Adaptive Pseudo Text Augmentation for Noise-Robust Text-to-Image Person Re-Identification.

ABSTRACT: Text-to-image person re-identification (T2I-ReID) aims to retrieve pedestrians from images/videos based on textual descriptions. However, most methods implicitly assume that training image-text pairs are correctly aligned, while in practice, issues such as under-correlated and falsely correlated image-text pairs arise due to coarse-grained text annotations and erroneous textual descriptions. To address this problem, we propose a T2I-ReID method based on noise identification and pseudo-text generation. We first extracts image-text features using the Contrastive Language-Image Pre-Training model (CLIP), then employs the token fusion model to select and fuse informative local token features, resulting in token fusion embedding (TFE) for fine-grained representations. To identify noisy image-te

SUBMITTER: Xiong L 

PROVIDER: S-EPMC12694070 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature