VECA learns effective visual representations using core-periphery attention where patches interact exclusively via a resolution-invariant set of learned core embeddings, achieving linear O(N) complexity while maintaining competitive performance.
Theia: Distilling diverse vision foundation models for robot learning
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A label-propagation pipeline combining a segment proposer with Hopfield networks on multi-model embeddings can automatically annotate 60% of household object data for up to 50 classes using only limited initial labels.
citing papers explorer
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Elastic Attention Cores for Scalable Vision Transformers
VECA learns effective visual representations using core-periphery attention where patches interact exclusively via a resolution-invariant set of learned core embeddings, achieving linear O(N) complexity while maintaining competitive performance.
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Efficient Image Annotation via Semi-Supervised Object Segmentation with Label Propagation
A label-propagation pipeline combining a segment proposer with Hopfield networks on multi-model embeddings can automatically annotate 60% of household object data for up to 50 classes using only limited initial labels.