Exploring Visual Context for Weakly Supervised Person Search

Person search has recently emerged as a challenging task that jointly addresses pedestrian detection and person reidentification. Existing approaches follow a fully supervised setting where both bounding box and identity annotations are available. However, annotating identities is labor-intensive, limiting the practicability and scalability of current frameworks. This paper inventively considers weakly supervised person search with only bounding box annotations. We proposed to address this novel task by investigating three levels of context clues (i. e. , detection, memory and scene) in unconstrained natural images. The first two are employed to promote local and global discriminative capabilities, while the latter enhances clustering accuracy. Despite its simple design, our CGPS achieves 80. 0% in mAP on CUHK-SYSU, boosting the baseline model by 8. 8%. Surprisingly, it even achieves comparable performance with several supervised person search models. Our code is available at https: //github. com/ljpadam/CGPS

Authors

• Yichao Yan Shanghai Jiao Tong University
• Jinpeng Li The Chinese University of Hong Kong
• Shengcai Liao
• Jie Qin Nanjing University of Aeronautics and Astronautics
• Bingbing Ni Shanghai Jiao Tong University
• Ke Lu University of Electronic Science and Technology of China
• Xiaokang Yang Shanghai Jiao Tong University, China

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