Abstract
Compared to visible modality person re-identification that handles only the intra-modality discrepancy, visible-infrared person re-identification (VI-ReID) aims to achieve the match between visible modality and infrared modality. However, supervised VI-ReID methods are limited in flexibility due to their dependence on labeled data. Although several unsupervised VI-ReID methods have been developed, they usually ignore the impact of hard samples and noisy labels. In this paper, we propose the modality-invariance modeling and refinement (MIMR) framework for unsupervised VI-ReID. For the first issue, we first conduct cross-modality image translation, then enable the encoder to extract modality-invariant feature representation by aligning translated versions with original samples, and finally use cross-modality clustering and hard sample contrastive loss to handle the hard samples. For the second issue, we believe that ambiguous samples are usually assigned noisy labels, so we design ambiguity-oriented pseudo label refinement (APLR), which evaluates ambiguity from both the sample itself and the corresponding translated version, rather than discards hard samples as existing methods. Extensive experiments demonstrate that MIMR achieves superior performance compared to state-of-the-art unsupervised methods and even surpasses early supervised methods.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.