Weakly Supervised Semantic Segmentation Using Color Adjacency Loss

Abstract

Large amount of training data is essential for deep learning-based computer vision tasks. However, in semantic segmentation, annotating pixel-wise labels for large-scale image data is laborious and time-consuming. To handle this problem, we propose a training framework for a CNN-based network using sparse labels. We propagate the sparse labels to produce the same performance as training on dense labels in the segmentation network. For effective label propagation, we take advantage of the observation that adjacent pixels sharing similar colors would be in the same class. Based on this insight, the label is propagated by our adjacency loss depending on the color similarity between the adjacent pixels. We perform on the PASCAL VOC 2012 dataset using scribbles annotations as sparse labels. The proposed algorithm achieves superior performance compared to the previous method in weakly supervised semantic segmentation task.