We present a novel confidence refinement scheme that enhances pseudo-labels in semi-supervised semantic segmentation. Unlike current leading methods, which filter pixels with low-confidence predictions in isolation, our approach leverages the spatial correlation of labels in segmentation maps by grouping neighboring pixels and considering their pseudo-labels collectively. With this contextual information, our method, named S4MC, increases the amount of unlabeled data used during training while maintaining the quality of the pseudo-labels, all with negligible computational overhead. Through extensive experiments on standard benchmarks, we demonstrate that S4MC outperforms existing state-of-the-art semi-supervised learning approaches, offering a promising solution for reducing the cost of acquiring dense annotations. For example, S4MC achieves a 1.29 mIoU improvement over the prior state-of-the-art method on PASCAL VOC 12 with 366 annotated images. The code to reproduce our experiments is available at https://s4mcontext.github.io/

我们提出了一种新颖的置信度细化方案，通过将伪标签在半监督语义分割中加强。与当前主流方法不同，我们的方法不仅孤立地过滤具有低置信度预测的像素，还通过将相邻像素进行分组并综合考虑它们的伪标签，利用分割图中标签的空间相关性。借助这些上下文信息，我们的方法S4MC在保持伪标签的质量的同时，增加了在训练期间使用的未标记数据量，而计算开销几乎可以忽略不计。通过在多个标准基准上进行广泛实验，我们证明了S4MC优于现有先进的半监督学习方法的性能，为降低获取密集注释的成本提供了有希望的解决方案。例如，在具有366个带有注释图像的PASCAL VOC 12上，S4MC相对于之前的先进方法达到了1.29个mIoU的提高。我们提供了复现我们实验的代码，位于此https URL。

基于边缘上下文信息的半监督语义分割