Framework certifies VLM robustness under semantic transformations via text prompt proxies, enabling quantitative certification of safe extent intervals without per-variation data.
Ants: Adaptive negative textual space shaping for ood detection via test-time mllm understanding and reasoning
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TINS improves OOD detection by learning negative semantics at test time with ID-prototype separation, cutting average FPR95 from 14.04% to 6.72% on the Four-OOD benchmark with ImageNet-1K.
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Semantic Robustness Certification for Vision-Language Models
Framework certifies VLM robustness under semantic transformations via text prompt proxies, enabling quantitative certification of safe extent intervals without per-variation data.
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TINS: Test-time ID-prototype-separated Negative Semantics Learning for OOD Detection
TINS improves OOD detection by learning negative semantics at test time with ID-prototype separation, cutting average FPR95 from 14.04% to 6.72% on the Four-OOD benchmark with ImageNet-1K.