Keypoint-Guided Modality-Invariant Discriminative Learning for Visible-Infrared Person Re-identification

The visible-infrared person re-identification (VI-ReID) task aims to retrieve images of pedestrians across cameras with different modalities. In this task, the major challenges arise from two aspects: intra-class variations among images of the same identity, and cross-modality discrepancies between visible and infrared images. Existing methods mainly focus on the latter, attempting to alleviate the impact of modality discrepancy, which ignore the former issue of identity variations and achieve limited discrimination. To address both aspects, we propose a Keypoint-guided Modality-invariant Discriminative Learning (KMDL) method, which can simultaneously adapt to intra-ID variations and bridge the cross-modality gap. By introducing human keypoints, our method makes further exploration in the image space, feature space and loss constraints to solve the above issues. Specifically, considering the modality discrepancy in original images, we first design a Hue Jitter Augmentation (HJA) strategy, introducing the hue disturbance to alleviate color dependence in the input stage. To obtain discriminative fine-grained representation for retrieval, we design the Global-Keypoint Graph Module (GKGM) in feature space, which can directly extract keypoint-aligned features and mine relationships within global and keypoint embeddings. Based on these semantic local embeddings, we further propose the Keypoint-Aware Center (KAC) loss that can effectively adjust the feature distribution under the supervision of ID and keypoint to learn discriminative representation for the matching. Extensive experiments on SYSU-MM01 and RegDB datasets demonstrate the effectiveness of our KMDL method.