Author

Shuo Wang

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Preprint Jul 2026

Robustifying Vision-Language Models via Test-Time Prompt Adaptation

This work proposes RITA, a Robust test-tIme prompt-TAdaptation framework that shifts from sample-level estimates to distribution-level alignment, and employs optimal transport to align the distribution of augmented visual features with textual prototypes, mitigating adversarial outliers and rectifying cross-modal seman...

Xingyu Zhu, Huanshen Wu, Shuo Wang et al. · 1 citation