Learning with Coupled Noisy Labels for Visible-Infrared Person Re-identification via Graph Consistency

In this paper, we focus on the issue of Couple Noisy Labels (CNL) in Visible-Infrared Person Re-identification. CNL which refers to the Noisy Annotations and the Noisy Correspondences. Existing methods have a drawback of wasting samples, as only clean samples selected based on confidence are considered for training. This means that samples with ambiguous predictions do not contribute to the training phase. We propose a robust method dubbed Coupled Noisy with Graph Consistency (CNGC), takes a graph perspective and consists of two components: Node Consistency and Edge Consistency. Node consistency tackles the issue of samples that are discarded due to noisy annotations, while edge consistency addresses the problem of noisy training pairs where both samples are incorrectly labeled. To validate the effectiveness of our method, we conduct extensive experiments on SYSU-MM01 and RegDB datasets. The results demonstrate that CNGC outperforms seven state-of-the-art methods when dealing with Couple Noisy Labels.