A Theoretical Analysis of Environment-Aware Out-of-Distribution Detection for Vision-Language Models
Continual vision-language models (VLMs) encounter a stream of environments and must also reject inputs that do not belong to the environment at hand. Class information from earlier environments can aid out-of-distribution (OOD) detection, yet existing detectors score each environment in isolation, and when previous environments help or hurt is theoretically unclear. We develop a theory of environment-aware OOD detection built on contribution scores, pairwise cosine expectations measuring how the image and text features of one environment align with those of another. We propose a cross-environment maximum-concept-matching (MCM) score with two terms: a primary term matching the test image a...
| Authors | Salimeh Sekeh, Mary Wisell |
|---|---|
| Primary category | cs.LG |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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