  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 those most unlikely
categories. Based on this insight, we develop an effective pipeline to make
sufficient use of unlabeled data. Concretely, we separate reliable and
unreliable pixels via the entropy of predictions, push each unreliable pixel to
a category-wise queue that consists of negative samples, and manage to train
the model with all candidate pixels. Considering the training evolution, where
the prediction becomes more and more accurate, we adaptively adjust the
threshold for the reliable-unreliable partition. Experimental results on
various benchmarks and training settings demonstrate the superiority of our
approach over the state-of-the-art alternatives.
