Weakly supervised semantic segmentation has been a subject of increased
interest due to the scarcity of fully annotated images. We introduce a new
approach for solving weakly supervised semantic segmentation with deep
Convolutional Neural Networks (CNNs). The method introduces a novel layer which
applies simplex projection on the output of a neural network using area
constraints of class objects. The proposed method is general and can be
seamlessly integrated into any CNN architecture. Moreover, the projection layer
allows strongly supervised models to be adapted to weakly supervised models
effortlessly by substituting ground truth labels. Our experiments have shown
that applying such an operation on the output of a CNN improves the accuracy of
semantic segmentation in a weakly supervised setting with image-level labels.

本文介绍了一种新的使用深度卷积神经网络解决弱监督语义分割问题的方法，该方法使用基于类对象面积约束的单形投影，可以通用于任何 CNN 架构。实验结果表明，在只有图像级标签的弱监督情况下，将此操作应用于 CNN 的输出可以提高语义分割的准确性。