SLAI Seminar 21st Session will be discussing the topic on "New Advances in Person Re-identification——From Individual Pedestrians to Small Groups, From Ground to Air-Ground Integration ", from 4pm to 5:30pm, January 12th (Monday) at B411 Lecture Hall, online participation is welcome (Tencent Meeting ID: 840-739-186)

About the Speaker:

Lai Jianhuang Professor (Second-Class), PhD Supervisor, School of Computer Science, Sun Yat-sen University. Honorary Vice Chairman and Fellow of the Chinese Society of Image and Graphics (CSIG); Chairman of the Guangdong Society of Image and Graphics (4th & 5th Terms). Distinguished Member of the China Computer Federation (CCF); Deputy Director of the CCF Technical Committee on Computer Vision (1st & 2nd Terms); Vice Chairman of the Guangdong Artificial Intelligence and Robotics Society (1st Term); Director of the Artificial Intelligence Professional Committee of the Guangdong Security and Protection Association.He received his Bachelor’s and Master’s degrees from Sun Yat-sen University in 1986 and 1989 respectively, and has been teaching at the university ever since. He obtained his PhD degree from Sun Yat-sen University in 1999. His main research fields include computer vision, pattern recognition, and machine learning.He has presided over a series of projects, including the Joint Key Project of the National Natural Science Foundation of China and Guangdong Province, the Science and Technology Support Program of the Ministry of Science and Technology, and projects funded by the National Natural Science Foundation of China. His academic achievements have been recognized with numerous awards, such as the First Prize of Natural Science Award of Guangdong Provincial Science and Technology Award (2018, Rank 1), the Dingying Award 丁颖奖 (2019), and he enjoys the Special Government Allowance of the State Council 国务院政府津贴.He has published approximately 200 academic papers, mainly in top-tier international conferences including ICCV, CVPR and ICDM, as well as authoritative international journals such as IEEE TPAMI, IEEE TIP, IEEE TNN, IEEE KDE and Pattern Recognition.

Abstract：

Small group re-identification aims to accurately associate group images with identical members captured by camera networks under non-overlapping fields of view. As a crucial extension of traditional person re-identification tasks, small group re-identification holds significant research value and promising application prospects in security surveillance scenarios. The unique challenges of small group re-identification lie in modeling the variations in the number and spatial layout of group members, as well as extracting stable and robust feature representations. In recent years, small group re-identification has attracted extensive attention from researchers and achieved rapid development. This report mainly introduces the scientific issues and related research progress of small group re-identification technology, including the research exploration and practical achievements of our laboratory: robust group feature extraction methods in cross-view scenarios based on salient key points, siamese networks, uncertainty modeling, and group 3D layout reconstruction; group metric methods based on person-group distance and nearest permutation distance; cross-domain re-identification technology based on person-group correlation; and the construction of large-scale virtual reality group datasets such as City1M and cross-modal group datasets such as CMGroup.