Generate And Adjust: A Novel Framework For Semi-Supervised Pedestrian Attribute Recognition

Xuebo Shan, Peixi Peng, Yunpeng Zhai, Chong Zhang, Tiejun Huang, Yonghong Tian

Abstract: Conventional pedestrian attribute recognition methods typically rely on the assumption that enough well-labeled training samples are available. However, it is hard to be satisfied due to the unaffordable cost of human annotation, especially on big surveillance data. To address this problem, we propose a novel task-semi-supervised pedestrian attribute recognition (Semi-PAR), in which only a few limited labeled data and plenty of unlabeled data are available during training. We tackle the Semi-PAR problem by presenting a novel semi-supervised learning framework. In this framework, a PAR network is firstly designed to extract attribute-specific features, and adopts a similarity graph-based algorithm to generate pseudo-labels. Then we utilize the relationship of each attribute to modify the pseudo-labels, and feed them back to the PAR network to guide the training. Finally, a harmonic confidence loss is introduced to diminish the impact of harmful pseudo-labels. Experiments on three widely-used datasets verify the superiority of our method.