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

• URL: http://arxiv.org/abs/2205.01233v1
• Date: Mon, 2 May 2022 21:46:41 GMT
• Title: One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model
• Authors: Wonho Bae, Junhyug Noh, Milad Jalali Asadabadi, Danica J. Sutherland
• Abstract summary: 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. We propose a method called prediction filtering, which instead of extracting pseudo-labels, just uses the classifier as a classifier. Adding this simple post-processing method to baselines gives results competitive with or better than prior SWSSS algorithms.
• Score: 8.388356030608886
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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