Person re-identification (re-id) is a critical problem in many real-world applications such as surveillance and security. It aims to identify the re-appearing persons from a large set of videos. In this talk, I will first introduce the re-id problem and the challenges. Then, I will talk about one image dataset: Market-1501, and one video dataset: Mars, which we construct to mimics the real-world setting. Last, I will present our works about part-based re-id solutions which imitate the way human differentiate person images: attention-based and pose-guided deep representations.

报告人简介（BIOGRAPHY）：

Jingdong Wang is a Senior Researcher with the Visual Computing Group, Microsoft Research, Beijing, China. His areas of current interest include efficient CNN architecture design, person re-identification, human pose estimation, semantic segmentation, large-scale indexing, and salient object detection. He has authored one book and 100+ papers in top conferences and prestigious international journals in computer vision, multimedia, and machine learning. His paper was selected into the Best Paper Finalist at the ACM MM 2015. He has shipped a dozen of technologies to Microsoft products, including Bing search, Cognitive service, and XiaoIce Chatbot. Dr. Wang is an Associate Editor of IEEE TPAMI, IEEE TCSVT and IEEE TMM. He was an Area Chair or a Senior Program Committee Member of top conferences, such as CVPR, ICCV, ECCV, AAAI, IJCAI, and ACM Multimedia. He is an ACM Distinguished Member and a Fellow of the IAPR. His homepage is https://jingdongwang2017.github.io/ /