Disentangled Concept Representation for Text-to-image Person Re-identification

• URL: http://arxiv.org/abs/2601.10053v1
• Date: Thu, 15 Jan 2026 04:08:53 GMT
• Title: Disentangled Concept Representation for Text-to-image Person Re-identification
• Abstract summary: TIReID is challenging due to the substantial modality gap between visual appearances and textual expressions.<n>We propose DiCo, a novel framework that achieves hierarchical and disentangled cross-modal alignment.<n>Our framework achieves competitive performance with state-of-the-art methods.
• Score: 4.581495198705364
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-to-image person re-identification (TIReID) aims to retrieve person images from a large gallery given free-form textual descriptions. TIReID is challenging due to the substantial modality gap between visual appearances and textual expressions, as well as the need to model fine-grained correspondences that distinguish individuals with similar attributes such as clothing color, texture, or outfit style. To address these issues, we propose DiCo (Disentangled Concept Representation), a novel framework that achieves hierarchical and disentangled cross-modal alignment. DiCo introduces a shared slot-based representation, where each slot acts as a part-level anchor across modalities and is further decomposed into multiple concept blocks. This design enables the disentanglement of complementary attributes (\textit{e.g.}, color, texture, shape) while maintaining consistent part-level correspondence between image and text. Extensive experiments on CUHK-PEDES, ICFG-PEDES, and RSTPReid demonstrate that our framework achieves competitive performance with state-of-the-art methods, while also enhancing interpretability through explicit slot- and block-level representations for more fine-grained retrieval results.

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