CLIP-RD adds VRD for cross-modality distillation consistency and XRD for bidirectional cross-modal symmetry to align student embedding geometry more closely with the teacher, yielding a 0.8 percentage point gain over prior distillation methods.
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Robust CLIP models amplify vulnerabilities to natural adversarial scenarios while standard CLIP shows large performance drops on natural language-induced adversarial examples in zero-shot classification, segmentation, and VQA.
citing papers explorer
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CLIP-RD: Relative Distillation for Efficient CLIP Knowledge Distillation
CLIP-RD adds VRD for cross-modality distillation consistency and XRD for bidirectional cross-modal symmetry to align student embedding geometry more closely with the teacher, yielding a 0.8 percentage point gain over prior distillation methods.
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Beyond Standard Benchmarks: A Systematic Audit of Vision-Language Model's Robustness to Natural Semantic Variation Across Diverse Tasks
Robust CLIP models amplify vulnerabilities to natural adversarial scenarios while standard CLIP shows large performance drops on natural language-induced adversarial examples in zero-shot classification, segmentation, and VQA.