Summary of Can Ood Object Detectors Learn From Foundation Models?, by Jiahui Liu et al.
Can OOD Object Detectors Learn from Foundation Models?
by Jiahui Liu, Xin Wen, Shizhen Zhao, Yingxian Chen, Xiaojuan Qi
First submitted to arxiv on: 8 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the use of generative models to synthesize out-of-distribution (OOD) object detection samples for enhancing OOD object detection. The authors propose a simple method called SyncOOD that leverages large foundation models to extract meaningful OOD data from text-to-image generative models, providing access to open-world knowledge. Synthetic OOD samples are then used to augment the training of a lightweight OOD detector, optimizing ID/OOD decision boundaries. The approach is evaluated across multiple benchmarks, achieving state-of-the-art performance with minimal synthetic data usage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computers to create new pictures that can help detect things in the world that we haven’t seen before. They show how they can use big models that can understand words and images to make these new pictures. These pictures are then used to train a smaller computer program that can tell what’s real or not. This helps the program get better at recognizing things it hasn’t seen before. The results are impressive, showing that this method works really well. |
Keywords
» Artificial intelligence » Object detection » Synthetic data
