TagFog: Textual Anchor Guidance and Fake Outlier Generation for Visual Out-of-Distribution Detection

• URL: http://arxiv.org/abs/2412.05292v1
• Date: Fri, 22 Nov 2024 14:40:25 GMT
• Title: TagFog: Textual Anchor Guidance and Fake Outlier Generation for Visual Out-of-Distribution Detection
• Authors: Jiankang Chen, Tong Zhang, Wei-Shi Zheng, Ruixuan Wang,
• Abstract summary: Out-of-distribution (OOD) detection is crucial in many real-world applications. We propose a new learning framework which leverage simple Jigsaw-based fake OOD data and rich semantic embeddings (anchors') from the ChatGPT description of ID knowledge to help guide the training of the image encoder.
• Score: 34.31570050254269
• License:
• Abstract: Out-of-distribution (OOD) detection is crucial in many real-world applications. However, intelligent models are often trained solely on in-distribution (ID) data, leading to overconfidence when misclassifying OOD data as ID classes. In this study, we propose a new learning framework which leverage simple Jigsaw-based fake OOD data and rich semantic embeddings (`anchors') from the ChatGPT description of ID knowledge to help guide the training of the image encoder. The learning framework can be flexibly combined with existing post-hoc approaches to OOD detection, and extensive empirical evaluations on multiple OOD detection benchmarks demonstrate that rich textual representation of ID knowledge and fake OOD knowledge can well help train a visual encoder for OOD detection. With the learning framework, new state-of-the-art performance was achieved on all the benchmarks. The code is available at \url{https://github.com/Cverchen/TagFog}.

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