Discovering attention-guided cross-modality correlation for visible–infrared person re-identification

Abstract

Abstract

Visible–infrared person re-identification (VI Re-ID) is an essential and challenging task. Existing studies mainly focus on learning the unified modality-invariant representations directly from visible and infrared images. However, it is hard to obtain the identity-aware patterns due to the co-existence of inter- and intra-modality discrepancies. In this paper, we propose a novel attention-guided cross-modality correlation method (AGCC) to achieve the modality-invariant and identity-discriminative representations for visible–infrared person Re-ID. Specifically, we introduce a modality-aware attention (MAA) mechanism to model the inter- and intra-modality variations, which generates attention masks of two modalities for preserving the most significant region and obtaining the discriminative patterns in each identity. Further, we present an attention-guided channel and spatial correlation scheme (AGCSC) to establish the attention-guided cross-modality correlation, which can bridge the gap between inter- and intra-modalities. Moreover, a novel joint-modality learning head (JMLH) is developed to promote the metric and mutual learning from both feature distribution and classification logit levels. Extensive experiments on two public SYSU-MM01 and RegDB datasets demonstrate the remarkable superiority of our method over the state of the arts. The implementation codes will be made available soon.