  Training a Convolutional Neural Network (CNN) for semantic segmentation
typically requires to collect a large amount of accurate pixel-level
annotations, a hard and expensive task. In contrast, simple image tags are
easier to gather. With this paper we introduce a novel weakly-supervised
semantic segmentation model able to learn from image labels, and just image
labels. Our model uses the prior knowledge of a network trained for image
recognition, employing these image annotations as an attention mechanism to
identify semantic regions in the images. We then present a methodology that
builds accurate class-specific segmentation masks from these regions, where
neither external objectness nor saliency algorithms are required. We describe
how to incorporate this mask generation strategy into a fully end-to-end
trainable process where the network jointly learns to classify and segment
images. Our experiments on PASCAL VOC 2012 dataset show that exploiting these
generated class-specific masks in conjunction with our novel end-to-end
learning process outperforms several recent weakly-supervised semantic
segmentation methods that use image tags only, and even some models that
leverage additional supervision or training data.
