  Visual Question Answering (VQA) is a complex semantic task requiring both
natural language processing and visual recognition. In this paper, we explore
whether VQA is solvable when images are captured in a sub-Nyquist compressive
paradigm. We develop a series of deep-network architectures that exploit
available compressive data to increasing degrees of accuracy, and show that VQA
is indeed solvable in the compressed domain. Our results show that there is
nominal degradation in VQA performance when using compressive measurements, but
that accuracy can be recovered when VQA pipelines are used in conjunction with
state-of-the-art deep neural networks for CS reconstruction. The results
presented yield important implications for resource-constrained VQA
applications.
