Respecting Modality Gap in Post-hoc Out-of-distribution Detection with Pre-trained Vision-Language Models

ArXi:2605.26661v1 Announce Type: cross Out-of-distribution (OOD) detection has emerged as a popular technique to enhance the reliability of machine learning models by identifying unexpected inputs from unknown classes. Recent progress in pre-trained vision-language models (VLMs) has enabled zero-shot OOD detection without access to in-distribution (ID)