Multi-Perspective Pseudo-Label Generation and Confidence-Weighted Training for Semi-Supervised Semantic Segmentation

Self-training has been shown to achieve remarkable gains in semi-supervised semantic segmentation by creating pseudo-labels using unlabeled data. This approach, however, suffers from the quality of the generated pseudo-labels, and generating higher quality pseudo-labels is the main challenge that needs to be addressed. In this paper, we propose a novel method for semi-supervised semantic segmentation based on Multi-perspective pseudo-label Generation and Confidence-weighted Training (MGCT). First, we present a multi-perspective pseudo-label generation strategy that considers both global and local semantic perspectives. This strategy prioritizes pixels in all images by the global and local predictions, and subsequently generates pseudo-labels for different pixels in stages according to the ranking results. Our pseudo-label generation method shows superior suitability for semi-supervised semantic segmentation compared to other approaches. Second, we propose a confidence-weighted training method to alleviate performance degradation caused by unstable pixels. Our training method assigns confident weights to unstable pixels, which reduces the interference of unstable pixels during training and facilitates the efficient training of the model. Finally, we validate our approach on the PASCAL VOC 2012 and Cityscapes datasets, and the results indicate that we achieve new state-of-the-art performance on both datasets in all settings.