FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval

• URL: http://arxiv.org/abs/2604.09114v1
• Date: Fri, 10 Apr 2026 08:50:47 GMT
• Title: FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval
• Abstract summary: Composed image retrieval (CIR) aims to retrieve a target image that depicts a reference image modified by a textual description.<n>Recent vision-language models (VLMs) achieve promising CIR performance by embedding images and text into a shared space for retrieval.<n>We introduce FIRE-CIR, a model that brings compositional reasoning and interpretability to fashion CIR.
• Score: 24.83808020078209
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Composed image retrieval (CIR) aims to retrieve a target image that depicts a reference image modified by a textual description. While recent vision-language models (VLMs) achieve promising CIR performance by embedding images and text into a shared space for retrieval, they often fail to reason about what to preserve and what to change. This limitation hinders interpretability and yields suboptimal results, particularly in fine-grained domains like fashion. In this paper, we introduce FIRE-CIR, a model that brings compositional reasoning and interpretability to fashion CIR. Instead of relying solely on embedding similarity, FIRE-CIR performs question-driven visual reasoning: it automatically generates attribute-focused visual questions derived from the modification text, and verifies the corresponding visual evidence in both reference and candidate images. To train such a reasoning system, we automatically construct a large-scale fashion-specific visual question answering dataset, containing questions requiring either single- or dual-image analysis. During retrieval, our model leverages this explicit reasoning to re-rank candidate results, filtering out images inconsistent with the intended modifications. Experimental results on the Fashion IQ benchmark show that FIRE-CIR outperforms state-of-the-art methods in retrieval accuracy. It also provides interpretable, attribute-level insights into retrieval decisions.

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