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

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 https://github.com/Cverchen/TagFog.

Authors

• Jiankang Chen School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China Key Laboratory of Machine Intelligence and Advanced Computing, MOE, Guangzhou, China
• Tong Zhang Nanjing University of Science and Technology
• Wei-Shi Zheng SUN YAT-SEN UNIVERSITY Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China
• Ruixuan Wang School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China Key Laboratory of Machine Intelligence and Advanced Computing, MOE, Guangzhou, China Peng Cheng Laboratory, Shenzhen, China

Keywords

• CV: Language and Vision
• CV: Object Detection & Categorization
• CV: Other Foundations of Computer Vision