good4cir: Generating Detailed Synthetic Captions for Composed Image Retrieval
Journal:
arXiv
Published Date:
Mar 22, 2025
Abstract
Composed image retrieval (CIR) enables users to search images using a
reference image combined with textual modifications. Recent advances in
vision-language models have improved CIR, but dataset limitations remain a
barrier. Existing datasets often rely on simplistic, ambiguous, or insufficient
manual annotations, hindering fine-grained retrieval. We introduce good4cir, a
structured pipeline leveraging vision-language models to generate high-quality
synthetic annotations. Our method involves: (1) extracting fine-grained object
descriptions from query images, (2) generating comparable descriptions for
target images, and (3) synthesizing textual instructions capturing meaningful
transformations between images. This reduces hallucination, enhances
modification diversity, and ensures object-level consistency. Applying our
method improves existing datasets and enables creating new datasets across
diverse domains. Results demonstrate improved retrieval accuracy for CIR models
trained on our pipeline-generated datasets. We release our dataset construction
framework to support further research in CIR and multi-modal retrieval.
