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Cross-modality Hierarchical Clustering and Refinement for Unsupervised Visible-Infrared Person Re-Identification
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  • Zhiqi Pang ,
  • Chunyu Wang ,
  • Lingling Zhao ,
  • Yang Liu ,
  • Gaurav Sharma
Zhiqi Pang
Harbin Institute of Technology

Corresponding Author:[email protected]

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Chunyu Wang
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Lingling Zhao
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Gaurav Sharma
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Abstract

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.
2023Published in IEEE Transactions on Circuits and Systems for Video Technology on pages 1-1. 10.1109/TCSVT.2023.3310015