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Semi-weakly supervised semantic segmentation (SWSSS) aims to train a model toidentify objects in images based on a small number of images with pixel-levellabels, and many more images with only image-level labels. Most existing SWSSSalgorithms extract pixel-level pseudo-labels from an image classifier - a verydifficult task to do well, hence requiring complicated architectures andextensive hyperparameter tuning on fully-supervised validation sets. We proposea method called prediction filtering, which instead of extractingpseudo-labels, just uses the classifier as a classifier: it ignores anysegmentation predictions from classes which the classifier is confident are notpresent. Adding this simple post-processing method to baselines gives resultscompetitive with or better than prior SWSSS algorithms. Moreover, it iscompatible with pseudo-label methods: adding prediction filtering to existingSWSSS algorithms further improves segmentation performance.

One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model