Methods › Computer Vision › Vision Transformers › CrossTransformers
CrossTransformers
Introduced by Carl Doersch et al. in CrossTransformers: spatially-aware few-shot transfer
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
CrossTransformers is a Transformer-based neural network architecture which can take a small number of labeled images and an unlabeled query, find coarse spatial correspondence between the query and the labeled images, and then infer class membership by computing distances between spatially-corresponding features.
Papers archive 2025-07-28
3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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FewSOL: A Dataset for Few-Shot Object Learning in Robotic Environments 6 Jul 2022 · 3 repositories · arXiv:2207.03333
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Temporal-Relational CrossTransformers for Few-Shot Action Recognition 15 Jan 2021 · 2 repositories · arXiv:2101.06184Syntology ran 2 of 5 samples · 3 unverified · 1 pointer-only (licence)
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CrossTransformers: spatially-aware few-shot transfer 22 Jul 2020 · 6 repositories · arXiv:2007.11498Syntology ran 5 of 6 samples · 1 unverified · 2 pointer-only (licence)
Tasks archive 2025-07-28
13 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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