Deep neural networks often exhibit the overconfidence issue when encountering out-of-distribution (OOD) samples. To address this, leveraging large-scale pre-trained models like CLIP has shown promise. While CLIP has the capability to encode a vast array of interconnected concepts, current OOD detection methods based on it primarily focus on ID categories and a limited set of OOD categories. In this paper, we propose a novel approach that harnesses the power of WordNet to fully exploit the rich knowledge encapsulated within CLIP, resulting in enhanced OOD detection performance. Our methodology involves constructing a word tree that includes both in-distribution (ID) words and a large set of semantically similar OOD words selected from WordNet. By matching a test image with the concepts of the words in the word tree using CLIP, we estimate the probability of the image being classified as either ID or OOD. Furthermore, we introduce a conditional random field model to effectively handle both the parent-child and the sibling-sibling conflicts in the concept matching results. Extensive experiments under various ID/OOD settings demonstrate the effectiveness of our approach and its superiority over state-of-the-art methods.

Deep neural networks (DNNs) are dominating various computer vision solutions. However, DNN classifiers suffer from the out-of-distribution (OOD) overconfidence issue, i.e., making overconfident predictions on OOD samples. In this paper, we consider a new OOD attack task, i.e., generating OOD examples that fool DNN classifiers to trap into this issue. Specifically, we first generate seed examples by sampling from common OOD distributions, and then lift the prediction to be overconfident. Extensive experiments with different seeds and confidence-lifting solutions under white-and black-box settings validate the feasibility of OOD attack. Besides, we demonstrate its usefulness in evaluating OOD detection and alleviating the OOD overconfidence issue.