Summary of Revisiting Network Perturbation For Semi-supervised Semantic Segmentation, by Sien Li et al.
Revisiting Network Perturbation for Semi-Supervised Semantic Segmentation
by Sien Li, Tao Wang, Ruizhe Hu, Wenxi Liu
First submitted to arxiv on: 8 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 presents a novel approach to semi-supervised semantic segmentation, building upon recent works that combine weak-to-strong consistency regularization with input-level and feature-level perturbations. The authors identify limitations in existing network perturbations for this task and introduce a new framework called MLPMatch (Multi-Level-Perturbation Match). This efficient and easy-to-implement approach is validated on the Pascal VOC and Cityscapes datasets, achieving state-of-the-art performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Semi-supervised semantic segmentation helps computers identify objects in images without using a lot of labeled data. The method combines two techniques: consistency regularization and network perturbations. Consistency regularization makes sure that a model’s predictions are the same when given different inputs or feature representations. Network perturbations modify the model’s behavior to help it generalize better. This paper proposes a new approach called MLPMatch, which is easy to use and achieves good results. |
Keywords
» Artificial intelligence » Regularization » Semantic segmentation » Semi supervised
