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ST++: Make Self-trainingWork Better for Semi-supervised Semantic Segmentation | IEEE Conference Publication | IEEE Xplore
Source: hfbic45521e79b0484907snnq65xokoqpn6n5cfiac.eds.tju.edu.cn
Article summary:
1. This article presents a strong baseline of self-training (ST) for semi-supervised semantic segmentation via injecting strong data augmentations (SDA) on unlabeled images.
2. The article further proposes an advanced self-training framework (ST++) that performs selective re-training via prioritizing reliable unlabeled images based on holistic prediction-level stability.
3. The article investigates the SDA and provides empirical analysis to demonstrate its effectiveness in improving performance.
Article analysis:
The article is overall trustworthy and reliable, as it provides a detailed overview of the proposed ST++ framework for semi-supervised semantic segmentation, along with empirical evidence to support its claims. The authors have also provided code for their proposed method, which can be used to verify the results presented in the paper. Furthermore, the authors have provided a thorough analysis of the SDA and its effects on performance, which adds to the credibility of their work.
However, there are some potential biases in the article that should be noted. For example, while the authors provide evidence for their claims regarding ST++'s effectiveness in improving performance, they do not explore any counterarguments or alternative approaches that could potentially yield better results than ST++. Additionally, while they discuss possible risks associated with using ST++, they do not present both sides equally; instead, they focus more on highlighting the benefits of using ST++ rather than exploring potential drawbacks or limitations of this approach. Finally, there is some promotional content in the article as well; while this does not necessarily detract from its credibility or trustworthiness, it should still be noted as a potential bias in the paper's reporting.