Jian-Fang Hu

Email: hujf5@mail.sysu.edu.cn
Address: Room 301, Building 571, No. 135, XinGangXi Road

Short Bio
I have joined Sun Yat-sen University, China as a Research Associate Professor, working closely with Prof. Wei-Shi Zheng and Prof. Jianhuang Lai. Before that, I obtained my PhD degree from the School of Mathematics, Sun Yat-sen University in December, 2016. I have also worked as a visiting student in Prof. Gang Wang's group at Nanyang Technological University, Singapore, from Sep. 2015 to Sep. 2016. My research interests are in computer vision and applied machine learning. Specifically, I mainly conduct reseaches on action/activity recognition, action/activity prediction and action/activity detection. Recently, I also work on developing deep models for video segementation, collaborating with Dr. Bing Shuai (Amazon, Seattle). I also work closely with Prof. Jianguo Zhang (University of Dundee, United Kingdom) for human action modelling. My CV can be found in the LINK.

研究组招研究生和本科生进行视频分割、视频行为动作分析方面的前沿研究,对人工智能与计算机视觉研究感兴趣且能坚持投入时间和精力的同学,可以联系我!

 

News  
1.
One paper (first author) is accepted by TPAMI.
2.
One paper (first author) is accepted by ECCV 2018.
3.
One paper is accepted by BMVC 2018. This is our first work for object segmentation.
4.
Our codes for our JOULE model are completely available now.
5.
Our CVPR extension work is accepted by TPAMI.
   
Selected Publications
(*) indicates corresponding author
DSD Jian-Fang Hu, Wei-Shi Zheng, Lianyang Ma, Gang Wang, Jianhuang Lai, and Jianguo Zhang, "Early Action Prediction by Soft Regression", IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2018. PDF, SUPP-PDF
Jian-Fang Hu, Wei-Shi Zheng, Jianhuang Lai, and Jianguo Zhang, "Jointly Learning Heterogeneous Features for RGB-D Activity Recognition", IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 39 (11), 2186-2200, 2017. PDF SUPP-PDF Codes & SYSU 3D HOI Set & Demo
ddd
Jian-Fang Hu, Wei-Shi Zheng, Jiahui Pan, Jianhuang Lai, and Jianguo Zhang, "Deep Bilinear Learning for RGB-D Action Recognition", European Conference on Computer Vision (ECCV), 2018. PDF
ddd Jiafeng Xie, Bing Shuai, Jian-Fang Hu (*), Jingyang Lin, and Wei-Shi Zheng, "Improving Fast Segmentation with Teacher-student Learning", British Machine Vision Conference (BMVC), 2018. PDF
ddd Shaofan Lai, Wei-Shi Zheng, Jian-Fang Hu, and Jianguo Zhang, "Global-Local Temporal Saliency Action Prediction", IEEE Transaction on Image Processing (TIP), 27(5), 2272-2285, 2018.
Jiachi He, Jian-Fang Hu, Xi Lu, Wei-Shi Zheng, "Multi-Task Mid-Level Feature Learning for Micro-Expression Recognition", Pattern Recognition (PR), 66 (6), 44-52, 2017.
DDS Jian-Fang Hu, Wei-Shi Zheng, Xiaohua Xie, and Jianhuang Lai, "Sparse Transfer for Facial Shape-from-Shading", Pattern Recognition (PR), 68(8), 272-285, 2017. pdf
Jian-Fang Hu, Wei-Shi Zheng, Lianyang Ma, Gang Wang, and Jianhuang Lai, "Real-time RGB-D Activity Prediction by Soft Regression", European Conference on Computer Vision (ECCV), 280-296, 2016. pdf. Prediction Results

Zhaoze Zhou, Wei-Shi Zheng, Jian-Fang Hu, Yong Xu, Jane You, " One-pass Online Learning: A Local Approach", Pattern Recognition (PR), 51, 346-357, 2016. pdfcode.

Jian-Fang Hu, Wei-Shi Zheng, Jianhuang Lai, and Jianguo Zhang, "Jointly Learning Heterogeneous Features for RGB-D Activity Recognition", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 5344-5352, 2015pdf

Jian-Fang Hu, Wei-Shi Zheng, Jianhuang Lai, Shaogang Gong, and Tao Xiang, "Exemplar-based Recognition of Human-Object Interactions", IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 26(4), 647-660, 2016. pdf, BibTexPHOW features (sports & PPMI set), SYSU Action Dataset, Project

Jian-Fang Hu, Wei-Shi Zheng, Jianhuang Lai, Shaogang Gong, and Tao Xiang, "Recognising Human-Object Interaction via Exemplar based Modelling", International Conference on Computer Vision (ICCV) , 3144-3151, 2013. pdf, BibTex
Jianfang Hu, Guocan Feng, Jianhuang Lai, and Wei-Shi Zheng, "Asymmetric Facial Shape based on Some Symmetry Assumptions", Chines Conference on Biometric Recognition (CCBR), 42-49, 2011.  VIDEO

 

 

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