Search Results for author: Zhiheng Wang

Found 7 papers, 0 papers with code

Do We Need Binary Features for 3D Reconstruction?

no code implementations14 Feb 2016 Bin Fan, Qingqun Kong, Wei Sui, Zhiheng Wang, Xinchao Wang, Shiming Xiang, Chunhong Pan, Pascal Fua

Binary features have been incrementally popular in the past few years due to their low memory footprints and the efficient computation of Hamming distance between binary descriptors.

3D Reconstruction

Complete Scene Reconstruction by Merging Images and Laser Scans

no code implementations21 Apr 2019 Xiang Gao, Shuhan Shen, Lingjie Zhu, Tianxin Shi, Zhiheng Wang, Zhanyi Hu

Experimental evaluations on two ancient Chinese architecture datasets demonstrate the effectiveness of our proposed complete scene reconstruction pipeline.

Progressive Multi-Stage Learning for Discriminative Tracking

no code implementations1 Apr 2020 Weichao Li, Xi Li, Omar Elfarouk Bourahla, Fuxian Huang, Fei Wu, Wei Liu, Zhiheng Wang, Hongmin Liu

Visual tracking is typically solved as a discriminative learning problem that usually requires high-quality samples for online model adaptation.

Visual Tracking

Tips and Tricks for Webly-Supervised Fine-Grained Recognition: Learning from the WebFG 2020 Challenge

no code implementations29 Dec 2020 Xiu-Shen Wei, Yu-Yan Xu, Yazhou Yao, Jia Wei, Si Xi, Wenyuan Xu, Weidong Zhang, Xiaoxin Lv, Dengpan Fu, Qing Li, Baoying Chen, Haojie Guo, Taolue Xue, Haipeng Jing, Zhiheng Wang, Tianming Zhang, Mingwen Zhang

WebFG 2020 is an international challenge hosted by Nanjing University of Science and Technology, University of Edinburgh, Nanjing University, The University of Adelaide, Waseda University, etc.

Content Popularity Prediction in Fog-RANs: A Clustered Federated Learning Based Approach

no code implementations13 Jun 2022 Zhiheng Wang, Yanxiang Jiang, Fu-Chun Zheng, Mehdi Bennis, Xiaohu You

Based on clustered federated learning, we propose a novel mobility-aware popularity prediction policy, which integrates content popularities in terms of local users and mobile users.

Federated Learning

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