AdvFLYP finetunes CLIP on web image-text pairs using adversarial contrastive learning and regularization to boost zero-shot adversarial robustness across domains better than prior proxy-dataset methods.
Robustness under Higher Attack Budgets We report the full tables of robustness evaluated under the attack strength of ϵ= 2/255 and ϵ= 4/255 in Tab
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Finetune Like You Pretrain: Boosting Zero-shot Adversarial Robustness in Vision-language Models
AdvFLYP finetunes CLIP on web image-text pairs using adversarial contrastive learning and regularization to boost zero-shot adversarial robustness across domains better than prior proxy-dataset methods.