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One Weird Trick to Improve Your Semi-Weakly Supervised Semantic Segmentation Model. (arXiv:2205.01233v1 [cs.CV])
May 4, 2022, 1:11 a.m. | Wonho Bae, Junhyug Noh, Milad Jalali Asadabadi, Danica J. Sutherland
cs.LG updates on arXiv.org arxiv.org
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 …
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