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.

Deep learning implementation of image segmentation in agricultural applications: a comprehensive review

“…GCNs have made significant progress in graph-centric tasks, such as image segmentation, resulting in the creation of numerous high-performing models. Notable works include Spatial Pyramid Based Graph Reasoning (Li et al 2020), Graph Matching Network (GMNet) (Michieli et al 2020), Boundary-Aware Semi-Supervised Segmentation Network (Graph-BAS3Net) (Huang et al 2021a, b, c), Boundary-aware Graph Convolution (BGC) (Hu et al 2021a, b), Exploit Visual Dependency Relations (EVDR) (Liu et al 2021a, b, c), and weakly supervised image semantic segmentation predicated on image-level class labels (Pan et al 2021). Despite Graph Convolutional Networks showcasing formidable performance across segmentation domains, they may confront computational and storage constraints when processing large-scale images and necessitate copious training data to attain superior prediction results.…”

Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation

“…Without the off-the-shelf knowledge of the saliency map, saliency-free approaches have proposed novel methods based on affinity learning [1,51], contrastive learning [18,95], graph neural network [64], self-attention [76], causal inference [16], antiadversarial attack [44], conditional random field [53,90]. Recently, AMN [47] has demonstrated the importance of the threshold in saliency-free WSSS.…”