ICCV图像处理相关论文总结（103篇）（粗）

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