Abstract:Systems for person re-identification (ReID) can achieve a high accuracy when trained on large fullylabeled image datasets. However, the domain shift typically associated with diverse operational capture conditions (e.g., camera viewpoints and lighting) may translate to a significant decline in performance. This paper focuses on unsupervised domain adaptation (UDA) for video-based ReID -a relevant scenario that is less explored in the literature. In this scenario, the ReID model must adapt to a complex target d… Show more
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