Dual-Path Imbalanced Feature Compensation Network for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (VI-ReID) presents significant challenges on account of the substantial cross-modality gap and intra-class variations. Most existing methods primarily concentrate on aligning cross-modality at the feature or image levels and training with an equal number of samples from different modalities. However, in the real world, there exists an issue of modality imbalance between visible and infrared data. Besides, imbalanced samples between train and test impact the robustness and generalization of the VI-ReID. To alleviate this problem, we propose a dual-path imbalanced feature compensation network (DICNet) for VI-ReID, which provides equal opportunities for each modality to learn inconsistent information from different identities of others, enhancing identity discrimination performance and generalization. First, a modality consistency perception (MCP) module is designed to assist the backbone focus on spatial and channel information, extracting diverse and salient features to enhance feature representation. Second, we propose a cross-modality features re-assignment strategy to simulate modality imbalance by grouping and re-organizing the cross-modality features. Third, we perform bidirectional heterogeneous cooperative compensation with cross-modality imbalanced feature interaction modules (CIFIMs), allowing our network to explore the identity-aware patterns from imbalanced features of multiple groups for cross-modality interaction and fusion. Further, we design a feature re-construction difference loss to reduce cross-modality discrepancy and enrich feature diversity within each modality. Extensive experiments on three mainstream datasets show the superiority of the DICNet. Additionally, competitive results in corrupted scenarios verify its generalization and robustness.