CLIP-driven Outliers Synthesis for few-shot OOD detection

• URL: http://arxiv.org/abs/2404.00323v1
• Date: Sat, 30 Mar 2024 11:28:05 GMT
• Title: CLIP-driven Outliers Synthesis for few-shot OOD detection
• Authors: Hao Sun, Rundong He, Zhongyi Han, Zhicong Lin, Yongshun Gong, Yilong Yin,
• Abstract summary: Few-shot OOD detection focuses on recognizing out-of-distribution (OOD) images that belong to classes unseen during training. Up to now, a mainstream strategy is based on large-scale vision-language models, such as CLIP. We propose CLIP-driven Outliers Synthesis(CLIP-OS) to overcome the lack of reliable OOD supervision information.
• Score: 40.6496321698913
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Few-shot OOD detection focuses on recognizing out-of-distribution (OOD) images that belong to classes unseen during training, with the use of only a small number of labeled in-distribution (ID) images. Up to now, a mainstream strategy is based on large-scale vision-language models, such as CLIP. However, these methods overlook a crucial issue: the lack of reliable OOD supervision information, which can lead to biased boundaries between in-distribution (ID) and OOD. To tackle this problem, we propose CLIP-driven Outliers Synthesis~(CLIP-OS). Firstly, CLIP-OS enhances patch-level features' perception by newly proposed patch uniform convolution, and adaptively obtains the proportion of ID-relevant information by employing CLIP-surgery-discrepancy, thus achieving separation between ID-relevant and ID-irrelevant. Next, CLIP-OS synthesizes reliable OOD data by mixing up ID-relevant features from different classes to provide OOD supervision information. Afterward, CLIP-OS leverages synthetic OOD samples by unknown-aware prompt learning to enhance the separability of ID and OOD. Extensive experiments across multiple benchmarks demonstrate that CLIP-OS achieves superior few-shot OOD detection capability.

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