Summary of The Last Mile to Supervised Performance: Semi-supervised Domain Adaptation For Semantic Segmentation, by Daniel Morales-brotons et al.
The Last Mile to Supervised Performance: Semi-Supervised Domain Adaptation for Semantic Segmentation
by Daniel Morales-Brotons, Grigorios Chrysos, Stratis Tzoumas, Volkan Cevher
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 paper proposes a novel Semi-Supervised Domain Adaptation (SSDA) framework that leverages consistency regularization, pixel contrastive learning, and self-training to effectively utilize a few target-domain labels. The method is designed to overcome the limitations of existing Unsupervised Domain Adaptation (UDA) and Semi-Supervised Learning (SSL) approaches, which often struggle to achieve supervised performance at a low annotation cost. By combining these techniques, the proposed SSDA framework outperforms prior art in various benchmarks, including GTA-to-Cityscapes, Synthia-to-Cityscapes, GTA-to-BDD, and Synthia-to-BDD. The authors also discuss design patterns for adapting existing UDA and SSL methods to the SSDA setting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to learn from images without needing lots of labeled data. It’s like trying to teach someone how to recognize objects in pictures by showing them just a few examples and some similar, but not identical, pictures. The researchers developed a special method that uses a combination of techniques to make the most of these limited training examples. They tested their method on several different tasks and showed that it can achieve results close to those obtained with much more labeled data. This is important because collecting lots of labeled data can be time-consuming and expensive. |
Keywords
» Artificial intelligence » Domain adaptation » Regularization » Self training » Semi supervised » Supervised » Unsupervised
