Summary of How to Benchmark Vision Foundation Models For Semantic Segmentation?, by Tommie Kerssies et al.
How to Benchmark Vision Foundation Models for Semantic Segmentation?
by Tommie Kerssies, Daan de Geus, Gijs Dubbelman
First submitted to arxiv on: 18 Apr 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 Medium Difficulty Summary: Recent vision foundation models (VFMs) have shown impressive results in various tasks, but require supervised fine-tuning to excel in semantic segmentation. To compare and guide future developments, a standardized benchmark is crucial. This paper investigates how VFMs should be evaluated for semantic segmentation. By fine-tuning different VFMs under various settings, the study assesses the impact of individual settings on performance ranking and training time. The recommended approach involves fine-tuning ViT-B variants with a 16×16 patch size, linear decoder, and reduced training time. Using multiple datasets for training and evaluation is also advised, as performance rankings vary across datasets and domain shifts. Linear probing is not recommended, as it does not represent end-to-end fine-tuning. The proposed benchmarking setup enables a performance analysis of VFMs for semantic segmentation, revealing that pretraining with promptable segmentation is not beneficial, while masked image modeling (MIM) with abstract representations is crucial. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This paper looks at how to make computer vision models better at identifying objects in images. These models are very good at some tasks, but need help to do this one thing. To compare different models and make them better, we need a way to test them that’s fair. The researchers tried different ways of testing the models and found the best approach. They also found out that using lots of different training data is important, because the models perform differently on different types of images. |
Keywords
» Artificial intelligence » Decoder » Fine tuning » Pretraining » Semantic segmentation » Supervised » Vit
