Summary of Segment Anything Model For Automated Image Data Annotation: Empirical Studies Using Text Prompts From Grounding Dino, by Fuseini Mumuni et al.
Segment Anything Model for automated image data annotation: empirical studies using text prompts from Grounding DINO
by Fuseini Mumuni, Alhassan Mumuni
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
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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 approach combines Grounding DINO and the Segment Anything Model (SAM) to achieve zero-shot object detection and image segmentation, respectively. This integration has potential applications in zero-shot semantic segmentation or data annotation. However, a limitation of the referring expression comprehension (REC) framework is its tendency to make false positive predictions when targets are absent from images. To address this, empirical studies were conducted on six publicly available datasets across different domains, revealing that prediction errors follow a predictable pattern and can be mitigated by filtering large image areas with appreciable confidence scores. The study also evaluates the performance of SAM on multiple datasets from various specialized domains, reporting significant improvements in segmentation performance and annotation time savings over manual approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper combines two models to detect objects and segment images without any prior training data. This can be useful for tasks like annotating medical images or understanding what’s in a picture. However, the approach isn’t perfect and sometimes makes mistakes when an object is not present in the image. To improve this, researchers studied how often these mistakes happen and found that they follow a pattern. By filtering out large areas of the image with high confidence scores, the mistakes can be reduced. The study also shows that using this combined model can help with tasks like segmenting organs in medical images or understanding what’s being talked about in an image. |
Keywords
» Artificial intelligence » Grounding » Image segmentation » Object detection » Sam » Semantic segmentation » Zero shot
