Robust Object Re-identification with Coupled Noisy Labels

Mouxing Yang, Zhenyu Huang, Xi Peng

Abstract: In this paper, we reveal and study a new challenging problem faced by object Re-IDentification (ReID), i.e., Coupled Noisy Labels (CNL) which refers to the Noisy Annotation (NA) and the accompanied Noisy Correspondence (NC). Specifically, NA refers to the wrongly-annotated identity of samples during manual labeling, and NC refers to the mismatched training pairs including false positives and false negatives whose correspondences are established based on the NA. Clearly, CNL will limit the success of the object ReID paradigm that simultaneously performs identity-aware discrimination learning on the data samples and pairwise similarity learning on the training pairs. To overcome this practical but ignored problem, we propose a robust object ReID method dubbed Learning with Coupled Noisy Labels (LCNL). In brief, LCNL first estimates the annotation confidences of samples and then adaptively divides the training pairs into four groups with the confidences to rectify the correspondences. After that, LCNL employs a novel objective function to achieve robust object ReID with theoretical guarantees. To verify the effectiveness of LCNL, we conduct extensive experiments on five benchmark datasets in single- and cross-modality object ReID tasks compared with 14 algorithms. The code could be accessed from https://github.com/XLearning-SCU/2024-IJCV-LCNL.