most citedOne Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models

1 citations · 1 across the 1 of their papers we have counts for

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2 papers

cs.CV2024★ 1 cited

One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models

Lin Li, Haoyan Guan, Jianing Qiu +1

Large pre-trained Vision-Language Models (VLMs) like CLIP, despite having remarkable generalization ability, are highly vulnerable to adversarial examples. This work studies the ad…

cs.LG2023

OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift

Lin Li, Yifei Wang, Chawin Sitawarin +1

Existing works have made great progress in improving adversarial robustness, but typically test their method only on data from the same distribution as the training data, i.e. in-d…