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.

该论文研究了三种不同的融合方法在远程遥感领域的视觉问答中的应用，并分析了与模型复杂度相关的精度提高。结果表明，采用更复杂的融合机制可以提高性能，但在实践中寻求模型复杂度和性能之间的平衡是值得的。