ICCV图像处理相关论文总结（103篇）
1、Person ReID（行人再识别）（15）
1、Neural Person Search Machines
Hao Liu, Jiashi Feng,Zequn Jie, Karlekar Jayashree, Bo Zhao, Meibin Qi, Jianguo Jiang, Shuicheng Yan
2、Cross-View Asymmetric Metric Learning for Unsupervised PersonRe-Identification
Hong-Xing Yu, Ancong Wu,Wei-Shi Zheng
3、SHaPE: A Novel Graph Theoretic Algorithm for MakingConsensus-Based Decisions in Person Re-Identification Systems
Arko Barman, Shishir K.Shah
4、A Two Stream Siamese Convolutional Neural Network for PersonRe-Identification
Dahjung Chung, KhalidTahboub, Edward J. Delp
5、Efficient Online Local Metric Adaptation via Negative Samplesfor Person Re-Identification
Jiahuan Zhou, Pei Yu, WeiTang, Ying Wu
6、Learning View-Invariant Features for Person Identification inTemporally Synchronized Videos Taken by Wearable Cameras
Kang Zheng, XiaochuanFan, Yuewei Lin, Hao Guo, Hongkai Yu, Dazhou Guo, Song Wang
7、Deeply-Learned Part-Aligned Representations for PersonRe-Identification
Liming Zhao, Xi Li,Yueting Zhuang, Jingdong Wang
8、Unlabeled Samples Generated by GAN Improve the PersonRe-Identification Baseline in Vitro
Zhedong Zheng, LiangZheng, Yi Yang
9、Pose-Driven Deep Convolutional Model for PersonRe-Identification
Chi Su, Jianing Li,Shiliang Zhang, Junliang Xing, Wen Gao, Qi Tian
10、Jointly Attentive Spatial-Temporal Pooling Networks forVideo-Based Person Re-Identification
Shuangjie Xu, Yu Cheng,Kang Gu, Yang Yang, Shiyu Chang, Pan Zhou
11、RGB-Infrared Cross-Modality Person Re-Identification
Ancong Wu, Wei-Shi Zheng,Hong-Xing Yu, Shaogang Gong, Jianhuang Lai
12、Multi-Scale Deep Learning Architectures for PersonRe-Identification
Xuelin Qian, Yanwei Fu,Yu-Gang Jiang, Tao Xiang, Xiangyang Xue
13、Stepwise Metric Promotion forUnsupervised Video PersonRe-Identification
Zimo Liu;Dong Wang;Huchuan Lu
14、Dynamic Label Graph Matching forUnsupervised VideoRe-Identification
Mang Ye; AndyJ. Ma; LiangZheng; Jiawei Li; Pong C. Yuen
15、Jointly Attentive Spatial-TemporalPooling Networks forVideo-Based Person Re-Identification
Shuangjie Xu;Yu Cheng;Kang Gu; Yang Yang; Shiyu Chang; Pan Zhou
2、Pedestrian recognition（行人识别）（10）
1、SBGAR: Semantics BasedGroup ActivityRecognition
XinLi, MooiChoo Chuah
2、R-C3D: RegionConvolutional 3D Network for Temporal ActivityDetection
HuijuanXu, Abir Das, Kate Saenko
3、Learning Long-TermDependenciesfor Action Recognition With a Biologically-Inspired Deep Network
YeminShi, YonghongTian, Yaowei Wang, Wei Zeng, Tiejun Huang
4、Ensemble Deep Learningfor Skeleton-Based ActionRecognition Using Temporal Sliding LSTM Networks
InwoongLee, Doyoung Kim,Seoungyoon Kang, Sanghoon Lee
5、Adaptive RNN Tree forLarge-Scale Human Action Recognition
WenboLi, Longyin Wen, Ming-Ching Chang,Ser Nam Lim, Siwei Lyu
6、View Adaptive RecurrentNeural Networks for High Performance HumanAction Recognition From Skeleton Data
PengfeiZhang, Cuiling Lan, JunliangXing, Wenjun Zeng, Jianru Xue, Nanning Zheng
7、Lattice Long Short-TermMemory for Human Action Recognition
LinSun, Kui Jia, Kevin Chen, Dit-YanYeung, Bertram E. Shi, Silvio Savarese
8、Single Image ActionRecognition Using Semantic Body Part Actions
ZhichenZhao, Huimin Ma, Shaodi You
9、RPAN: An End-To-EndRecurrent Pose-Attention Network for ActionRecognition in Videos
WenbinDu, Yali Wang, Yu Qiao
10、Learning ActionRecognition Model From Depth and Skeleton Videos
HosseinRahmani, Mohammed Bennamoun
3、Pedestrian Retrieval（行人检索）（3）
1、SVDNet forPedestrian Retrieval
Yifan Sun;Liang Zheng; Weijian Deng;Shengjin Wang
2、HydraPlus-Net:Attentive Deep Featuresfor Pedestrian Analysis
Xihui Liu;Haiyu Zhao; Maoqing Tian; LuSheng; Jing Shao; Shuai Yi; Junjie Yan; XiaogangWang
3、Spatio-TemporalPerson Retrieval viaNatural Language Queries
Masataka Yamaguchi;Kuniaki Saito; YoshitakaUshiku; Tatsuya Harada
4、Tracking（跟踪）（16）
1、Non-Markovian GloballyConsistent Multi-Object Tracking
AndriiMaksai, Xinchao Wang, Francois Fleuret, Pascal Fua
2、CDTS: CollaborativeDetection, Tracking, and Segmentation for Online Multiple Object Segmentationin Videos
YeongJun Koh, Chang-Su Kim
3、Online Multi-ObjectTracking Using CNN-Based Single Object Tracker With Spatial-Temporal AttentionMechanism
QiChu, Wanli Ouyang, Hongsheng Li, Xiaogang Wang, Bin Liu, Nenghai Yu
4、Tracking theUntrackable: Learning to Track Multiple Cues With Long-Term Dependencies
AmirSadeghian, Alexandre Alahi, Silvio Savarese
5、Learning Dynamic Siamese Networkfor Visual Object Tracking
QingGuo, Wei Feng, Ce Zhou, Rui Huang, Liang Wan, Song Wang
6、CREST: ConvolutionalResidual Learningfor Visual Tracking
YibingSong,Chao Ma, Lijun Gong, Jiawei Zhang, Rynson W. H. Lau, Ming-Hsuan Yang
7、LearningBackground-Aware CorrelationFilters for Visual Tracking
HamedKianiGaloogahi, Ashton Fagg, Simon Lucey
8、Need for Speed: ABenchmark for HigherFrame Rate Object Tracking
HamedKianiGaloogahi, Ashton Fagg, Chen Huang, Deva Ramanan, Simon Lucey
9、Parallel Tracking andVerifying: AFramework for Real-Time and High Accuracy Visual Tracking
HengFan,Haibin Ling
10、Non-Rigid ObjectTracking viaDeformable Patches Using Shape-Preserved KCF and Level Sets
XinSun,Ngai-Man Cheung, Hongxun Yao, Yiluan Guo
11、Tracking as OnlineDecision-Making:Learning a Policy From Streaming Videos With ReinforcementLearning
JamesSupancic,III,Deva Ramanan
12、Learning Policies forAdaptive TrackingWith Deep Feature Cascades
ChenHuang,Simon Lucey, Deva Ramanan
13、Robust Object TrackingBased onTemporal and Spatial Deep Networks
ZhuTeng,Junliang Xing, Qiang Wang, Congyan Lang, Songhe Feng, Yi Jin
14、Non-Markovian GloballyConsistentMulti-Object Tracking
AndriiMaksai,Xinchao Wang, Francois Fleuret, Pascal Fua
15、Beyond StandardBenchmarks:Parameterizing Performance Evaluation in Visual Object Tracking
LukaCehovinZajc, Alan Lukezic, Ales Leonardis, Matej Kristan
16、Online Multi-ObjectTracking UsingCNN-Based Single Object Tracker With Spatial-Temporal AttentionMechanism
QiChu, WanliOuyang, Hongsheng Li, Xiaogang Wang, Bin Liu, Nenghai Yu
5、Object Detection（目标检测）（13）
1、Amulet: AggregatingMulti-LevelConvolutional Features for Salient Object Detection
PingpingZhang, Dong Wang, Huchuan Lu,Hongyu Wang, Xiang Ruan
2、Flow-Guided FeatureAggregation forVideo Object Detection
Xizhou Zhu,Yujie Wang, Jifeng Dai, Lu Yuan,Yichen Wei
3、DeNet: ScalableReal-Time ObjectDetection With Directed Sparse Sampling
LachlanTychsen-Smith, Lars Petersson
4、Recurrent ScaleApproximation forObject Detection in CNN
Yu Liu,Hongyang Li, Junjie Yan, FangyinWei, Xiaogang Wang, Xiaoou Tang
5、Adversarial Examplesfor SemanticSegmentation and Object Detection
Cihang Xie,Jianyu Wang, Zhishuai Zhang,Yuyin Zhou, Lingxi Xie, Alan Yuille
6、Temporal DynamicGraph LSTM forAction-Driven Video Object Detection
Yuan Yuan,Xiaodan Liang, Xiaolong Wang,Dit-Yan Yeung, Abhinav Gupta
7、Chained CascadeNetwork for ObjectDetection
Wanli Ouyang,Kun Wang, Xin Zhu, XiaogangWang
8、Online Video ObjectDetection UsingAssociation LSTM
Yongyi Lu,Cewu Lu, Chi-Keung Tang
9、Focal Loss for DenseObject Detection
Tsung-Yi Lin,Priya Goyal, Ross Girshick,Kaiming He, Piotr Dollar
10、Spatial Memory forContext Reasoning inObject Detection
Xinlei Chen,Abhinav Gupta
11、2D-Driven 3D ObjectDetection in RGB-DImages
Jean Lahoud,Bernard Ghanem
12、Moving ObjectDetection in Time-Lapseor Motion Trigger Image Sequences Using Low-Rank andInvariant SparseDecomposition
MoeinShakeri, Hong Zhang
13、Soft-NMS --Improving Object DetectionWith One Line of Code
NavaneethBodla, Bharat Singh, RamaChellappa, Larry S. Davis
6、Pose estimation（姿态估计）（15）
1、Real-TimeMonocular Pose Estimation of 3D Objects Using Temporally Consistent Local ColorHistograms
Henning Tjaden, Ulrich Schwanecke, Elmar Schomer
2、Benchmarkingand Error Diagnosis in Multi-Instance Pose Estimation
Matteo Ruggero Ronchi, Pietro Perona
3、Towards3D Human Pose Estimation in the Wild: A Weakly-Supervised Approach
Xingyi Zhou, Qixing Huang, Xiao Sun, Xiangyang Xue, YichenWei
4、AdversarialPoseNet: A Structure-Aware Convolutional Network for Human Pose Estimation
Yu Chen, Chunhua Shen, Xiu-Shen Wei, Lingqiao Liu, JianYang
5、LearningFeature Pyramids for Human Pose Estimation
Wei Yang, Shuang Li, Wanli Ouyang, Hongsheng Li, XiaogangWang
6、MakingMinimal Solvers for Absolute Pose Estimation Compact and Robust
Viktor Larsson, Zuzana Kukelova, Yinqiang Zheng
7、RMPE:Regional Multi-Person Pose Estimation
Hao-Shu Fang, Shuqin Xie, Yu-Wing Tai, Cewu Lu
8、ASimple yet Effective Baseline for 3D Human Pose Estimation
Julieta Martinez, Rayat Hossain, Javier Romero, James J.Little
9、RobustHand Pose Estimation During the Interaction With an Unknown Object
Chiho Choi, Sang Ho Yoon, Chin-Ning Chen, Karthik Ramani
10、Monocular3D Human Pose Estimation by Predicting Depth on Joints
Bruce Xiaohan Nie, Ping Wei, Song-Chun Zhu
11、DeepGlobally Constrained MRFs for Human Pose Estimation
Ioannis Marras, Petar Palasek, Ioannis Patras
12、BinarizedConvolutional Landmark Localizers for Human Pose Estimation and Face AlignmentWith Limited Resources
Adrian Bulat, Georgios Tzimiropoulos
13、Learningto Fuse 2D and 3D Image Cues for Monocular Body Pose Estimation
Bugra Tekin, Pablo Marquez-Neila, Mathieu Salzmann, PascalFua
14、ActiveLearning for Human Pose Estimation
Buyu Liu, Vittorio Ferrari
15、Human Pose Estimation Using Global and LocalNormalization
Ke Sun, Cuiling Lan, Junliang Xing, Wenjun Zeng, Dong Liu,Jingdong Wang
7、Semantic Segmentation（语义分割）（12）
1、Predicting DeeperInto the Future of Semantic Segmentation
Pauline Luc, NataliaNeverova, Camille Couprie, Jakob Verbeek, Yann LeCun
2、Cascaded Feature Network for SemanticSegmentation of RGB-D Images
Di Lin, GuangyongChen, Daniel Cohen-Or, Pheng-Ann Heng, Hui Huang
3、Video Deblurring via Semantic Segmentation andPixel-Wise Non-Linear Kernel
Wenqi Ren, JinshanPan, Xiaochun Cao, Ming-Hsuan Yang
4、Adversarial Examples for Semantic Segmentationand Object Detection
Cihang Xie, JianyuWang, Zhishuai Zhang, Yuyin Zhou, Lingxi Xie, Alan Yuille
5、VQS: Linking Segmentations to Questions andAnswers for Supervised Attention in VQA and Question-Focused SemanticSegmentation
Chuang Gan, YandongLi, Haoxiang Li, Chen Sun, Boqing Gong
6、Curriculum Domain Adaptation for SemanticSegmentation of Urban Scenes
Yang Zhang, PhilipDavid, Boqing Gong
7、Bringing Background Into the Foreground:Making All Classes Equal in Weakly-Supervised Video Semantic Segmentation
Fatemeh Sadat Saleh,Mohammad Sadegh Aliakbarian, Mathieu Salzmann, Lars Petersson, Jose M. Alvarez
8、RDFNet: RGB-D Multi-Level Residual FeatureFusion for Indoor Semantic Segmentation
Seong-Jin Park,Ki-Sang Hong, Seungyong Lee
9、3D Graph Neural Networks for RGBD SemanticSegmentation
Xiaojuan Qi, RenjieLiao, Jiaya Jia, Sanja Fidler, Raquel Urtasun
10、Semi Supervised Semantic Segmentation UsingGenerative Adversarial Network
Nasim Souly, ConcettoSpampinato, Mubarak Shah
11、Deep Dual Learning for SemanticImageSegmentation
Ping Luo, GuangrunWang, LiangLin, Xiaogang Wang
12、Universal Adversarial PerturbationsAgainstSemantic Image Segmentation
Jan Hendrik Metzen,MummadiChaithanya Kumar, Thomas Brox, Volker Fischer
8、Instance Segmentation（实例分割）（2）
1、SGN: Sequential Grouping Networks for InstanceSegmentation
Shu Liu, Jiaya Jia,Sanja Fidler, Raquel Urtasun
2、Mask R-CNN
Kaiming He, GeorgiaGkioxari, Piotr Dollar, Ross Girshick
9、Crowd counting（人群计数）（2）
1、Generating High-Quality Crowd Density MapsUsing Contextual Pyramid CNNs
Vishwanath A.Sindagi, Vishal M. Patel
2、Spatiotemporal Modeling for Crowd Counting inVideos
Feng Xiong, XingjianShi, Dit-Yan Yeung
10、Vehicle ReID（车辆识别）（2）
1、Learning Deep Neural Networksfor Vehicle Re-ID With Visual-Spatio-Temporal Path Proposals
Yantao Shen, Tong Xiao, Hongsheng Li, Shuai Yi, Xiaogang Wang
2、Orientation Invariant FeatureEmbedding and Spatial Temporal Regularization for Vehicle Re-Identification
Zhongdao Wang, Luming Tang, Xihui Liu, Zhuliang Yao, Shuai Yi, JingShao, Junjie Yan, Shengjin Wang, Hongsheng Li, Xiaogang Wang
11、GAN（生成对抗网络）（11）
1、Unlabeled SamplesGenerated by GAN Improve the Person Re-Identification Baseline in Vitro
ZhedongZheng, Liang Zheng, Yi Yang
2、Xudong Mao, Qing Li,Haoran Xie, Raymond Y.K. Lau, Zhen Wang, Stephen Paul Smolley
3、StackGAN: Text toPhoto-Realistic Image Synthesis With Stacked Generative Adversarial Networks
HanZhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang,Dimitris N. Metaxas
4、Semantic Image Synthesisvia Adversarial Learning
HaoDong, Simiao Yu, Chao Wu, Yike Guo
5、Dual Motion GAN forFuture-Flow Embedded Video Prediction
XiaodanLiang, Lisa Lee, Wei Dai, Eric P. Xing
6、GANs for Biological ImageSynthesis
AntonOsokin, Anatole Chessel, Rafael E. Carazo Salas, Federico Vaggi
7、Beyond Face Rotation:Global and Local Perception GAN for Photorealistic and Identity PreservingFrontal View Synthesis
RuiHuang, Shu Zhang, Tianyu Li, Ran He
8、CVAE-GAN: Fine-GrainedImage Generation Through Asymmetric Training
JianminBao, Dong Chen, Fang Wen, Houqiang Li, Gang Hua
9、DualGAN: UnsupervisedDual Learning for Image-To-Image Translation
ZiliYi, Hao Zhang, Ping Tan, Minglun Gong
10、Recurrent Topic-TransitionGAN for Visual Paragraph Generation
XiaodanLiang, Zhiting Hu, Hao Zhang, Chuang Gan, Eric P. Xing
11、Realistic Dynamic Facial Textures From a Single ImageUsing GANs
KyleOlszewski, Zimo Li, Chao Yang, Yi Zhou, Ronald Yu, Zeng Huang, Sitao Xiang,Shunsuke Saito, Pushmeet Kohli, Hao Li
12、video summarization（视频摘要）（2）
1、Weakly SupervisedSummarization of Web Videos
RameswarPanda, Abir Das, Ziyan Wu, Jan Ernst, Amit K. Roy-Chowdhury
2、Summarization andClassification of Wearable Camera Streams by Learning the Distributions OverDeep Features of Out-Of-Sample Image Sequences
AlessandroPerina, Sadegh Mohammadi, Nebojsa Jojic, Vittorio Murino
