Summary of Clipsam: Clip and Sam Collaboration For Zero-shot Anomaly Segmentation, by Shengze Li et al.
ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation
by Shengze Li, Jianjian Cao, Peng Ye, Yuhan Ding, Chongjun Tu, Tao Chen
First submitted to arxiv on: 23 Jan 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 The proposed ClipSAM framework for Zero-Shot Anomaly Segmentation (ZSAS) leverages the strengths of CLIP and SAM models to overcome their individual limitations. By integrating semantic understanding from CLIP with the refinement capabilities of SAM, ClipSAM achieves optimal segmentation performance on MVTec-AD and VisA datasets. The framework consists of a Unified Multi-scale Cross-modal Interaction module for anomaly localization and a Multi-level Mask Refinement module that utilizes positional information as prompts for SAM to generate hierarchical masks. This collaborative approach enables precise segmentation of local anomalous parts while reducing redundant mask generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ClipSAM is a new way to find abnormal areas in pictures or videos without needing specific training data. Current methods like CLIP and SAM have limitations, such as not being good at finding small abnormal parts or generating too many masks that need to be processed further. The ClipSAM framework combines the strengths of these models by using CLIP’s understanding of what things mean to help identify where anomalies are, and then using SAM to refine those results. This makes it easier to find and separate abnormal areas from normal ones. |
Keywords
» Artificial intelligence » Mask » Sam » Zero shot
