  Visual question answering (VQA) has recently been introduced to remote
sensing to make information extraction from overhead imagery more accessible to
everyone. VQA considers a question (in natural language, therefore easy to
formulate) about an image and aims at providing an answer through a model based
on computer vision and natural language processing methods. As such, a VQA
model needs to jointly consider visual and textual features, which is
frequently done through a fusion step. In this work, we study three different
fusion methodologies in the context of VQA for remote sensing and analyse the
gains in accuracy with respect to the model complexity. Our findings indicate
that more complex fusion mechanisms yield an improved performance, yet that
seeking a trade-of between model complexity and performance is worthwhile in
practice.
