GEA: Generation-Enhanced Alignment for Text-to-Image Person Retrieval

• URL: http://arxiv.org/abs/2511.10154v1
• Date: Fri, 14 Nov 2025 01:35:34 GMT
• Title: GEA: Generation-Enhanced Alignment for Text-to-Image Person Retrieval
• Authors: Hao Zou, Runqing Zhang, Xue Zhou, Jianxiao Zou,
• Abstract summary: Text-to-Image Person Retrieval (TIPR) aims to retrieve person images based on natural language descriptions.<n>To address these limitations, we propose the Generation-Enhanced Alignment (GEA) from a generative perspective.<n>We conduct extensive experiments on three public TIPR datasets, CUHK-PEDES, RSTPReid, and ICFG-PEDES, to evaluate the performance of GEA.
• Score: 12.483996028288407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-to-Image Person Retrieval (TIPR) aims to retrieve person images based on natural language descriptions. Although many TIPR methods have achieved promising results, sometimes textual queries cannot accurately and comprehensively reflect the content of the image, leading to poor cross-modal alignment and overfitting to limited datasets. Moreover, the inherent modality gap between text and image further amplifies these issues, making accurate cross-modal retrieval even more challenging. To address these limitations, we propose the Generation-Enhanced Alignment (GEA) from a generative perspective. GEA contains two parallel modules: (1) Text-Guided Token Enhancement (TGTE), which introduces diffusion-generated images as intermediate semantic representations to bridge the gap between text and visual patterns. These generated images enrich the semantic representation of text and facilitate cross-modal alignment. (2) Generative Intermediate Fusion (GIF), which combines cross-attention between generated images, original images, and text features to generate a unified representation optimized by triplet alignment loss. We conduct extensive experiments on three public TIPR datasets, CUHK-PEDES, RSTPReid, and ICFG-PEDES, to evaluate the performance of GEA. The results justify the effectiveness of our method. More implementation details and extended results are available at https://github.com/sugelamyd123/Sup-for-GEA.

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