The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
COTR: Correspondence Transformer for Matching Across Images
Open accessAbstract
We propose a novel framework for finding correspondences in images based on a deep neural network that, given two images and a query point in one of them, finds its correspondence in the other. By doing so, one has the option to query only the points of interest and retrieve sparse correspondences, or to query all points in an image and obtain dense mappings. Importantly, in order to capture both local and global priors, and to let our model relate between image regions using the most relevant among said priors, we realize our network using a transformer. At inference time, we apply our correspondence network by recursively zooming in around the estimates, yielding a multiscale pipeline able to provide highly-accurate correspondences. Our method significantly outperforms the state of the art on both sparse and dense correspondence problems on multiple datasets and tasks, ranging from wide-baseline stereo to optical flow, without any retraining for a specific dataset. We commit to releasing data, code, and all the tools necessary to train from scratch and ensure reproducibility.
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Adam: A Method for Stochastic Optimization
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End-to-End Object Detection with Transformers
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Grounding Image Matching in 3D with MASt3R
Leroy, V., Cabon, Y., & Revaud, J. (2024) · arXiv · doi:10.48550/arxiv.2406.09756 · Cited by 309
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
Grauman, K., Westbury, A., Torresani, L., Kitani, K., Malik, J., Afouras, T., Ashutosh, K., Baiyya, V., Bansal, S., Boote, B., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01834 · Cited by 182
Multimodal Remote Sensing Image Matching via Learning Features and Attention Mechanism
Zhang, Y., Lan, C., Zhang, H., Ma, G., & Li, H. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3348980 · Cited by 37
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives
Grauman, K., Westbury, A., Torresani, L., Kitani, K., Malik, J., Afouras, T., Ashutosh, K., Baiyya, V., Bansal, S., Boote, B., et al. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02557-6 · Cited by 3
Metadata
- Authors
- Jiang, W., Trulls, E., Hosang, J., Tagliasacchi, A., & Yi, K. M.
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 6187-6197
- OpenAlex
- W3203518786
- Cited by
- 282
- References
- 14
- FWCI
- 17.93
- Type
- conference paper
- Language
- en
- Open access
- green
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- Jiang, W., Trulls, E., Hosang, J., Tagliasacchi, A., & Yi, K. M. (2021). COTR: Correspondence Transformer for Matching Across Images. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv48922.2021.00615
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