Near OOD Detection for Vision-Language Prompt Learning with Contrastive Logit Score

Understand

Prompt learning has emerged as an efficient and effective method for fine-tuning vision-language models such as CLIP.

• While many studies have explored generalisation abilities of these models in few-shot classification tasks and a few studies have addressed far out-of-distribution (OOD) of the models, their potential for addressing near OOD detection remains underexplored.
• Existing methods either require training from scratch, need fine-tuning, or are not designed for vision-language prompt learning.
• To address this, we introduce the Contrastive Logit Score (CLS), a novel post-hoc, plug-and-play scoring function.