Modality-specific adversarial generators trained with metric disruption, simulated cross-modal, and collaborative multi-modal losses transfer to black-box single-, cross-, and multi-modality person re-id models, reaching mean mAP drop rates of 55.9%, 24.4%, 49.0%, and 62.7%.
Pseudo-label noise prevention, suppression and softening for unsupervised person re- identification,
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Modality Unified Attack for Omni-Modality Person Re-Identification
Modality-specific adversarial generators trained with metric disruption, simulated cross-modal, and collaborative multi-modal losses transfer to black-box single-, cross-, and multi-modality person re-id models, reaching mean mAP drop rates of 55.9%, 24.4%, 49.0%, and 62.7%.