Unsupervised Visible-Infrared ReID via Pseudo-label Correction and
  Modality-level Alignment
Yexin Liu Weiming Zhang Athanasios V. Vasilakos Lin Wang
Unsupervised visible-infrared person re-identification (UVI-ReID) has
recently gained great attention due to its potential for enhancing human
detection in diverse environments without labeling. Previous methods utilize
intra-modality clustering and cross-modality feature matching to achieve
UVI-ReID. However, there exist two challenges: 1) noisy pseudo labels might be
generated in the clustering process, and 2) the cross-modality feature
alignment via matching the marginal distribution of visible and infrared
modalities may misalign the different identities from two modalities. In this
paper, we first conduct a theoretic analysis where an interpretable
generalization upper bound is introduced. Based on the analysis, we then
propose a novel unsupervised cross-modality person re-identification framework
(PRAISE). Specifically, to address the first challenge, we propose a
pseudo-label correction strategy that utilizes a Beta Mixture Model to predict
the probability of mis-clustering based network's memory effect and rectifies
the correspondence by adding a perceptual term to contrastive learning. Next,
we introduce a modality-level alignment strategy that generates paired
visible-infrared latent features and reduces the modality gap by aligning the
labeling function of visible and infrared features to learn identity
discriminative and modality-invariant features. Experimental results on two
benchmark datasets demonstrate that our method achieves state-of-the-art
performance than the unsupervised visible-ReID methods.