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05ebacde-4da0-4c32-be66-a8526c2e8ca8 | gan-supervised-dense-visual-alignment | 2112.05143 | null | https://arxiv.org/abs/2112.05143v2 | https://arxiv.org/pdf/2112.05143v2.pdf | GAN-Supervised Dense Visual Alignment | We propose GAN-Supervised Learning, a framework for learning discriminative models and their GAN-generated training data jointly end-to-end. We apply our framework to the dense visual alignment problem. Inspired by the classic Congealing method, our GANgealing algorithm trains a Spatial Transformer to map random sample... | ['Alexei A. Efros', 'Eli Shechtman', 'Antonio Torralba', 'Richard Zhang', 'Jun-Yan Zhu', 'William Peebles'] | 2021-12-09 | null | http://openaccess.thecvf.com//content/CVPR2022/html/Peebles_GAN-Supervised_Dense_Visual_Alignment_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Peebles_GAN-Supervised_Dense_Visual_Alignment_CVPR_2022_paper.pdf | cvpr-2022-1 | ['dense-pixel-correspondence-estimation'] | ['computer-vision'] | [ 6.54285491e-01 5.57049453e-01 3.12506072e-02 -6.47574723e-01
-1.34492707e+00 -6.54781759e-01 9.28991437e-01 -4.87284333e-01
-8.36092457e-02 7.58998811e-01 4.54697073e-01 -2.33957116e-02
2.02097774e-01 -6.13153994e-01 -1.14871085e+00 -5.62367678e-01
3.42321187e-01 1.07043386e+00 -2.37579986e-01 -2.21259072... | [11.640295028686523, -0.44223713874816895] |
8241716a-ba1b-415c-a56e-a234525f5c7f | generating-multiple-diverse-responses-for | 1811.05696 | null | http://arxiv.org/abs/1811.05696v3 | http://arxiv.org/pdf/1811.05696v3.pdf | Generating Multiple Diverse Responses for Short-Text Conversation | Neural generative models have become popular and achieved promising
performance on short-text conversation tasks. They are generally trained to
build a 1-to-1 mapping from the input post to its output response. However, a
given post is often associated with multiple replies simultaneously in real
applications. Previous... | ['Xiaojiang Liu', 'Jun Gao', 'Shuming Shi', 'Junhui Li', 'Wei Bi'] | 2018-11-14 | null | null | null | null | ['short-text-conversation'] | ['natural-language-processing'] | [ 3.06097895e-01 6.21646494e-02 9.32704657e-03 -8.14643562e-01
-1.12532687e+00 -3.39340001e-01 7.88130879e-01 -1.82984725e-01
-8.86139870e-02 1.01981235e+00 6.85421109e-01 1.87349662e-01
2.01614290e-01 -8.29958737e-01 -3.73874158e-01 -5.99339664e-01
7.14730382e-01 9.95125473e-01 1.39232688e-02 -6.07132912... | [12.56264877319336, 8.335223197937012] |
24d2a096-9f92-44d3-8646-b1a491914cbe | developing-a-multi-variate-prediction-model | 2209.03727 | null | https://arxiv.org/abs/2209.03727v1 | https://arxiv.org/pdf/2209.03727v1.pdf | Developing a multi-variate prediction model for the detection of COVID-19 from Crowd-sourced Respiratory Voice Data | COVID-19 has affected more than 223 countries worldwide. There is a pressing need for non invasive, low costs and highly scalable solutions to detect COVID-19, especially in low-resource countries where PCR testing is not ubiquitously available. Our aim is to develop a deep learning model identifying COVID-19 using voi... | ['Visara Urovi', 'Sami O. Simmons', 'Wafaa Aljbawi'] | 2022-09-08 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [-2.08845418e-02 -5.35786569e-01 1.94079146e-01 9.38498750e-02
-8.48528385e-01 -5.52619636e-01 1.49401918e-01 3.53993297e-01
-6.76074266e-01 8.60389769e-01 1.27383143e-01 -3.88665706e-01
-3.75617057e-01 -5.07187307e-01 -2.06591696e-01 -5.31065643e-01
-2.10130617e-01 7.46653438e-01 -2.22004071e-01 7.45805204... | [14.45466136932373, 3.9248528480529785] |
150ea2f6-62f3-4edf-99bd-fa0ca23db32d | a-simple-but-powerful-graph-encoder-for | null | null | https://openreview.net/forum?id=TBimhW-8Fk6 | https://openreview.net/pdf?id=TBimhW-8Fk6 | A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion | While knowledge graphs contain rich semantic knowledge about various entities and the relational information among them, temporal knowledge graphs (TKGs) describe and model the interactions of the entities over time. In this context, automatic temporal knowledge graph completion (TKGC) has gained great interest. Recent... | ['Anonymous'] | 2022-01-16 | null | null | null | acl-arr-january-2022-1 | ['temporal-knowledge-graph-completion'] | ['knowledge-base'] | [-3.30581158e-01 1.20428741e-01 -6.99650109e-01 -1.36074916e-01
-3.60417217e-01 -4.80302453e-01 7.19324529e-01 2.05367789e-01
-4.72156584e-01 5.17186046e-01 3.69832873e-01 -1.81444988e-01
-2.45334417e-01 -9.64403272e-01 -8.30569744e-01 -4.74676400e-01
-3.31301779e-01 3.22798103e-01 5.34202993e-01 -3.06170374... | [8.617423057556152, 7.917392730712891] |
3fe3df91-2a33-4452-b41f-9a7d7911c128 | winning-solution-for-the-cvpr2023-visual | 2306.09067 | null | https://arxiv.org/abs/2306.09067v1 | https://arxiv.org/pdf/2306.09067v1.pdf | Winning Solution for the CVPR2023 Visual Anomaly and Novelty Detection Challenge: Multimodal Prompting for Data-centric Anomaly Detection | This technical report introduces the winning solution of the team \textit{Segment Any Anomaly} for the CVPR2023 Visual Anomaly and Novelty Detection (VAND) challenge. Going beyond uni-modal prompt, \textit{e.g.}, language prompt, we present a novel framework, \textit{i.e.}, Segment Any Anomaly + (SAA$+$), for zero-shot... | ['Weiming Shen', 'Liang Gao', 'Yuqi Cheng', 'Chen Sun', 'Xiaohao Xu', 'Yunkang Cao'] | 2023-06-15 | null | null | null | null | ['anomaly-detection'] | ['methodology'] | [ 1.50410146e-01 3.67854722e-02 2.54236817e-01 -3.14584583e-01
-1.20634389e+00 -5.66379368e-01 3.94691020e-01 2.68475682e-01
-1.73535854e-01 -1.29041210e-01 -2.57469058e-01 -3.50888044e-01
-2.22786590e-02 -2.89979577e-01 -8.59267950e-01 -4.90626633e-01
6.68985322e-02 2.91075170e-01 3.79034251e-01 -3.27593058... | [7.837815761566162, 1.6641185283660889] |
2c7a11fd-50ae-4cef-80eb-a510be099bc2 | convolutional-graph-auto-encoder-a-deep | 1809.03538 | null | http://arxiv.org/abs/1809.03538v1 | http://arxiv.org/pdf/1809.03538v1.pdf | Convolutional Graph Auto-encoder: A Deep Generative Neural Architecture for Probabilistic Spatio-temporal Solar Irradiance Forecasting | Machine Learning on graph-structured data is an important and omnipresent
task for a vast variety of applications including anomaly detection and dynamic
network analysis. In this paper, a deep generative model is introduced to
capture continuous probability densities corresponding to the nodes of an
arbitrary graph. I... | ['Jianhui Wang', 'Saeed Mohammadi', 'Mohammad Khodayar', 'Mahdi Khodayar', 'Guangyi Liu'] | 2018-09-10 | null | null | null | null | ['solar-irradiance-forecasting'] | ['time-series'] | [ 6.07054494e-02 2.45795641e-02 2.22572237e-01 -1.75914913e-01
-3.54752034e-01 -4.02463645e-01 7.34995425e-01 1.17243998e-01
3.90774906e-01 7.43889153e-01 2.03039691e-01 -3.78472537e-01
-6.41157985e-01 -1.36945736e+00 -8.96484137e-01 -1.14859629e+00
-1.02135003e-01 3.22161347e-01 -3.53683889e-01 3.79046276... | [6.72052001953125, 2.952145576477051] |
0e1cce4e-3cee-4a6d-84cd-980e880fa433 | self-supervised-learning-of-motion-capture | 1712.01337 | null | http://arxiv.org/abs/1712.01337v1 | http://arxiv.org/pdf/1712.01337v1.pdf | Self-supervised Learning of Motion Capture | Current state-of-the-art solutions for motion capture from a single camera
are optimization driven: they optimize the parameters of a 3D human model so
that its re-projection matches measurements in the video (e.g. person
segmentation, optical flow, keypoint detections etc.). Optimization models are
susceptible to loca... | ['Hsiao-Wei Tung', 'Hsiao-Yu Fish Tung', 'Ersin Yumer', 'Katerina Fragkiadaki'] | 2017-12-04 | self-supervised-learning-of-motion-capture-1 | http://papers.nips.cc/paper/7108-self-supervised-learning-of-motion-capture | http://papers.nips.cc/paper/7108-self-supervised-learning-of-motion-capture.pdf | neurips-2017-12 | ['3d-human-reconstruction', 'weakly-supervised-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [ 2.41049185e-01 1.13849584e-02 7.52022117e-02 -1.99656636e-01
-6.30167663e-01 -5.66653848e-01 5.05879581e-01 -3.69706452e-01
-6.17926061e-01 5.14353096e-01 1.04125015e-01 1.37298256e-01
3.11275482e-01 -5.05532205e-01 -9.50685978e-01 -4.13758039e-01
1.00446105e-01 9.56053913e-01 5.31772316e-01 -6.05637915... | [7.144565582275391, -1.0049095153808594] |
d1320e11-24e5-4249-b2b2-157adc16786c | adaptive-policy-learning-to-additional-tasks | 2305.15193 | null | https://arxiv.org/abs/2305.15193v1 | https://arxiv.org/pdf/2305.15193v1.pdf | Adaptive Policy Learning to Additional Tasks | This paper develops a policy learning method for tuning a pre-trained policy to adapt to additional tasks without altering the original task. A method named Adaptive Policy Gradient (APG) is proposed in this paper, which combines Bellman's principle of optimality with the policy gradient approach to improve the converg... | ['Shaoshuai Mou', 'Tianyu Zhou', 'Zihao Liang', 'Zehui Lu', 'Wenjian Hao'] | 2023-05-24 | null | null | null | null | ['policy-gradient-methods'] | ['methodology'] | [-3.17199171e-01 7.51170143e-03 -3.31737876e-01 -1.74699739e-01
-4.83875602e-01 -4.27130163e-01 2.79641122e-01 -5.49051240e-02
-9.99814510e-01 1.47590673e+00 -3.70681763e-01 -8.72642159e-01
-4.41067994e-01 -3.47766489e-01 -7.76772141e-01 -8.02289188e-01
-1.81615725e-01 2.79865295e-01 2.73264702e-02 -2.42848456... | [4.204388618469238, 2.488438844680786] |
89d95b00-fa0d-42bc-bba3-1d3744b59ec8 | hidden-gems-4d-radar-scene-flow-learning | 2303.00462 | null | https://arxiv.org/abs/2303.00462v3 | https://arxiv.org/pdf/2303.00462v3.pdf | Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision | This work proposes a novel approach to 4D radar-based scene flow estimation via cross-modal learning. Our approach is motivated by the co-located sensing redundancy in modern autonomous vehicles. Such redundancy implicitly provides various forms of supervision cues to the radar scene flow estimation. Specifically, we i... | ['Chris Xiaoxuan Lu', 'Dariu M. Gavrila', 'Andras Palffy', 'Fangqiang Ding'] | 2023-03-01 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Ding_Hidden_Gems_4D_Radar_Scene_Flow_Learning_Using_Cross-Modal_Supervision_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Ding_Hidden_Gems_4D_Radar_Scene_Flow_Learning_Using_Cross-Modal_Supervision_CVPR_2023_paper.pdf | cvpr-2023-1 | ['motion-segmentation', 'scene-flow-estimation'] | ['computer-vision', 'computer-vision'] | [ 2.03302845e-01 -1.24382950e-01 -6.42103434e-01 -6.24085963e-01
-1.17763853e+00 -4.46710467e-01 8.09156299e-01 -6.21721506e-01
-2.17254996e-01 6.09006822e-01 1.90491483e-01 -5.48695803e-01
-1.99893326e-01 -6.67894125e-01 -6.19881272e-01 -4.95245069e-01
-2.92827308e-01 3.71649235e-01 1.07250832e-01 8.98253173... | [8.476984024047852, -1.9870703220367432] |
7c88b678-b2e5-4842-be12-53e7ed4076b6 | mfnet-multi-feature-fusion-network-for-real | null | null | https://ieeexplore.ieee.org/document/9839297 | https://ieeexplore.ieee.org/document/9839297 | MFNet: Multi-Feature Fusion Network for Real-Time Semantic Segmentation in Road Scenes | Although high-accuracy networks have been applied to semantic segmentation at present, their inference speeds remain slow. A trade-off between accuracy and speed is demanded for real-time applications. To approach this problem, we propose Multi-Feature Fusion Network (MFNet) with real-time efficient prediction capacity... | ['Mengxu Lu; Zhenxue Chen; Chengyun Liu; Sile Ma; Lei Cai; Hao Qin'] | 2022-11-01 | null | null | null | ieee-transactions-on-intelligent-13 | ['real-time-semantic-segmentation'] | ['computer-vision'] | [ 1.42238244e-01 -1.77910104e-01 -1.75965333e-03 -5.56557894e-01
-7.18587339e-01 -2.86433846e-01 5.12447238e-01 -7.77963847e-02
-8.29577923e-01 5.38109779e-01 -4.63926375e-01 -2.98541248e-01
-1.84976101e-01 -9.75764096e-01 -5.44821024e-01 -7.19622731e-01
-1.14812367e-01 3.50509793e-01 7.70580053e-01 -1.86037019... | [9.21386432647705, -0.5601849555969238] |
a7d30e65-4f8e-4f14-8104-a6cae43d2ec4 | technical-report-for-valence-arousal | 2105.01502 | null | https://arxiv.org/abs/2105.01502v2 | https://arxiv.org/pdf/2105.01502v2.pdf | Technical Report for Valence-Arousal Estimation on Affwild2 Dataset | In this work, we describe our method for tackling the valence-arousal estimation challenge from ABAW FG-2020 Competition. The competition organizers provide an in-the-wild Aff-Wild2 dataset for participants to analyze affective behavior in real-life settings. We use MIMAMO Net \cite{deng2020mimamo} model to achieve inf... | ['I-Hsuan Li'] | 2021-05-04 | null | null | null | null | ['video-emotion-recognition'] | ['computer-vision'] | [-0.6072364 -0.1677435 -0.18302754 -0.96179765 -0.49712566 -0.44753522
0.11065575 -0.23720627 -0.4126133 0.7216593 0.46513698 0.71784055
0.3604531 -0.21056013 -0.1263927 -0.4562712 -0.5852224 -0.2565553
-0.44721776 -0.46545994 -0.44200408 -0.1777801 -1.3998502 0.9349158
0.25982165 1.6196884 -0.62... | [13.575961112976074, 2.2870049476623535] |
13a8220f-4e97-4673-b37d-ef204451cf5b | common-sense-bias-in-semantic-role-labeling | null | null | https://aclanthology.org/2021.wnut-1.14 | https://aclanthology.org/2021.wnut-1.14.pdf | Common Sense Bias in Semantic Role Labeling | Large-scale language models such as ELMo and BERT have pushed the horizon of what is possible in semantic role labeling (SRL), solving the out-of-vocabulary problem and enabling end-to-end systems, but they have also introduced significant biases. We evaluate three SRL parsers on very simple transitive sentences with v... | ['Anders Søgaard', 'Heather Lent'] | null | null | null | null | wnut-acl-2021-11 | ['semantic-role-labeling'] | ['natural-language-processing'] | [ 1.77442044e-01 5.21096110e-01 -2.25064576e-01 -5.93653738e-01
-5.24783492e-01 -7.55436063e-01 8.79605770e-01 5.01001596e-01
-9.96695638e-01 4.60889369e-01 9.46642101e-01 -2.80225724e-01
-7.92289246e-03 -6.76913738e-01 -3.15647960e-01 -4.24061865e-01
1.95391208e-01 7.62497663e-01 4.22064692e-01 -6.58840716... | [10.355871200561523, 9.298208236694336] |
e417c859-2628-4e42-b011-47f32242c391 | zs-slr-zero-shot-sign-language-recognition | 2108.10059 | null | https://arxiv.org/abs/2108.10059v1 | https://arxiv.org/pdf/2108.10059v1.pdf | ZS-SLR: Zero-Shot Sign Language Recognition from RGB-D Videos | Sign Language Recognition (SLR) is a challenging research area in computer vision. To tackle the annotation bottleneck in SLR, we formulate the problem of Zero-Shot Sign Language Recognition (ZS-SLR) and propose a two-stream model from two input modalities: RGB and Depth videos. To benefit from the vision Transformer c... | ['Sergio Escalera', 'Kourosh Kiani', 'Razieh Rastgoo'] | 2021-08-23 | null | null | null | null | ['sign-language-recognition'] | ['computer-vision'] | [ 3.00187945e-01 -6.77685589e-02 -2.06684604e-01 -1.72568470e-01
-6.39790118e-01 -1.25200778e-01 7.39316106e-01 -8.38563144e-01
-6.58823192e-01 1.77740470e-01 4.16530162e-01 -7.73486774e-03
2.83735812e-01 -5.06172419e-01 -5.53857625e-01 -4.63500708e-01
3.12288076e-01 3.83088082e-01 6.92633748e-01 -2.58928686... | [9.16440486907959, -6.467400550842285] |
e8ea4c6e-8930-4947-960d-eaa6df1592e0 | persistence-based-structural-recognition | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Li_Persistence-based_Structural_Recognition_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Li_Persistence-based_Structural_Recognition_2014_CVPR_paper.pdf | Persistence-based Structural Recognition | This paper presents a framework for object recognition using topological persistence. In particular, we show that the so-called persistence diagrams built from functions defined on the objects can serve as compact and informative descriptors for images and shapes. Complementary to the bag-of-features representation, wh... | ['Maks Ovsjanikov', 'Frederic Chazal', 'Chunyuan Li'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['3d-shape-retrieval'] | ['computer-vision'] | [ 5.33480888e-05 -4.75461513e-01 -3.46626103e-01 -2.96388417e-01
-4.22252417e-01 -7.70748734e-01 9.47310686e-01 4.48327810e-01
3.91852995e-03 3.89303863e-01 -2.54079923e-02 -6.71067238e-02
-5.45483708e-01 -1.18220484e+00 -4.27931309e-01 -1.05214381e+00
-3.55378360e-01 6.02256179e-01 4.88112509e-01 -1.06276527... | [10.057907104492188, -0.47107452154159546] |
69d13904-13e6-4d85-9ca5-e89b19b6006a | two-heads-are-better-than-one-geometric | 2111.00231 | null | https://arxiv.org/abs/2111.00231v1 | https://arxiv.org/pdf/2111.00231v1.pdf | Two Heads are Better than One: Geometric-Latent Attention for Point Cloud Classification and Segmentation | We present an innovative two-headed attention layer that combines geometric and latent features to segment a 3D scene into semantically meaningful subsets. Each head combines local and global information, using either the geometric or latent features, of a neighborhood of points and uses this information to learn bette... | ['Robert B. Fisher', 'Antonio Javier Gallego', 'Hanz Cuevas-Velasquez'] | 2021-10-30 | null | null | null | null | ['point-cloud-classification'] | ['computer-vision'] | [-1.60296500e-01 2.32303962e-01 -1.56723678e-01 -3.20139974e-01
-8.04768145e-01 -3.85015786e-01 6.50005698e-01 2.65918285e-01
-4.17191356e-01 3.07294250e-01 -1.93948206e-02 -2.88032293e-02
-2.90280938e-01 -1.11687005e+00 -1.00890934e+00 -5.71246564e-01
-3.64231080e-01 1.00704265e+00 6.32989526e-01 -1.11343907... | [7.986633777618408, -3.516296148300171] |
438c69ed-d0a5-4b33-8cd3-cda2193663c2 | artificial-benchmark-for-community-detection-1 | 2301.05749 | null | https://arxiv.org/abs/2301.05749v2 | https://arxiv.org/pdf/2301.05749v2.pdf | Artificial Benchmark for Community Detection with Outliers (ABCD+o) | The Artificial Benchmark for Community Detection graph (ABCD) is a random graph model with community structure and power-law distribution for both degrees and community sizes. The model generates graphs with similar properties as the well-known LFR one, and its main parameter $\xi$ can be tuned to mimic its counterpart... | ['François Théberge', 'Paweł Prałat', 'Bogumił Kamiński'] | 2023-01-13 | null | null | null | null | ['community-detection'] | ['graphs'] | [-3.62490864e-05 1.86420470e-01 2.43556444e-02 1.64918095e-01
1.81664433e-02 -5.68058312e-01 5.98518968e-01 3.80396754e-01
9.94348675e-02 8.11571479e-01 -1.79481804e-01 -5.32797098e-01
-3.22957128e-01 -9.87400711e-01 -3.68814379e-01 -5.07887006e-01
-1.05346334e+00 6.60280287e-01 7.04360843e-01 -3.01663756... | [6.9709272384643555, 5.290719509124756] |
6edbd1ca-760f-48d4-bb22-9bd56443de0e | investigating-the-dynamics-of-hand-and-lips | 2306.08290 | null | https://arxiv.org/abs/2306.08290v1 | https://arxiv.org/pdf/2306.08290v1.pdf | Investigating the dynamics of hand and lips in French Cued Speech using attention mechanisms and CTC-based decoding | Hard of hearing or profoundly deaf people make use of cued speech (CS) as a communication tool to understand spoken language. By delivering cues that are relevant to the phonetic information, CS offers a way to enhance lipreading. In literature, there have been several studies on the dynamics between the hand and the l... | ['Thomas Hueber', 'Olivier Perrotin', 'Frédéric Elisei', 'Denis Beautemps', 'Sanjana Sankar'] | 2023-06-14 | null | null | null | null | ['lipreading'] | ['computer-vision'] | [ 4.21762168e-01 2.53448337e-01 -3.03754300e-01 -3.46468925e-01
-8.31246078e-01 -3.25038552e-01 7.83941090e-01 -9.69421417e-02
-4.68678772e-01 3.91947210e-01 6.01004720e-01 -6.40426949e-02
-4.22173999e-02 -2.19303489e-01 -7.09656060e-01 -9.21451330e-01
1.33294895e-01 -2.16218196e-02 2.74724956e-03 -2.77325153... | [14.335798263549805, 5.025183200836182] |
8a85dbbd-a442-4be5-92fe-42c59e4180cd | deep-attention-guided-fusion-network-for | 1807.08471 | null | http://arxiv.org/abs/1807.08471v2 | http://arxiv.org/pdf/1807.08471v2.pdf | Deep attention-guided fusion network for lesion segmentation | We participated the Task 1: Lesion Segmentation. The paper describes our
algorithm and the final result of validation set for the ISIC Challenge 2018 -
Skin Lesion Analysis Towards Melanoma Detection. | ['Yangyang Hao', 'Ruixing Li', 'Hua Wang', 'Lizhuang Ma', 'Hengliang Zhu'] | 2018-07-23 | null | null | null | null | ['deep-attention', 'deep-attention'] | ['computer-vision', 'natural-language-processing'] | [ 1.00338101e+00 1.06042787e-01 -5.72891951e-01 1.25581669e-02
-1.21993876e+00 -4.99917060e-01 4.82029378e-01 8.42274055e-02
-8.76897156e-01 4.49910253e-01 -1.16994202e-01 -5.21770298e-01
3.77566874e-01 -9.11487266e-02 -2.34584674e-01 -9.13972795e-01
-8.18908289e-02 -1.64137751e-01 5.80380142e-01 5.44062480... | [15.804120063781738, -3.070793628692627] |
d66429b0-9ae8-48f4-9abc-44e584718c98 | mean-variance-hybrid-portfolio-optimization | 2303.15830 | null | https://arxiv.org/abs/2303.15830v2 | https://arxiv.org/pdf/2303.15830v2.pdf | Mean-variance hybrid portfolio optimization with quantile-based risk measure | This paper addresses the importance of incorporating various risk measures in portfolio management and proposes a dynamic hybrid portfolio optimization model that combines the spectral risk measure and the Value-at-Risk in the mean-variance formulation. By utilizing the quantile optimization technique and martingale re... | ['Ke Zhou', 'WeiPing Wu', 'Yu Lin', 'Jianjun Gao'] | 2023-03-28 | null | null | null | null | ['portfolio-optimization'] | ['time-series'] | [-3.44519645e-01 -5.51195219e-02 -1.91901118e-01 -8.88744276e-03
-5.51808417e-01 -7.64962614e-01 3.70725393e-01 -1.22125849e-01
-2.29830876e-01 1.03795123e+00 -7.60676637e-02 -5.43267429e-01
-9.68173981e-01 -1.21905565e+00 -1.21860608e-01 -9.44574714e-01
-2.11657211e-02 4.45804894e-01 -5.05452370e-03 -1.53090656... | [4.956301689147949, 3.926947832107544] |
42157991-3426-4717-b601-2c8af0cbf999 | transductive-universal-transport-for-zero | null | null | https://openreview.net/forum?id=Yp4sR6rmgFt | https://openreview.net/pdf?id=Yp4sR6rmgFt | Transductive Universal Transport for Zero-Shot Action Recognition | This work addresses the problem of recognizing action categories in videos for which no training examples are available. The current state-of-the-art enables such a zero-shot recognition by learning universal mappings from videos to a shared semantic space, either trained on large-scale seen actions or on objects. Whil... | ['Pascal Mettes'] | 2021-09-29 | null | null | null | null | ['zero-shot-action-recognition'] | ['computer-vision'] | [ 4.72766370e-01 1.02273032e-01 -4.11300391e-01 -2.35179350e-01
-6.19691730e-01 -3.35189193e-01 9.05958116e-01 -6.53520882e-01
-3.96388292e-01 6.49299860e-01 3.98118645e-01 3.81439269e-01
-1.70920238e-01 -7.82017887e-01 -8.74543250e-01 -1.11138952e+00
5.59349246e-02 5.14400482e-01 3.91763508e-01 2.64512878... | [8.723834991455078, 1.1220688819885254] |
207687db-2919-4982-992c-fbc80407b18a | relpose-predicting-probabilistic-relative | 2208.05963 | null | https://arxiv.org/abs/2208.05963v2 | https://arxiv.org/pdf/2208.05963v2.pdf | RelPose: Predicting Probabilistic Relative Rotation for Single Objects in the Wild | We describe a data-driven method for inferring the camera viewpoints given multiple images of an arbitrary object. This task is a core component of classic geometric pipelines such as SfM and SLAM, and also serves as a vital pre-processing requirement for contemporary neural approaches (e.g. NeRF) to object reconstruct... | ['Shubham Tulsiani', 'Deva Ramanan', 'Jason Y. Zhang'] | 2022-08-11 | null | null | null | null | ['object-reconstruction'] | ['computer-vision'] | [ 8.15461203e-02 -1.14593342e-01 -1.77141666e-01 -6.76800549e-01
-9.41908240e-01 -7.29400277e-01 8.97006094e-01 -3.99233520e-01
-3.31448615e-02 1.40650153e-01 5.02507925e-01 9.00438279e-02
-4.82768640e-02 -3.97912741e-01 -1.11233044e+00 -4.71736372e-01
4.20507938e-01 8.74367476e-01 5.89037165e-02 -2.89452840... | [8.151713371276855, -2.6076724529266357] |
e8f1658d-4ff6-4fe5-bef4-3fa321c1d9e7 | non-uniform-motion-deblurring-with-blurry | 2101.06021 | null | https://arxiv.org/abs/2101.06021v1 | https://arxiv.org/pdf/2101.06021v1.pdf | Non-uniform Motion Deblurring with Blurry Component Divided Guidance | Blind image deblurring is a fundamental and challenging computer vision problem, which aims to recover both the blur kernel and the latent sharp image from only a blurry observation. Despite the superiority of deep learning methods in image deblurring have displayed, there still exists major challenge with various non-... | ['Yanning Zhang', 'Jinqiu Sun', 'Yu Zhu', 'Rui Li', 'Axi Niu', 'Qingsen Yan', 'Wei Sun', 'Pei Wang'] | 2021-01-15 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 6.12713955e-02 -6.57746732e-01 1.82670176e-01 -2.26134539e-01
-6.04528546e-01 -3.13321739e-01 4.65042710e-01 -6.73311591e-01
-5.00017516e-02 7.42983997e-01 7.33448982e-01 6.94357902e-02
-1.85850233e-01 -1.66570708e-01 -5.78688562e-01 -9.67325330e-01
3.69160473e-01 -4.49750006e-01 1.62155554e-01 -1.18643986... | [11.502859115600586, -2.6508946418762207] |
9ae43e28-ca38-41c7-9ea2-b2ed5a7c8c35 | towards-multi-label-unknown-intent-detection | null | null | https://aclanthology.org/2022.coling-1.52 | https://aclanthology.org/2022.coling-1.52.pdf | Towards Multi-label Unknown Intent Detection | Multi-class unknown intent detection has made remarkable progress recently. However, it has a strong assumption that each utterance has only one intent, which does not conform to reality because utterances often have multiple intents. In this paper, we propose a more desirable task, multi-label unknown intent detection... | ['Jiajun Chen', 'ShuJian Huang', 'Xinyu Dai', 'Zhen Wu', 'Yawen Ouyang'] | null | null | null | null | coling-2022-10 | ['intent-detection'] | ['natural-language-processing'] | [ 3.24425578e-01 -1.96727868e-02 -1.66760236e-01 -6.70213997e-01
-9.77733910e-01 -7.39921153e-01 2.86242843e-01 1.03991434e-01
-8.01300704e-02 6.00251615e-01 2.29930162e-01 -2.74200708e-01
4.62301195e-01 -4.47042286e-01 -5.23697317e-01 -7.33230770e-01
2.48484969e-01 4.31312472e-01 1.71093836e-01 -2.65077174... | [12.48637866973877, 7.4926252365112305] |
9f17f0ef-cea5-473f-8b69-9237cff29ea0 | self-supervised-multi-view-synchronization | 2010.06218 | null | https://arxiv.org/abs/2010.06218v1 | https://arxiv.org/pdf/2010.06218v1.pdf | Self-Supervised Multi-View Synchronization Learning for 3D Pose Estimation | Current state-of-the-art methods cast monocular 3D human pose estimation as a learning problem by training neural networks on large data sets of images and corresponding skeleton poses. In contrast, we propose an approach that can exploit small annotated data sets by fine-tuning networks pre-trained via self-supervised... | ['Paolo Favaro', 'Simon Jenni'] | 2020-10-13 | null | null | null | null | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [ 1.82621568e-01 9.21818912e-02 -1.73550978e-01 -4.96515095e-01
-3.77892584e-01 -5.12735248e-01 5.53078532e-01 -3.87352169e-01
-6.85807228e-01 4.28896546e-01 1.83889404e-01 4.74534601e-01
2.58480251e-01 -3.77177000e-01 -1.04065883e+00 -3.59442025e-01
-3.64676751e-02 1.13731015e+00 1.50220916e-01 -1.88375995... | [7.045149803161621, -0.8801019191741943] |
99931752-a435-4e65-9401-e88a59014954 | few-shot-nested-named-entity-recognition | 2212.00953 | null | https://arxiv.org/abs/2212.00953v1 | https://arxiv.org/pdf/2212.00953v1.pdf | Few-Shot Nested Named Entity Recognition | While Named Entity Recognition (NER) is a widely studied task, making inferences of entities with only a few labeled data has been challenging, especially for entities with nested structures. Unlike flat entities, entities and their nested entities are more likely to have similar semantic feature representations, drast... | ['Ning An', 'Yan Pan', 'Lili Jiang', 'Jiaoyun Yang', 'Hong Ming'] | 2022-12-02 | null | null | null | null | ['nested-named-entity-recognition'] | ['natural-language-processing'] | [-1.50432691e-01 1.98939115e-01 -1.58643901e-01 -5.74799418e-01
-6.84642494e-01 -4.84733135e-01 5.32637060e-01 4.84985977e-01
-9.06961203e-01 6.74178839e-01 3.26742470e-01 -8.51276219e-02
-3.54358577e-03 -1.07569242e+00 -5.92442870e-01 -3.20953995e-01
-1.71402842e-01 3.12461197e-01 5.10594726e-01 -1.71933621... | [9.650012016296387, 9.412233352661133] |
60c5f399-730a-4240-bf5d-d204b728cd6d | pi2-text-vec-policy-representations-with | 2306.09800 | null | https://arxiv.org/abs/2306.09800v1 | https://arxiv.org/pdf/2306.09800v1.pdf | $\pi2\text{vec}$: Policy Representations with Successor Features | This paper describes $\pi2\text{vec}$, a method for representing behaviors of black box policies as feature vectors. The policy representations capture how the statistics of foundation model features change in response to the policy behavior in a task agnostic way, and can be trained from offline data, allowing them to... | ['Misha Denil', 'Yutian Chen', 'Tom Le Paine', 'Claudio Fantacci', 'Ksenia Konyushkova', 'Gianluca Scarpellini'] | 2023-06-16 | null | null | null | null | ['offline-rl'] | ['playing-games'] | [-2.40675300e-01 -3.26128900e-01 -9.43563104e-01 -4.97180194e-01
-2.67497897e-01 -9.41383004e-01 8.39626193e-01 1.18803136e-01
-5.40478647e-01 8.87963235e-01 3.29991281e-01 -7.35807538e-01
-8.10586568e-03 -4.25681561e-01 -5.15044332e-01 -5.37050843e-01
-4.17761266e-01 4.28860158e-01 1.34678513e-01 -4.97529060... | [4.13698148727417, 2.0735127925872803] |
2a5b1c97-47d3-4ad2-a78e-423488df69b7 | fgn-fusion-glyph-network-for-chinese-named | 2001.05272 | null | https://arxiv.org/abs/2001.05272v6 | https://arxiv.org/pdf/2001.05272v6.pdf | FGN: Fusion Glyph Network for Chinese Named Entity Recognition | Chinese NER is a challenging task. As pictographs, Chinese characters contain latent glyph information, which is often overlooked. In this paper, we propose the FGN, Fusion Glyph Network for Chinese NER. Except for adding glyph information, this method may also add extra interactive information with the fusion mechanis... | ['Zhenyu Xuan', 'Rui Bao', 'Shengyi Jiang'] | 2020-01-15 | null | null | null | null | ['chinese-named-entity-recognition'] | ['natural-language-processing'] | [ 6.44854456e-02 -1.05733141e-01 1.15269996e-01 -4.25505340e-01
-7.86565721e-01 -6.11682117e-01 5.27312100e-01 9.67401937e-02
-5.62102318e-01 5.55630505e-01 9.44215536e-01 -1.33546919e-01
6.00284219e-01 -9.58341777e-01 -5.90518832e-01 -5.68691790e-01
2.04668462e-01 -1.25956669e-01 1.60112962e-01 -3.51136506... | [9.780567169189453, 9.862372398376465] |
cdc700d4-9c9a-4b82-9894-2ee7eb5033e9 | inference-of-the-dynamic-aging-related | 1807.05637 | null | https://arxiv.org/abs/1807.05637v3 | https://arxiv.org/pdf/1807.05637v3.pdf | Inference of the Dynamic Aging-related Biological Subnetwork via Network Propagation | Gene expression (GE) data capture valuable condition-specific information ("condition" can mean a biological process, disease stage, age, patient, etc.) However, GE analyses ignore physical interactions between gene products, i.e., proteins. Since proteins function by interacting with each other, and since biological n... | ['Tijana Milenkovic', 'Khalique Newaz'] | 2018-07-15 | null | null | null | null | ['human-aging'] | ['miscellaneous'] | [ 3.44900668e-01 -1.31052895e-03 -4.07319933e-01 -1.72617301e-01
3.02569985e-01 -5.04889309e-01 8.30338001e-02 3.16772074e-01
-7.99131095e-02 1.36312568e+00 1.73900396e-01 -4.23294127e-01
-4.98547196e-01 -1.07635546e+00 -6.49546444e-01 -8.12066078e-01
-5.91660440e-01 5.56494117e-01 3.35739285e-01 -1.27488285... | [6.648803234100342, 5.496265411376953] |
257d2724-fd0f-4eb5-8f1b-40bc5f2d0720 | a-new-perspective-to-boost-vision-transformer | 2301.00989 | null | https://arxiv.org/abs/2301.00989v1 | https://arxiv.org/pdf/2301.00989v1.pdf | A New Perspective to Boost Vision Transformer for Medical Image Classification | Transformer has achieved impressive successes for various computer vision tasks. However, most of existing studies require to pretrain the Transformer backbone on a large-scale labeled dataset (e.g., ImageNet) for achieving satisfactory performance, which is usually unavailable for medical images. Additionally, due to ... | ['Yefeng Zheng', 'Kai Ma', 'Nanjun He', 'Yawen Huang', 'Yuexiang Li'] | 2023-01-03 | null | null | null | null | ['skin-lesion-classification', 'diabetic-retinopathy-grading'] | ['medical', 'medical'] | [ 5.51669300e-01 2.82386154e-01 -3.91510159e-01 -2.36855179e-01
-5.18213749e-01 5.86017109e-02 2.59462386e-01 2.52362132e-01
-3.73744220e-01 2.19180226e-01 1.38232827e-01 -4.50119972e-02
-2.69729793e-01 -6.34779751e-01 -4.23765272e-01 -8.38780522e-01
1.17991030e-01 2.87661701e-01 2.80032717e-02 -1.99769706... | [14.772627830505371, -2.190380096435547] |
91d3cdfb-5658-4f52-b6d2-ea3408b24a32 | appliance-detection-using-very-low-frequency | 2305.10352 | null | https://arxiv.org/abs/2305.10352v2 | https://arxiv.org/pdf/2305.10352v2.pdf | Appliance Detection Using Very Low-Frequency Smart Meter Time Series | In recent years, smart meters have been widely adopted by electricity suppliers to improve the management of the smart grid system. These meters usually collect energy consumption data at a very low frequency (every 30min), enabling utilities to bill customers more accurately. To provide more personalized recommendatio... | ['Themis Palpanas', 'Paul Boniol', 'Philippe Charpentier', 'Adrien Petralia'] | 2023-05-10 | null | null | null | null | ['time-series-classification'] | ['time-series'] | [-6.66186661e-02 -5.03633559e-01 -1.25472825e-02 -4.67697978e-01
-4.62074459e-01 -5.34995973e-01 6.71810091e-01 2.88924932e-01
-1.34046048e-01 4.99469787e-01 2.93030799e-03 -1.17127039e-01
-4.10468191e-01 -1.06672454e+00 -9.09714550e-02 -1.15734732e+00
-4.34527904e-01 5.39533675e-01 -1.86430305e-01 -1.42591238... | [6.023406982421875, 2.6074423789978027] |
d5802824-a962-44c8-a14b-5d4646f9d8a4 | convex-space-learning-improves-deep | 2206.09812 | null | https://arxiv.org/abs/2206.09812v2 | https://arxiv.org/pdf/2206.09812v2.pdf | ConvGeN: Convex space learning improves deep-generative oversampling for tabular imbalanced classification on smaller datasets | Data is commonly stored in tabular format. Several fields of research are prone to small imbalanced tabular data. Supervised Machine Learning on such data is often difficult due to class imbalance. Synthetic data generation, i.e., oversampling, is a common remedy used to improve classifier performance. State-of-the-art... | ['Olaf Wolkenhauer', 'Prashant Srivastava', 'Markus Wolfien', 'Waldemar Hahn', 'Saptarshi Bej', 'Kristian Schultz'] | 2022-06-20 | null | null | null | null | ['imbalanced-classification'] | ['miscellaneous'] | [-6.82564229e-02 2.12762743e-01 -3.39181751e-01 -2.91573435e-01
-9.81486201e-01 -2.32290506e-01 3.91031802e-01 -7.01880082e-02
2.74104506e-01 1.19631481e+00 5.66148832e-02 -1.74503744e-01
1.19606890e-01 -1.23349702e+00 -1.08361018e+00 -8.26264083e-01
3.56265008e-01 8.38046610e-01 -4.68437105e-01 -3.85288179... | [8.901068687438965, 4.168900489807129] |
7c638760-0d55-4c08-aac8-24f630517b85 | improving-trustworthiness-of-ai-disease | 2207.02238 | null | https://arxiv.org/abs/2207.02238v1 | https://arxiv.org/pdf/2207.02238v1.pdf | Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction Sets | The regulatory approval and broad clinical deployment of medical AI have been hampered by the perception that deep learning models fail in unpredictable and possibly catastrophic ways. A lack of statistically rigorous uncertainty quantification is a significant factor undermining trust in AI results. Recent development... | ['Stuart Pomerantz', 'Anastasios N. Angelopoulos', 'Charles Lu'] | 2022-07-05 | null | null | null | null | ['prediction-intervals'] | ['miscellaneous'] | [ 3.18644047e-01 7.24499166e-01 -2.72733241e-01 -6.99231207e-01
-1.29867327e+00 -4.07065034e-01 2.62487918e-01 5.16269028e-01
-3.68107587e-01 1.04850459e+00 2.88144767e-01 -6.87234581e-01
-8.29551280e-01 -5.50069273e-01 -9.05050039e-01 -5.81202924e-01
-4.65871185e-01 9.93494391e-01 -1.98153500e-02 5.13743341... | [14.350351333618164, -2.0513322353363037] |
f68920fc-adcd-4454-be76-ac1691c12bb0 | polynomial-implicit-neural-representations | 2303.11424 | null | https://arxiv.org/abs/2303.11424v1 | https://arxiv.org/pdf/2303.11424v1.pdf | Polynomial Implicit Neural Representations For Large Diverse Datasets | Implicit neural representations (INR) have gained significant popularity for signal and image representation for many end-tasks, such as superresolution, 3D modeling, and more. Most INR architectures rely on sinusoidal positional encoding, which accounts for high-frequency information in data. However, the finite encod... | ['Pavan Turaga', 'Ankita Shukla', 'Rajhans Singh'] | 2023-03-20 | null | http://openaccess.thecvf.com//content/CVPR2023/html/Singh_Polynomial_Implicit_Neural_Representations_for_Large_Diverse_Datasets_CVPR_2023_paper.html | http://openaccess.thecvf.com//content/CVPR2023/papers/Singh_Polynomial_Implicit_Neural_Representations_for_Large_Diverse_Datasets_CVPR_2023_paper.pdf | cvpr-2023-1 | ['conditional-image-generation'] | ['computer-vision'] | [ 4.46418338e-02 6.96954504e-02 -2.57100053e-02 -4.46965039e-01
-8.38151157e-01 -2.87120908e-01 8.39710355e-01 -1.39997870e-01
-1.86767116e-01 4.51062679e-01 3.00283104e-01 4.25426289e-02
-3.42691690e-02 -8.20032120e-01 -7.00318456e-01 -7.90916979e-01
-2.46313661e-02 2.19851241e-01 9.95125845e-02 -1.92471161... | [11.092337608337402, -1.7768861055374146] |
6aa44b8e-82bf-477b-9e2b-ea9f343e1c5c | dialogue-term-extraction-using-transfer | 2208.10448 | null | https://arxiv.org/abs/2208.10448v1 | https://arxiv.org/pdf/2208.10448v1.pdf | Dialogue Term Extraction using Transfer Learning and Topological Data Analysis | Goal oriented dialogue systems were originally designed as a natural language interface to a fixed data-set of entities that users might inquire about, further described by domain, slots, and values. As we move towards adaptable dialogue systems where knowledge about domains, slots, and values may change, there is an i... | ['Milica Gašić', 'Marcus Zibrowius', 'Carel van Niekerk', 'Benjamin Matthias Ruppik', 'Michael Heck', 'Renato Vukovic'] | 2022-08-22 | null | https://aclanthology.org/2022.sigdial-1.53 | https://aclanthology.org/2022.sigdial-1.53.pdf | sigdial-acl-2022-9 | ['term-extraction', 'goal-oriented-dialogue-systems'] | ['natural-language-processing', 'natural-language-processing'] | [ 9.95574743e-02 7.17601359e-01 -4.83308844e-02 -4.64281142e-01
-5.17010212e-01 -8.66899490e-01 1.12838912e+00 6.51742995e-01
-5.19487202e-01 7.85135627e-01 9.57448900e-01 -4.80797172e-01
-3.75974000e-01 -1.09398091e+00 -9.53582674e-02 -2.88661182e-01
-2.47037828e-01 8.76312494e-01 4.57767606e-01 -1.07409024... | [12.73884105682373, 7.914146423339844] |
19e46c0e-7abf-4ce5-bcf6-791fb0a6004d | learning-trajectory-dependencies-for-human | 1908.05436 | null | https://arxiv.org/abs/1908.05436v3 | https://arxiv.org/pdf/1908.05436v3.pdf | Learning Trajectory Dependencies for Human Motion Prediction | Human motion prediction, i.e., forecasting future body poses given observed pose sequence, has typically been tackled with recurrent neural networks (RNNs). However, as evidenced by prior work, the resulted RNN models suffer from prediction errors accumulation, leading to undesired discontinuities in motion prediction.... | ['Miaomiao Liu', 'Mathieu Salzmann', 'Wei Mao', 'Hongdong Li'] | 2019-08-15 | learning-trajectory-dependencies-for-human-1 | http://openaccess.thecvf.com/content_ICCV_2019/html/Mao_Learning_Trajectory_Dependencies_for_Human_Motion_Prediction_ICCV_2019_paper.html | http://openaccess.thecvf.com/content_ICCV_2019/papers/Mao_Learning_Trajectory_Dependencies_for_Human_Motion_Prediction_ICCV_2019_paper.pdf | iccv-2019-10 | ['human-pose-forecasting'] | ['computer-vision'] | [-1.28382305e-03 1.16815634e-01 -3.05723011e-01 -4.73366454e-02
-1.93345368e-01 -2.72692204e-01 5.38673401e-01 -1.91076890e-01
-4.95408684e-01 5.04499257e-01 4.45889562e-01 -1.59897506e-01
-5.48773594e-02 -7.06604421e-01 -8.62805009e-01 -5.53745985e-01
-4.01723951e-01 3.13529998e-01 2.28086188e-01 -2.68123001... | [7.255952835083008, -0.33201199769973755] |
080ee9f6-531a-4aa0-922f-c3763dd1d072 | crown-conversational-passage-ranking-by | 1911.02850 | null | https://arxiv.org/abs/1911.02850v3 | https://arxiv.org/pdf/1911.02850v3.pdf | CROWN: Conversational Passage Ranking by Reasoning over Word Networks | Information needs around a topic cannot be satisfied in a single turn; users typically ask follow-up questions referring to the same theme and a system must be capable of understanding the conversational context of a request to retrieve correct answers. In this paper, we present our submission to the TREC Conversationa... | ['Magdalena Kaiser', 'Rishiraj Saha Roy', 'Gerhard Weikum'] | 2019-11-07 | null | null | null | null | ['passage-ranking'] | ['natural-language-processing'] | [ 3.63794491e-02 9.38606262e-02 -7.17873275e-02 -5.28590739e-01
-1.06226826e+00 -7.71770656e-01 8.48165572e-01 6.86602831e-01
-6.60197258e-01 7.35162437e-01 1.13439393e+00 -3.18546951e-01
-4.24819052e-01 -5.96016109e-01 -3.58249575e-01 -3.69799793e-01
-1.76380828e-01 7.34506845e-01 5.30379713e-01 -6.58629417... | [12.057699203491211, 7.859766006469727] |
0f5807ab-873c-475c-83cd-fe34f9898e4e | evaluation-of-gpt-and-bert-based-models-on | 2303.17728 | null | https://arxiv.org/abs/2303.17728v1 | https://arxiv.org/pdf/2303.17728v1.pdf | Evaluation of GPT and BERT-based models on identifying protein-protein interactions in biomedical text | Detecting protein-protein interactions (PPIs) is crucial for understanding genetic mechanisms, disease pathogenesis, and drug design. However, with the fast-paced growth of biomedical literature, there is a growing need for automated and accurate extraction of PPIs to facilitate scientific knowledge discovery. Pre-trai... | ['Junguk Hur', 'Arzucan Özgür', 'Yongqun He', 'Mert Basmaci', 'Nur Bengisu Çam', 'Hasin Rehana'] | 2023-03-30 | null | null | null | null | ['literature-mining'] | ['natural-language-processing'] | [ 1.97312757e-01 3.67088497e-01 -3.73575926e-01 -2.42256939e-01
-1.07450533e+00 -4.66139197e-01 4.48897451e-01 7.31998682e-01
-3.23995262e-01 1.13455725e+00 1.75823420e-01 -5.34458339e-01
-2.39428222e-01 -7.05329657e-01 -9.64651048e-01 -6.62095428e-01
-1.54360548e-01 7.42864192e-01 -1.12183183e-01 6.42842725... | [8.43008041381836, 8.726710319519043] |
bcf8a224-dce2-4068-b38f-b2a998352e84 | explore-image-deblurring-via-blur-kernel | 2104.00317 | null | https://arxiv.org/abs/2104.00317v2 | https://arxiv.org/pdf/2104.00317v2.pdf | Explore Image Deblurring via Blur Kernel Space | This paper introduces a method to encode the blur operators of an arbitrary dataset of sharp-blur image pairs into a blur kernel space. Assuming the encoded kernel space is close enough to in-the-wild blur operators, we propose an alternating optimization algorithm for blind image deblurring. It approximates an unseen ... | ['Minh Hoai', 'Quynh Phung', 'Anh Tran', 'Phong Tran'] | 2021-04-01 | null | null | null | null | ['blind-image-deblurring'] | ['computer-vision'] | [ 7.39692226e-02 -2.86075622e-01 3.71195972e-01 -2.99697608e-01
-2.99966305e-01 -5.41982830e-01 2.98093081e-01 -7.02444971e-01
-2.78403997e-01 7.86980569e-01 3.92666847e-01 -1.17447570e-01
-2.60021597e-01 -3.27342808e-01 -7.92081594e-01 -8.22061598e-01
3.30785573e-01 -1.98799506e-01 -5.80122285e-02 2.15430647... | [11.566532135009766, -2.7653286457061768] |
d2de63f1-581d-44cc-bb8c-c9054cd7048e | diffusion-dataset-generation-towards-closing | 2305.09401 | null | https://arxiv.org/abs/2305.09401v1 | https://arxiv.org/pdf/2305.09401v1.pdf | Diffusion Dataset Generation: Towards Closing the Sim2Real Gap for Pedestrian Detection | We propose a method that augments a simulated dataset using diffusion models to improve the performance of pedestrian detection in real-world data. The high cost of collecting and annotating data in the real-world has motivated the use of simulation platforms to create training datasets. While simulated data is inexpen... | ['Michael Greenspan', 'Mohsen Zand', 'Andrew Farley'] | 2023-05-16 | null | null | null | null | ['pedestrian-detection'] | ['computer-vision'] | [-2.36499887e-02 -9.60672572e-02 3.39751393e-01 -3.94553721e-01
-6.23846531e-01 -5.16425133e-01 7.78576374e-01 3.03052545e-01
-9.32800472e-01 8.36230278e-01 2.16660071e-02 -4.02995646e-01
7.09797263e-01 -1.12349534e+00 -8.08601975e-01 -3.62064570e-01
-1.46844581e-01 8.56199026e-01 7.01278329e-01 -2.13963136... | [8.362221717834473, -1.0758014917373657] |
f289396d-e1e2-4215-891f-d8eb2df1d50e | fan-trans-online-knowledge-distillation-for | 2211.06143 | null | https://arxiv.org/abs/2211.06143v1 | https://arxiv.org/pdf/2211.06143v1.pdf | FAN-Trans: Online Knowledge Distillation for Facial Action Unit Detection | Due to its importance in facial behaviour analysis, facial action unit (AU) detection has attracted increasing attention from the research community. Leveraging the online knowledge distillation framework, we propose the ``FANTrans" method for AU detection. Our model consists of a hybrid network of convolution and tran... | ['Maja Pantic', 'Yordan Hristov', 'Yiming Lin', 'Jie Shen', 'Jing Yang'] | 2022-11-11 | null | null | null | null | ['face-alignment', 'action-unit-detection', 'facial-action-unit-detection'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 3.21744263e-01 2.79819220e-01 4.75290455e-02 -6.32980764e-01
-4.23387021e-01 -1.14467032e-01 5.60027659e-01 -2.51940489e-01
-4.38234180e-01 2.98109293e-01 2.23959059e-01 2.30463669e-01
3.14008236e-01 -7.49755442e-01 -7.18856514e-01 -7.83784091e-01
-9.59522650e-02 -1.44055143e-01 6.27696142e-02 -1.27470046... | [13.618390083312988, 1.6044385433197021] |
ecc8f510-69ee-4e54-9277-595ad9a8f0b9 | maximum-mean-discrepancy-is-aware-of | 2010.11415 | null | https://arxiv.org/abs/2010.11415v3 | https://arxiv.org/pdf/2010.11415v3.pdf | Maximum Mean Discrepancy Test is Aware of Adversarial Attacks | The maximum mean discrepancy (MMD) test could in principle detect any distributional discrepancy between two datasets. However, it has been shown that the MMD test is unaware of adversarial attacks -- the MMD test failed to detect the discrepancy between natural and adversarial data. Given this phenomenon, we raise a q... | ['Masashi Sugiyama', 'Gang Niu', 'Tongliang Liu', 'Bo Han', 'Jingfeng Zhang', 'Feng Liu', 'Ruize Gao'] | 2020-10-22 | null | null | null | null | ['adversarial-attack-detection', 'adversarial-attack-detection'] | ['computer-vision', 'knowledge-base'] | [-6.26862049e-02 2.38898069e-01 2.87439495e-01 6.58470998e-03
-8.09730887e-01 -1.09720767e+00 6.08293653e-01 6.25098869e-03
-2.30751380e-01 1.01241755e+00 -1.10643968e-01 -6.76414847e-01
-1.20616734e-01 -9.82173204e-01 -8.29440296e-01 -8.32062066e-01
-1.80321574e-01 2.59626746e-01 4.77623194e-01 -2.25165993... | [5.710664749145508, 7.7580180168151855] |
3b52b264-96d4-45ac-b350-29c5b8cfb6ce | impact-of-uavs-equipped-with-ads-b-on-the | 2307.01534 | null | https://arxiv.org/abs/2307.01534v1 | https://arxiv.org/pdf/2307.01534v1.pdf | Impact of UAVs Equipped with ADS-B on the Civil Aviation Monitoring System | In recent years, there is an increasing demand for unmanned aerial vehicles (UAVs) to complete multiple applications. However, as unmanned equipments, UAVs lead to some security risks to general civil aviations. In order to strengthen the flight management of UAVs and guarantee the safety, UAVs can be equipped with aut... | ['Bin Wang', 'Huiling Hu', 'Qihui Wu', 'Yifan Zhang', 'Chao Dong', 'Ziye Jia', 'Lei Zhang', 'Yiyang Liao'] | 2023-07-04 | null | null | null | null | ['blocking'] | ['natural-language-processing'] | [-1.06575437e-01 -1.61018327e-01 9.68011394e-02 2.36357585e-01
3.54721785e-01 -8.92843425e-01 3.15021984e-02 -1.88845903e-01
-3.32207590e-01 9.85398352e-01 -4.92166877e-01 -8.64445210e-01
-6.46781087e-01 -1.13944733e+00 -5.47064960e-01 -8.91797125e-01
-5.14396846e-01 -5.04060090e-01 7.48516619e-01 -6.13022506... | [5.961727142333984, 1.4682537317276] |
2a02039c-ed7b-41a5-9ab0-d8ac6d4d2c6f | micro-objective-learning-accelerating-deep | 1703.03933 | null | http://arxiv.org/abs/1703.03933v1 | http://arxiv.org/pdf/1703.03933v1.pdf | Micro-Objective Learning : Accelerating Deep Reinforcement Learning through the Discovery of Continuous Subgoals | Recently, reinforcement learning has been successfully applied to the logical
game of Go, various Atari games, and even a 3D game, Labyrinth, though it
continues to have problems in sparse reward settings. It is difficult to
explore, but also difficult to exploit, a small number of successes when
learning policy. To so... | ['Byoung-Tak Zhang', 'Dong-Hyun Kwak', 'Sang-Woo Lee', 'Jinyoung Choi', 'Sungtae Lee'] | 2017-03-11 | null | null | null | null | ['game-of-go', 'montezumas-revenge'] | ['playing-games', 'playing-games'] | [-4.32781845e-01 -1.30916107e-02 -4.31959659e-01 9.87084061e-02
-7.94111431e-01 -6.30727470e-01 3.33915770e-01 -1.48597971e-01
-7.97611475e-01 1.26447189e+00 -1.92607120e-02 -4.07021314e-01
-3.40512335e-01 -6.90482557e-01 -6.21644378e-01 -5.15175879e-01
-6.85815930e-01 7.10860908e-01 3.49749029e-01 -8.33520114... | [3.7287917137145996, 1.698229193687439] |
3102a062-cc74-470c-8327-6c421dc6ccc6 | predicting-human-card-selection-in-magic-the | 2105.11864 | null | https://arxiv.org/abs/2105.11864v2 | https://arxiv.org/pdf/2105.11864v2.pdf | Predicting Human Card Selection in Magic: The Gathering with Contextual Preference Ranking | Drafting, i.e., the selection of a subset of items from a larger candidate set, is a key element of many games and related problems. It encompasses team formation in sports or e-sports, as well as deck selection in many modern card games. The key difficulty of drafting is that it is typically not sufficient to simply e... | ['Martin Müller', 'Johannes Fürnkranz', 'Timo Bertram'] | 2021-05-25 | null | null | null | null | ['card-games'] | ['playing-games'] | [ 2.51242071e-01 -1.77031025e-01 -2.43290424e-01 -1.75578743e-01
-3.83937925e-01 -7.40015805e-01 -5.63947000e-02 4.54610467e-01
-8.01146686e-01 8.47730517e-01 1.97012633e-01 -1.56741589e-01
-8.79910767e-01 -1.02076340e+00 -4.71175879e-01 -4.11855578e-01
-2.51391977e-01 1.18167543e+00 5.25411189e-01 -8.45390975... | [3.461162805557251, 1.4257694482803345] |
7f6b3479-3ac8-4430-b2ec-58194590866c | explicitly-controllable-3d-aware-portrait | 2209.05434 | null | https://arxiv.org/abs/2209.05434v3 | https://arxiv.org/pdf/2209.05434v3.pdf | 3DFaceShop: Explicitly Controllable 3D-Aware Portrait Generation | In contrast to the traditional avatar creation pipeline which is a costly process, contemporary generative approaches directly learn the data distribution from photographs. While plenty of works extend unconditional generative models and achieve some levels of controllability, it is still challenging to ensure multi-vi... | ['Fang Wen', 'Lizhuang Ma', 'Dong Chen', 'Ting Zhang', 'Binxin Yang', 'Bo Zhang', 'Junshu Tang'] | 2022-09-12 | null | null | null | null | ['3d-face-animation', 'face-reenactment', 'face-model'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.18036175e-01 1.40951812e-01 1.21343911e-01 -4.72662777e-01
-4.30236578e-01 -8.09961259e-01 7.79842317e-01 -7.11518943e-01
3.61883283e-01 7.11019099e-01 2.35553965e-01 4.60877389e-01
2.82865800e-02 -9.45140839e-01 -7.55666256e-01 -8.90899718e-01
3.69582981e-01 6.36314273e-01 -2.18515366e-01 -4.51401234... | [12.299912452697754, -0.47503042221069336] |
328152d8-da1c-4c64-ad1c-073a97f4526a | differentiable-tracking-based-training-of | 2111.00030 | null | https://arxiv.org/abs/2111.00030v1 | https://arxiv.org/pdf/2111.00030v1.pdf | Differentiable Tracking-Based Training of Deep Learning Sound Source Localizers | Data-based and learning-based sound source localization (SSL) has shown promising results in challenging conditions, and is commonly set as a classification or a regression problem. Regression-based approaches have certain advantages over classification-based, such as continuous direction-of-arrival estimation of stati... | ['Tuomas Virtanen', 'Archontis Politis', 'Sharath Adavanne'] | 2021-10-29 | null | null | null | null | ['direction-of-arrival-estimation', 'sound-classification'] | ['audio', 'audio'] | [ 2.53396556e-02 -5.80408871e-01 1.06093600e-01 -4.17265207e-01
-1.64636743e+00 -6.35475576e-01 3.63312095e-01 1.63723990e-01
-4.98286664e-01 4.16557342e-01 5.41266054e-03 -2.49876991e-01
-2.38471776e-01 -5.76581098e-02 -8.10365915e-01 -8.41156721e-01
-3.72282654e-01 9.32008997e-02 5.02423346e-01 1.56829938... | [15.194772720336914, 5.344437599182129] |
3722904b-2808-4d0b-bb3b-0ff33217a19b | simulslt-end-to-end-simultaneous-sign | 2112.04228 | null | https://arxiv.org/abs/2112.04228v1 | https://arxiv.org/pdf/2112.04228v1.pdf | SimulSLT: End-to-End Simultaneous Sign Language Translation | Sign language translation as a kind of technology with profound social significance has attracted growing researchers' interest in recent years. However, the existing sign language translation methods need to read all the videos before starting the translation, which leads to a high inference latency and also limits th... | ['Xiaofei He', 'Xingshan Zeng', 'Meng Zhang', 'Weike Jin', 'Jinglin Liu', 'Zhou Zhao', 'Aoxiong Yin'] | 2021-12-08 | null | null | null | null | ['sign-language-translation'] | ['computer-vision'] | [ 3.09971094e-01 -3.91899467e-01 -2.30431005e-01 -2.54158050e-01
-5.38022816e-01 -4.06704128e-01 4.37284678e-01 -8.59500051e-01
-4.94653076e-01 4.13552046e-01 5.90404749e-01 -2.23471910e-01
3.51020753e-01 -5.20325422e-01 -6.61223888e-01 -6.23486578e-01
4.16932821e-01 1.09968707e-01 4.70232636e-01 4.06874344... | [9.186925888061523, -6.484431266784668] |
c0ca3ab8-5653-4dff-a7d5-eef9655e943e | a-3d-face-modelling-approach-for-pose | 1606.00474 | null | http://arxiv.org/abs/1606.00474v1 | http://arxiv.org/pdf/1606.00474v1.pdf | A 3D Face Modelling Approach for Pose-Invariant Face Recognition in a Human-Robot Environment | Face analysis techniques have become a crucial component of human-machine
interaction in the fields of assistive and humanoid robotics. However, the
variations in head-pose that arise naturally in these environments are still a
great challenge. In this paper, we present a real-time capable 3D face
modelling framework f... | ['Matthias Rätsch', 'Philipp Kopp', 'Michael Grupp', 'Patrik Huber'] | 2016-06-01 | null | null | null | null | ['robust-face-recognition', '3d-face-modeling'] | ['computer-vision', 'computer-vision'] | [ 3.56714576e-02 3.27205688e-01 3.89823079e-01 -6.20554209e-01
-4.13036980e-02 -1.70122415e-01 4.43539679e-01 -4.80037719e-01
-5.11059821e-01 2.73438275e-01 -3.36505532e-01 1.67457536e-01
-8.67637023e-02 -3.58157277e-01 -5.93984962e-01 -5.15954137e-01
-1.85487047e-01 1.03879511e+00 -5.00592068e-02 -1.82651699... | [13.460610389709473, 0.21938800811767578] |
1a1a301e-f9d1-4d99-b41a-ec35ac8ff70e | recursive-gaussian-process-over-graphs-for | 2209.01703 | null | https://arxiv.org/abs/2209.01703v1 | https://arxiv.org/pdf/2209.01703v1.pdf | Recursive Gaussian Process over graphs for Integrating Multi-timescale Measurements in Low-Observable Distribution Systems | The transition to a smarter grid is empowered by enhanced sensor deployments and smart metering infrastructure in the distribution system. Measurements from these sensors and meters can be used for many applications, including distribution system state estimation (DSSE). However, these measurements are typically sample... | ['Balasubramaniam Natarajan', 'Shweta Dahale'] | 2022-09-04 | null | null | null | null | ['matrix-completion'] | ['methodology'] | [ 1.22904498e-02 -3.64588022e-01 5.41471720e-01 -2.74104416e-01
-1.07559121e+00 -4.32818532e-01 4.93308246e-01 6.89069331e-01
3.68351042e-02 1.04739618e+00 2.93504819e-02 -9.57469866e-02
-6.84464872e-01 -9.76836383e-01 -3.16985011e-01 -1.23098862e+00
-7.13387251e-01 8.37725103e-01 -1.91168860e-01 8.37723613... | [5.799465179443359, 2.6110517978668213] |
0b39aa90-b049-4cd6-87a4-a54bd80e68c0 | surfit-learning-to-fit-surfaces-improves-few | 2112.13942 | null | https://arxiv.org/abs/2112.13942v2 | https://arxiv.org/pdf/2112.13942v2.pdf | PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud Segmentation | We present PriFit, a semi-supervised approach for label-efficient learning of 3D point cloud segmentation networks. PriFit combines geometric primitive fitting with point-based representation learning. Its key idea is to learn point representations whose clustering reveals shape regions that can be approximated well by... | ['Subhransu Maji', 'Rui Wang', 'Matheus Gadelha', 'Aruni RoyChowdhury', 'Erik Learned-Miller', 'Liangliang Cao', 'Evangelos Kalogerakis', 'Marios Loizou', 'Bidya Dash', 'Gopal Sharma'] | 2021-12-27 | null | null | null | null | ['point-cloud-segmentation'] | ['computer-vision'] | [ 5.44251651e-02 3.81185532e-01 -2.25637570e-01 -3.59133422e-01
-7.94800520e-01 -7.70312905e-01 8.22026789e-01 3.16307962e-01
-4.90346402e-02 -9.29921344e-02 -1.84523597e-01 -2.19702333e-01
-4.76699956e-02 -7.25142479e-01 -1.03604090e+00 -3.59970927e-01
-1.71789050e-01 1.25101161e+00 6.67720735e-01 -1.46883339... | [8.030754089355469, -3.348069667816162] |
f4f1ae41-b6ed-43d3-b364-2dc4476c3fbe | supervised-dimensionality-reduction-and | 2002.11934 | null | https://arxiv.org/abs/2002.11934v2 | https://arxiv.org/pdf/2002.11934v2.pdf | Supervised Dimensionality Reduction and Visualization using Centroid-encoder | Visualizing high-dimensional data is an essential task in Data Science and Machine Learning. The Centroid-Encoder (CE) method is similar to the autoencoder but incorporates label information to keep objects of a class close together in the reduced visualization space. CE exploits nonlinearity and labels to encode high ... | ['Tomojit Ghosh', 'Michael Kirby'] | 2020-02-27 | null | null | null | null | ['supervised-dimensionality-reduction'] | ['computer-vision'] | [-2.68814206e-01 -6.57891762e-03 -9.77320448e-02 -2.91751623e-01
-6.92195669e-02 -6.81163132e-01 7.54472077e-01 2.62831271e-01
-4.77554947e-02 3.06738049e-01 7.24511504e-01 -1.33172274e-01
-8.01360846e-01 -5.04288435e-01 -1.57025456e-01 -8.87719870e-01
-7.06437409e-01 4.92144972e-01 -3.48731726e-01 8.45997185... | [8.044784545898438, 4.442458629608154] |
222a64d7-d86a-4d38-be63-63e49c8a44a6 | generalizing-hierarchical-bayesian-bandits | 2205.15124 | null | https://arxiv.org/abs/2205.15124v3 | https://arxiv.org/pdf/2205.15124v3.pdf | Mixed-Effect Thompson Sampling | A contextual bandit is a popular framework for online learning to act under uncertainty. In practice, the number of actions is huge and their expected rewards are correlated. In this work, we introduce a general framework for capturing such correlations through a mixed-effect model where actions are related through mul... | ['Sumeet Katariya', 'Branislav Kveton', 'Imad Aouali'] | 2022-05-30 | null | null | null | null | ['thompson-sampling'] | ['methodology'] | [ 7.97154307e-02 2.94871092e-01 -7.87070215e-01 -4.49377388e-01
-1.06997681e+00 -4.87098694e-01 5.59907377e-01 -1.27183005e-01
-3.74537468e-01 1.27257359e+00 3.67506832e-01 -2.82511890e-01
-5.98559797e-01 -5.37458956e-01 -1.07030141e+00 -8.62886548e-01
-4.03403819e-01 4.46970701e-01 2.49146111e-02 8.98088366... | [4.488411903381348, 3.168287992477417] |
1b1504c9-7367-4888-bef1-8568ef44b6a5 | meccano-a-multimodal-egocentric-dataset-for | 2209.08691 | null | https://arxiv.org/abs/2209.08691v1 | https://arxiv.org/pdf/2209.08691v1.pdf | MECCANO: A Multimodal Egocentric Dataset for Humans Behavior Understanding in the Industrial-like Domain | Wearable cameras allow to acquire images and videos from the user's perspective. These data can be processed to understand humans behavior. Despite human behavior analysis has been thoroughly investigated in third person vision, it is still understudied in egocentric settings and in particular in industrial scenarios. ... | ['Giovanni Maria Farinella', 'Antonino Furnari', 'Francesco Ragusa'] | 2022-09-19 | null | null | null | null | ['human-object-interaction-detection', 'action-anticipation'] | ['computer-vision', 'computer-vision'] | [ 3.41068834e-01 7.64258322e-04 5.11146225e-02 -3.38922501e-01
6.01238310e-02 -5.66788256e-01 6.43079638e-01 1.07933357e-01
-4.81029153e-01 3.69685262e-01 8.97787418e-03 2.80021161e-01
-1.84231445e-01 -1.42399952e-01 -7.90486872e-01 -8.35798442e-01
-1.10520367e-02 4.07049596e-01 -3.08548729e-03 -8.94310176... | [7.9915289878845215, 0.34627124667167664] |
d7067ee5-5ab6-4233-941d-fb48809066a9 | analysis-of-skin-lesion-images-with-deep | 2101.03814 | null | https://arxiv.org/abs/2101.03814v1 | https://arxiv.org/pdf/2101.03814v1.pdf | Analysis of skin lesion images with deep learning | Skin cancer is the most common cancer worldwide, with melanoma being the deadliest form. Dermoscopy is a skin imaging modality that has shown an improvement in the diagnosis of skin cancer compared to visual examination without support. We evaluate the current state of the art in the classification of dermoscopic image... | ['Sten Hanke', 'Josef Steppan'] | 2021-01-11 | null | null | null | null | ['skin-lesion-classification'] | ['medical'] | [ 6.84634507e-01 7.82641321e-02 -3.79131347e-01 -2.92182863e-01
-7.05861449e-01 -4.09363210e-01 6.10186577e-01 4.17205751e-01
-6.73036575e-01 5.89911461e-01 -1.37717053e-01 -4.57661778e-01
-1.12069689e-01 -6.39310241e-01 -3.33357036e-01 -7.76484609e-01
1.26832217e-01 2.77264386e-01 2.19990209e-01 3.33805420... | [15.69419002532959, -3.0080161094665527] |
9d362cc8-214e-4ad7-94b0-24aa309abdc9 | transmatting-enhancing-transparent-objects | 2208.03007 | null | https://arxiv.org/abs/2208.03007v3 | https://arxiv.org/pdf/2208.03007v3.pdf | TransMatting: Enhancing Transparent Objects Matting with Transformers | Image matting refers to predicting the alpha values of unknown foreground areas from natural images. Prior methods have focused on propagating alpha values from known to unknown regions. However, not all natural images have a specifically known foreground. Images of transparent objects, like glass, smoke, web, etc., ha... | ['Lili Guo', 'Lele Xu', 'Fanglei Xue', 'Huanqia Cai'] | 2022-08-05 | null | null | null | null | ['transparent-objects', 'image-matting'] | ['computer-vision', 'computer-vision'] | [ 4.95501816e-01 1.81473419e-02 7.23233968e-02 -6.34639561e-01
-4.11872894e-01 -2.06911922e-01 3.91018808e-01 -6.27387822e-01
1.11272261e-01 6.84471548e-01 1.35433316e-01 1.48395956e-01
4.29825664e-01 -9.08335447e-01 -1.31038427e+00 -9.79754686e-01
4.29463416e-01 3.09332758e-01 7.36184120e-01 1.89909831... | [10.573429107666016, -0.9400648474693298] |
a0485bbb-e219-4f18-9574-19aaa77ecfe9 | personalized-image-enhancement-featuring | 2306.09334 | null | https://arxiv.org/abs/2306.09334v1 | https://arxiv.org/pdf/2306.09334v1.pdf | Personalized Image Enhancement Featuring Masked Style Modeling | We address personalized image enhancement in this study, where we enhance input images for each user based on the user's preferred images. Previous methods apply the same preferred style to all input images (i.e., only one style for each user); in contrast to these methods, we aim to achieve content-aware personalizati... | ['Toshihiko Yamasaki', 'Satoshi Kosugi'] | 2023-06-15 | null | null | null | null | ['image-enhancement'] | ['computer-vision'] | [ 4.74082887e-01 -2.60727495e-01 -8.95945802e-02 -4.72681403e-01
-4.06613767e-01 -6.75963819e-01 3.01079601e-01 -2.77308941e-01
-6.28122747e-01 2.55612999e-01 3.76488924e-01 -6.02946384e-03
2.60199100e-01 -7.29498029e-01 -7.23213851e-01 -4.63829339e-01
4.82304752e-01 -2.24962667e-01 8.95277411e-02 -2.11956590... | [11.384010314941406, -0.7023983597755432] |
1dad70b2-c40c-4727-92a2-58de8d51246a | adaptive-adversarial-network-for-source-free | null | null | http://openaccess.thecvf.com//content/ICCV2021/html/Xia_Adaptive_Adversarial_Network_for_Source-Free_Domain_Adaptation_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Xia_Adaptive_Adversarial_Network_for_Source-Free_Domain_Adaptation_ICCV_2021_paper.pdf | Adaptive Adversarial Network for Source-Free Domain Adaptation | Unsupervised Domain Adaptation solves knowledge transfer along with the coexistence of well-annotated source domain and unlabeled target instances. However, the source domain in many practical applications is not always accessible due to data privacy or the insufficient memory storage for small devices. This scenar... | ['Zhengming Ding', 'Handong Zhao', 'Haifeng Xia'] | 2021-01-01 | null | null | null | iccv-2021-1 | ['source-free-domain-adaptation'] | ['computer-vision'] | [ 0.44871536 0.20746702 -0.5322131 -0.37525 -0.7065857 -0.764929
0.41248557 -0.21866024 -0.36492363 0.9266798 -0.12495644 -0.05717435
0.07227294 -0.7456856 -0.8803345 -0.8565614 0.4209853 0.4590098
0.17458017 -0.03321573 -0.06387831 0.40511385 -1.20612 0.16582826
1.093026 1.288898 0.083... | [10.411221504211426, 3.1398110389709473] |
728c8472-7310-45bb-97e0-66dfb39e5e46 | storygraphs-visualizing-character | null | null | http://openaccess.thecvf.com/content_cvpr_2014/html/Tapaswi_StoryGraphs_Visualizing_Character_2014_CVPR_paper.html | http://openaccess.thecvf.com/content_cvpr_2014/papers/Tapaswi_StoryGraphs_Visualizing_Character_2014_CVPR_paper.pdf | StoryGraphs: Visualizing Character Interactions as a Timeline | We present a novel way to automatically summarize and represent the storyline of a TV episode by visualizing character interactions as a chart. We also propose a scene detection method that lends itself well to generate over-segmented scenes which is used to partition the video. The positioning of character lines in th... | ['Rainer Stiefelhagen', 'Martin Bauml', 'Makarand Tapaswi'] | 2014-06-01 | null | null | null | cvpr-2014-6 | ['person-identification'] | ['computer-vision'] | [ 3.31931680e-01 6.06921390e-02 1.10035375e-01 -3.33936572e-01
-7.65698075e-01 -1.06778109e+00 7.16802657e-01 5.00240088e-01
2.28869915e-01 5.76244712e-01 7.11420000e-01 -5.22706993e-02
-7.72426724e-02 -4.53426242e-01 -4.53614563e-01 -1.76234782e-01
-1.66414112e-01 1.90820232e-01 1.16345003e-01 -9.42853093... | [11.034138679504395, 0.6358421444892883] |
a64d94a6-c07c-444a-97c3-e13dffdcbb41 | a-data-centric-framework-for-improving-domain | 2304.00483 | null | https://arxiv.org/abs/2304.00483v2 | https://arxiv.org/pdf/2304.00483v2.pdf | A Data-centric Framework for Improving Domain-specific Machine Reading Comprehension Datasets | Low-quality data can cause downstream problems in high-stakes applications. Data-centric approach emphasizes on improving dataset quality to enhance model performance. High-quality datasets are needed for general-purpose Large Language Models (LLMs) training, as well as for domain-specific models, which are usually sma... | ['Josip Car', 'Shafiq Joty', 'Mathieu Ravaut', 'Duy Phung', 'Qi Chwen Ong', 'Sreeja Tar', 'Verena Suharman', 'Josef Halim', 'Iva Bojic'] | 2023-04-02 | null | null | null | null | ['reading-comprehension', 'machine-reading-comprehension'] | ['natural-language-processing', 'natural-language-processing'] | [ 2.97443748e-01 4.00184155e-01 -4.46323723e-01 -5.95314562e-01
-1.56628597e+00 -5.13292551e-01 4.10507709e-01 6.17024422e-01
-7.03153253e-01 1.19146788e+00 4.50213313e-01 -3.12388480e-01
-3.75589639e-01 -7.92138755e-01 -1.07294953e+00 -3.32372606e-01
7.17264712e-01 5.32697439e-01 -1.40053168e-01 1.48990685... | [8.467530250549316, 8.65436840057373] |
61258548-e9cf-43e4-9188-2d9dbabcf15a | ranking-differential-privacy | 2301.00841 | null | https://arxiv.org/abs/2301.00841v1 | https://arxiv.org/pdf/2301.00841v1.pdf | Ranking Differential Privacy | Rankings are widely collected in various real-life scenarios, leading to the leakage of personal information such as users' preferences on videos or news. To protect rankings, existing works mainly develop privacy protection on a single ranking within a set of ranking or pairwise comparisons of a ranking under the $\ep... | ['Guang Cheng', 'Will Wei Sun', 'SHIRONG XU'] | 2023-01-02 | null | null | null | null | ['inference-attack'] | ['adversarial'] | [ 1.34112462e-01 -9.79188457e-02 2.75144950e-02 -7.16063738e-01
-9.87346828e-01 -1.12208796e+00 2.06677675e-01 -8.44559968e-02
-4.44523901e-01 7.14184344e-01 1.65387079e-01 -2.07889065e-01
-7.41821408e-01 -8.25301230e-01 -5.73149800e-01 -6.41969979e-01
-4.56062913e-01 -4.38616350e-02 -2.94459816e-02 -2.50290960... | [5.976934909820557, 6.697381019592285] |
116b76be-72ed-4571-8cbd-ce45eab15e75 | good-better-best-textual-distractors | 1910.09134 | null | https://arxiv.org/abs/1910.09134v3 | https://arxiv.org/pdf/1910.09134v3.pdf | Good, Better, Best: Textual Distractors Generation for Multiple-Choice Visual Question Answering via Reinforcement Learning | Multiple-choice VQA has drawn increasing attention from researchers and end-users recently. As the demand for automatically constructing large-scale multiple-choice VQA data grows, we introduce a novel task called textual Distractors Generation for VQA (DG-VQA) focusing on generating challenging yet meaningful distract... | ['Yi Ren', 'Xin Ye', 'Jiaying Lu', 'Yezhou Yang'] | 2019-10-21 | null | null | null | null | ['distractor-generation'] | ['natural-language-processing'] | [-8.60011801e-02 1.34272501e-01 7.09175318e-02 -1.84943825e-01
-1.27617705e+00 -7.45391965e-01 7.51790464e-01 1.26766175e-01
-4.96006638e-01 7.16903687e-01 2.08883598e-01 -4.20270652e-01
2.73268551e-01 -5.84117055e-01 -6.81884408e-01 -4.58193839e-01
4.24747676e-01 7.83462405e-01 1.98737606e-01 -7.30240047... | [11.624666213989258, 8.340675354003906] |
64a73033-f8fa-4b6b-b8db-48592598542e | camerapose-weakly-supervised-monocular-3d | 2301.02979 | null | https://arxiv.org/abs/2301.02979v1 | https://arxiv.org/pdf/2301.02979v1.pdf | CameraPose: Weakly-Supervised Monocular 3D Human Pose Estimation by Leveraging In-the-wild 2D Annotations | To improve the generalization of 3D human pose estimators, many existing deep learning based models focus on adding different augmentations to training poses. However, data augmentation techniques are limited to the "seen" pose combinations and hard to infer poses with rare "unseen" joint positions. To address this pro... | ['Jenq-Neng Hwang', 'Zhongyu Jiang', 'Ke Zhang', 'Nan Qiao', 'Yuyin Sun', 'Lu Xia', 'Jiajia Luo', 'Cheng-Yen Yang'] | 2023-01-08 | null | null | null | null | ['3d-human-pose-estimation', 'monocular-3d-human-pose-estimation'] | ['computer-vision', 'computer-vision'] | [-1.43473312e-01 3.38278770e-01 -1.32197618e-01 -4.23672408e-01
-1.00279617e+00 -4.57316816e-01 4.14334863e-01 -1.89638078e-01
-6.99082017e-01 4.84576762e-01 3.46264482e-01 4.52520907e-01
3.04498434e-01 -2.87827730e-01 -1.02583539e+00 -3.39058340e-01
1.39716879e-01 9.51109350e-01 3.83803129e-01 -4.37000960... | [7.020162582397461, -0.9128594994544983] |
323243c4-d970-4da7-899b-29791e95e54d | counterfactual-graph-learning-for-link | 2106.02172 | null | https://arxiv.org/abs/2106.02172v2 | https://arxiv.org/pdf/2106.02172v2.pdf | Learning from Counterfactual Links for Link Prediction | Learning to predict missing links is important for many graph-based applications. Existing methods were designed to learn the association between observed graph structure and existence of link between a pair of nodes. However, the causal relationship between the two variables was largely ignored for learning to predict... | ['Meng Jiang', 'Wenhao Yu', 'Daheng Wang', 'Gang Liu', 'Tong Zhao'] | 2021-06-03 | counterfactual-graph-learning-for-link-1 | https://openreview.net/forum?id=N2dEPuK0vom | https://openreview.net/pdf?id=N2dEPuK0vom | neurips-2021-12 | ['counterfactual-inference'] | ['miscellaneous'] | [ 4.01568890e-01 8.61707687e-01 -1.01432502e+00 -3.08449149e-01
-2.81092320e-02 -2.77384102e-01 7.66898453e-01 5.46933055e-01
4.55321908e-01 1.14554560e+00 6.29687846e-01 -9.52144742e-01
-4.10541028e-01 -1.49329126e+00 -1.19574022e+00 -1.75211653e-01
-8.10211480e-01 3.32315266e-01 -4.57253028e-03 -1.78481206... | [7.940249919891357, 5.931220054626465] |
d8ce1049-3585-47f2-aa89-d3ef38715c81 | gowvis-a-web-application-for-graph-of-words | null | null | https://aclanthology.org/P16-4026 | https://aclanthology.org/P16-4026.pdf | GoWvis: A Web Application for Graph-of-Words-based Text Visualization and Summarization | null | ['Antoine Tixier', 'Michalis Vazirgiannis', 'Konstantinos Skianis'] | 2016-08-01 | null | null | null | acl-2016-8 | ['ad-hoc-information-retrieval'] | ['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... | [-7.272640705108643, 3.4766929149627686] |
ec0e1549-d11c-433d-979c-8ab8358832d2 | co-attention-based-neural-network-for-source-2 | 1908.01993 | null | https://arxiv.org/abs/1908.01993v1 | https://arxiv.org/pdf/1908.01993v1.pdf | Co-Attention Based Neural Network for Source-Dependent Essay Scoring | This paper presents an investigation of using a co-attention based neural network for source-dependent essay scoring. We use a co-attention mechanism to help the model learn the importance of each part of the essay more accurately. Also, this paper shows that the co-attention based neural network model provides reliabl... | ['Haoran Zhang', 'Diane Litman'] | 2019-08-06 | co-attention-based-neural-network-for-source-1 | https://aclanthology.org/W18-0549 | https://aclanthology.org/W18-0549.pdf | ws-2018-6 | ['automated-essay-scoring'] | ['natural-language-processing'] | [-2.39152655e-01 2.12140128e-01 -4.30348068e-01 -8.16862702e-01
-1.03186059e+00 -3.21631193e-01 3.09335649e-01 1.36984482e-01
-5.99221706e-01 7.70736039e-01 8.73095095e-01 -1.20534465e-01
-1.20977998e-01 -8.27961266e-01 -3.95502150e-01 3.94853428e-02
7.55588412e-01 6.50347710e-01 -1.49718774e-02 -6.55332208... | [11.355890274047852, 9.342557907104492] |
585ed52c-6356-4113-aab9-9b2188390bba | simulating-single-photon-detector-array | 2210.05644 | null | https://arxiv.org/abs/2210.05644v1 | https://arxiv.org/pdf/2210.05644v1.pdf | Simulating single-photon detector array sensors for depth imaging | Single-Photon Avalanche Detector (SPAD) arrays are a rapidly emerging technology. These multi-pixel sensors have single-photon sensitivities and pico-second temporal resolutions thus they can rapidly generate depth images with millimeter precision. Such sensors are a key enabling technology for future autonomous system... | ['Jonathan Leach', 'Phil Soan', 'Istvan Gyongy', 'Feng Zhu', 'Germán Mora-Martín', 'Stirling Scholes'] | 2022-10-07 | null | null | null | null | ['pico'] | ['natural-language-processing'] | [ 6.04892075e-01 -2.91800201e-01 7.36625254e-01 -1.86589032e-01
-6.48536623e-01 -5.82459033e-01 5.50936878e-01 9.49387923e-02
-9.41322446e-01 7.84741104e-01 -5.83644271e-01 -6.01349212e-02
-2.00232953e-01 -9.30761278e-01 -4.17826176e-01 -1.11573589e+00
-1.41991377e-01 5.88253200e-01 8.29976141e-01 -4.63515595... | [9.694669723510742, -2.687131881713867] |
e70709d5-f8e2-45d5-bad4-a05102a3506b | conditional-infilling-gans-for-data | 1807.08093 | null | http://arxiv.org/abs/1807.08093v2 | http://arxiv.org/pdf/1807.08093v2.pdf | Conditional Infilling GANs for Data Augmentation in Mammogram Classification | Deep learning approaches to breast cancer detection in mammograms have
recently shown promising results. However, such models are constrained by the
limited size of publicly available mammography datasets, in large part due to
privacy concerns and the high cost of generating expert annotations. Limited
dataset size is ... | ['William Lotter', 'Kevin Wu', 'Eric Wu', 'David Cox'] | 2018-07-21 | null | null | null | null | ['breast-cancer-detection', 'breast-cancer-detection'] | ['knowledge-base', 'medical'] | [ 9.76294160e-01 1.00709581e+00 4.20003757e-02 -4.95107293e-01
-1.31585646e+00 -4.56533164e-01 4.87117589e-01 1.91741635e-03
-2.99668014e-01 1.03952432e+00 2.88024455e-01 -4.75385040e-01
3.28706056e-01 -9.96332705e-01 -9.64638114e-01 -6.73256516e-01
2.42334269e-02 5.90049386e-01 -8.28726217e-02 6.19973987... | [14.32275676727295, -1.9574916362762451] |
8c6573e4-078d-4675-a384-07afecf119c2 | detecting-rumours-with-latency-guarantees | 2205.06580 | null | https://arxiv.org/abs/2205.06580v1 | https://arxiv.org/pdf/2205.06580v1.pdf | Detecting Rumours with Latency Guarantees using Massive Streaming Data | Today's social networks continuously generate massive streams of data, which provide a valuable starting point for the detection of rumours as soon as they start to propagate. However, rumour detection faces tight latency bounds, which cannot be met by contemporary algorithms, given the sheer volume of high-velocity st... | ['Quoc Viet Hung Nguyen', 'Thai Son Mai', 'Thanh Thi Nguyen', 'Matthias Weidlich', 'Hongzhi Yin', 'Thanh Trung Huynh', 'Thanh Tam Nguyen'] | 2022-05-13 | null | null | null | null | ['rumour-detection'] | ['natural-language-processing'] | [ 7.19583854e-02 -4.55295704e-02 -1.71001673e-01 1.19111866e-01
-1.95939824e-01 -5.73791027e-01 5.38773715e-01 1.10756445e+00
-1.79237872e-01 5.53993940e-01 -2.38344204e-02 -2.44825646e-01
-2.85037845e-01 -1.02532256e+00 -3.00407737e-01 -2.67204762e-01
-8.92429173e-01 8.47774327e-01 1.05619621e+00 -5.29176414... | [8.15554428100586, 10.089387893676758] |
b9216c2b-7a01-4e73-bddb-97137df4b9e2 | vrt-a-video-restoration-transformer | 2201.12288 | null | https://arxiv.org/abs/2201.12288v2 | https://arxiv.org/pdf/2201.12288v2.pdf | VRT: A Video Restoration Transformer | Video restoration (e.g., video super-resolution) aims to restore high-quality frames from low-quality frames. Different from single image restoration, video restoration generally requires to utilize temporal information from multiple adjacent but usually misaligned video frames. Existing deep methods generally tackle w... | ['Luc van Gool', 'Radu Timofte', 'Yawei Li', 'Rakesh Ranjan', 'Kai Zhang', 'Yuchen Fan', 'JieZhang Cao', 'Jingyun Liang'] | 2022-01-28 | null | null | null | null | ['space-time-video-super-resolution', 'video-super-resolution', 'video-denoising', 'video-restoration'] | ['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision'] | [ 5.08427203e-01 -5.20002782e-01 -1.61570877e-01 -9.15171057e-02
-8.77683461e-01 3.37655134e-02 4.85486597e-01 -5.50897121e-01
-2.84410387e-01 7.58803785e-01 6.79862499e-01 1.34064198e-01
-1.67887568e-01 -3.79492491e-01 -8.00028980e-01 -9.88887191e-01
5.73855154e-02 -4.00180548e-01 3.65458846e-01 -2.70830274... | [11.090298652648926, -1.9228957891464233] |
d4afac2b-8937-483f-aabc-5edd749ed2e3 | framework-for-certification-of-ai-based | 2302.11049 | null | https://arxiv.org/abs/2302.11049v1 | https://arxiv.org/pdf/2302.11049v1.pdf | Framework for Certification of AI-Based Systems | The current certification process for aerospace software is not adapted to "AI-based" algorithms such as deep neural networks. Unlike traditional aerospace software, the precise parameters optimized during neural network training are as important as (or more than) the code processing the network and they are not direct... | ['Evan Wilson', 'Rob Timpe', 'Brian Shimanuki', 'Maxime Gariel'] | 2023-02-21 | null | null | null | null | ['common-sense-reasoning'] | ['reasoning'] | [-1.30115703e-01 2.44245544e-01 3.03137787e-02 -4.47606951e-01
9.04259831e-02 -7.89160728e-01 2.39431202e-01 1.06988706e-01
-4.31418233e-02 4.15938973e-01 -4.76656854e-01 -1.08119845e+00
-5.56618452e-01 -6.52981460e-01 -7.40787089e-01 -2.37461567e-01
5.00230044e-02 4.84169930e-01 -1.72421470e-01 -5.11218846... | [7.6597676277160645, 7.446241855621338] |
63b8f2e3-e73a-4a18-bcc6-3a413a29aed2 | merkel-podcast-corpus-a-multimodal-dataset-1 | null | null | https://aclanthology.org/2022.lrec-1.270 | https://aclanthology.org/2022.lrec-1.270.pdf | Merkel Podcast Corpus: A Multimodal Dataset Compiled from 16 Years of Angela Merkel’s Weekly Video Podcasts | We introduce the Merkel Podcast Corpus, an audio-visual-text corpus in German collected from 16 years of (almost) weekly Internet podcasts of former German chancellor Angela Merkel. To the best of our knowledge, this is the first single speaker corpus in the German language consisting of audio, visual and text modaliti... | ['Timo Baumann', 'Shravan Nayak', 'Debjoy Saha'] | null | null | null | null | lrec-2022-6 | ['face-detection', 'talking-face-generation', 'face-generation'] | ['computer-vision', 'computer-vision', 'computer-vision'] | [ 2.96630353e-01 4.11306262e-01 4.32183534e-01 -5.16761899e-01
-1.07277393e+00 -7.94392645e-01 8.94905150e-01 -3.89200933e-02
-2.36162357e-02 3.98823559e-01 6.16190732e-01 5.13899773e-02
-5.31102866e-02 -6.80154711e-02 -3.60032469e-01 -6.98114932e-01
1.24881957e-02 6.21739805e-01 2.02848613e-02 -1.87680960... | [14.403573036193848, 5.2171149253845215] |
fe7adcf4-887e-4afe-9f5d-29c753fc6b6e | improving-and-analyzing-neural-speaker | 2301.04571 | null | https://arxiv.org/abs/2301.04571v1 | https://arxiv.org/pdf/2301.04571v1.pdf | Improving And Analyzing Neural Speaker Embeddings for ASR | Neural speaker embeddings encode the speaker's speech characteristics through a DNN model and are prevalent for speaker verification tasks. However, few studies have investigated the usage of neural speaker embeddings for an ASR system. In this work, we present our efforts w.r.t integrating neural speaker embeddings in... | ['Hermann Ney', 'Ralf Schlüter', 'Mohammad Zeineldeen', 'Jingjing Xu', 'Christoph Lüscher'] | 2023-01-11 | null | null | null | null | ['speaker-verification'] | ['speech'] | [ 3.72400843e-02 3.08502614e-01 1.19667739e-01 -7.39569664e-01
-1.25192714e+00 -3.68279308e-01 4.08386230e-01 1.02823317e-01
-6.14130497e-01 5.13581783e-02 4.86843377e-01 -7.00204372e-01
3.29128146e-01 -2.11590827e-01 -4.45098221e-01 -3.87826502e-01
9.87690613e-02 3.87919307e-01 9.84890293e-03 -4.33620691... | [14.390109062194824, 6.256379127502441] |
33a7aed9-55a1-49d1-83ae-6c3d89108520 | absolute-value-constraint-the-reason-for | 2101.10942 | null | https://arxiv.org/abs/2101.10942v2 | https://arxiv.org/pdf/2101.10942v2.pdf | Absolute Value Constraint: The Reason for Invalid Performance Evaluation Results of Neural Network Models for Stock Price Prediction | Neural networks for stock price prediction(NNSPP) have been popular for decades. However, most of its study results remain in the research paper and cannot truly play a role in the securities market. One of the main reasons leading to this situation is that the prediction error(PE) based evaluation results have statist... | ['Yi Wei'] | 2021-01-10 | null | null | null | null | ['stock-price-prediction'] | ['time-series'] | [-8.93325150e-01 -6.97523534e-01 -2.26285696e-01 -3.73864383e-01
-3.00538801e-02 -3.28336120e-01 3.88140172e-01 -1.94407314e-01
-4.28618371e-01 7.08363354e-01 5.33161089e-02 -7.15209424e-01
-2.50012755e-01 -1.33614981e+00 -2.76317537e-01 -6.44092441e-01
7.13604912e-02 1.91194057e-01 3.27548593e-01 -5.75487375... | [4.5225982666015625, 4.192911148071289] |
5d930e54-4f9f-4cd9-9b3d-d5dc29f2b846 | mixpro-data-augmentation-with-maskmix-and | 2304.12043 | null | https://arxiv.org/abs/2304.12043v1 | https://arxiv.org/pdf/2304.12043v1.pdf | MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer | The recently proposed data augmentation TransMix employs attention labels to help visual transformers (ViT) achieve better robustness and performance. However, TransMix is deficient in two aspects: 1) The image cropping method of TransMix may not be suitable for vision transformer. 2) At the early stage of training, th... | ['Jun Liu', 'Fan Zhang', 'Wei Hu', 'Yangyu Huang', 'QiHao Zhao'] | 2023-04-24 | null | null | null | null | ['image-cropping'] | ['computer-vision'] | [ 2.95564830e-01 5.50236627e-02 -1.47631794e-01 -2.97932699e-02
-3.99800718e-01 -3.13989252e-01 3.73973817e-01 -1.97882533e-01
-3.95978779e-01 3.31876516e-01 -1.19407922e-01 -4.14118081e-01
3.07357848e-01 -7.74984956e-01 -8.73127639e-01 -6.17876828e-01
5.38702071e-01 2.23196000e-01 3.98238659e-01 -1.42926574... | [9.834083557128906, 0.4078010022640228] |
e5f76e35-747b-44e2-8f68-7f0e666e8994 | deeply-coupled-convolution-transformer-with | 2304.14122 | null | https://arxiv.org/abs/2304.14122v1 | https://arxiv.org/pdf/2304.14122v1.pdf | Deeply-Coupled Convolution-Transformer with Spatial-temporal Complementary Learning for Video-based Person Re-identification | Advanced deep Convolutional Neural Networks (CNNs) have shown great success in video-based person Re-Identification (Re-ID). However, they usually focus on the most obvious regions of persons with a limited global representation ability. Recently, it witnesses that Transformers explore the inter-patch relations with gl... | ['Huchuan Lu', 'Pingping Zhang', 'Chenyang Yu', 'Xuehu Liu'] | 2023-04-27 | null | null | null | null | ['person-re-identification'] | ['computer-vision'] | [-2.15945661e-01 -5.39822459e-01 -1.69866443e-01 -4.81485814e-01
-3.48808795e-01 -2.63021171e-01 7.31122911e-01 -2.66517729e-01
-4.42944139e-01 4.75513905e-01 7.08234370e-01 1.21624805e-01
-1.88822865e-01 -7.28154004e-01 -5.26143610e-01 -6.01609766e-01
-2.90307007e-03 5.70651665e-02 8.47174376e-02 -3.16344470... | [14.747115135192871, 0.9137153625488281] |
b50f00ba-5a9e-4ca1-8015-ae85e6f385c8 | beyond-weak-perspective-for-monocular-3d | 2009.06549 | null | https://arxiv.org/abs/2009.06549v1 | https://arxiv.org/pdf/2009.06549v1.pdf | Beyond Weak Perspective for Monocular 3D Human Pose Estimation | We consider the task of 3D joints location and orientation prediction from a monocular video with the skinned multi-person linear (SMPL) model. We first infer 2D joints locations with an off-the-shelf pose estimation algorithm. We use the SPIN algorithm and estimate initial predictions of body pose, shape and camera pa... | ['Imry Kissos', 'Mark Kliger', 'Lior Fritz', 'Eduard Oks', 'Omer Meir', 'Matan Goldman'] | 2020-09-14 | null | null | null | null | ['monocular-3d-human-pose-estimation'] | ['computer-vision'] | [-1.33655414e-01 3.87223005e-01 -1.61134288e-01 -4.12116796e-01
-7.96181738e-01 -4.62409765e-01 6.42634630e-01 -6.20387256e-01
-6.31919503e-01 4.31375742e-01 4.02433008e-01 2.71953791e-01
2.28299424e-01 -9.43210274e-02 -9.88340855e-01 -2.69069374e-01
-6.47431314e-02 1.16003680e+00 2.58905709e-01 -3.20934430... | [7.0495285987854, -0.9998615384101868] |
73ae4340-7ab0-4c5e-bd03-ab3a760e7f53 | multi-view-graph-convolutional-networks-for | 2005.04955 | null | https://arxiv.org/abs/2005.04955v3 | https://arxiv.org/pdf/2005.04955v3.pdf | Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction | Stock price movement prediction is commonly accepted as a very challenging task due to the volatile nature of financial markets. Previous works typically predict the stock price mainly based on its own information, neglecting the cross effect among involved stocks. However, it is well known that an individual stock pri... | ['Cheng-Zhong Xu', 'Juanjuan Zhao', 'Jiexia Ye', 'Kejiang Ye'] | 2020-05-11 | null | null | null | null | ['stock-prediction'] | ['time-series'] | [-6.41811669e-01 -3.82702321e-01 -2.75551528e-01 -2.04623327e-01
-1.45557344e-01 -8.03781390e-01 5.83665788e-01 -1.56518042e-01
-1.00098878e-01 6.00820839e-01 5.22564352e-01 -5.15851974e-01
-3.65712605e-02 -1.36921525e+00 -7.03228652e-01 -4.09566045e-01
-4.75903034e-01 2.96536654e-01 3.67149591e-01 -6.92088783... | [4.314165115356445, 4.337586402893066] |
9236230f-3f07-43fa-ac34-9c3211259dda | a-recommender-system-approach-for-very-large | 2304.04067 | null | https://arxiv.org/abs/2304.04067v1 | https://arxiv.org/pdf/2304.04067v1.pdf | A Recommender System Approach for Very Large-scale Multiobjective Optimization | We define very large multi-objective optimization problems to be multiobjective optimization problems in which the number of decision variables is greater than 100,000 dimensions. This is an important class of problems as many real-world problems require optimizing hundreds of thousands of variables. Existing evolution... | ['Kay Chen Tan', 'Qiuzhen Lin', 'Jonathan M. Garibaldi', 'Min Jiang', 'Haokai Hong'] | 2023-04-08 | null | null | null | null | ['thompson-sampling', 'multiobjective-optimization'] | ['methodology', 'methodology'] | [-1.17720827e-01 -4.37798589e-01 8.86732638e-02 -1.83444336e-01
-3.30082178e-01 -5.21547258e-01 -2.34337579e-02 4.19263691e-02
-6.13986909e-01 1.06993437e+00 9.28260684e-02 -5.38324900e-02
-7.55751073e-01 -9.73165274e-01 -4.37414080e-01 -7.94417143e-01
-1.79504126e-01 9.85145390e-01 1.88273951e-01 -6.03151023... | [5.714217185974121, 3.5622336864471436] |
ab880633-dddd-4a79-a636-f5c42fb1731a | implementing-a-wall-in-building-placement-in | 1306.4460 | null | http://arxiv.org/abs/1306.4460v1 | http://arxiv.org/pdf/1306.4460v1.pdf | Implementing a Wall-In Building Placement in StarCraft with Declarative Programming | In real-time strategy games like StarCraft, skilled players often block the
entrance to their base with buildings to prevent the opponent's units from
getting inside. This technique, called "walling-in", is a vital part of
player's skill set, allowing him to survive early aggression. However, current
artificial players... | ['Michal Certicky'] | 2013-06-19 | null | null | null | null | ['real-time-strategy-games'] | ['playing-games'] | [-2.38045678e-01 8.67754668e-02 3.11276197e-01 6.86253160e-02
-1.08343638e-01 -9.42543507e-01 2.36370973e-02 1.49834797e-01
-4.67555076e-01 7.68199921e-01 -2.87576854e-01 -7.34322667e-01
-3.45609784e-01 -1.20852208e+00 -6.91374689e-02 -3.25982690e-01
1.44771636e-01 5.38608491e-01 1.00439358e+00 -9.33027804... | [3.4738125801086426, 1.4811068773269653] |
110f9735-ee7f-4d87-89b0-5a86bbcf3ce3 | learning-to-start-for-sequence-to-sequence | 1608.05554 | null | http://arxiv.org/abs/1608.05554v1 | http://arxiv.org/pdf/1608.05554v1.pdf | Learning to Start for Sequence to Sequence Architecture | The sequence to sequence architecture is widely used in the response
generation and neural machine translation to model the potential relationship
between two sentences. It typically consists of two parts: an encoder that
reads from the source sentence and a decoder that generates the target sentence
word by word accor... | ['Wei-Nan Zhang', 'Ting Liu', 'Lianqiang Zhou', 'Qingfu Zhu'] | 2016-08-19 | null | null | null | null | ['short-text-conversation'] | ['natural-language-processing'] | [ 9.05920744e-01 3.30250055e-01 2.21875578e-01 -4.76696312e-01
-8.32053542e-01 -3.59005123e-01 7.54051805e-01 1.31507590e-01
-4.62866485e-01 1.08045292e+00 2.91664988e-01 -4.50264931e-01
3.93126875e-01 -6.81242764e-01 -6.83660686e-01 -5.72186053e-01
5.35571098e-01 4.28181142e-01 2.16520175e-01 -6.48536086... | [11.988985061645508, 8.96153736114502] |
d85f5f79-55cd-4159-a472-f003794a8eac | brain-mri-image-super-resolution-using-phase | 1807.11643 | null | http://arxiv.org/abs/1807.11643v1 | http://arxiv.org/pdf/1807.11643v1.pdf | Brain MRI Image Super Resolution using Phase Stretch Transform and Transfer Learning | A hallucination-free and computationally efficient algorithm for enhancing
the resolution of brain MRI images is demonstrated. | ['Sifeng He', 'Bahram Jalali'] | 2018-07-31 | null | null | null | null | ['medical-super-resolution'] | ['medical'] | [ 2.47427419e-01 -1.14234231e-01 5.14189899e-01 -2.44199365e-01
-4.59950149e-01 1.66752756e-01 2.31462657e-01 -3.20271969e-01
-6.65103555e-01 9.29845989e-01 4.50669199e-01 -2.40996778e-02
-1.51792750e-01 -4.70676869e-01 -3.97606194e-02 -6.65453672e-01
-5.86754858e-01 5.62909663e-01 1.20034568e-01 -3.33724618... | [13.676896095275879, -2.382397174835205] |
e8062a97-d532-4ece-a7ba-2010c9f976d8 | hit-hierarchical-transformer-with-momentum | 2103.15049 | null | https://arxiv.org/abs/2103.15049v2 | https://arxiv.org/pdf/2103.15049v2.pdf | HiT: Hierarchical Transformer with Momentum Contrast for Video-Text Retrieval | Video-Text Retrieval has been a hot research topic with the growth of multimedia data on the internet. Transformer for video-text learning has attracted increasing attention due to its promising performance. However, existing cross-modal transformer approaches typically suffer from two major limitations: 1) Exploitatio... | ['Zhongyuan Wang', 'Wenkui Ding', 'Yiru Chen', 'Shengsheng Qian', 'Haoqi Fan', 'Song Liu'] | 2021-03-28 | null | http://openaccess.thecvf.com//content/ICCV2021/html/Liu_HiT_Hierarchical_Transformer_With_Momentum_Contrast_for_Video-Text_Retrieval_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Liu_HiT_Hierarchical_Transformer_With_Momentum_Contrast_for_Video-Text_Retrieval_ICCV_2021_paper.pdf | iccv-2021-1 | ['video-text-retrieval'] | ['computer-vision'] | [ 6.48420453e-02 -1.04907107e+00 -3.60286891e-01 -3.49878699e-01
-1.29421818e+00 -3.75094891e-01 6.89639390e-01 1.46216288e-01
-4.48284268e-01 1.85883120e-01 3.72293383e-01 2.79717743e-01
-4.02421921e-01 -4.32345033e-01 -3.38302374e-01 -7.39953041e-01
1.28604561e-01 3.34885865e-01 3.27728659e-01 -1.62074357... | [10.503836631774902, 1.0525139570236206] |
36a07a63-eff5-4626-b50e-7b73e241f97b | deep-graph-convolutional-encoders-for | 1810.09995 | null | http://arxiv.org/abs/1810.09995v1 | http://arxiv.org/pdf/1810.09995v1.pdf | Deep Graph Convolutional Encoders for Structured Data to Text Generation | Most previous work on neural text generation from graph-structured data
relies on standard sequence-to-sequence methods. These approaches linearise the
input graph to be fed to a recurrent neural network. In this paper, we propose
an alternative encoder based on graph convolutional networks that directly
exploits the i... | ['Laura Perez-Beltrachini', 'Diego Marcheggiani'] | 2018-10-23 | deep-graph-convolutional-encoders-for-1 | https://aclanthology.org/W18-6501 | https://aclanthology.org/W18-6501.pdf | ws-2018-11 | ['graph-to-sequence', 'kg-to-text'] | ['natural-language-processing', 'natural-language-processing'] | [ 8.06620061e-01 8.02619457e-01 -2.30511338e-01 -3.53438735e-01
-3.03811908e-01 -6.10773623e-01 7.20082700e-01 1.62457883e-01
-1.37741193e-01 9.06912506e-01 5.62701344e-01 -9.05283332e-01
4.96526927e-01 -1.12661362e+00 -1.07244802e+00 -8.59724581e-02
-1.38187498e-01 2.95115918e-01 -1.01395391e-01 -5.13577521... | [10.287700653076172, 8.345161437988281] |
5a2c2c7a-9812-47d0-a287-4cd0c9f12696 | improving-adversarially-robust-few-shot-image | null | null | http://openaccess.thecvf.com//content/CVPR2022/html/Dong_Improving_Adversarially_Robust_Few-Shot_Image_Classification_With_Generalizable_Representations_CVPR_2022_paper.html | http://openaccess.thecvf.com//content/CVPR2022/papers/Dong_Improving_Adversarially_Robust_Few-Shot_Image_Classification_With_Generalizable_Representations_CVPR_2022_paper.pdf | Improving Adversarially Robust Few-Shot Image Classification With Generalizable Representations | Few-Shot Image Classification (FSIC) aims to recognize novel image classes with limited data, which is significant in practice. In this paper, we consider the FSIC problem in the case of adversarial examples. This is an extremely challenging issue because current deep learning methods are still vulnerable when hand... | ['Xiaohua Xie', 'Jian-Huang Lai', 'YuAn Wang', 'Junhao Dong'] | 2022-01-01 | null | null | null | cvpr-2022-1 | ['few-shot-image-classification'] | ['computer-vision'] | [ 6.64349377e-01 -1.63085088e-01 -1.21822360e-03 -2.75942236e-01
-9.60998774e-01 -6.42625034e-01 6.90769672e-01 -3.84070933e-01
-2.86993235e-01 6.91201866e-01 3.31986658e-02 8.28163847e-02
5.89516610e-02 -9.93773282e-01 -1.01796782e+00 -8.16300035e-01
3.36195201e-01 5.53268678e-02 2.18540907e-01 -5.46673238... | [5.572395324707031, 7.937742233276367] |
bdac168d-251d-4a4e-bc58-d41dd3233cfb | event-extraction-a-survey | 2210.03419 | null | https://arxiv.org/abs/2210.03419v2 | https://arxiv.org/pdf/2210.03419v2.pdf | Event Extraction: A Survey | Extracting the reported events from text is one of the key research themes in natural language processing. This process includes several tasks such as event detection, argument extraction, role labeling. As one of the most important topics in natural language processing and natural language understanding, the applicati... | ['Viet Dac Lai'] | 2022-10-07 | null | null | null | null | ['event-extraction'] | ['natural-language-processing'] | [ 4.41436052e-01 2.10658878e-01 -5.69134295e-01 -2.63723880e-01
-4.93711144e-01 -7.34702885e-01 9.32303309e-01 1.32279682e+00
-8.05974722e-01 1.32873797e+00 7.36538291e-01 -1.01086661e-01
-1.20618679e-01 -9.33139503e-01 -1.61708474e-01 -3.77192259e-01
-3.82549077e-01 1.29420161e-01 3.81878585e-01 -9.46745202... | [9.069578170776367, 9.198287010192871] |
84e27799-f946-41ea-92b1-318db2fb1fd2 | improved-latent-tree-induction-with-distant | 2109.05112 | null | https://arxiv.org/abs/2109.05112v2 | https://arxiv.org/pdf/2109.05112v2.pdf | Improved Latent Tree Induction with Distant Supervision via Span Constraints | For over thirty years, researchers have developed and analyzed methods for latent tree induction as an approach for unsupervised syntactic parsing. Nonetheless, modern systems still do not perform well enough compared to their supervised counterparts to have any practical use as structural annotation of text. In this w... | ['Andrew McCallum', 'Mohit Iyyer', 'Shilpa Suresh', 'Dylan Finkbeiner', 'Subendhu Rongali', "Tim O'Gorman", 'Jay Yoon Lee', 'Andrew Drozdov', 'Zhiyang Xu'] | 2021-09-10 | null | https://aclanthology.org/2021.emnlp-main.395 | https://aclanthology.org/2021.emnlp-main.395.pdf | emnlp-2021-11 | ['constituency-parsing'] | ['natural-language-processing'] | [ 4.89179343e-01 7.92930305e-01 -2.66995788e-01 -7.15578794e-01
-1.32207859e+00 -7.51649737e-01 2.35979602e-01 6.68704152e-01
-6.25945807e-01 9.26082909e-01 3.27961892e-01 -5.93307793e-01
2.26984665e-01 -6.92759633e-01 -5.50186515e-01 -4.34861511e-01
5.02471579e-03 6.20786309e-01 3.69583517e-01 -1.57283366... | [10.32093620300293, 9.713637351989746] |
5642e97b-2d18-43ab-ac38-7d475656f0d4 | a-computer-vision-assisted-approach-to | 2202.13285 | null | https://arxiv.org/abs/2202.13285v1 | https://arxiv.org/pdf/2202.13285v1.pdf | A Computer Vision-assisted Approach to Automated Real-Time Road Infrastructure Management | Accurate automated detection of road pavement distresses is critical for the timely identification and repair of potentially accident-inducing road hazards such as potholes and other surface-level asphalt cracks. Deployment of such a system would be further advantageous in low-resource environments where lack of govern... | ['Philippe Heitzmann'] | 2022-02-27 | null | null | null | null | ['road-damage-detection'] | ['computer-vision'] | [ 9.03484672e-02 2.44691044e-01 1.59471348e-01 -2.00105578e-01
-1.07676494e+00 -1.63980260e-01 2.62142986e-01 1.99854940e-01
-2.81776071e-01 3.45639378e-01 1.19597159e-01 -7.09993660e-01
-1.51165411e-01 -1.21223056e+00 -7.35328078e-01 -4.49158072e-01
8.59152973e-02 1.49647063e-02 4.81972247e-01 -3.42531770... | [7.418722152709961, 1.154784917831421] |
2c9f1305-fc64-461a-97ed-2b8f163f4153 | deep-learning-for-music | 1606.04930 | null | http://arxiv.org/abs/1606.04930v1 | http://arxiv.org/pdf/1606.04930v1.pdf | Deep Learning for Music | Our goal is to be able to build a generative model from a deep neural network
architecture to try to create music that has both harmony and melody and is
passable as music composed by humans. Previous work in music generation has
mainly been focused on creating a single melody. More recent work on polyphonic
music mode... | ['Raymond Wu', 'Allen Huang'] | 2016-06-15 | null | null | null | null | ['music-modeling'] | ['music'] | [-9.95645002e-02 1.01843029e-02 2.81812489e-01 9.82354488e-03
-6.95420742e-01 -2.78706670e-01 6.28202856e-01 -6.06868327e-01
-1.29344940e-01 8.32943499e-01 5.29715955e-01 1.15446530e-01
-1.24650441e-01 -1.00248528e+00 -5.25178850e-01 -5.98436296e-01
1.47456989e-01 8.84089351e-01 -1.88412100e-01 -2.94218183... | [15.985312461853027, 5.501816749572754] |
b1c66877-7fc1-4136-93a3-ce1818eb3a4b | deep-reinforcement-learning-for-sequence-to | 1805.09461 | null | http://arxiv.org/abs/1805.09461v4 | http://arxiv.org/pdf/1805.09461v4.pdf | Deep Reinforcement Learning For Sequence to Sequence Models | In recent times, sequence-to-sequence (seq2seq) models have gained a lot of
popularity and provide state-of-the-art performance in a wide variety of tasks
such as machine translation, headline generation, text summarization, speech to
text conversion, and image caption generation. The underlying framework for all
these... | ['Tian Shi', 'Naren Ramakrishnan', 'Chandan K. Reddy', 'Yaser Keneshloo'] | 2018-05-24 | null | null | null | null | ['headline-generation'] | ['natural-language-processing'] | [ 6.74415946e-01 3.36967528e-01 -2.10438251e-01 -1.84352368e-01
-1.08061707e+00 -3.95879418e-01 9.28407073e-01 -5.26570044e-02
-2.62328774e-01 1.17476368e+00 6.96176708e-01 -2.73092687e-01
1.64449736e-01 -6.81106687e-01 -8.61256599e-01 -5.97680986e-01
2.56784827e-01 5.06932259e-01 -4.83311936e-02 -5.32502472... | [12.04637622833252, 9.180880546569824] |
4ec368de-e41b-4f81-b948-c5c7132de745 | triplet-contrastive-learning-for-unsupervised | 2301.09498 | null | https://arxiv.org/abs/2301.09498v2 | https://arxiv.org/pdf/2301.09498v2.pdf | Triplet Contrastive Representation Learning for Unsupervised Vehicle Re-identification | Part feature learning is critical for fine-grained semantic understanding in vehicle re-identification. However, existing approaches directly model part features and global features, which can easily lead to serious gradient vanishing issues due to their unequal feature information and unreliable pseudo-labels for unsu... | ['Xiangbo Shu', 'Jinhui Tang', 'Liyan Zhang', 'Xiaoyu Du', 'Fei Shen'] | 2023-01-23 | null | null | null | null | ['vehicle-re-identification'] | ['computer-vision'] | [-1.26780555e-01 -1.86372221e-01 -4.65509027e-01 -5.85716784e-01
-8.61045361e-01 -3.01462978e-01 7.86721528e-01 2.71464288e-01
-2.97841996e-01 3.35738987e-01 -1.41929582e-01 -5.89332841e-02
-1.14458077e-01 -7.29056120e-01 -6.24829531e-01 -8.87102842e-01
1.93905786e-01 3.70716244e-01 2.49399900e-01 -1.08680740... | [8.148111343383789, -0.9390479922294617] |
92e87f25-64d7-4d44-952a-1a8347970a70 | spin-an-empirical-evaluation-on-sharing | 2207.10237 | null | https://arxiv.org/abs/2207.10237v1 | https://arxiv.org/pdf/2207.10237v1.pdf | SPIN: An Empirical Evaluation on Sharing Parameters of Isotropic Networks | Recent isotropic networks, such as ConvMixer and vision transformers, have found significant success across visual recognition tasks, matching or outperforming non-isotropic convolutional neural networks (CNNs). Isotropic architectures are particularly well-suited to cross-layer weight sharing, an effective neural netw... | ['Mohammad Rastegari', 'Maxwell Horton', 'Anurag Ranjan', 'Sachin Mehta', 'Thomas Merth', 'Anish Prabhu', 'Chien-Yu Lin'] | 2022-07-21 | null | null | null | null | ['neural-network-compression', 'neural-network-compression'] | ['methodology', 'miscellaneous'] | [ 3.17421198e-01 1.02930740e-01 -3.91374975e-01 -5.50071895e-01
-2.40364149e-01 -4.45851028e-01 7.05024302e-01 -3.95685494e-01
-1.06072938e+00 2.66345412e-01 3.21984291e-01 -7.07192540e-01
-3.50746036e-01 -5.70679069e-01 -6.98343515e-01 -5.29859424e-01
-1.94367170e-02 1.27766788e-01 1.32198572e-01 3.18292469... | [8.579537391662598, 3.054499864578247] |
cbda1a92-4fa6-4a35-8837-267e7819e191 | short-term-density-forecasting-of-low-voltage | 2204.13939 | null | https://arxiv.org/abs/2204.13939v3 | https://arxiv.org/pdf/2204.13939v3.pdf | Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows | The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level to increase efficiency and ensure reliable control. However, high fluctuations and increasing electrification cause huge forecast variability, not reflected in traditional point estimates. Probabilistic load f... | ['Oliver Dürr', 'Mark Nigge-Uricher', 'Beate Sick', 'Marcus Voss', 'Marcel Arpogaus'] | 2022-04-29 | null | null | null | null | ['probabilistic-deep-learning', 'probabilistic-time-series-forecasting'] | ['computer-vision', 'time-series'] | [-5.37409484e-01 -3.39739949e-01 -2.48547196e-01 -5.30880332e-01
-5.71540415e-01 -5.56125283e-01 5.11318445e-01 2.68881619e-01
4.83973585e-02 1.24333906e+00 1.41077682e-01 -6.70257270e-01
-3.41573685e-01 -1.24770439e+00 -3.30360800e-01 -9.32797074e-01
-1.91306457e-01 9.00430381e-01 -5.06136119e-01 3.82421523... | [6.110777378082275, 2.863161087036133] |
a30e61dc-9cc9-48bf-9a4f-b85d64e1a566 | safl-a-self-attention-scene-text-recognizer-1 | 2201.00132 | null | https://arxiv.org/abs/2201.00132v1 | https://arxiv.org/pdf/2201.00132v1.pdf | SAFL: A Self-Attention Scene Text Recognizer with Focal Loss | In the last decades, scene text recognition has gained worldwide attention from both the academic community and actual users due to its importance in a wide range of applications. Despite achievements in optical character recognition, scene text recognition remains challenging due to inherent problems such as distortio... | ['Phi Le Nguyen', 'Thanh Hung Nguyen', 'Duc Anh Le', 'Huu Manh Nguyen', 'Thanh Le-Cong', 'Bao Hieu Tran'] | 2022-01-01 | safl-a-self-attention-scene-text-recognizer | https://ieeexplore.ieee.org/document/9356232 | https://ieeexplore.ieee.org/document/9356232 | null | ['scene-text-recognition'] | ['computer-vision'] | [ 4.40768391e-01 -6.43576503e-01 8.60284735e-03 -3.31517845e-01
-1.40557036e-01 -2.01153718e-02 6.10900700e-01 -3.37875485e-02
-5.02964437e-01 3.98647219e-01 1.63200349e-01 -2.94491231e-01
-1.25623971e-01 -6.81925595e-01 -5.95517635e-01 -7.55598307e-01
4.71023947e-01 1.65406987e-02 3.61357778e-01 4.44287546... | [11.907398223876953, 2.1962296962738037] |
550ec98c-f9b9-4a1e-9f18-3efebc3518cb | pvt-cov19d-pyramid-vision-transformer-for | 2206.15069 | null | https://arxiv.org/abs/2206.15069v1 | https://arxiv.org/pdf/2206.15069v1.pdf | PVT-COV19D: Pyramid Vision Transformer for COVID-19 Diagnosis | With the outbreak of COVID-19, a large number of relevant studies have emerged in recent years. We propose an automatic COVID-19 diagnosis framework based on lung CT scan images, the PVT-COV19D. In order to accommodate the different dimensions of the image input, we first classified the images using Transformer models,... | ['Zhaoyan Yan', 'Rui Zhou', 'Tianyi Wang', 'Jiaxin Fan', 'Hanzhang Li', 'Xiaorun Tang', 'Jiaxuan Fang', 'Lilang Zheng'] | 2022-06-30 | null | null | null | null | ['covid-19-detection'] | ['medical'] | [-3.65978852e-02 -4.62825835e-01 5.23681343e-02 -2.54749298e-01
-2.30567962e-01 -2.29215682e-01 3.13263535e-01 -3.65152508e-01
-1.94071800e-01 4.69467491e-01 -5.44773117e-02 -4.28632766e-01
-2.89374202e-01 -7.50764132e-01 -1.56528234e-01 -6.97547019e-01
-1.35672927e-01 1.06160712e+00 7.52533972e-01 4.04452950... | [15.538105010986328, -1.7410807609558105] |
5ac2f329-ad99-41e1-9ac9-651ae84f1841 | modnet-v-improving-portrait-video-matting-via | 2109.11818 | null | https://arxiv.org/abs/2109.11818v1 | https://arxiv.org/pdf/2109.11818v1.pdf | MODNet-V: Improving Portrait Video Matting via Background Restoration | To address the challenging portrait video matting problem more precisely, existing works typically apply some matting priors that require additional user efforts to obtain, such as annotated trimaps or background images. In this work, we observe that instead of asking the user to explicitly provide a background image, ... | ['Rynson W. H. Lau', 'Huchuan Lu', 'Lihe Zhang', 'Zhanghan Ke', 'Jiayu Sun'] | 2021-09-24 | null | null | null | null | ['image-matting', 'video-matting'] | ['computer-vision', 'computer-vision'] | [ 4.67782617e-01 -1.64996326e-01 2.37455871e-02 -6.27310649e-02
-3.78031105e-01 -2.88041472e-01 3.22426498e-01 -2.32783407e-01
-3.04728389e-01 4.65885848e-01 -1.39752924e-01 -2.83180654e-01
5.54266870e-01 -8.15519750e-01 -9.71721113e-01 -5.19706249e-01
4.55577105e-01 1.93307593e-01 4.82901931e-01 -1.89594239... | [10.63322925567627, -0.9379869699478149] |
6187d43f-3241-4c62-b288-0b8c829c9074 | multi-stage-fault-warning-for-large-electric | 1903.06700 | null | http://arxiv.org/abs/1903.06700v1 | http://arxiv.org/pdf/1903.06700v1.pdf | Multi-Stage Fault Warning for Large Electric Grids Using Anomaly Detection and Machine Learning | In the monitoring of a complex electric grid, it is of paramount importance
to provide operators with early warnings of anomalies detected on the network,
along with a precise classification and diagnosis of the specific fault type.
In this paper, we propose a novel multi-stage early warning system prototype
for electr... | ['Ernest Fokoué', 'Sanjeev Raja'] | 2019-03-15 | null | null | null | null | ['subgroup-discovery'] | ['methodology'] | [-1.71742961e-01 -7.08299339e-01 3.65315199e-01 -8.29259381e-02
-4.26329762e-01 -6.28930867e-01 5.33725739e-01 6.93720818e-01
7.29089901e-02 6.08495772e-01 -5.00603989e-02 -4.57864046e-01
-7.44375050e-01 -9.39412773e-01 2.66655862e-01 -1.07651365e+00
-7.84711123e-01 6.29231453e-01 1.85205892e-01 -9.99826044... | [6.347107410430908, 2.5676074028015137] |
f4f2148a-95b8-406b-b105-d0c1700fd188 | efficient-neural-architecture-search-for | 2303.13653 | null | https://arxiv.org/abs/2303.13653v1 | https://arxiv.org/pdf/2303.13653v1.pdf | Efficient Neural Architecture Search for Emotion Recognition | Automated human emotion recognition from facial expressions is a well-studied problem and still remains a very challenging task. Some efficient or accurate deep learning models have been presented in the literature. However, it is quite difficult to design a model that is both efficient and accurate at the same time. M... | ['Santosh Kumar Vipparthi', 'Yashwanth Reddy Meedimale', 'Satish Kumar Reddy', 'Murari Mandal', 'Monu Verma'] | 2023-03-23 | null | null | null | null | ['micro-expression-recognition', 'architecture-search'] | ['computer-vision', 'methodology'] | [-2.97860265e-01 -5.54530501e-01 1.56880826e-01 -7.00229228e-01
-3.98003131e-01 -2.17625692e-01 3.46910387e-01 -4.05115545e-01
-4.92584974e-01 6.09057963e-01 -2.07386956e-01 4.21052247e-01
8.92410949e-02 -2.25203738e-01 -3.56016338e-01 -8.06940675e-01
-1.87095344e-01 3.46814208e-02 -3.29369903e-01 -5.05525351... | [13.607269287109375, 1.8129751682281494] |
c7402001-821c-4e38-b788-50b7a92bbace | uolo-automatic-object-detection-and | 1810.05729 | null | http://arxiv.org/abs/1810.05729v1 | http://arxiv.org/pdf/1810.05729v1.pdf | UOLO - automatic object detection and segmentation in biomedical images | We propose UOLO, a novel framework for the simultaneous detection and
segmentation of structures of interest in medical images. UOLO consists of an
object segmentation module which intermediate abstract representations are
processed and used as input for object detection. The resulting system is
optimized simultaneousl... | ['Aurélio Campilho', 'Ana Maria Mendonça', 'Teresa Araújo', 'Adrian Galdran', 'Pedro Costa', 'Guilherme Aresta'] | 2018-10-09 | null | null | null | null | ['fovea-detection'] | ['medical'] | [ 5.72471499e-01 3.42576236e-01 -8.34210142e-02 -3.18676591e-01
-7.02731133e-01 -4.69669133e-01 2.84587294e-01 6.02775097e-01
-7.72303462e-01 3.77739936e-01 -3.29162180e-01 -3.03890407e-01
7.53383413e-02 -6.14988387e-01 -5.25908470e-01 -7.01116502e-01
-6.14815503e-02 6.12106383e-01 7.16792703e-01 4.66939121... | [15.758807182312012, -3.9084229469299316] |
1d2c815b-86d4-4faa-bb79-7f0e6bb5400c | pose-guided-person-image-generation | 1705.09368 | null | http://arxiv.org/abs/1705.09368v6 | http://arxiv.org/pdf/1705.09368v6.pdf | Pose Guided Person Image Generation | This paper proposes the novel Pose Guided Person Generation Network (PG$^2$)
that allows to synthesize person images in arbitrary poses, based on an image
of that person and a novel pose. Our generation framework PG$^2$ utilizes the
pose information explicitly and consists of two key stages: pose integration
and image ... | ['Luc van Gool', 'Tinne Tuytelaars', 'Xu Jia', 'Qianru Sun', 'Liqian Ma', 'Bernt Schiele'] | 2017-05-25 | pose-guided-person-image-generation-1 | http://papers.nips.cc/paper/6644-pose-guided-person-image-generation | http://papers.nips.cc/paper/6644-pose-guided-person-image-generation.pdf | neurips-2017-12 | ['gesture-to-gesture-translation', 'pose-transfer'] | ['computer-vision', 'computer-vision'] | [ 4.73154843e-01 2.39963025e-01 5.36838830e-01 -2.88875133e-01
-5.52406788e-01 -4.29435909e-01 6.09723926e-01 -5.49040496e-01
-5.05549014e-01 8.89355242e-01 -7.43960664e-02 2.82770723e-01
2.46467084e-01 -9.05597389e-01 -6.99461162e-01 -5.25522709e-01
4.71674725e-02 5.76305389e-01 -1.51767433e-01 -1.69320703... | [12.020356178283691, -0.7995086312294006] |
65fc5d81-bb22-458a-a7c1-3105b95927cb | a-theoretical-investigation-of-graph-degree | 1801.07889 | null | http://arxiv.org/abs/1801.07889v3 | http://arxiv.org/pdf/1801.07889v3.pdf | A Theoretical Investigation of Graph Degree as an Unsupervised Normality Measure | For a graph representation of a dataset, a straightforward normality measure
for a sample can be its graph degree. Considering a weighted graph, degree of a
sample is the sum of the corresponding row's values in a similarity matrix. The
measure is intuitive given the abnormal samples are usually rare and they are
dissi... | ['Emre Aksu', 'Lixin Fan', 'Francesco Cricri', 'Caglar Aytekin'] | 2018-01-24 | null | null | null | null | ['spectral-graph-clustering'] | ['graphs'] | [ 2.62453891e-02 1.83269918e-01 9.92771424e-03 -2.26423860e-01
2.35263072e-02 -3.59695792e-01 2.73647845e-01 6.71014488e-01
1.06037498e-01 1.17850378e-01 -1.21772185e-01 -2.07350984e-01
-5.70979118e-01 -9.02372539e-01 -1.58859700e-01 -7.96639621e-01
-8.07306111e-01 2.85625130e-01 3.90280157e-01 -5.81209213... | [6.986027240753174, 5.530588626861572] |
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