Adaptive Multi-prompt Contrastive Network for Few-shot Out-of-distribution Detection 事件

Adaptive Multi-prompt Contrastive Network for Few-shot Out-of-distribution Detection arXiv:2506.17633v2 Announce Type: replace Abstract: Out-of-distribution (OOD) detection attempts to distinguish outlier samples to prevent models trained on the in-distribution (ID) dataset from producing unavailable outputs. Most OOD detection methods require many IID samples for training, which seriously limits their real-world applications. To this end, we target a challenging setting: few-shot OOD detection