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Fondazione Bruno Kessler
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Selective querying of VLMs can lead to autonomous policies that outperform their teachers while drastically reducing reliance on expensive model calls.
Unlabeled data can be transformed into powerful training examples, boosting classification performance by nearly 20 points without the need for costly annotations.
