Summary of Mvrec: a General Few-shot Defect Classification Model Using Multi-view Region-context, by Shuai Lyu et al.
MVREC: A General Few-shot Defect Classification Model Using Multi-View Region-Context
by Shuai Lyu, Fangjian Liao, Zeqi Ma, Rongchen Zhang, Dongmei Mo, Waikeung Wong
First submitted to arxiv on: 22 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel general framework for few-shot defect multi-classification (FSDMC) is proposed, called MVREC. This approach extracts general features for defects by incorporating the pre-trained AlphaCLIP model and utilizes a region-context framework to enhance defect features through mask region input and multi-view context augmentation. The Few-shot Zip-Adapter(-F) classifiers within the model cache visual features of the support set and perform few-shot classification. A new FSDMC benchmark, MVTec-FS, is introduced, featuring 1228 defect images with instance-level mask annotations and 46 defect types. Extensive experiments demonstrate MVREC’s effectiveness in general defect classification and its ability to incorporate contextual information to improve performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to identify defects in products is being developed. This method, called MVREC, uses a pre-trained model and special techniques to look at images of defects and decide what kind they are. It works well even when it only sees a few examples of each type of defect. To test this method, the researchers created a big dataset with lots of different types of defects. They then used this method on both their own dataset and others to show that it really works. |
Keywords
» Artificial intelligence » Classification » Few shot » Mask
