# One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model Source: [One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:11:46.889Z Authors: Wonho Bae, Junhyug Noh, Milad Jalali Asadabadi, Danica J. Sutherland Published: 2022-05-02T21:46:41.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()