Zhongdao Wang

I am a third-year Ph.D candidate at the Department of Electronic Engineering, Tsinghua Univerisity, advised by Prof. Shengjin Wang. Before that, I received bechalor's degree in Mathematics and Physics from Tsinghua University. My current research interests are task-oriented self-supervised learning, espicially applications on multiple object tracking (MOT) and re-identification (re-ID). Previously, I also did research on metric learning and its applications on face recognition, person/vehicle re-identification. I work closely with Prof. Liang Zheng.

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• [NEW] Two papers are accepted to ECCV 2020!
• One paper is accepted to CVPR 2020 as oral presentation!
• One paper is accepted to AAAI 2020 as oral presentation!
• One paper isaccepted to CVPR 2019.

Conference papers

Learning re-identifiable features by self-supervised learning. The supervision signal is cycle consistency emerging from instance association in a forward-then-backward cycle.

By incorporating the appearance embedding model into the detector, we introduce JDE, the first open-source real-time multiple object trackor with a running speed of 22 ~ 38 FPS. This speed takes all the steps into account, including detection, appearance embedding extraction and association. Code is released! If you are looking for an easy-to-use and fast pedestrian detector/tracker, JDE is a good option!

A unified perspective for proxy-based and pair-based metric learning.

Investigation on intra- and inter-class objectives of the softmax cross-entropy loss function, and a new loss that dissects the two parts for accelerating massive classification.

A supervised solution to the face clustering task using graph convolutional networks.

An orientation-invariant solution to the vehicle re-identification problem.

Journal papers

Multi-graph fusion using extreme value theory for multi-view clustering.

Design and source code modified based on Jon Barron's website.