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There has been significant work on understanding the In-Context Learning capabilities of Large Language Models, especially on the induction circuit. For a few-shot classification task, the induction circuit leverages linear representations of each labeled example in-context in order to classify an unlabeled query. Howe...
This work proposes an appearance-aware RPC refinement pipeline that combines learned local feature matching for season-invariant correspondences with global image descriptors for selecting visually compatible image pairs, which reduces redundant and error-prone matching while preserving the connectivity of the matching...
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