Papers › Zero-shot Composed Text-Image Retrieval
Zero-shot Composed Text-Image Retrieval
Yikun Liu, Jiangchao Yao, Ya zhang, Yanfeng Wang, Weidi Xie
In this paper, we consider the problem of composed image retrieval (CIR), it aims to train a model that can fuse multi-modal information, e.g., text and images, to accurately retrieve images that match the query, extending the user's expression ability. We make the following contributions: (i) we initiate a scalable pipeline to automatically construct datasets for training CIR model, by simply exploiting a large-scale dataset of image-text pairs, e.g., a subset of LAION-5B; (ii) we introduce a transformer-based adaptive aggregation model, TransAgg, which employs a simple yet efficient fusion mechanism, to adaptively combine information from diverse modalities; (iii) we conduct extensive ablation studies to investigate the usefulness of our proposed data construction procedure, and the effectiveness of core components in TransAgg; (iv) when evaluating on the publicly available benckmarks under the zero-shot scenario, i.e., training on the automatically constructed datasets, then directly conduct inference on target downstream datasets, e.g., CIRR and FashionIQ, our proposed approach either performs on par with or significantly outperforms the existing state-of-the-art (SOTA) models. Project page: https://code-kunkun.github.io/ZS-CIR/
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Zero-Shot Composed Image Retrieval (ZS-CIR) | CIRR | TransAgg (Laion-CIR-Combined) | R@1 | 37.87 | #6 of 47 | Archive leaderboard | report |
| Zero-Shot Composed Image Retrieval (ZS-CIR) | CIRR | TransAgg (Laion-CIR-Combined) | R@5 | 68.88 | #6 of 47 | Archive leaderboard | report |
| Zero-Shot Composed Image Retrieval (ZS-CIR) | CIRR | TransAgg (Laion-CIR-Combined) | R@50 | 93.86 | #6 of 47 | Archive leaderboard | report |
| Zero-Shot Composed Image Retrieval (ZS-CIR) | Fashion IQ | TransAgg (Laion-CIR-Combined) | (Recall@10+Recall@50)/2 | 44.75 | #14 of 41 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections