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.
