Weakly-Supervised Image Semantic Segmentation Using Graph Convolutional Networks
Abstract
This work addresses weakly-supervised image semantic segmentation based on image-level class labels. One common approach to this task is to propagate the activation scores of Class Activation Maps (CAMs) using a random-walk mechanism in order to arrive at complete pseudo labels for training a semantic segmentation network in a fully-supervised manner. However, the feed-forward nature of the random walk imposes no regularization on the quality of the resulting complete pseudo labels. To overcome this issue, we propose a Graph Convolutional Network (GCN)-based feature propagation framework. We formulate the generation of complete pseudo labels as a semi-supervised learning task and learn a 2-layer GCN separately for every training image by back-propagating a Laplacian and an entropy regularization loss. Experimental results on the PASCAL VOC 2012 dataset confirm the superiority of our scheme to several state-of-the-art baselines. Our code is available at https://github.com/Xavier-Pan/WSGCN.
Cite
@article{arxiv.2103.16762,
title = {Weakly-Supervised Image Semantic Segmentation Using Graph Convolutional Networks},
author = {Shun-Yi Pan and Cheng-You Lu and Shih-Po Lee and Wen-Hsiao Peng},
journal= {arXiv preprint arXiv:2103.16762},
year = {2021}
}
Comments
Accepted by ICME 2021