Negative Label Guided OOD Detection with Pretrained Vision-Language
  Models
Xue Jiang Feng Liu Zhen Fang Hong Chen Tongliang Liu Feng Zheng Bo Han
Out-of-distribution (OOD) detection aims at identifying samples from unknown
classes, playing a crucial role in trustworthy models against errors on
unexpected inputs. Extensive research has been dedicated to exploring OOD
detection in the vision modality. Vision-language models (VLMs) can leverage
both textual and visual information for various multi-modal applications,
whereas few OOD detection methods take into account information from the text
modality. In this paper, we propose a novel post hoc OOD detection method,
called NegLabel, which takes a vast number of negative labels from extensive
corpus databases. We design a novel scheme for the OOD score collaborated with
negative labels. Theoretical analysis helps to understand the mechanism of
negative labels. Extensive experiments demonstrate that our method NegLabel
achieves state-of-the-art performance on various OOD detection benchmarks and
generalizes well on multiple VLM architectures. Furthermore, our method
NegLabel exhibits remarkable robustness against diverse domain shifts. The
codes are available at https://github.com/tmlr-group/NegLabel.