3 papers
cs.CV2026
DynProto: Dynamic Prototype Evolution for Out-of-Distribution Detection
Yanqi Wu, Xinhua Lu, Runhe Lai +4
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). Ho…
cs.CV2026
DCAC: Dynamic Class-Aware Cache Creates Stronger Out-of-Distribution Detectors
Yanqi Wu, Qichao Chen, Runhe Lai +5
Out-of-distribution (OOD) detection remains a fundamental challenge for deep neural networks, particularly due to overconfident predictions on unseen OOD samples during testing. We…
cs.CV2024
TagFog: Textual Anchor Guidance and Fake Outlier Generation for Visual Out-of-Distribution Detection
Jiankang Chen, Tong Zhang, Wei-Shi Zheng +1
Out-of-distribution (OOD) detection is crucial in many real-world applications. However, intelligent models are often trained solely on in-distribution (ID) data, leading to overco…