Summary of Investigating the Semantic Robustness Of Clip-based Zero-shot Anomaly Segmentation, by Kevin Stangl et al.
Investigating the Semantic Robustness of CLIP-based Zero-Shot Anomaly Segmentation
by Kevin Stangl, Marius Arvinte, Weilin Xu, Cory Cornelius
First submitted to arxiv on: 13 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 investigates the performance of zero-shot anomaly segmentation using pre-trained foundation models like WinCLIP [14] in various environmental conditions and distribution shifts. The authors perturb test data with semantic transformations such as bounded angular rotations, saturation shifts, and hue shifts to empirically measure a lower performance bound. They find that average performance drops by up to 20% in area under the ROC curve and 40% in area under the per-region overlap curve across three CLIP backbones, regardless of model architecture or learning objective. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well an algorithm called WinCLIP can identify unusual things in pictures without needing a lot of practice data. They changed the test pictures in different ways to see how it would do. What they found was that the algorithm didn’t do as well when the pictures were changed, and some parts did worse than others. This shows that we need to be careful when testing this kind of technology. |
Keywords
» Artificial intelligence » Roc curve » Zero shot
