H
Honggang Zhang
Researcher at Beijing University of Posts and Telecommunications
Publications - 91
Citations - 7069
Honggang Zhang is an academic researcher from Beijing University of Posts and Telecommunications. The author has contributed to research in topics: Image retrieval & Feature extraction. The author has an hindex of 23, co-authored 91 publications receiving 5062 citations.
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Proceedings ArticleDOI
Residual Attention Network for Image Classification
Fei Wang,Mengqing Jiang,Chen Qian,Shuo Yang,Cheng Li,Honggang Zhang,Xiaogang Wang,Xiaoou Tang +7 more
TL;DR: Residual Attention Network as mentioned in this paper is a convolutional neural network using attention mechanism which can incorporate with state-of-the-art feed forward network architecture in an end-to-end training fashion.
Posted Content
Residual Attention Network for Image Classification
Fei Wang,Mengqing Jiang,Chen Qian,Shuo Yang,Cheng Li,Honggang Zhang,Xiaogang Wang,Xiaoou Tang +7 more
TL;DR: Residual Attention Network as discussed by the authors is a convolutional neural network using attention mechanism which can incorporate with state-of-the-art feed forward network architecture in an end-to-end training fashion.
Book ChapterDOI
The sixth visual object tracking VOT2018 challenge results
Matej Kristan,Ales Leonardis,Jiří Matas,Michael Felsberg,Roman Pflugfelder,Roman Pflugfelder,Luka Čehovin Zajc,Tomas Vojir,Goutam Bhat,Alan Lukežič,Abdelrahman Eldesokey,Gustavo Fernandez,Alvaro Garcia-Martin,Álvaro Iglesias-Arias,A. Aydin Alatan,Abel Gonzalez-Garcia,Alfredo Petrosino,Alireza Memarmoghadam,Andrea Vedaldi,Andrej Muhič,Anfeng He,Arnold W. M. Smeulders,Asanka G. Perera,Bo Li,Boyu Chen,Changick Kim,Changsheng Xu,Changzhen Xiong,Cheng Tian,Chong Luo,Chong Sun,Cong Hao,Daijin Kim,Deepak Mishra,Deming Chen,Dong Wang,Dongyoon Wee,Efstratios Gavves,Erhan Gundogdu,Erik Velasco-Salido,Fahad Shahbaz Khan,Fan Yang,Fei Zhao,Feng Li,Francesco Battistone,George De Ath,Gorthi R. K. Sai Subrahmanyam,Guilherme Sousa Bastos,Haibin Ling,Hamed Kiani Galoogahi,Hankyeol Lee,Haojie Li,Haojie Zhao,Heng Fan,Honggang Zhang,Horst Possegger,Houqiang Li,Huchuan Lu,Hui Zhi,Huiyun Li,Hyemin Lee,Hyung Jin Chang,Isabela Drummond,Jack Valmadre,Jaime Spencer Martin,Javaan Chahl,Jin-Young Choi,Jing Li,Jinqiao Wang,Jinqing Qi,Jinyoung Sung,Joakim Johnander,João F. Henriques,Jongwon Choi,Joost van de Weijer,Jorge Rodríguez Herranz,Jorge Rodríguez Herranz,José M. Martínez,Josef Kittler,Junfei Zhuang,Junyu Gao,Klemen Grm,Lichao Zhang,Lijun Wang,Lingxiao Yang,Litu Rout,Liu Si,Luca Bertinetto,Lutao Chu,Manqiang Che,Mario Edoardo Maresca,Martin Danelljan,Ming-Hsuan Yang,Mohamed H. Abdelpakey,Mohamed Shehata,Myunggu Kang,Namhoon Lee,Ning Wang,Ondrej Miksik,Payman Moallem,Pablo Vicente-Moñivar,Pedro Senna,Peixia Li,Philip H. S. Torr,Priya Mariam Raju,Qian Ruihe,Qiang Wang,Qin Zhou,Qing Guo,Rafael Martin-Nieto,Rama Krishna Sai Subrahmanyam Gorthi,Ran Tao,Richard Bowden,Richard M. Everson,Runling Wang,Sangdoo Yun,Seokeon Choi,Sergio Vivas,Shuai Bai,Shuangping Huang,Sihang Wu,Simon Hadfield,Siwen Wang,Stuart Golodetz,Tang Ming,Tianyang Xu,Tianzhu Zhang,Tobias Fischer,Vincenzo Santopietro,Vitomir Struc,Wang Wei,Wangmeng Zuo,Wei Feng,Wei Wu,Wei Zou,Weiming Hu,Wengang Zhou,Wenjun Zeng,Xiaofan Zhang,Xiaohe Wu,Xiaojun Wu,Xinmei Tian,Yan Li,Yan Lu,Yee Wei Law,Yi Wu,Yi Wu,Yiannis Demiris,Yicai Yang,Yifan Jiao,Yuhong Li,Yuhong Li,Yunhua Zhang,Yuxuan Sun,Zheng Zhang,Zheng Zhu,Zhen-Hua Feng,Zhihui Wang,Zhiqun He +158 more
TL;DR: The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative; results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years.
Proceedings ArticleDOI
Dual Attention Matching Network for Context-Aware Feature Sequence Based Person Re-identification
TL;DR: A novel end-to-end trainable framework, called Dual ATtention Matching network (DuATM), to learn context-aware feature sequences and perform attentive sequence comparison simultaneously, in which both intrasequence and inter-sequence attention strategies are used for feature refinement and feature-pair alignment.
Proceedings ArticleDOI
Sketch-based image retrieval via Siamese convolutional neural network
TL;DR: A novel convolutional neural network based on Siamese network for SBIR is proposed, which is to pull output feature vectors closer for input sketch-image pairs that are labeled as similar, and push them away if irrelevant.