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KAIST
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Adapting visual features directly in a low-rank subspace can drastically improve VLMs' adversarial robustness without sacrificing performance.
Few-shot tuning can distort embeddings, but Subspace Tuning stabilizes adaptation, leading to robust generalization even for unseen classes.
