Semi-Supervised Semantic Segmentation via Marginal Contextual
  Information
Moshe Kimhi Shai Kimhi Evgenii Zheltonozhskii Or Litany Chaim Baskin
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/