The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
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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Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval
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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
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