Summary of Sam4udass: When Sam Meets Unsupervised Domain Adaptive Semantic Segmentation in Intelligent Vehicles, by Weihao Yan et al.
SAM4UDASS: When SAM Meets Unsupervised Domain Adaptive Semantic Segmentation in Intelligent Vehicles
by Weihao Yan, Yeqiang Qian, Xingyuan Chen, Hanyang Zhuang, Chunxiang Wang, Ming Yang
First submitted to arxiv on: 22 Nov 2023
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 This paper presents SAM4UDASS, a novel approach for semantic segmentation in domain shift scenarios. The method combines the Segment Anything Model (SAM) with unsupervised domain adaptation (UDA) techniques to refine pseudo-labels and mitigate semantic granularity inconsistency between SAM masks and target domains. By incorporating Semantic-Guided Mask Labeling, SAM4UDASS assigns semantic labels to unlabeled SAM masks using UDA pseudo-labels, achieving state-of-the-art results on synthetic-to-real and normal-to-adverse driving datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps cars understand their surroundings better by improving a type of computer vision called “semantic segmentation.” The problem is that current methods don’t work well when the environment changes. The researchers developed a new approach called SAM4UDASS that uses two existing techniques: Segment Anything Model and unsupervised domain adaptation. This combination helps the method create more accurate labels for objects in the scene, which is important for autonomous vehicles to make good decisions. |
Keywords
» Artificial intelligence » Domain adaptation » Mask » Sam » Semantic segmentation » Unsupervised
