Online Semantic Gaussian Banks for Test-Time OOD Detection

Pre-trained vision-language models (VLMs) have shown strong performance for out-of-distribution (OOD) detection, while recent test-time methods further improve detection by adapting to unlabeled test streams. However, incorrect ID/OOD assignments during adaptation can introduce unreliable test samples and degrade detection performance. We propose OSGB (Online Semantic Gaussian Banks), an optimization-free framework for online test-time OOD detection. Using frozen VLM features and positive and negative textual semantics, OSGB selectively updates separate Gaussian banks for ID and OOD samples. As test-time statistics accumulate, a global Gaussian likelihood-ratio score is used for OOD detection. Each sample is scored before updating the banks, preserving causal online inference. Experiments on ImageNet-1K, shifted ImageNet variants, CIFAR-100, and OpenOOD show consistent improvements over the compared methods.