[TOC]
截至2019年12月11日 2019年度阅读论文:
视觉领域论文
Fashion 相关论文:
1611.09577 Fast Face-swap Using Convolutional Neural Networks.pdf
1711.08447 [VITON] An Image-based Virtual Try-on Network.pdf
1807.07688 [CP-VTON] Toward Characteristic-Preserving Image-based Virtual Try-On Network.pdf
1906.01347 [WUTON] End-to-End Learning of Geometric Deformations of Feature Maps for Virtual Try-On .pdf
1906.07251 (ncsu JD Oppo)Pose Guided Fashion Image Synthesis Using Deep Generative Model.pdf
[MG-VTON] Towards Multi-pose Guided Virtual Try-on Network.pdf
[ClothFlow] Han_ClothFlow_A_Flow-Based_Model_for_Clothed_Person_Generation_ICCV_2019_paper.pdf
[DeepFashion] Liu_DeepFashion_Powering_Robust_CVPR_2016_paper.pdf
[DeepFashion2]—A Versatile Benchmark for Detection, Pose Estimation,Segmentation and Re-Identification of Clothing Images.pdf
[OpenPose] 1611.08050 Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields .pdf
[OpenPose] 1812.08008 [OpenPose] Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields.pdf
[Parsing] Liang_Human_Parsing_With_Contextualized Convolutional Neural Network ICCV_2015_paper.pdf
[Parsing] Zhao_Self-Supervised_Neural_Aggregation_CVPR_2017_paper.pdf
Fashion is my profession.pdf
Parsing Clothing in Fashion Photographs .pdf
MG-VTON-sysu.edu/
ViTON-xintong/
中山大学：
Dong_7329-[SG-WGAN] soft-gated-warping-gan-for-pose-guided-person-image-synthesis.pdf
Dong_FW-GAN_Flow-Navigated_Warping_GAN_for_Video_Virtual_Try-On_ICCV_2019_paper.pdf
Viton相关：
1805.04953 Learning Rich Features for Image Manipulation Detection.pdf
1902.01096 FiNet-Compatible and Diverse Fashion Image Inpainting.pdf
1910.02624 Label-PEnet .pdf
automatic-fashion-concept-final.pdf
Han_ClothFlow_A_Flow-Based_Model_for_Clothed_Person_Generation_ICCV_2019_paper.pdf
Han_FiNet_Compatible_and_Diverse_Fashion_Image_Inpainting_ICCV_2019_paper.pdf
iFAN_ Image-Instance Full Alignment Networks for Adaptive Object Detection.pdf
CV 相关论文:
0700.00000 A duality based approach for realtime tv-l 1 optical flow.pdf
1911.05722 [MoCo] Momentum Contrast for Unsupervised Visual Representation Learning .pdf
2010 视觉挑战赛 The PASCAL Visual Object Classes (VOC) Challenge .pdf
hodan2017tless [T-LESS] An RGB-D Dataset for 6D Pose Estimation of Texture-less Objects .pdf
CV classfication:
1406.4729 [SPP] PPT .pdf
1406.4729 [SPP] Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.pdf
1412.0767 [C3D] Learning Spatiotemporal Features with 3D Convolutional Networks.pdf
1512.03385 [ResNet] -Deep Residual Learning for Image Recognition.pdf
1608.00859 [TSN] Temporal Segment Networks-Towards Good Practices for Deep Action Recognition.pdf
1703.05593 [Geometric Matching] Convolutional neural network architecture for geometric matching.pdf
1705.07750 [I3D] Quo Vadis, Action Recognition A New Model and the Kinetics Dataset .pdf
1708.05038 [R3D] ConvNet Architecture Search for Spatiotemporal Feature Learning.pdf
1709.01507 [SeNet] Squeeze-and-Excitation Networks.pdf
1711.07971 [NoLocal] Non-local Neural Networks.pdf
1711.08200 [T3D] Temporal 3D ConvNets-New Architecture and Transfer Learning for video Classification.pdf
1711.11248 [R21D] A Closer Look at Spatiotemporal Convolutions for Action Recognition.pdf
1711.11248v3 [R21D] A Closer Look at Spatiotemporal Convolutions for Action Recognition.pdf
1712.00559 [PNasNet] Progressive Neural Architecture Search.pdf
I3D_PPT.pdf
NVIDIA_R3DCNN_cvpr2016.pdf
*3D *
1803.11527v3 [SpiderCNN] Deep Learning on Point Sets with .pdf
attention
‘1809.00916 [OCNet] Object Context Network for Scene Parsing.pdf’
‘1809.02983 [DANet] Dual Attention Network for Scene Segmentation.pdf’
‘1811.11721 [CCNet] Criss-Cross Attention for Semantic Segmentation.pdf’
‘1908.06955 [DGMN] Dynamic Graph Message Passing Networks.pdf’
‘7318 [A^2Net] -a2-nets-double-attention-networks.pdf’
GAN
‘1411.1784 [CGAN] Conditional Generative Adversarial Nets.pdf’
‘1611.02200 UNSUPERVISED CROSS-DOMAIN IMAGE GENERATION.pdf’
‘1710.00962 [GP-GAN] Gender Ptreserving GAN for Synathesizing Faces from Landmarks.pdf’
‘1803.04189 [N2N] Noise2Noise- learning Image restoration with Clean Data.pdf’
‘1909.04988 How Old Are You–Face Age Translation with Identity Preservation Using GANs .pdf’
‘1910.10334 Relation Modeling with Graph Convolutional Networks for Facial Action Unit Detection.pdf’
‘1910.10344 Facial Expression Restoration Based on Improved Graph Convolutional Networks.pdf’
‘CVPR-2019-Drawing [APDrawingGAN]Generating Artistic Portrait Drawings from Face Photos with Hierarchical GANs.pdf’
object detection
‘1611.10012 [vs] Speed-accuracy trade-offs for modern convolutional object detectors.pdf’
‘1707.09605v2 [Crowd Counting] CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting.pdf’
segmentation
‘1411.4038 [FCN] Fully Convolutional Networks for Semantic Segmentation.pdf’
‘1500.00000 [FCN -CVPR ] Fully convolutional networks for semantic segmentation.pdf’
‘1505.04597 [U-Net] Convolutional Networks for Biomedical Image Segmentation.pdf’
‘1511.00561 [SegNet] SegNet A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.pdf’
‘1605.06211 [FCN] Fully Convolutional Networks for Semantic Segmentation.pdf’
‘1700.00000 [LIP] CVPR_2017_paper Look into Person Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing.pdf’
‘1703.06870 [2018.1] Mark R-CNN.pdf’
‘1803.10683v3 [Human Instance Segmentation] Pose2Seg-Detection Free Human Instance Segmentation.pdf’
‘1804.01984 [LIP] Look into Person Joint Body Parsing and Pose Estimation Network and A New Benchmark.pdf’
‘1811.12596v1 [Human Part segmentation] Parsing R-CNN for Instance-Level Human Analysis.pdf’
‘1910.09777 [CVPR2019] Self-Correction for Human Parsing .pdf’
GeometricMatching
‘1511.05065 【Proposal Flow】.pdf’ ‘Articulated Human Detection with Flexible Mixtures-of-Parts.pdf’
GAME Papers
‘[DRL] Playing FPS Games with Deep Reinforcement Learning 14456-66873-1-PB.pdf’
‘[facebook AI]Better Computer Go Player with Neural Network and Long-Term Prediction 1511.06410.pdf’
‘[MLL] 2006 Multi-Label Neural Networks with Applications to Functional Genomics and Text Categorization.pdf’
‘[MLL] gibaja2015 A Tutorial on Multilabel Learning.pdf’
‘[MLL] TKDE14- A Review On Multi-Label Learning Algorithms.pdf’
‘[MLL][PPT] A Review on Multi-Label Learning Algorithms.pdf’
‘[PaperRead] Accurate, Large Minibatch SGD- Training ImageNet in One Hour .docx’
0709/
‘1603.05027_Identity Mappings in Deep Residual Networks.pdf’
‘1706.02677_[Facebook] Accurate,LargeMinibatch SGD-training ImageNet in 1 Hour.pdf’
‘1806.07366 [NIPs2018 best paper] Neural Ordinary Differential Equations.pdf’
‘1810.09026 Actor-Critic Policy Optimization in Partially Observable Multiagent Enviroments.pdf’
‘1811.08883v1 [kaiming.he] Rethinking ImageNet Pre-training .pdf’
‘baier2018 [[Baier et al] Emulating human play in a leading mobile card game.pdf’
DDZ/
‘Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability 10.0000@dl.acm.org@3305958.pdf’
Game/
‘hierarchical-deep-reinforcement-learning-integrating-temporal-abstraction-and-intrinsic-motivation 6233-.pdf’
lstm_cnn/
‘mcts-A survery of Monte Carlo Tree Search Methods REVIEW.pdf’
mcts-survey.pdf
‘MiniMax etc’/
‘Multi-armed Bandits with Episode Context.pdf’
‘sample statistical gradient-following algorithms for connectionist reinforcement learning -williams92simple.pdf’
Texas/
发表/
基于深度森林算法的慢性胃炎中医证候分类.pdf
深度强化学习进展-从AlphaGo到AlphaGoZero.pdf
RL:
‘[Professor]Peter Stone_ Publications Sorted by Date.pdf的注释概要.pdf’
rl_ppt/
7-pg Lecture 7 Policy Gradient.pdf
8-dyna Lecture 8 Integrating Learning and Planning.pdf
‘RL-1-Playing Atari with Deep Reinforcement Learning_1312.5602.pdf’
‘RL-2-human-level control through deep reinforcement learing - mnih2015.pdf’
‘RL-3-Deep Recurrent Q-Learing for Partially Observable MDPs 1507.06527.pdf’
RLbook2018.pdf
‘RL-ppt-Deep Recurrent Q-Learning for Partially - SDMIA15-Hausknecht.slides.pdf’
“强化学习—DQN算法原理详解 _ Wanjun’s blog.pdf”
“强化学习-基本概念 _ Wanjun’s blog.pdf”
0709:
‘1602.02867_v1_Value Iteration Networks.pdf’
‘1602.02867_v2_Value Iteration Networks.pdf’
‘1611.01626_v2_PGQ-Combining Policy Gradietn And Q-Learning.pdf’
‘1611.05763_Learning to reinforcement learn.pdf’
‘1702.03037_Multi-agent Reinforcement Learning in Sequential Social Dilemmas.pdf’
‘1706.02677[Facebook] Accurate,LargeMinibatch SGD-training ImageNet in 1 Hour.pdf’
‘mu-thesis Scaling Distributed Machine Learning with System and Algorithm Co-design.pdf’
‘phd_perolat_Reinforcement Learning- The Multi-Player Case.pdf’
‘stochgames.ijcai09_Computing Equilibria in Multiplayer Stochastic Games of Imperfect Information.pdf’
Temporal-difference_search_in_Computer_Go.pdf
深度强化学习进展-从AlphaGo到AlphaGoZero.pdf
*DDZ: *
‘0176 [IJCAI] DeltaDou- Expert-level Doudizhu AI through Self-play.pdf’
‘A Solution to China Competitive Poker Using Deep learning .pdf’
‘1901.08925_Combinational Q-Learning for Dou Di Zhu.pdf’
*GAME: *
‘[DeepMind] Mastering the Game of Go With Deep Neural Networks and Tree Search.pdf’
‘[DeepMind] Mastering the Game of Go With Deep Neural Networks and Tree Search-DESKTOP-828K1I6.pdf’
‘[DeepMind] Mastering the game of Go without human knowledge.pdf’
‘[DeepMind] Move Evaluation in Go Using Deep Convolutional Neural Networks 1412.6564.pdf’
‘[DeepMind][IN] 1612.00222 Interaction Networks for Learning about Objects, Relations and Physics .pdf’
‘[DeepMind][smooth_uct] Smooth UCT Search in Computer Poker.pdf’
‘[DeepMind][StarCraft II]1708.04782 StarCraft II- A New Challenge for .pdf’
‘[DeepMind][VIN]1706.01433 Visual Interaction Networks.pdf’
‘[DeepStack] 1701.01724 Expert-Level Artificail Intelligence in Heads-Up No-Limit Poker.pdf’
‘[Google] 1706.03762 Attention Is All You Need.pdf’
‘[SP] [FSP] Fictitious self-play in extensive-form games [UCL & DeepMind]heinrich15.pdf’
‘[SP] [MC-NFSP][Othello] 1903.09569 [ZJU] Monte Carlo Neural Fictitious Self-Play Approach to Approximate Nash Equilibrium of Imperfect-Information Games.pdf’
‘[SP] [NFSP] 1603.01121 Deep Reinforcement Learning from Self-Play in Imperfect-Information Games.pdf’
‘1807.06813 [Scopone] Traditional Wisdom and Monte Carlo Tree Search Face-to-Face in the Card Game Scopone.pdf’
‘1910.04376 [RLCard] A Toolkit for Reinforcement Learning in Card Games.pdf’
Bridge/
‘1509.06731 Poker-CNN APattern Learning Strategy for Making Draws and Bets In Poker Game.pdf’
‘1607.03290 [CN] Automatic Bridge Bidding Using Deep Reinforcement Learning.pdf’
‘1903.00900v2 Competitive Bridge Bidding with Deep Neural Networks .pdf’
‘2010 [NYU] The State of Automated Bridge Play.pdf’
Double_Dummy_Analysis.pdf
DraftBoostingBridge.pdf
Skat/
‘09-IJCAI [Skat] Improving State Evaluation, Inference, and Search in Trick-Based Card Games.pdf’
‘11 [UA] [Skat] Search, Inference and Opponent Modelling in an Expert-Caliber Skat Player.pdf’
‘11 [UA] [Skat] Search, Inference and Opponent Modelling in an Expert-Caliber Skat Player-DESKTOP-828K1I6.pdf’
‘13 [UA] [Skat] Symmetries and Search in Trick-Taking Card Games.pdf’
‘1903.09604 [Skat] Improving Search with Supervised Learning in Trick-Based Card Games.pdf’
‘1905.10907 [Skat] Learning Policies from Human Data for Skat.pdf’
‘1906.00000 [Skat] Policy Based Inference in Trick-Taking Card Games.pdf’
*Lstm_CNN: *
‘[CLDNN] Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks - sainath2015.pdf’
‘[ConvLSTM] 5955-convolutional-lstm-network-a-machine-learning-approach-for-precipitation-nowcasting.pdf’
‘[ConvLSTM]-21-Unsupervised Learning of Video Representations using LSTMs - 1502.04681.pdf’
‘[LRCN] Long-term Recurrent Convolutional Networks for Visual Recognition and Description - 1411.4389.pdf’
‘[lstm + RL-PG] Recurrent Policy Gradients joa2009.pdf’
‘[lstm+ DRL] Language Understanding for Text-based Games using Deep 1506.08941v2.pdf’
‘[lstm+ RL] 1953-reinforcement-learning-with-long-short-term-memory.pdf’
‘[lstm-cards] Implementing a Doppelkopf Card Game - BA-Obenaus.pdf’
‘1502.04681 [LSTMs] Unsupervised Learning of Video Representations using LSTMs.pdf’
MiniMax etc:
allis-thesis.pdf
Proof-number search allis1994.pdf
*Texas: *
‘1809.04040-Solving Imperfect-Information Games.pdf’
CFR/
‘1407.5042-Solving Large Imperfect Information Gmae Using CFR+.pdf’
‘17-AAAI-Refinement Safe and Nested Endgame Solving for Imperfect-Information Games.pdf’
‘17-AAAI-Safe and Nested Endgame Solving for Imperfect.docx’
‘CFR Variant.pptx’
CFR+.pptx
‘REGRET MINIMIZATION IN GAMES AND THE DEVELOPMENT OF CHAMPION MULTIPLAYER Computer POKER_PLAYINGACENTS.pdf’
‘Generalized Sampling and Variance in Counterfactual Regret Minimization.pdf’
Libratus.pptx
‘Superhuman AI for heads up no limit poker Libratus beats top professionals.pdf’
‘Superhuman AI for multiplayer poker.pdf’
‘博论全文-REGRET MINIMIZATION IN GAMES AND THE DEVELOPMENT OF CHAMPION MULTIPLAYER Computer POKER_PLAYINGACENTS.pdf’