Summary of Quantifying the Limits Of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-like and Low-contrast Objects, by Yixin Zhang et al.
Quantifying the Limits of Segmentation Foundation Models: Modeling Challenges in Segmenting Tree-Like and Low-Contrast Objects
by Yixin Zhang, Nicholas Konz, Kevin Kramer, Maciej A. Mazurowski
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); 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 This paper investigates the limitations of image segmentation foundation models (SFMs) like Segment Anything Model (SAM), which excel in zero-shot and interactive segmentation across diverse domains. However, they struggle with objects featuring dense, tree-like morphology and low textural contrast from their surroundings. To understand these failure modes, the authors introduce interpretable metrics quantifying object tree-likeness and textural separability. Experimental results on synthetic datasets and real-world benchmarks show that SFM performance (e.g., SAM, SAM 2, HQ-SAM) is noticeably correlated with these factors. The study links these failures to “textural confusion”, where models misinterpret local structure as global texture, leading to over-segmentation or difficulty distinguishing objects from similar backgrounds. Notably, targeted fine-tuning fails to resolve this issue, indicating a fundamental limitation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well image segmentation models do when they have trouble recognizing certain types of objects with complex shapes and poor contrast. These models are really good at recognizing objects in lots of different situations, but they struggle when the objects have dense tree-like structures and blend into their surroundings. The researchers created new metrics to understand why these models fail, and they found that the models get confused when trying to distinguish between local patterns and overall texture. They also tried fine-tuning the models to make them better, but it didn’t help with this specific problem. |
Keywords
» Artificial intelligence » Fine tuning » Image segmentation » Sam » Zero shot
