  We study the problem of few-shot out-of-distribution (OOD) detection, which
aims to detect OOD samples from unseen categories during inference time with
only a few labeled in-domain (ID) samples. Existing methods mainly focus on
training task-aware prompts for OOD detection. However, training on few-shot
data may cause severe overfitting and textual prompts alone may not be enough
for effective detection. To tackle these problems, we propose a prior-based
Training-free Dual Adaptation method (Dual-Adapter) to detect OOD samples from
both textual and visual perspectives. Specifically, Dual-Adapter first extracts
the most significant channels as positive features and designates the remaining
less relevant channels as negative features. Then, it constructs both a
positive adapter and a negative adapter from a dual perspective, thereby better
leveraging previously outlooked or interfering features in the training
dataset. In this way, Dual-Adapter can inherit the advantages of CLIP not
having to train, but also excels in distinguishing between ID and OOD samples.
Extensive experimental results on four benchmark datasets demonstrate the
superiority of Dual-Adapter.
