  Visual question answering (VQA) comprises a variety of language capabilities.
The diagnostic benchmark dataset CLEVR has fueled progress by helping to better
assess and distinguish models in basic abilities like counting, comparing and
spatial reasoning in vitro. Following this approach, we focus on spatial
language capabilities and investigate the question: what are the key
ingredients to handle simple visual-spatial relations? We look at the SAN,
RelNet, FiLM and MC models and evaluate their learning behavior on diagnostic
data which is solely focused on spatial relations. Via comparative analysis and
targeted model modification we identify what really is required to
substantially improve upon the CNN-LSTM baseline.
