Summary of Am-sam: Automated Prompting and Mask Calibration For Segment Anything Model, by Yuchen Li et al.
AM-SAM: Automated Prompting and Mask Calibration for Segment Anything Model
by Yuchen Li, Li Zhang, Youwei Liang, Pengtao Xie
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 Segment Anything Model (SAM) is a well-known framework for semantic segmentation that has excelled in various applications. However, SAM faces two primary limitations: it relies heavily on human-provided prompts and the mask decoder’s feature representation can be inaccurate due to dot product operations. To address these issues, we propose an automated prompting and mask calibration method called AM-SAM based on a bi-level optimization framework. This approach automatically generates prompts for input images, eliminating the need for human involvement and achieving faster convergence. Additionally, we modify the mask decoder with Low-Rank Adaptation (LoRA), enhancing its feature representation by incorporating advanced techniques that capture and utilize feature correlations. Experimental results demonstrate that AM-SAM achieves accurate segmentation, matching or exceeding the effectiveness of human-generated and default prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Segment Anything Model is a tool used in computer vision to identify objects in images. While it’s good at what it does, there are two main problems with it. First, it needs people to provide specific information about the image before it can work well. Second, it has trouble understanding certain details within the image. To fix these issues, we created a new way of using SAM that automatically generates this needed information and improves its ability to understand the image. Our method is called AM-SAM and uses a special kind of optimization framework. We tested AM-SAM and found that it works better than previous methods at identifying objects in images. |
Keywords
» Artificial intelligence » Decoder » Dot product » Lora » Low rank adaptation » Mask » Optimization » Prompting » Sam » Semantic segmentation
