Semi-Supervised Semantic Segmentation With Region Relevance
This work addresses a key bottleneck in semi-supervised semantic segmentation for computer vision applications, offering incremental improvements over existing methods.
The paper tackles the problem of noisy pseudo-labels causing errors and inconsistency in semi-supervised semantic segmentation by proposing a Region Relevance Network (RRN) with local filtering and dynamic correction modules, achieving state-of-the-art performance on PASCAL VOC 2012 and Cityscapes datasets.
Semi-supervised semantic segmentation aims to learn from a small amount of labeled data and plenty of unlabeled ones for the segmentation task. The most common approach is to generate pseudo-labels for unlabeled images to augment the training data. However, the noisy pseudo-labels will lead to cumulative classification errors and aggravate the local inconsistency in prediction. This paper proposes a Region Relevance Network (RRN) to alleviate the problem mentioned above. Specifically, we first introduce a local pseudo-label filtering module that leverages discriminator networks to assess the accuracy of the pseudo-label at the region level. A local selection loss is proposed to mitigate the negative impact of wrong pseudo-labels in consistency regularization training. In addition, we propose a dynamic region-loss correction module, which takes the merit of network diversity to further rate the reliability of pseudo-labels and correct the convergence direction of the segmentation network with a dynamic region loss. Extensive experiments are conducted on PASCAL VOC 2012 and Cityscapes datasets with varying amounts of labeled data, demonstrating that our proposed approach achieves state-of-the-art performance compared to current counterparts.