Summary of Diffusion For Out-of-distribution Detection on Road Scenes and Beyond, by Silvio Galesso et al.
Diffusion for Out-of-Distribution Detection on Road Scenes and Beyond
by Silvio Galesso, Philipp Schröppel, Hssan Driss, Thomas Brox
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed approach introduces a novel method for out-of-distribution (OoD) detection in semantic segmentation tasks, which is robust to increased semantic diversity. The researchers develop the ADE-OoD benchmark, based on the ADE20k dataset, featuring indoor and outdoor images with diverse domains and 150 semantic categories. They also propose a diffusion-based OoD detection (DOoD) method that uses diffusion score matching for pixel-wise OoD scores at inference time. The DOoD model outperforms state-of-the-art approaches on common road scene OoD benchmarks, without relying on outliers or making assumptions about the data domain. On the ADE-OoD benchmark, DOoD shows promising results, indicating potential future improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research introduces a new way to detect things that don’t belong in pictures. The approach is designed for images with lots of different objects and scenes. It uses a special type of model called a diffusion model, which helps identify unusual parts of the image. This method works well on regular road scene images and even better on more diverse images like indoor and outdoor scenes. It’s an important step forward in helping computers understand what makes sense in pictures. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Inference » Semantic segmentation
