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InfoResearchPeer-reviewed
Causal Bootstrapped Alignment for Unsupervised Video-Based Visible–Infrared Person Re-Identification
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Summary
Researchers propose Causal Bootstrapped Alignment (CBA), an unsupervised framework for video-based visible-infrared person re-identification that learns identity representations from unlabeled video tracklets. CBA combines Causal Intervention Warm-up, which suppresses modality- and motion-induced spurious correlations, with Prototype-Guided Uncertainty Refinement, which resolves cross-modality clustering granularity mismatch. Experiments on the HITSZ-VCM and BUPTCampus benchmarks show CBA significantly outperforms existing USL-VI-ReID methods in this setting.