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Bernard Ghanem
Researcher at King Abdullah University of Science and Technology
Publications - 347
Citations - 18959
Bernard Ghanem is an academic researcher from King Abdullah University of Science and Technology. The author has contributed to research in topics: Computer science & Video tracking. The author has an hindex of 49, co-authored 268 publications receiving 12453 citations. Previous affiliations of Bernard Ghanem include University of Illinois at Urbana–Champaign & Agency for Science, Technology and Research.
Papers
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Proceedings ArticleDOI
ActivityNet: A large-scale video benchmark for human activity understanding
TL;DR: This paper introduces ActivityNet, a new large-scale video benchmark for human activity understanding that aims at covering a wide range of complex human activities that are of interest to people in their daily living.
Book ChapterDOI
A Benchmark and Simulator for UAV Tracking
TL;DR: A new aerial video dataset and benchmark for low altitude UAV target tracking, as well as, a photo-realistic UAV simulator that can be coupled with tracking methods to easily extend existing real-world datasets.
Proceedings ArticleDOI
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing
Jian Zhang,Bernard Ghanem +1 more
TL;DR: This paper proposes a novel structured deep network, dubbed ISTA-Net, which is inspired by the Iterative Shrinkage-Thresholding Algorithm (ISTA) for optimizing a general $$ norm CS reconstruction model and develops an effective strategy to solve the proximal mapping associated with the sparsity-inducing regularizer using nonlinear transforms.
Book ChapterDOI
The Visual Object Tracking VOT2016 Challenge Results
Matej Kristan,Ales Leonardis,Jiří Matas,Michael Felsberg,Roman Pflugfelder,Luka Cehovin,Tomas Vojir,Gustav Häger,Alan Lukežič,Gustavo Fernandez,Abhinav Gupta,Alfredo Petrosino,Alireza Memarmoghadam,Alvaro Garcia-Martin,Andres Solis Montero,Andrea Vedaldi,Andreas Robinson,Andy J. Ma,Anton Varfolomieiev,A. Aydin Alatan,Aykut Erdem,Bernard Ghanem,Bin Liu,Bohyung Han,Brais Martinez,Chang-Ming Chang,Changsheng Xu,Chong Sun,Daijin Kim,Dapeng Chen,Dawei Du,Deepak Mishra,Dit-Yan Yeung,Erhan Gundogdu,Erkut Erdem,Fahad Shahbaz Khan,Fatih Porikli,Fatih Porikli,Fei Zhao,Filiz Bunyak,Francesco Battistone,Gao Zhu,Giorgio Roffo,Gorthi R. K. Sai Subrahmanyam,Guilherme Sousa Bastos,Guna Seetharaman,Henry Medeiros,Hongdong Li,Honggang Qi,Horst Bischof,Horst Possegger,Huchuan Lu,Hyemin Lee,Hyeonseob Nam,Hyung Jin Chang,Isabela Drummond,Jack Valmadre,Jae-chan Jeong,Jaeil Cho,Jae-Yeong Lee,Jianke Zhu,Jiayi Feng,Jin Gao,Jin-Young Choi,Jingjing Xiao,Ji-Wan Kim,Jiyeoup Jeong,João F. Henriques,Jochen Lang,Jongwon Choi,José M. Martínez,Junliang Xing,Junyu Gao,Kannappan Palaniappan,Karel Lebeda,Ke Gao,Krystian Mikolajczyk,Lei Qin,Lijun Wang,Longyin Wen,Luca Bertinetto,Madan Kumar Rapuru,Mahdieh Poostchi,Mario Edoardo Maresca,Martin Danelljan,Matthias Mueller,Mengdan Zhang,Michael Arens,Michel Valstar,Ming Tang,Mooyeol Baek,Muhammad Haris Khan,Naiyan Wang,Nana Fan,Noor M. Al-Shakarji,Ondrej Miksik,Osman Akin,Payman Moallem,Pedro Senna,Philip H. S. Torr,Pong C. Yuen,Qingming Huang,Qingming Huang,Rafael Martin-Nieto,Rengarajan Pelapur,Richard Bowden,Robert Laganiere,Rustam Stolkin,Ryan Walsh,Sebastian B. Krah,Shengkun Li,Shengping Zhang,Shizeng Yao,Simon Hadfield,Simone Melzi,Siwei Lyu,Siyi Li,Stefan Becker,Stuart Golodetz,Sumithra Kakanuru,Sunglok Choi,Tao Hu,Thomas Mauthner,Tianzhu Zhang,Tony P. Pridmore,Vincenzo Santopietro,Weiming Hu,Wenbo Li,Wolfgang Hübner,Xiangyuan Lan,Xiaomeng Wang,Xin Li,Yang Li,Yiannis Demiris,Yifan Wang,Yuankai Qi,Zejian Yuan,Zexiong Cai,Zhan Xu,Zhenyu He,Zhizhen Chi +140 more
TL;DR: The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.
Proceedings ArticleDOI
Robust visual tracking via multi-task sparse learning
TL;DR: Experimental results show that MTT methods consistently outperform state-of-the-art trackers and mining the interdependencies between particles improves tracking performance and overall computational complexity.