Dynamic Uncertainty Learning with Noisy Correspondence for Text-based Person Retrieval

Abstract

Text-Based Person Retrieval (TBPR) aims to locate a person in an image database based on a natural language description. While effective in theory, TBPR faces substantial challenges in real-world scenarios due to noisy correspondences—misaligned or weakly related image–text pairs—that significantly degrade retrieval performance. Existing methods often overemphasize hard negative mining, which inadvertently magnifies the impact of such noise. To address this issue, we propose Dynamic Uncertainty with Noisy Correspondences (DUNC), a novel framework that incorporates two key components: (1) Cross-modal Evidential Learning (CEL), which models bidirectional alignment uncertainty using a Dirichlet distribution to capture the confidence in image–text similarity, and (2) Dynamic Robust Loss (DRL), which adaptively selects and aggregates hard negative samples to reduce the influence of noisy instances and improve model robustness. Unlike conventional global-alignment approaches, DUNC exploits fine-grained local correspondences to enhance semantic alignment between modalities. By integrating uncertainty-aware modeling and adaptive contrastive supervision, our method is capable of effectively disentangling noisy from reliable training pairs. Extensive experiments conducted on three benchmark datasets—CUHK-PEDES, ICFG-PEDES, and RSTPReid—demonstrate that DUNC consistently achieves state-of-the-art performance and exhibits strong robustness across a wide range of noise conditions. Code is publicly available at https://github.com/zqxie77/DUNC