Homogeneous and Heterogeneous Consistency progressive Re-ranking for Visible-Infrared Person Re-identification

TL;DR

This paper introduces a novel re-ranking method for visible-infrared person re-identification that effectively addresses intra-modal and inter-modal discrepancies, achieving state-of-the-art results.

Contribution

The paper proposes a new Progressive Modal Relationship Re-ranking method with heterogeneous and homogeneous modules, along with a baseline inference network for cross-modal re-identification.

Findings

Achieved state-of-the-art performance on re-identification benchmarks.

Demonstrated the effectiveness of heterogeneous and homogeneous consistency modules.

Proved the generalization capability of the proposed re-ranking method.

Abstract

Visible-infrared person re-identification faces greater challenges than traditional person re-identification due to the significant differences between modalities. In particular, the differences between these modalities make effective matching even more challenging, mainly because existing re-ranking algorithms cannot simultaneously address the intra-modal variations and inter-modal discrepancy in cross-modal person re-identification. To address this problem, we propose a novel Progressive Modal Relationship Re-ranking method consisting of two modules, called heterogeneous and homogeneous consistency re-ranking(HHCR). The first module, heterogeneous consistency re-ranking, explores the relationship between the query and the gallery modalities in the test set. The second module, homogeneous consistency reranking, investigates the intrinsic relationship within each modality between the query and the gallery in the test set. Based on this, we propose a baseline for cross-modal person re-identification, called a consistency re-ranking inference network (CRI). We conducted comprehensive experiments demonstrating that our proposed re-ranking method is generalized, and both the re-ranking and the baseline achieve state-of-the-art performance.