A reference-based geometric hashing method recovers cross-model vector correspondences by exploiting local isometric consistency in contrastive embeddings and iteratively bootstrapping from a seed of paired anchors.
Bootstrapping parallel anchors for relative representations,
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Lightweight model stitching preserves over 91% of driving performance in cross-domain perception updates for end-to-end autonomous driving, cutting adaptation time from 22 hours to under 1 hour.
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Vector Linking via Cross-Model Local Isometric Consistency
A reference-based geometric hashing method recovers cross-model vector correspondences by exploiting local isometric consistency in contrastive embeddings and iteratively bootstrapping from a seed of paired anchors.