Visible-infrared person re-identification (VI-ReID) aims to match pedestrian images captured under visible and infrared modalities, which suffer from significant domain discrepancies. Existing approaches either synthesize cross-modal images or learn modality-invariant representations, yet often encounter semantic degradation or limited alignment capacity. Recent vision-language models leverage textual semantics for modality bridging; however, CLIP-based frameworks typically rely on learnable token proxies with limited expressiveness. In this paper, we propose a novel semantic-driven framework that explicitly generates rich, modality-agnostic textual descriptions from images as alignment cues. Specifically, we design a dual-branch Text Semantic Generation Module that includes: (1) an Attribute-Aware text descriptions Generation module using prompt-based templates to capture modality-invariant identity cues, and (2) a Contour-Aware text prompt Module that provides complementary structural information often missing in textual descriptions. To reconcile semantic heterogeneity, a Text Re-definition Module(TRM) fuses instance-level and class-level semantics into unified representations, enabling fine-grained alignment with image features. Furthermore, we construct category-level textual prototypes as global semantic anchors to enhance cross-modal consistency. Extensive experiments on two standard VI-ReID benchmarks demonstrate that our method achieves superior performance, validating its effectiveness in semantic-guided modality alignment.
Tao et al. (Sat,) studied this question.
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