  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.
