Cross-modality Hierarchical Clustering and Refinement for Unsupervised Visible-Infrared Person Re-Identification.pdf (8.2 MB)
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Cross-modality Hierarchical Clustering and Refinement for Unsupervised Visible-Infrared Person Re-Identification

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posted on 2023-08-15, 14:14 authored by Zhiqi PangZhiqi Pang, Chunyu WangChunyu Wang, Lingling Zhao, Yang Liu, Gaurav SharmaGaurav Sharma

Visible-infrared person re-identification (VI-ReID) is a challenging cross-modality image retrieval task. Compared to visible modality person re-identification that handles only the intra-modality discrepancy, VI-ReID suffers from an additional modality gap. Most existing VI-ReID methods achieve promising accuracy in a supervised setting, but the high annotation cost limits their scalability to real-world scenarios. Although a few unsupervised VI-ReID methods already exist, they typically rely on intra-modality initialization and cross-modality instance selection, despite the additional computational time required for intra-modality initialization. In this paper, we study the fully unsupervised VI-ReID problem and propose a novel cross-modality hierarchical clustering and refinement (CHCR) method by promoting modality-invariant feature learning and improving the reliability of pseudo-labels. Unlike conventional VI-ReID methods, CHCR does not rely on any manual identity annotation and intra-modality initialization. First, we design a simple and effective cross-modality clustering baseline that clusters between modalities. Then, to provide sufficient inter-modality positive sample pairs for modality-invariant feature learning, we propose a cross-modality hierarchical clustering algorithm to promote the clustering of inter-modality positive samples into the same cluster. In addition, we develop an inter-channel pseudo-label refinement algorithm to eliminate unreliable pseudo-labels by checking the clustering results of three channels in the visible modality. Extensive experiments demonstrate that CHCR outperforms state-of-the-art unsupervised methods and achieves performance competitive with many supervised methods. 


National Natural Science Foundation of China (NSFC, Grant no. 62231013, 62272136, 62171164, 61872114 and 62131004)


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Submitting Author's Institution

Harbin Institute of Technology

Submitting Author's Country

  • China

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