CIRCLED: A Multi-turn CIR Dataset with Consistent Dialogues across Domains
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...
| Authors | Tomohisa Takeda, Yu-Chieh Lin, Yuji Nozawa et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 May 2026 |
| Last updated at source | 21 August 2026 |
Models and datasets referencing this paper
- tk1441/CIRCLEDDataset
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.