DynProto: Dynamic Prototype Evolution for Out-of-Distribution Detection
Recent studies show that using potential out-of-distribution (OOD) labels from large corpora as auxiliary information can improve OOD detection in vision-language models (VLMs). However, these methods often fail when real-world OOD samples fall outside the predefined OOD label set. To address this limitation, we propose DynProto, a novel approach that learns OOD prototypes dynamically during testing using only in-distribution (ID) information. DynProto is inspired by a key observation: OOD samples predicted as the same ID class tend to cluster in the feature space. With this insight, we leverage easy-to-detect OOD samples as ``anchors'' to find their harder-to-detect, similar counterparts...
| Authors | Yanqi Wu, Xinhua Lu, Runhe Lai et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 April 2026 |
| Last updated at source | 18 June 2026 |
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