  Current state of the art methods for generating semantic segmentation rely
heavily on a large set of images that have each pixel labeled with a class of
interest label or background. Coming up with such labels, especially in domains
that require an expert to do annotations, comes at a heavy cost in time and
money. Several methods have shown that we can learn semantic segmentation from
less expensive image-level labels, but the effectiveness of point level labels,
a healthy compromise between all pixels labelled and none, still remains
largely unexplored. This paper presents a novel procedure for producing
semantic segmentation from images given some point level annotations. This
method includes point annotations in the training of a convolutional neural
network (CNN) for producing improved localization and class activation maps.
Then, we use another CNN for predicting semantic affinities in order to
propagate rough class labels and create pseudo semantic segmentation labels.
Finally, we propose training a CNN that is normally fully supervised using our
pseudo labels in place of ground truth labels, which further improves
performance and simplifies the inference process by requiring just one CNN
during inference rather than two. Our method achieves state of the art results
for point supervised semantic segmentation on the PASCAL VOC 2012 dataset
\cite{everingham2010pascal}, even outperforming state of the art methods for
stronger bounding box and squiggle supervision.
