://www.zdzheng.xyz/files/Han_TPAMI22.pdf | DMRNet++: Learning Discriminative Features with Decoupled Networks and Enriched Pairs for One-Step Person Search | Person search aims at localizing and recognizing query persons from raw video frames, which is a combination of two sub-tasks, i.e., pedestrian detection and person re-identification. The dominant fashion is termed as the one-step person search that jointly optimizes detection and identification in a unified network, e... | ['Yi Yang', 'Nong Sang', 'Changxin Gao', 'Zehuan Yuan', 'Dongdong Yu', 'Kai Su', 'Zhedong Zheng', 'Chuchu Han'] | 2022-11-10 | null | null | null | ieee-transactions-on-pattern-analysis-and-24 | ['person-re-identification', 'pedestrian-detection', 'person-search'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [-6.32661954e-02 -4.51703250e-01 -5.16090281e-02 -4.31992441e-01
-6.42256260e-01 -3.75499368e-01 6.62532210e-01 -3.64601284e-01
-8.53576303e-01 6.72225654e-01 8.22175741e-02 1.48064584e-01
1.09512143e-01 -5.40872395e-01 -6.74927294e-01 -6.85892522e-01
2.07785055e-01 4.12734270e-01 3.01805019e-01 3.08874637... | [14.799138069152832, 0.8417012691497803] |
ceead40d-3d84-474d-8036-2b5b6cedb388 | pointclustering-unsupervised-point-cloud-pre | null | null | http://openaccess.thecvf.com//content/CVPR2023/html/Long_PointClustering_Unsupervised_Point_Cloud_Pre-Training_Using_Transformation_Invariance_in_Clustering_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Long_PointClustering_Unsupervised_Point_Cloud_Pre-Training_Using_Transformation_Invariance_in_Clustering_CVPR_2023_paper.pdf | PointClustering: Unsupervised Point Cloud Pre-Training Using Transformation Invariance in Clustering | Feature invariance under different data transformations, i.e., transformation invariance, can be regarded as a type of self-supervision for representation learning. In this paper, we present PointClustering, a new unsupervised representation learning scheme that leverages transformation invariance for point cloud p... | ['Tao Mei', 'Lusong Li', 'Zhaofan Qiu', 'Ting Yao', 'Fuchen Long'] | 2023-01-01 | null | null | null | cvpr-2023-1 | ['point-cloud-pre-training', 'deep-clustering', 'philosophy', 'deep-clustering'] | ['computer-vision', 'miscellaneous', 'miscellaneous', 'natural-language-processing'] | [-1.49326427e-02 1.37540996e-01 -5.14619648e-01 -7.31557786e-01
-7.61910617e-01 -7.22335994e-01 5.77674747e-01 3.05895954e-01
-1.05806932e-01 -1.09133795e-01 -7.30221672e-03 -2.69183159e-01
-2.34095857e-01 -8.56917500e-01 -1.23666608e+00 -7.06954181e-01
-8.40636566e-02 5.27059376e-01 2.56715357e-01 -1.52927473... | [7.9609527587890625, -3.535647392272949] |
b2110c2c-82ef-41e9-8d6d-cb6e80096c62 | unsupervised-video-domain-adaptation-a | 2208.07365 | null | https://arxiv.org/abs/2208.07365v2 | https://arxiv.org/pdf/2208.07365v2.pdf | Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement Perspective | Unsupervised video domain adaptation is a practical yet challenging task. In this work, for the first time, we tackle it from a disentanglement view. Our key idea is to handle the spatial and temporal domain divergence separately through disentanglement. Specifically, we consider the generation of cross-domain videos f... | ['Yi Ren', 'Jing Jiang', 'Zhiqiang Xu', 'Xiang Yin', 'Xinghua Qu', 'Lingdong Kong', 'Pengfei Wei'] | 2022-08-15 | null | null | null | null | ['video-domain-adapation'] | ['computer-vision'] | [ 2.50863343e-01 -2.82008886e-01 -1.61961243e-01 -9.69393030e-02
-8.19229543e-01 -7.20913112e-01 7.56310999e-01 -5.05743504e-01
-2.95444101e-01 7.93058932e-01 2.55438060e-01 8.20269585e-02
-7.50272572e-02 -3.37764174e-01 -6.68410003e-01 -1.04707778e+00
5.18121310e-02 1.37199640e-01 2.27400675e-01 -5.67627400... | [10.728326797485352, -0.01215418428182602] |
1c0cc511-2a6c-4ba6-a220-87dc75a94053 | mrfalign-protein-homology-detection-through | 1401.2668 | null | http://arxiv.org/abs/1401.2668v2 | http://arxiv.org/pdf/1401.2668v2.pdf | MRFalign: Protein Homology Detection through Alignment of Markov Random Fields | Sequence-based protein homology detection has been extensively studied and so
far the most sensitive method is based upon comparison of protein sequence
profiles, which are derived from multiple sequence alignment (MSA) of sequence
homologs in a protein family. A sequence profile is usually represented as a
position-sp... | ['Zhiyong Wang', 'Sheng Wang', 'Jianzhu Ma', 'Jinbo Xu'] | 2014-01-12 | null | null | null | null | ['multiple-sequence-alignment'] | ['medical'] | [ 6.02279186e-01 -2.18213499e-01 -3.13222498e-01 -3.76101166e-01
-6.76540971e-01 -4.65143532e-01 2.84526259e-01 5.85438907e-01
-4.03055668e-01 1.16293490e+00 -3.74799907e-01 -5.04775941e-01
-3.33494097e-02 -4.64451104e-01 -7.11172640e-01 -1.07148337e+00
-1.71872169e-01 5.47805309e-01 6.89897776e-01 -4.35087562... | [4.793907165527344, 5.29660177230835] |
8c6ed46e-06eb-4abf-96d2-ebbb668eee0e | meld-a-multimodal-multi-party-dataset-for | 1810.02508 | null | https://arxiv.org/abs/1810.02508v6 | https://arxiv.org/pdf/1810.02508v6.pdf | MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations | Emotion recognition in conversations is a challenging task that has recently gained popularity due to its potential applications. Until now, however, a large-scale multimodal multi-party emotional conversational database containing more than two speakers per dialogue was missing. Thus, we propose the Multimodal Emotion... | ['Soujanya Poria', 'Navonil Majumder', 'Erik Cambria', 'Devamanyu Hazarika', 'Rada Mihalcea', 'Gautam Naik'] | 2018-10-05 | meld-a-multimodal-multi-party-dataset-for-1 | https://aclanthology.org/P19-1050 | https://aclanthology.org/P19-1050.pdf | acl-2019-7 | ['emotion-recognition-in-conversation'] | ['natural-language-processing'] | [-1.56925768e-01 -1.50942847e-01 2.40873341e-02 -6.72877550e-01
-1.25556982e+00 -7.59462595e-01 6.54036462e-01 8.98959264e-02
-3.17247599e-01 6.23815298e-01 8.17933798e-01 2.89570987e-01
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-1.15418613e-01 1.94734991e-01 -5.70418119e-01 -4.43902254... | [13.093202590942383, 5.686499118804932] |
ce4ef6c3-1a01-49a7-a152-2b8a1ab7f446 | keys-to-better-image-inpainting-structure-and | 2208.03382 | null | https://arxiv.org/abs/2208.03382v2 | https://arxiv.org/pdf/2208.03382v2.pdf | Keys to Better Image Inpainting: Structure and Texture Go Hand in Hand | Deep image inpainting has made impressive progress with recent advances in image generation and processing algorithms. We claim that the performance of inpainting algorithms can be better judged by the generated structures and textures. Structures refer to the generated object boundary or novel geometric structures wit... | ['Humphrey Shi', 'Ning Yu', 'Yuqian Zhou', 'Jitesh Jain'] | 2022-08-05 | null | null | null | null | ['texture-synthesis'] | ['computer-vision'] | [ 4.72656876e-01 2.20697984e-01 7.92496949e-02 -8.88108462e-02
-6.30298555e-01 -1.55021876e-01 5.81929266e-01 -5.18693745e-01
1.54468521e-01 8.26655447e-01 3.39439541e-01 1.41003132e-01
1.92171067e-01 -1.13514447e+00 -1.14311659e+00 -6.67632699e-01
1.04213305e-01 1.64959267e-01 1.37004778e-01 -4.72798228... | [11.410807609558105, -1.0743569135665894] |
93255f72-8e04-499c-ae4e-d13811f642c9 | strong-baselines-for-parameter-efficient-few | 2304.01917 | null | https://arxiv.org/abs/2304.01917v1 | https://arxiv.org/pdf/2304.01917v1.pdf | Strong Baselines for Parameter Efficient Few-Shot Fine-tuning | Few-shot classification (FSC) entails learning novel classes given only a few examples per class after a pre-training (or meta-training) phase on a set of base classes. Recent works have shown that simply fine-tuning a pre-trained Vision Transformer (ViT) on new test classes is a strong approach for FSC. Fine-tuning Vi... | ['Soheil Feizi', 'Shell Xu Hu', 'Daniela Massiceti', 'Samyadeep Basu'] | 2023-04-04 | null | null | null | null | ['few-shot-image-classification'] | ['computer-vision'] | [ 5.58755934e-01 -3.90116870e-02 -1.18537366e-01 -4.62471217e-01
-9.76347268e-01 -4.80691552e-01 8.28726649e-01 -1.61062062e-01
-6.23893023e-01 4.54839647e-01 2.26219699e-01 -2.98959613e-01
-1.81494251e-01 -5.89521527e-01 -9.84346092e-01 -6.16304874e-01
2.36226216e-01 3.97748053e-01 4.99230146e-01 -3.59051168... | [9.876113891601562, 2.8590681552886963] |
2824fc42-65a2-4fc0-9a13-68b04b11a871 | self-attention-amortized-distributional | 2301.04791 | null | https://arxiv.org/abs/2301.04791v2 | https://arxiv.org/pdf/2301.04791v2.pdf | Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction | Max sliced Wasserstein (Max-SW) distance has been widely known as a solution for less discriminative projections of sliced Wasserstein (SW) distance. In applications that have various independent pairs of probability measures, amortized projection optimization is utilized to predict the ``max" projecting directions giv... | ['Nhat Ho', 'Dang Nguyen', 'Khai Nguyen'] | 2023-01-12 | null | null | null | null | ['point-cloud-reconstruction'] | ['computer-vision'] | [ 1.59973744e-02 -1.29012480e-01 7.57099465e-02 -6.19666636e-01
-1.01760745e+00 -4.27489758e-01 4.16147232e-01 -1.37801602e-01
-6.80140853e-01 6.47502422e-01 1.74138650e-01 -5.56234062e-01
-4.73185629e-01 -8.44377220e-01 -8.62002552e-01 -8.93500924e-01
1.28268555e-01 6.26831293e-01 2.99610049e-02 1.57274365... | [7.638640403747559, 4.116988182067871] |
c7c845c7-fbb2-49db-9f9a-e509d32a2902 | two-stream-consensus-network-for-weakly-1 | 2010.11594 | null | https://arxiv.org/abs/2010.11594v1 | https://arxiv.org/pdf/2010.11594v1.pdf | Two-Stream Consensus Network for Weakly-Supervised Temporal Action Localization | Weakly-supervised Temporal Action Localization (W-TAL) aims to classify and localize all action instances in an untrimmed video under only video-level supervision. However, without frame-level annotations, it is challenging for W-TAL methods to identify false positive action proposals and generate action proposals with... | ['Gang Hua', 'Junsong Yuan', 'Qilin Zhang', 'Wei Tang', 'Le Wang', 'Yuanhao Zhai'] | 2020-10-22 | two-stream-consensus-network-for-weakly | https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/4602_ECCV_2020_paper.php | https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123510035.pdf | eccv-2020-8 | ['weakly-supervised-action-localization', 'weakly-supervised-temporal-action'] | ['computer-vision', 'computer-vision'] | [ 7.30902791e-01 1.31276190e-01 -7.33840346e-01 -3.24041009e-01
-8.15810978e-01 -5.44009916e-02 4.96474892e-01 -2.50762969e-01
-5.68084240e-01 7.67811179e-01 3.83765519e-01 1.61823004e-01
2.24657223e-01 -2.14323655e-01 -6.97853386e-01 -6.97107315e-01
3.01437220e-03 1.41642243e-01 8.87264073e-01 1.85481012... | [8.449689865112305, 0.5860508680343628] |
37497341-a600-48ae-962f-c6f3d7220184 | unsupervised-domain-adaptation-through-shape | 2207.02529 | null | https://arxiv.org/abs/2207.02529v1 | https://arxiv.org/pdf/2207.02529v1.pdf | Unsupervised Domain Adaptation through Shape Modeling for Medical Image Segmentation | Shape information is a strong and valuable prior in segmenting organs in medical images. However, most current deep learning based segmentation algorithms have not taken shape information into consideration, which can lead to bias towards texture. We aim at modeling shape explicitly and using it to help medical image s... | ['Yongyi Lu', 'Alan Yuille', 'Wei Shen', 'Yan Wang', 'Zongwei Zhou', 'Fengze Liu', 'Yuan YAO'] | 2022-07-06 | null | null | null | null | ['pancreas-segmentation'] | ['medical'] | [ 1.09940499e-01 4.18521941e-01 -4.61474210e-02 -6.81807578e-01
-8.99306059e-01 -6.69834375e-01 2.99977362e-01 4.15945947e-01
-4.39470679e-01 3.35232973e-01 2.27064908e-01 -1.77693844e-01
7.06662834e-02 -7.25964129e-01 -9.20389235e-01 -7.95928895e-01
1.70100942e-01 9.63089943e-01 1.38690054e-01 1.96738303... | [14.5833158493042, -2.4046952724456787] |
c70d70c8-517a-4ee4-b4bc-ac5a61d797f2 | future-video-synthesis-with-object-motion | 2004.00542 | null | https://arxiv.org/abs/2004.00542v2 | https://arxiv.org/pdf/2004.00542v2.pdf | Future Video Synthesis with Object Motion Prediction | We present an approach to predict future video frames given a sequence of continuous video frames in the past. Instead of synthesizing images directly, our approach is designed to understand the complex scene dynamics by decoupling the background scene and moving objects. The appearance of the scene components in the f... | ['Jaesik Park', 'Rongrong Gao', 'Qifeng Chen', 'Yue Wu'] | 2020-04-01 | future-video-synthesis-with-object-motion-1 | http://openaccess.thecvf.com/content_CVPR_2020/html/Wu_Future_Video_Synthesis_With_Object_Motion_Prediction_CVPR_2020_paper.html | http://openaccess.thecvf.com/content_CVPR_2020/papers/Wu_Future_Video_Synthesis_With_Object_Motion_Prediction_CVPR_2020_paper.pdf | cvpr-2020-6 | ['predict-future-video-frames'] | ['computer-vision'] | [ 1.60222903e-01 -3.08955330e-02 3.15282762e-01 -2.99011320e-01
-8.17384347e-02 -4.38087404e-01 8.06965530e-01 -6.34318352e-01
-6.32054284e-02 5.41384339e-01 6.78437129e-02 2.64409035e-02
4.02152210e-01 -4.86262172e-01 -8.35809231e-01 -6.41571820e-01
-2.14373901e-01 -2.71377042e-02 8.61861050e-01 6.85705021... | [9.631426811218262, -2.053640842437744] |
ae9ecaf3-57f5-456b-9d5a-b8074564d8d5 | contrastnet-a-contrastive-learning-framework | 2305.09269 | null | https://arxiv.org/abs/2305.09269v1 | https://arxiv.org/pdf/2305.09269v1.pdf | ContrastNet: A Contrastive Learning Framework for Few-Shot Text Classification | Few-shot text classification has recently been promoted by the meta-learning paradigm which aims to identify target classes with knowledge transferred from source classes with sets of small tasks named episodes. Despite their success, existing works building their meta-learner based on Prototypical Networks are unsatis... | ['Jie Xu', 'Yongyi Mao', 'Richong Zhang', 'Junfan Chen'] | 2023-05-16 | null | null | null | null | ['few-shot-text-classification'] | ['natural-language-processing'] | [ 4.91419196e-01 -7.23061990e-03 -3.57562751e-01 -5.19785166e-01
-5.91547668e-01 1.89081728e-01 8.66632402e-01 6.84479415e-01
-4.69407618e-01 6.87194586e-01 1.78585947e-01 2.84626812e-01
-2.42584303e-01 -7.76987195e-01 -2.47764558e-01 -6.35281563e-01
4.17237878e-01 5.24386525e-01 3.72089952e-01 -2.43825123... | [10.274604797363281, 3.637838125228882] |
a5865a83-7d7b-40e9-81f3-93f022f18f15 | a-three-player-gan-generating-hard-samples-to | 1903.03496 | null | http://arxiv.org/abs/1903.03496v1 | http://arxiv.org/pdf/1903.03496v1.pdf | A Three-Player GAN: Generating Hard Samples To Improve Classification Networks | We propose a Three-Player Generative Adversarial Network to improve
classification networks. In addition to the game played between the
discriminator and generator, a competition is introduced between the generator
and the classifier. The generator's objective is to synthesize samples that are
both realistic and hard t... | ['Luc van Gool', 'Bert de Brabandere', 'Simon Vandenhende', 'Davy Neven'] | 2019-03-08 | null | null | null | null | ['traffic-sign-recognition'] | ['computer-vision'] | [ 5.79676867e-01 5.53167462e-01 -6.86581954e-02 -2.88441449e-01
-9.56872761e-01 -7.55375087e-01 6.77276969e-01 -6.81916654e-01
-2.74416327e-01 1.03456473e+00 -2.47863173e-01 -3.83743286e-01
4.28476870e-01 -9.31509912e-01 -7.91475773e-01 -8.18377316e-01
1.87022284e-01 5.98407626e-01 7.98818991e-02 -1.66459814... | [11.47636604309082, -0.2606852948665619] |
dcc3ad52-9e5d-43f0-9a71-f26c81c1ab41 | uncertainty-in-real-time-semantic | 2301.01201 | null | https://arxiv.org/abs/2301.01201v3 | https://arxiv.org/pdf/2301.01201v3.pdf | Uncertainty in Real-Time Semantic Segmentation on Embedded Systems | Application for semantic segmentation models in areas such as autonomous vehicles and human computer interaction require real-time predictive capabilities. The challenges of addressing real-time application is amplified by the need to operate on resource constrained hardware. Whilst development of real-time methods for... | ['Clinton Fookes', 'Ethan Goan'] | 2022-12-20 | null | null | null | null | ['real-time-semantic-segmentation'] | ['computer-vision'] | [ 2.57836401e-01 7.20016718e-01 -3.44924122e-01 -9.75639582e-01
-9.48056996e-01 -2.29862273e-01 7.14814484e-01 1.68012619e-01
-4.22129035e-01 7.37047791e-01 -4.88940358e-01 -4.77338165e-01
-1.38642743e-01 -6.05727732e-01 -7.99316525e-01 -3.47128958e-01
-2.21336395e-01 8.46947312e-01 7.14053571e-01 2.68288851... | [6.966721534729004, -0.34941309690475464] |
b6335061-cde3-4e46-82a5-c461edf06650 | dataset-reproducibility-and-ir-methods-in | null | null | https://aclanthology.org/2020.lrec-1.218 | https://aclanthology.org/2020.lrec-1.218.pdf | Dataset Reproducibility and IR Methods in Timeline Summarization | Timeline summarization (TLS) generates a dated overview of real-world events based on event-specific corpora. The two standard datasets for this task were collected using Google searches for news reports on given events. Not only is this IR method not reproducible at different search times, it also uses components (suc... | ['Leo Born', 'Katja Markert', 'Maximilian Bacher'] | 2020-05-01 | null | null | null | lrec-2020-5 | ['timeline-summarization'] | ['natural-language-processing'] | [ 1.70278654e-01 -1.46979373e-02 -2.70899266e-01 -3.47044080e-01
-1.60599256e+00 -9.00892496e-01 1.09209812e+00 8.28653932e-01
-8.01935673e-01 7.74778485e-01 9.32020962e-01 -2.20414147e-01
-1.56949490e-01 -5.21370411e-01 -5.99807680e-01 -1.85784489e-01
3.76641542e-01 7.32046247e-01 6.72207475e-01 -5.99193156... | [12.397475242614746, 9.420120239257812] |
d51880ed-be7e-4775-bb4e-5e487df648c3 | space-time-separable-graph-convolutional-1 | 2110.04573 | null | https://arxiv.org/abs/2110.04573v1 | https://arxiv.org/pdf/2110.04573v1.pdf | Space-Time-Separable Graph Convolutional Network for Pose Forecasting | Human pose forecasting is a complex structured-data sequence-modelling task, which has received increasing attention, also due to numerous potential applications. Research has mainly addressed the temporal dimension as time series and the interaction of human body joints with a kinematic tree or by a graph. This has de... | ['Fabio Galasso', 'Luca Franco', 'Alessio Sampieri', 'Theodoros Sofianos'] | 2021-10-09 | space-time-separable-graph-convolutional | http://openaccess.thecvf.com//content/ICCV2021/html/Sofianos_Space-Time-Separable_Graph_Convolutional_Network_for_Pose_Forecasting_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Sofianos_Space-Time-Separable_Graph_Convolutional_Network_for_Pose_Forecasting_ICCV_2021_paper.pdf | iccv-2021-1 | ['human-pose-forecasting'] | ['computer-vision'] | [-2.45884255e-01 1.49307072e-01 -6.49958327e-02 1.28624411e-02
-1.95226237e-01 -4.10180598e-01 5.74634314e-01 -1.37042150e-01
-3.68453681e-01 4.22247529e-01 2.45673865e-01 -1.25872910e-01
-3.94503027e-01 -4.46810633e-01 -8.12050641e-01 -4.99634832e-01
-8.70500624e-01 5.87424695e-01 2.81618595e-01 -5.50963700... | [7.287280082702637, -0.3189002275466919] |
3254edf1-7bb4-4efa-9f8b-7cb461598345 | towards-moocs-for-lip-reading-using-synthetic | 2208.09796 | null | https://arxiv.org/abs/2208.09796v2 | https://arxiv.org/pdf/2208.09796v2.pdf | Towards MOOCs for Lipreading: Using Synthetic Talking Heads to Train Humans in Lipreading at Scale | Many people with some form of hearing loss consider lipreading as their primary mode of day-to-day communication. However, finding resources to learn or improve one's lipreading skills can be challenging. This is further exacerbated in the COVID19 pandemic due to restrictions on direct interactions with peers and speec... | ['C. V Jawahar', 'Vinay Namboodiri', 'Rudrabha Mukhopadhyay', 'Bipasha Sen', 'Aditya Agarwal'] | 2022-08-21 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [-1.07815817e-01 3.59288365e-01 -1.93196699e-01 -2.15162393e-02
-1.09024537e+00 -3.72335523e-01 4.39105183e-01 -3.76016885e-01
-5.03055632e-01 8.27870488e-01 8.11971486e-01 -2.97819942e-01
1.09556213e-01 -2.62654454e-01 -5.35344005e-01 -8.82911608e-02
6.02341950e-01 5.65204024e-01 2.17942163e-01 -4.69440997... | [14.276481628417969, 4.927881717681885] |
32ce5dd8-59c7-4db3-9231-2b5041186bc6 | graphfc-customs-fraud-detection-with-label | 2305.11377 | null | https://arxiv.org/abs/2305.11377v1 | https://arxiv.org/pdf/2305.11377v1.pdf | GraphFC: Customs Fraud Detection with Label Scarcity | Custom officials across the world encounter huge volumes of transactions. With increased connectivity and globalization, the customs transactions continue to grow every year. Associated with customs transactions is the customs fraud - the intentional manipulation of goods declarations to avoid the taxes and duties. Wit... | ['Shou-De Lin', 'Meeyoug Cha', 'Cheng-Te Li', 'Yu-Che Tsai', 'Karandeep Singh'] | 2023-05-19 | null | null | null | null | ['fraud-detection'] | ['miscellaneous'] | [-8.61492082e-02 -1.95723642e-02 -3.78527373e-01 -4.37163830e-01
-3.45302373e-01 -4.73925322e-01 3.03825289e-01 5.79296291e-01
-4.90286380e-01 7.24121451e-01 -3.32956284e-01 -8.67305279e-01
-2.12321430e-02 -1.10847485e+00 -6.93248391e-01 -1.36810482e-01
-1.28976479e-01 5.60368061e-01 -1.43911034e-01 -5.57508647... | [7.306859970092773, 5.828357219696045] |
3737d1d7-e232-488f-a4c2-202d525bbe9f | effective-conditioned-and-composed-image | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Baldrati_Effective_Conditioned_and_Composed_Image_Retrieval_Combining_CLIP-Based_Features_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Baldrati_Effective_Conditioned_and_Composed_Image_Retrieval_Combining_CLIP-Based_Features_CVPR_2022_paper.pdf | Effective Conditioned and Composed Image Retrieval Combining CLIP-Based Features | Conditioned and composed image retrieval extend CBIR systems by combining a query image with an additional text that expresses the intent of the user, describing additional requests w.r.t. the visual content of the query image. This type of search is interesting for e-commerce applications, e.g. to develop interact... | ['Alberto del Bimbo', 'Tiberio Uricchio', 'Marco Bertini', 'Alberto Baldrati'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['composed-image-retrieval'] | ['computer-vision'] | [ 2.52593547e-01 -4.19871449e-01 -3.31557781e-01 -3.64250153e-01
-9.02395010e-01 -9.90887284e-01 6.84921741e-01 1.51507765e-01
-4.99068767e-01 6.28050268e-02 -5.09264283e-02 -6.09071255e-02
-2.24482387e-01 -4.32838023e-01 -5.01652658e-01 -6.54951632e-01
1.88865528e-01 9.20873642e-01 1.55934647e-01 -9.18264091... | [10.82569694519043, 1.1655329465866089] |
af06f050-2426-4d54-b52e-c5cd8a2d734e | colt5-faster-long-range-transformers-with | 2303.09752 | null | https://arxiv.org/abs/2303.09752v2 | https://arxiv.org/pdf/2303.09752v2.pdf | CoLT5: Faster Long-Range Transformers with Conditional Computation | Many natural language processing tasks benefit from long inputs, but processing long documents with Transformers is expensive -- not only due to quadratic attention complexity but also from applying feedforward and projection layers to every token. However, not all tokens are equally important, especially for longer do... | ['Sumit Sanghai', 'Yun-Hsuan Sung', 'Yi Tay', 'James Lee-Thorp', 'Mandy Guo', 'David Uthus', 'Yury Zemlyanskiy', 'Siddhartha Brahma', 'Santiago Ontañón', 'Michiel de Jong', 'Tao Lei', 'Joshua Ainslie'] | 2023-03-17 | null | null | null | null | ['long-range-modeling'] | ['natural-language-processing'] | [ 9.36911926e-02 2.38107994e-01 -7.61854574e-02 -3.57129693e-01
-9.25258994e-01 -7.16879129e-01 6.39262497e-01 4.21539634e-01
-7.28035510e-01 6.66987062e-01 5.34844339e-01 -8.71917725e-01
1.23198368e-01 -1.03338766e+00 -9.47620928e-01 -2.52253681e-01
-2.85729412e-02 8.58776748e-01 1.10114798e-01 -8.07525143... | [10.90000057220459, 7.669509410858154] |
7c2a2dad-eec0-49a7-b8c4-134fda82530b | neural-graph-matching-networks-for-fewshot-3d | null | null | http://openaccess.thecvf.com/content_ECCV_2018/html/Michelle_Guo_Neural_Graph_Matching_ECCV_2018_paper.html | http://openaccess.thecvf.com/content_ECCV_2018/papers/Michelle_Guo_Neural_Graph_Matching_ECCV_2018_paper.pdf | Neural Graph Matching Networks for Fewshot 3D Action Recognition | We propose Neural Graph Matching (NGM) Networks, a novel framework that can learn to recognize a previous unseen 3D action class with only a few examples. We achieve this by leveraging the inherent structure of 3D data through a graphical representation. This allows us to modularize our model and lead to strong data-ef... | ['Li Fei-Fei', 'De-An Huang', 'Edward Chou', 'Serena Yeung', 'Michelle Guo', 'Shuran Song'] | 2018-09-01 | null | null | null | eccv-2018-9 | ['3d-human-action-recognition'] | ['computer-vision'] | [ 4.66152310e-01 2.74768829e-01 -4.07577187e-01 -4.54581827e-01
-8.06110501e-01 -3.76907229e-01 7.88314939e-01 -1.80095285e-01
-5.71957901e-02 8.74423459e-02 4.88149792e-01 -9.68772992e-02
8.69558230e-02 -7.43472159e-01 -8.12208593e-01 -2.99143583e-01
-1.20471418e-01 5.29434621e-01 2.35316530e-01 6.52437508... | [8.692953109741211, 0.9577859044075012] |
15c253e8-1ff2-48a0-952e-25bbff1c536b | refining-image-categorization-by-exploiting | 1703.05451 | null | http://arxiv.org/abs/1703.05451v1 | http://arxiv.org/pdf/1703.05451v1.pdf | Refining Image Categorization by Exploiting Web Images and General Corpus | Studies show that refining real-world categories into semantic subcategories
contributes to better image modeling and classification. Previous image
sub-categorization work relying on labeled images and WordNet's hierarchy is
not only labor-intensive, but also restricted to classify images into NOUN
subcategories. To t... | ['Xian-Sheng Hua', 'Wankou Yang', 'Yazhou Yao', 'Fumin Shen', 'Zhenmin Tang', 'Jian Zhang'] | 2017-03-16 | null | null | null | null | ['image-categorization'] | ['computer-vision'] | [ 5.74516177e-01 2.58109383e-02 -4.32141095e-01 -4.71999049e-01
-9.64338899e-01 -5.70924163e-01 4.60468799e-01 2.50658363e-01
-5.82358539e-01 4.95241731e-01 6.94827884e-02 6.48515895e-02
-3.22810143e-01 -8.27607274e-01 -4.65671390e-01 -7.01248765e-01
5.83453119e-01 4.65065122e-01 5.09436905e-01 -4.13561463... | [9.463478088378906, 2.684633731842041] |
51167a46-3b67-451e-9e7f-250454dfd20b | abdomenct-1k-is-abdominal-organ-segmentation | 2010.14808 | null | https://arxiv.org/abs/2010.14808v2 | https://arxiv.org/pdf/2010.14808v2.pdf | AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem? | With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have achieved comparable results with inter-rater variability on many benchmark datasets. However, most of the existing abdominal datasets only contain si... | ['Yuhui Li', 'Yunpeng Wang', 'Shangqing Liu', 'Qi Zhang', 'Shucheng Cao', 'Xin Liu', 'Qiyuan Wang', 'Congcong Wang', 'Xingle An', 'Yichi Zhang', 'Cheng Ge', 'Xiaoping Yang', 'Jian He', 'Cheng Zhu', 'Song Gu', 'Yao Zhang', 'Jun Ma'] | 2020-10-28 | null | null | null | null | ['pancreas-segmentation'] | ['medical'] | [-1.66006386e-01 8.62131303e-04 -6.71565413e-01 -3.80867511e-01
-9.53097582e-01 -7.29015529e-01 9.63416398e-02 4.61222142e-01
-2.73864925e-01 5.47727764e-01 1.89686641e-01 -5.12873054e-01
-7.59435296e-02 -4.01809990e-01 -5.85473716e-01 -8.13471735e-01
-3.23930949e-01 6.96719885e-01 1.46866128e-01 2.05282852... | [14.674345970153809, -2.4450056552886963] |
3737eb6e-4cc9-42e4-8c5c-59c0b49652d7 | docred-a-large-scale-document-level-relation | 1906.06127 | null | https://arxiv.org/abs/1906.06127v3 | https://arxiv.org/pdf/1906.06127v3.pdf | DocRED: A Large-Scale Document-Level Relation Extraction Dataset | Multiple entities in a document generally exhibit complex inter-sentence relations, and cannot be well handled by existing relation extraction (RE) methods that typically focus on extracting intra-sentence relations for single entity pairs. In order to accelerate the research on document-level RE, we introduce DocRED, ... | ['Zheng-Hao Liu', 'Jie zhou', 'Yuan Yao', 'Deming Ye', 'Peng Li', 'Yankai Lin', 'Maosong Sun', 'Zhiyuan Liu', 'Xu Han', 'Lixin Huang'] | 2019-06-14 | docred-a-large-scale-document-level-relation-1 | https://aclanthology.org/P19-1074 | https://aclanthology.org/P19-1074.pdf | acl-2019-7 | ['document-level-relation-extraction'] | ['natural-language-processing'] | [-1.56081870e-01 3.61925870e-01 -4.29730564e-01 -3.65837932e-01
-6.12460196e-01 -6.39010131e-01 7.38004506e-01 3.99413347e-01
-3.62743020e-01 9.59820688e-01 6.26461685e-01 -1.50756478e-01
-2.84952730e-01 -7.83645570e-01 -3.99664074e-01 -1.52714849e-01
-1.62580788e-01 4.89350766e-01 4.08179134e-01 -5.24059713... | [9.312660217285156, 8.682700157165527] |
b86da363-d8b8-4229-83d4-67bf3a3a8767 | measuring-few-shot-extrapolation-with-program | null | null | https://openreview.net/forum?id=UZTzGNV_64a | https://openreview.net/pdf?id=UZTzGNV_64a | Measuring few-shot extrapolation with program induction | Neural networks are capable of learning complex functions, but still have problems generalizing from few examples and beyond their training distribution. Meta-learning provides a paradigm to train networks to learn from few examples, but it has been shown that its most popular benchmarks require very limited generaliza... | ['Leslie Pack Kaelbling', 'Tomas Perez', 'Joshua B. Tenenbaum', 'Javier Lopez-Contreras', 'Ferran Alet'] | 2020-10-13 | null | null | null | neurips-workshop-cap-2020-12 | ['program-induction'] | ['computer-code'] | [ 1.18570246e-01 -1.02943502e-01 -6.84464872e-01 -4.91132081e-01
-7.45639443e-01 -4.35765982e-01 4.82678771e-01 3.19238901e-01
-5.12066960e-01 4.06563491e-01 2.95469281e-03 -6.14607096e-01
8.44803303e-02 -7.80856133e-01 -1.05185449e+00 -2.44755000e-01
-4.07507390e-01 4.64421839e-01 2.16572806e-01 -4.67274427... | [8.181859016418457, 7.513160705566406] |
ed2f7665-beea-4e55-b177-b35e7129700b | augmented-policy-gradient-methods-for | null | null | https://openreview.net/forum?id=S1gN8yrYwB | https://openreview.net/pdf?id=S1gN8yrYwB | AUGMENTED POLICY GRADIENT METHODS FOR EFFICIENT REINFORCEMENT LEARNING | We propose a new mixture of model-based and model-free reinforcement learning
(RL) algorithms that combines the strengths of both RL methods. Our goal is to reduce the sample complexity of model-free approaches utilizing fictitious trajectory
rollouts performed on a learned dynamics model to improve the data efficiency... | ['Frank Hees', 'Rene Vossen', 'Christoph Henke', 'Gregor Roering', 'Kai Lagemann'] | 2019-09-25 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-9.15634483e-02 2.41883680e-01 -3.22113484e-01 6.02157414e-02
-3.55958015e-01 -3.88526946e-01 7.74836004e-01 1.58906728e-01
-1.01805747e+00 1.45415318e+00 6.58578891e-03 -8.32672566e-02
-3.02557081e-01 -8.83333504e-01 -8.83358896e-01 -8.29761744e-01
-1.72450274e-01 7.26672411e-01 5.16506135e-01 -4.72562104... | [4.29914665222168, 2.1455538272857666] |
e44ce7b9-a4b3-4716-94c4-9c6e7fdd8f40 | guided-stereo-matching-1 | 1905.10107 | null | https://arxiv.org/abs/1905.10107v1 | https://arxiv.org/pdf/1905.10107v1.pdf | Guided Stereo Matching | Stereo is a prominent technique to infer dense depth maps from images, and deep learning further pushed forward the state-of-the-art, making end-to-end architectures unrivaled when enough data is available for training. However, deep networks suffer from significant drops in accuracy when dealing with new environments.... | ['Davide Pallotti', 'Stefano Mattoccia', 'Matteo Poggi', 'Fabio Tosi'] | 2019-05-24 | guided-stereo-matching | http://openaccess.thecvf.com/content_CVPR_2019/html/Poggi_Guided_Stereo_Matching_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Poggi_Guided_Stereo_Matching_CVPR_2019_paper.pdf | cvpr-2019-6 | ['stereo-matching'] | ['computer-vision'] | [ 5.41427970e-01 1.00774616e-01 9.10657421e-02 -3.97592962e-01
-7.05370843e-01 -3.31062138e-01 7.05047667e-01 9.63387415e-02
-6.40218496e-01 8.54782820e-01 1.06651828e-01 1.77497208e-01
-1.32589579e-01 -8.81935656e-01 -9.87426996e-01 -6.69939399e-01
-1.60704646e-02 5.00667989e-01 5.19842923e-01 -3.30420524... | [8.767915725708008, -2.3717997074127197] |
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