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Chalmers University of Technology
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Proprietary models may have powerful reasoning engines, but they fail to accurately estimate metrics and leverage structural insights, revealing a crucial gap in VLM performance.
Adversarial training unlocks domain-invariant prompts for CLIP, boosting zero-shot generalization beyond standard prompt tuning.
