Summary of A Sam Based Tool For Semi-automatic Food Annotation, by Lubnaa Abdur Rahman et al.
A SAM based Tool for Semi-Automatic Food Annotation
by Lubnaa Abdur Rahman, Ioannis Papathanail, Lorenzo Brigato, Stavroula Mougiakakou
First submitted to arxiv on: 11 Oct 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 paper presents a semi-automatic food image annotation tool that leverages the Segment Anything Model (SAM) to enable prompt-based food segmentation. The tool allows users to interactively segment and categorize food items within meal images, specifying weight/volume if needed. A fine-tuned version of SAM’s mask decoder, called MealSAM, is also released with a ViT-B backbone tailored for food image segmentation. The authors aim to contribute to the field by encouraging participation, collaboration, and annotated data gathering, while making AI technology accessible to a broader audience. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a tool that helps people in the food and nutrition field use artificial intelligence (AI) to identify different foods in pictures. This is important because there isn’t enough labeled data for AI to learn from. The tool allows users to teach the AI what foods are by interacting with it, making it easier for non-AI experts to get involved. |
Keywords
» Artificial intelligence » Decoder » Image segmentation » Mask » Prompt » Sam » Vit
