Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels
Official PyTorch implementation of Semi-Supervised Semantic Segmentation Using Unreliable Pseudo Labels, CVPR 2022.
Please refer to our project page for qualitative results.
Abstract.
The crux of semi-supervised semantic segmentation is to assign adequate pseudo-labels to the pixels of unlabeled images.
A common practice is to select the highly confident predictions as the pseudo ground-truth, but it leads to a problem that most pixels may be left unused due to their unreliability.
We argue that every pixel matters to the model training, even its prediction is ambiguous.
Intuitively, an unreliable prediction may get confused among the top classes (i.e., those with the highest probabilities),
however, it should be confident about the pixel not belonging to the remaining classes.
Hence, such a pixel can be convincingly treated as a negative sample to