The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
ST++: Make Self-trainingWork Better for Semi-supervised Semantic Segmentation
Open accessAbstract
Self-training via pseudo labeling is a conventional, simple, and popular pipeline to leverage unlabeled data. In this work, we first construct a strong baseline of self-training (namely ST) for semi-supervised semantic segmentation via injecting strong data augmentations (SDA) on unlabeled images to alleviate overfitting noisy labels as well as decouple similar predictions between the teacher and student. With this simple mechanism, our ST outperforms all existing methods without any bells and whistles, e.g., iterative retraining. Inspired by the impressive results, we thoroughly investigate the SDA and provide some empirical analysis. Nevertheless, incorrect pseudo labels are still prone to accumulate and degrade the performance. To this end, we further propose an advanced self-training framework (namely ST++), that performs selective re-training via prioritizing reliable unlabeled images based on holistic prediction-level stability. Concretely, several model checkpoints are saved in the first stage supervised training, and the discrepancy of their predictions on the unlabeled image serves as a measurement for reliability. Our image-level selection offers holistic contextual information for learning. We demonstrate that it is more suitable for segmentation than common pixel-wise selection. As a result, ST+ further boosts the performance of our ST. Code is available at https://github.com/LiheYoung/ST-PlusPlus.
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Ma, Y., Wang, J., Yang, J., & Wang, L. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3348982 · Cited by 55
Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks
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Metadata
- Authors
- Yang, L., Zhuo, W., Qi, L., Shi, Y., & Gao, Y.
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 4258-4267
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4312310512
- Cited by
- 518
- References
- 61
- FWCI
- 66.80
- Type
- conference paper
- Language
- en
- Open access
- green
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