  Semi-weakly supervised semantic segmentation (SWSSS) aims to train a model to
identify objects in images based on a small number of images with pixel-level
labels, and many more images with only image-level labels. Most existing SWSSS
algorithms extract pixel-level pseudo-labels from an image classifier - a very
difficult task to do well, hence requiring complicated architectures and
extensive hyperparameter tuning on fully-supervised validation sets. We propose
a method called prediction filtering, which instead of extracting
pseudo-labels, just uses the classifier as a classifier: it ignores any
segmentation predictions from classes which the classifier is confident are not
present. Adding this simple post-processing method to baselines gives results
competitive with or better than prior SWSSS algorithms. Moreover, it is
compatible with pseudo-label methods: adding prediction filtering to existing
SWSSS algorithms further improves segmentation performance.
