Dual Reverse Attention Networks for Person Re-Identification

Abstract

In this paper, we enhance feature representation ability of person re-identification (Re-ID) by learning invariances to hard examples. Unlike previous works of hard examples mining and generating in image level, we propose a dual reverse attention networks (DRANet) to generate hard examples in the convolutional feature space. Specifically, we use a classification branch of attention mechanism to model that ‘what’ in channel and ‘where’ in spatial dimensions are informative in the feature maps. Meanwhile, we introduce two branches of reverse attention modules in parallel way, which convert informative feature maps into hard examples of uninformative ones. In the proposed framework, both classification and dual reverse attention branches are learned in a joint way. Experimental results on three mainstream datasets demonstrate the efficacy of the proposed method.