Reinforced Sample Re-weighting for Pedestrian Attribute Recognition

Abstract

Pedestrian Attribute Recognition aims to recognise person attributes including age, gender, clothing and accessories in a given image. It is challenging due to the high variance in sample quality and attribute content. Many network structures have been proposed to better capture the fine-grained details in an image. However, how to learn from each training sample based on their importance to the model still remains to be addressed. In this paper, we propose Reinforced Sample Re-weighting (RSR), a novel approach to re-weight samples in a batch during back-propagation through reinforcement learning. RSR agents are proposed to assign sample weights based on both the sample itself and the recognition model status. The agent learns in an on-policy manner, where it learns together with the attribute recognition model and no additional training is required. The proposed approach achieves state-of-the-art performance against other existing methods on three large scale pedestrian attribute datasets PETA, PA-100K and RAP, which demonstrates the effectiveness of our method.