  The crux of label-efficient semantic segmentation is to produce high-quality
pseudo-labels to leverage a large amount of unlabeled or weakly labeled data. A
common practice is to select the highly confident predictions as the
pseudo-ground-truths for each pixel, but it leads to a problem that most pixels
may be left unused due to their unreliability. However, we argue that every
pixel matters to the model training, even those unreliable and ambiguous
pixels. Intuitively, an unreliable prediction may get confused among the top
classes, 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 key to those most unlikely categories. Therefore, 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 keys,
and manage to train the model with all candidate pixels. Considering the
training evolution, 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.
