Category-level Text-to-Image Retrieval Improved: Bridging the Domain Gap with Diffusion Models and Vision Encoders

• URL: http://arxiv.org/abs/2509.00177v1
• Date: Fri, 29 Aug 2025 18:24:38 GMT
• Title: Category-level Text-to-Image Retrieval Improved: Bridging the Domain Gap with Diffusion Models and Vision Encoders
• Authors: Faizan Farooq Khan, Vladan Stojnić, Zakaria Laskar, Mohamed Elhoseiny, Giorgos Tolias,
• Abstract summary: This work explores text-to-image retrieval for queries that specify or describe a semantic category.<n>We transform the text query into a visual query using a generative diffusion model.<n>Then, we estimate image-to-image similarity with a vision model.
• Score: 41.08205377881149
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work explores text-to-image retrieval for queries that specify or describe a semantic category. While vision-and-language models (VLMs) like CLIP offer a straightforward open-vocabulary solution, they map text and images to distant regions in the representation space, limiting retrieval performance. To bridge this modality gap, we propose a two-step approach. First, we transform the text query into a visual query using a generative diffusion model. Then, we estimate image-to-image similarity with a vision model. Additionally, we introduce an aggregation network that combines multiple generated images into a single vector representation and fuses similarity scores across both query modalities. Our approach leverages advancements in vision encoders, VLMs, and text-to-image generation models. Extensive evaluations show that it consistently outperforms retrieval methods relying solely on text queries. Source code is available at: https://github.com/faixan-khan/cletir

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