首页|End-to-end weakly supervised semantic segmentation with reliable region mining
End-to-end weakly supervised semantic segmentation with reliable region mining
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NSTL
Elsevier
Weakly supervised semantic segmentation is a challenging task that only takes image-level labels as supervision but produces pixel-level predictions for testing. To address such a challenging task, most current approaches generate pseudo pixel masks first that are then fed into a separate semantic seg-mentation network. However, these two-step approaches suffer from high complexity and being hard to train as a whole. In this work, we harness the image-level labels to produce reliable pixel-level anno-tations and design a fully end-to-end network to learn to predict segmentation maps. Concretely, we firstly leverage an image classification branch to generate class activation maps for the annotated cate-gories, which are further pruned into tiny reliable object/background regions. Such reliable regions are then directly served as ground-truth labels for the segmentation branch, where both global information and local information sub-branches are used to generate accurate pixel-level predictions. Furthermore, a new joint loss is proposed that considers both shallow and high-level features. Despite its apparent sim-plicity, our end-to-end solution achieves competitive mIoU scores ( val : 65.4%, test : 65.3%) on Pascal VOC compared with the two-step counterparts. By extending our one-step method to two-step, we get a new state-of-the-art performance on the Pascal VOC 2012 dataset(val: 69.3%, test : 69.2%). Code is available at: https://github.com/zbf1991/RRM . (c) 2022 Elsevier Ltd. All rights reserved.
NSTL
Elsevier
