Weakly-Supervised Image Semantic Segmentation Using Graph Convolutional Networks

• URL: http://arxiv.org/abs/2103.16762v2
• Date: Thu, 1 Apr 2021 11:42:30 GMT
• Title: Weakly-Supervised Image Semantic Segmentation Using Graph Convolutional Networks
• Authors: Shun-Yi Pan, Cheng-You Lu, Shih-Po Lee, Wen-Hsiao Peng
• Abstract summary: 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. We propose a Graph Convolutional Network (GCN)-based feature propagation framework.
• Score: 9.066817971329899
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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