Visible-infrared person re-identification (VI-ReID) is a challenging cross-modality retrieval task due to the large modality gap. While numerous efforts have been devoted to the supervised setting with a large amount of labeled cross-modality correspondences, few studies have tried to mitigate the modality gap by mining cross-modality correspondences in an unsupervised manner. However, existing works failed to capture the intrinsic relations among samples across two modalities, resulting in limited performance outcomes. In this paper, we propose a novel Progressive Graph Matching (PGM) approach to globally model the cross-modality relationships and instance-level affinities. PGM formulates cross-modality correspondence mining as a graph matching procedure, aiming to integrate global information by minimizing global matching costs. Considering that samples in wrong clusters cannot find reliable cross-modality correspondences by PGM, we further introduce a robust Dual-Level Matching (DLM) mechanism, combining the cluster-level PGM and Nearest Instance-Cluster Searching (NICS) with instance-level affinity optimization. Additionally, we design an Outlier Filter Strategy (OFS) to filter out unreliable cross-modality correspondences based on the dual-level relation constraints. To mitigate false accumulation in cross-modal correspondence learning, an Alternate Cross Contrastive Learning (ACCL) module is proposed to alternately adjust the dominated matching, i.e., visible-to-infrared or infrared-to-visible matching. Empirical results demonstrate the superiority of our unsupervised solution, achieving comparable performance with supervised counterparts.

This paper introduces a simple yet powerful channel augmentation for visible-infrared re-identification. Most existing augmentation operations designed for single-modality visible images do not fully consider the imagery properties in visible to infrared matching. Our basic idea is to homogeneously generate color-irrelevant images by randomly exchanging the color channels. It can be seamlessly integrated into existing augmentation operations, consistently improving the robustness against color variations. For cross-modality metric learning, we design an enhanced channel-mixed learning strategy to simultaneously handle the intra- and cross-modality variations with squared difference for stronger discriminability. Besides, a weak-and-strong augmentation joint learning strategy is further developed to explicitly optimize the outputs of augmented images, which mutually integrates the channel augmented images (strong) and the general augmentation operations (weak) with consistency regularization. Furthermore, by conducting the label association between the channel augmented images and infrared modalities with modality-specific clustering, a simple yet effective unsupervised learning baseline is designed, which significantly outperforms existing unsupervised single-modality solutions. Extensive experiments with insightful analysis on two visible-infrared recognition tasks show that the proposed strategies consistently improve the accuracy. Without auxiliary information, the Rank-1/mAP achieves 71.48%/68.15% on the large-scale SYSU-MM01 dataset.

Unsupervised visible-infrared person re-identification is a challenging task due to the large modality gap and the unavailability of cross-modality correspondences. Cross-modality correspondences are very crucial to bridge the modality gap. Some existing works try to mine cross-modality correspondences, but they focus only on local information. They do not fully exploit the global relationship across identities, thus limiting the quality of the mined correspondences. Worse still, the number of clusters of the two modalities is often inconsistent, exacerbating the unreliability of the generated correspondences. In response, we devise a Progressive Graph Matching method to globally mine cross-modality correspondences under cluster imbalance scenarios. PGM formulates correspondence mining as a graph matching process and considers the global information by minimizing the global matching cost, where the matching cost measures the dissimilarity of clusters. Besides, PGM adopts a progressive strategy to address the imbalance issue with multiple dynamic matching processes. Based on PGM, we design an Alternate Cross Contrastive Learning (ACCL) module to reduce the modality gap with the mined cross-modality correspondences, while mitigating the effect of noise in correspondences through an alternate scheme. Extensive experiments demonstrate the reliability of the generated correspondences and the effectiveness of our method.

Visible infrared person re-identification (VI-ReID) aims at searching out the corresponding infrared (visible) images from a gallery set captured by other spectrum cameras. Recent works mainly focus on supervised VI-ReID methods that require plenty of cross-modality (visible-infrared) identity labels which are more expensive than the annotations in single-modality person ReID. For the unsupervised learning visible infrared re-identification (USL-VI-ReID), the large cross-modality discrepancies lead to difficulties in generating reliable cross-modality labels and learning modality-invariant features without any annotations. To address this problem, we propose a novel Augmented Dual-Contrastive Aggregation (ADCA) learning framework. Specifically, a dual-path contrastive learning framework with two modality-specific memories is proposed to learn the intra-modality person representation. To associate positive cross-modality identities, we design a cross-modality memory aggregation module with count priority to select highly associated positive samples, and aggregate their corresponding memory features at the cluster level, ensuring that the optimization is explicitly concentrated on the modality-irrelevant perspective. Extensive experiments demonstrate that our proposed ADCA significantly outperforms existing unsupervised methods under various settings, and even surpasses some supervised counterparts, facilitating VI-ReID to real-world deployment. Code is available at https://github.com/yangbincv/ADCA.