TagFog: Textual Anchor Guidance and Fake Outlier Generation for Visual Out-of-Distribution Detection
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913600522682368 |
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| author | Chen, Jiankang Zhang, Tong Zheng, Wei-Shi Wang, Ruixuan |
| author_facet | Chen, Jiankang Zhang, Tong Zheng, Wei-Shi Wang, Ruixuan |
| contents | 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}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05292 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TagFog: Textual Anchor Guidance and Fake Outlier Generation for Visual Out-of-Distribution Detection Chen, Jiankang Zhang, Tong Zheng, Wei-Shi Wang, Ruixuan Computer Vision and Pattern Recognition Machine Learning 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}. |
| title | TagFog: Textual Anchor Guidance and Fake Outlier Generation for Visual Out-of-Distribution Detection |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2412.05292 |