Summary of A Study on Unsupervised Domain Adaptation For Semantic Segmentation in the Era Of Vision-language Models, by Manuel Schwonberg et al.
A Study on Unsupervised Domain Adaptation for Semantic Segmentation in the Era of Vision-Language Models
by Manuel Schwonberg, Claus Werner, Hanno Gottschalk, Carsten Meyer
First submitted to arxiv on: 25 Nov 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 This paper focuses on improving semantic segmentation for autonomous driving in the presence of domain shifts. Domain shifts occur when the environment or conditions change, making it challenging for models trained on one set of data to generalize well to new situations. The authors explore unsupervised domain adaptation (UDA) methods that adapt a model to a new target domain using only unlabeled data from that domain. They find that replacing the encoder of existing UDA methods with a vision-language pre-trained encoder can result in significant performance improvements, up to 10% mIoU on the GTA5-to-Cityscapes domain shift. The authors also investigate how well these adapted models generalize to unseen domains, achieving gains of up to 13.7% mIoU across three datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making self-driving cars better at understanding what’s around them, even when the weather or location changes. Right now, computers are good at recognizing things like roads and buildings, but they struggle when these things look different from what they learned in training data. The authors tried a new way of adapting models to handle these changes by using special computer vision models that can understand both pictures and words. They found that this approach can significantly improve how well the model works on unseen data, with some gains as high as 13.7%. However, not all approaches work equally well, and more research is needed to fully understand what makes a good adaptation method. |
Keywords
» Artificial intelligence » Domain adaptation » Encoder » Semantic segmentation » Unsupervised
