Pentadent-Net: Pedestrian Attribute Recognition With Distance Refinement And Correlation Mining

Pedestrian attribute recognition aims to accurately identify attributes from pedestrian images. Visually similar pedestrian images tend to share similar attributes, and the upper and lower clothing attributes normally match in style. However, how to effectively capture these relations remains a challenging topic. In this paper, we propose a novel network Pentadent-Net (PD-Net). The network consists of a “Representation Refining Branch (RRB)” and an “Attribute Fusion Branch (AFB)”. The RRB reduces the feature distances among visually similar images, and the AFB generates a joint-representation for each attribute based on its correlation with other attributes. The polished features from the two branches are then applied to capture the inter-sample relations and attribute dependencies respectively. We also further associate features from the two branches to enrich feature representations. Experimental results on two benchmark datasets (PETA and PA-100K) by our proposed approach demonstrate its advantages over state-of-the-art methods.