Summary of Robustsam: Segment Anything Robustly on Degraded Images, by Wei-ting Chen and Yu-jiet Vong and Sy-yen Kuo and Sizhuo Ma and Jian Wang
RobustSAM: Segment Anything Robustly on Degraded Images
by Wei-Ting Chen, Yu-Jiet Vong, Sy-Yen Kuo, Sizhuo Ma, Jian Wang
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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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 Segment Anything Model (SAM) is a cutting-edge approach in image segmentation that excels at zero-shot segmentation and has a flexible prompting system. However, its performance is hampered by images with degraded quality. To address this limitation, the Robust Segment Anything Model (RobustSAM) is proposed, which enhances SAM’s performance on low-quality images while maintaining its promptability and zero-shot generalization capabilities. The additional parameters of RobustSAM can be optimized within 30 hours on eight GPUs, demonstrating its feasibility for typical research laboratories. The Robust-Seg dataset, a collection of 688K image-mask pairs with different degradations, is also introduced to train and evaluate the model optimally. Extensive experiments across various segmentation tasks and datasets confirm RobustSAM’s superior performance, especially under zero-shot conditions, underscoring its potential for extensive real-world application. Additionally, the method has been shown to effectively improve the performance of SAM-based downstream tasks such as single image dehazing and deblurring. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine taking a picture that’s blurry or low-quality, but still wants it to be clear and precise? The Segment Anything Model (SAM) is really good at doing this, but sometimes it doesn’t work well with pictures that are damaged. To fix this, researchers created a new model called RobustSAM that makes SAM better for low-quality images while keeping its best features. This new model uses only a few extra parts and can be trained quickly on computers. They also created a special dataset of 688K image-mask pairs to test the model. The results show that RobustSAM is much better than before, especially when it doesn’t need any training beforehand. It even helps with other tasks like making blurry images clear again. |
Keywords
» Artificial intelligence » Generalization » Image segmentation » Mask » Prompting » Sam » Zero shot
