Jia He
ABSTRACT
In the study of unsupervised domain adaptation for pedestrian re-identification, one prevalent approach during training on unlabeled target domains involves generating pseudo-labels. However, due to the inability to guarantee the correctness of pseudo-labels, they inevitably carry noise, subsequently affecting the network's training process. In this paper, we propose a novel approach that better constrains noise and demonstrates strong performance. First, building upon the DBSCAN clustering method, we generate soft labels for samples, thereby providing fine-grained category loss supervision. Specifically, we introduce an adaptive reverse cross-entropy loss to impose adaptive constraints on the pseudo-label generation process. We conduct various experiments on four datasets across three transfer tasks, and the results consistently highlight the superiority of our method, providing strong evidence for its effectiveness.
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Index Terms
- Unsupervised Multi-Source Domain Adaptation for Pedestrian Re-identification ??A Study in Noise-Resilient Learning
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