  The group affect or emotion in an image of people can be inferred by
extracting features about both the people in the picture and the overall makeup
of the scene. The state-of-the-art on this problem investigates a combination
of facial features, scene extraction and even audio tonality. This paper
combines three additional modalities, namely, human pose, text-based tagging
and CNN extracted features / predictions. To the best of our knowledge, this is
the first time all of the modalities were extracted using deep neural networks.
We evaluate the performance of our approach against baselines and identify
insights throughout this paper.
