  Designing efficient and reliable VQA systems remains a challenging problem,
more so in the case of disaster management and response systems. In this work,
we revisit fundamental combination methods like concatenation, addition and
element-wise multiplication with modern image and text feature abstraction
models. We design a simple and efficient system which outperforms pre-existing
methods on the FloodNet dataset and achieves state-of-the-art performance. This
simplified system requires significantly less training and inference time than
modern VQA architectures. We also study the performance of various backbones
and report their consolidated results. Code is available at
https://github.com/sahilkhose/floodnet_vqa.
