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Image Retrieval on Real-life Images with Pre-trained Vision-and-Language Models
Open accessAbstract
We extend the task of composed image retrieval, where an input query consists of an image and short textual description of how to modify the image. Existing methods have only been applied to non-complex images within narrow domains, such as fashion products, thereby limiting the scope of study on in-depth visual reasoning in rich image and language contexts. To address this issue, we collect the Compose Image Retrieval on Real-life images (CIRR) dataset, which consists of over 36,000 pairs of crowd-sourced, open-domain images with human-generated modifying text. To extend current methods to the open-domain, we propose CIRPLANT, a transformer based model that leverages rich pre-trained vision-and-language (V&L) knowledge for modifying visual features conditioned on natural language. Retrieval is then done by nearest neighbor lookup on the modified features. We demonstrate that with a relatively simple architecture, CIRPLANT outperforms existing methods on open-domain images, while matching state-of-the-art accuracy on the existing narrow datasets, such as fashion. Together with the release of CIRR, we believe this work will inspire further research on composed image retrieval. Our dataset, code and pre-trained models are available at https://cuberick-orion.github.io/CIRR/.
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“We note that the simple prediction-based late fusion [247], [248] is less adopted in MML Transformers.”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [247]
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FIGROTD: A Friendly-to-Handle Dataset for Image Guided Retrieval with Optional Text
Le, H.-B., Tran, L.-D., Nguyen, B. T., Zhou, L., & Gurrin, C. G. (2026) · Lecture notes in computer science · doi:10.1007/978-981-95-6950-2_9
Fine-Grained Zero-Shot Composed Image Retrieval with Complementary Visual-Semantic Integration
Ye, Y., Zhang, K., Zhang, Y., Chen, E., Li, L., & Zhou, J. (2025) · Proceedings · doi:10.1109/icdm65498.2025.00093
SOP: Selective Orthogonal Projection for Composed Image Retrieval
Cheng, S., & Liu, G. (2026) · Sensors · doi:10.3390/s26051621
Le, H.-B., Tran, L.-D., Nguyen, B. T., Zhou, L., & Gurrin, C. G. (2025) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw69685.2025.00176
Multimodal deep learning for audiovisual production
Ouenniche, K. (2023) · theses.fr (ABES) · doi:10.70675/a01becf4z4ed9z4955z940cz49c821c1b8a8
ARCHER: Shooting Straight in Multimodal E-Commerce Search at Alibaba with Progressive Alignment
Wang, M., Fu, L., Chu, J., Guo, K., Qin, C., Wang, X., Wu, S., Jiang, W., & Zhao, X. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774904.3792819
Ge, X., Wang, C., Wang, X., Qin, Z., Chen, Z., & Xin, X. (2026) · International World Wide Web Conference (WWW) · doi:10.1145/3774904.3792287
Weakly Supervised Composed Object Re-Identification With Large Models
Huang, Y., Lü, J., Wu, Q., & Zhang, G. (2026) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2026.3681841
BenchCIR: Benchmarking robustness in composed image retrieval across modalities
Sun, S., Li, Q., Gong, S., Cai, W., Torr, P. H. S., & Gu, J. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113724
Metadata
- Authors
- Liu, Z., Rodriguez-Opazo, C., Teney, D., & Gould, S.
- Affiliations
- Australian National University (AU)
- University of Adelaide,Australian Institute for Machine Learning
- The University of Adelaide (AU)
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 2105-2114
- OpenAlex
- W3203247393
- Cited by
- 238
- References
- 43
- Citations by year
- 2026: 47 · 2025: 91 · 2024: 55 · 2023: 28 · 2022: 16 · 2021: 1
- FWCI
- 8.18
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- composed image retrieval, image modification, vision-language models, pre-trained language models, visual reasoning, nearest neighbor search
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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