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Targeted interventions can significantly enhance the robustness of CLIP models against Typographic Attacks without additional training, outperforming existing defense methods.
Counterfactual examples supercharge visual in-context learning, enabling smaller vision-language models to outperform larger ones by focusing on causal relationships rather than superficial correlations.
