CIRCLED: A Multi-turn CIR Dataset with Consistent Dialogues across Domains

Abstract

Existing Multi-Turn Composed Image Retrieval (MTCIR) datasets lack dialogue-historyconsistency and are restricted to the fashion domain. To address these limitations, we construct CIRCLED by extending FashionIQ, CIRR, and CIRCO. In CIRCLED, the query ateach turn progressively approaches the target image. Data are generated via a CIReVLbased retrieval pipeline and curated with multiple filters on retrieval success, turn length, consistency, and information redundancy to ensure quality. In total, we collect 22,608 multiturn sessions across nine subsets, substantially exceeding Multi-turn FashionIQ (11,505 sessions) in both scale and generality. We further apply multiple baseline methods and quantitatively assess retrieval accuracy on CIRCLED. Our work provides a practical, highquality benchmark to facilitate future research on multi-turn CIR. The dataset is publicly available at https://huggingface.co/datasets/tk1441/CIRCLED, and the code at https://github.com/mti-lab/circled.

Accepted at DMLR (Journal of Data-centric Machine Learning Research). Published 19 August 2026