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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Simple prediction-based late fusion is less adopted in MML Transformers.
“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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Align and Retrieve: Composition and Decomposition Learning in Image Retrieval With Text Feedback
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From text to mask: Localizing entities using the attention of text-to-image diffusion models
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CoVR-2: Automatic Data Construction for Composed Video Retrieval
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SafeBench: A Safety Evaluation Framework for Multimodal Large Language Models
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Composed Video Retrieval via Enriched Context and Discriminative Embeddings
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Semantic Editing Increment Benefits Zero-Shot Composed Image Retrieval
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PAIR: Complementarity-guided Disentanglement for Composed Image Retrieval
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Tang, Y., Zhang, J., Qin, X., Yu, J., Gou, G., GangXiong, G., Lin, Q., Rajmohan, S., Zhang, D., & Wu, Q. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01343 · Cited by 18 · see citing works
Fashion-GPT: Integrating LLMs with Fashion Retrieval System
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Multimodal Composition Example Mining for Composed Query Image Retrieval
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Improving Composed Image Retrieval via Contrastive Learning with Scaling Positives and Negatives
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SPIRIT: Style-guided Patch Interaction for Fashion Image Retrieval with Text Feedback
Chen, Y., Zhou, J., & Peng, Y. (2024) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3640345 · Cited by 15 · see citing works
FontCLIP: A Semantic Typography Visual‐Language Model for Multilingual Font Applications
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Visual Delta Generator with Large Multi-Modal Models for Semi-Supervised Composed Image Retrieval
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CaLa: Complementary Association Learning for Augmenting Comoposed Image Retrieval
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FashionERN: Enhance-and-Refine Network for Composed Fashion Image Retrieval
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SyncMask: Synchronized Attentional Masking for Fashion-centric Vision-Language Pretraining
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iSEARLE: Improving Textual Inversion for Zero-Shot Composed Image Retrieval
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FaD-VLP: Fashion Vision-and-Language Pre-training towards Unified Retrieval and Captioning
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Unsupervised deep hashing with multiple similarity preservation for cross-modal image-text retrieval
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EgoCVR: An Egocentric Benchmark for Fine-Grained Composed Video Retrieval
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Zhang, G., Wei, S., Pang, H., Qiu, S., & Zhao, Y. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3273466 · Cited by 12 · see citing works
Efficient and Secure Content-Based Image Retrieval in Cloud-Assisted Internet of Things
Chen, L., Yang, Y., Yang, L., Xu, C., Miao, Y., Liu, Z., Liu, X., Choo, K. R., Liang, Z., & Hong, C. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3489957 · Cited by 11 · see citing works
Self-citationOFFSET: Segmentation-based Focus Shift Revision for Composed Image Retrieval
Chen, Z., Hu, Y., Li, Z., Fu, Z., Song, X., & Nie, L. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3755366 · Cited by 11 · see citing works
Improving Commonsense in Vision-Language Models via Knowledge Graph Riddles
Ye, S., Xie, Y., Chen, D., Yi‐chong, X., Yuan, L., Zhu, C., & Liao, J. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.00259 · Cited by 11 · see citing works
The ECOLANG Multimodal Corpus of adult-child and adult-adult Language
Gu, Y., Donnellan, E., Grzyb, B. J., Brekelmans, G., Murgiano, M., Brieke, R., Perniss, P., & Vigliocco, G. (2025) · Scientific Data · doi:10.1038/s41597-025-04405-1 · Cited by 11 · see citing works
Generative Zero-Shot Composed Image Retrieval
Wang, L., Ao, W., Boddeti, V. N., & Lim, S.-N. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.02764 · Cited by 10 · see citing works
TagCLIP: Improving Discrimination Ability of Zero-Shot Semantic Segmentation
Li, J., Chen, P., Qian, S., Liu, S., & Jia, J. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3454647 · Cited by 10 · see citing works
PhotoScout: Synthesis-Powered Multi-Modal Image Search
Barnaby, C., Chen, Q., Wang, C., & Dillig, I. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642319 · Cited by 9 · see citing works
Set of Diverse Queries With Uncertainty Regularization for Composed Image Retrieval
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Attribute-Driven Multimodal Hierarchical Prompts for Image Aesthetic Quality Assessment
Zhu, H., Shi, J., Shao, Z., Yao, R., Zhou, Y. Z., Zhao, J., & Li, L. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681175 · Cited by 9 · see citing works
A survey of multimodal composite editing and retrieval
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Training-Free Zero-Shot Composed Image Retrieval via Weighted Modality Fusion and Similarity
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Learning-To-Rank Approach for Identifying Everyday Objects Using a Physical-World Search Engine
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Visual-Linguistic Alignment and Composition for Image Retrieval with Text Feedback
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Image Retrieval with Composed Query by Multi-Scale Multi-Modal Fusion
Sun, Z., Yang, G., Lu, Z., Jiang, H., Zhu, G., & Cao, Z. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446291 · Cited by 7 · see citing works
UIGR: Unified Interactive Garment Retrieval
Xiao, H. M., He, S., Zhang, L., Song, Y.-Z., & Xiang, T. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00241 · Cited by 7 · see citing works
Detours for Navigating Instructional Videos
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Liu, J. X., Shah, A., Konidaris, G., Tellex, S., & Paulius, D. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802696 · Cited by 6 · see citing works
A Survey on Composed Image Retrieval
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Small object detection in remote sensing images through multi-scale feature fusion
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CoLLM: A Large Language Model for Composed Image Retrieval
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CoTMR: Chain-of-Thought Multi-Scale Reasoning for Training-Free Zero-Shot Composed Image Retrieval
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Negative-Sensitive Framework With Semantic Enhancement for Composed Image Retrieval
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Composed image retrieval: a survey on recent research and development
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SCORE: Scene Context Matters in Open-Vocabulary Remote Sensing Instance Segmentation
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FashionNTM: Multi-turn Fashion Image Retrieval via Cascaded Memory
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Similarity Retrieval Based on Image Background Analysis
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Survey of Semantic-Based Image-to-Image Retrieval
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FiRE: Enhancing MLLMs with Fine-Grained Context Learning for Complex Image Retrieval
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ConText-CIR: Learning from Concepts in Text for Composed Image Retrieval
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- 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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