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Text-to-Image Person Re-Identification (TI-ReID) aims to retrieve target pedestrians from large-scale image galleries using natural language descriptions. Despite recent progress achieved by dual-tower architectures based on vision-language pre-training, these methods remain susceptible to semantic misalignment and noise induced by occlusions, background clutter, and fine-grained attribute distractions. To mitigate these issues, we propose a Global Collaborative Discriminative Denoising Network (GCDD), a dual-tower fine-tuning framework built upon a CLIP visual encoder and a BERT text encoder. Specifically, GCDD introduces three complementary branches for robust feature enhancement. First, Discriminative Token Selection (DTS) performs adaptive hard filtering to suppress low-informative tokens. Second, Global-Guided Feature Adaptation (GFA) leverages modality-specific global semantics to recalibrate local features. Third, Query-Driven Aggregation (QDA) constructs more discriminative global representations via attentive pooling, where the backbone global feature serves as the query. The outputs of the three branches are fused through a parameter-free averaging strategy to produce the final representation. Extensive experiments on three standard TI-ReID benchmarks demonstrate that GCDD achieves strong competitive performance, validating the effectiveness of the proposed feature enhancement framework.
Han et al. (Fri,) studied this question.