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

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.

Figures (3)

• Figure 1: The crosshair represents the center of the same identity, the same color indicates the same identity, and the circles and rectangles represent the two modalities. The arrows signify the process of reducing or increasing distances. Currently, the re-ranking methods applied to visible-infrared ReID consist of a single-stage re-ranking process, including only homogeneous re-ranking or heterogeneous re-ranking. The re-ranking method we propose includes Homogeneous Consistency Re-ranking and Heterogeneous Consistency Re-ranking.
• Figure 2: The pipeline of the proposed network. The network structure processes the input visible and infrared images through ResNet to extract features, followed by a BN (Batch Normalization) layer for normalization, and finally, computes the loss function. During the testing phase, HHCR first concatenates the test set to compute the original similarity matrix $A_{sim}^{ori}$. Then, it separately calculates Homogeneous and Heterogeneous Consistency Re-ranking. Finally, the initial similarity matrix is filtered and summed, resulting in the final similarity matrix. The yellow line represents the noise filtering step for the original features, while the blue line indicates the noise filtering step for the original features after HECR graph convolution processing.
• Figure 3: Comparison of Top 10 Retrieval Results with and without PMRR.