Shape-centered Representation Learning for Visible-Infrared Person Re-identification

Current Visible-Infrared Person Re-Identification (VI-ReID) methods prioritize extracting distinguishing appearance features, ignoring the natural resistance of body shape against modality changes. Initially, we gauged the discriminative potential of shapes by a straightforward concatenation of shape and appearance features. However, two unresolved issues persist in the utilization of shape features. One pertains to the dependence on auxiliary models for shape feature extraction in the inference phase, along with the errors in generated infrared shapes due to the intrinsic modality disparity. The other issue involves the inadequately explored correlation between shape and appearance features. To tackle the aforementioned challenges, we propose the Shape-centered Representation Learning framework (ScRL), which focuses on learning shape features and appearance features associated with shapes. Specifically, we devise the Shape Feature Propagation (SFP), facilitating direct extraction of shape features from original images with minimal complexity costs during inference. To restitute inaccuracies in infrared body shapes at the feature level, we present the Infrared Shape Restitution (ISR). Furthermore, to acquire appearance features related to shape, we design the Appearance Feature Enhancement (AFE), which accentuates identity-related features while suppressing identity-unrelated features guided by shape features. Extensive experiments are conducted to validate the effectiveness of the proposed ScRL. Achieving remarkable results, the Rank-1 (mAP) accuracy attains 76.1%, 71.2%, 92.4% (72.6%, 52.9%, 86.7%) on the SYSU-MM01, HITSZ-VCM, RegDB datasets respectively, outperforming existing state-of-the-art methods.