Distribution aware soft alignment with boundary pseudo label correction for unsupervised visible-infrared person re-identification.
TL;DR
A multi-level contrastive learning loss that integrates unimodal contrast, cross-modal alignment, and unified contrast supervision to learn modality-invariant feature representations is designed.
Abstract
Unsupervised Visible-Infrared Person Re-identification (US-VI-ReID) aims to achieve cross-modal identity matching without manual annotations. However, existing methods overlook the unreliable cross-modal cluster associations caused by hard matching and pseudo-label noise generated by cross-modal joint clustering. To address these issues, this paper proposes a novel Distribution-Aware Soft Alignment with Boundary Correction (DASBC) framework which consists of two collaborative modules, the Distribution-Aware Soft Alignment (DASA) module and the Boundary Pseudo-Label Correction (BPLC) module. The DASA module constructs cross-modal inter-cluster associations via a probabilistic soft alignment mechanism, quantifying the association strength between cluster pairs to flexibly and robustly capture both strong associations and weak ambiguous relationships. The BPLC module leverages the Silhouette Score to locate cluster boundary samples, verifies and corrects label rationality to eliminate noise, and provides reliable supervision signals. In addition, this paper designs a multi-level contrastive learning loss that integrates unimodal contrast, cross-modal alignment, and unified contrast supervision to learn modality-invariant feature representations. Extensive experiments on the SYSU-MM01 and RegDB datasets demonstrate the superiority of the proposed DASBC framework.