  There has been a rapid progress in the task of Visual Question Answering with
improved model architectures. Unfortunately, these models are usually
computationally intensive due to their sheer size which poses a serious
challenge for deployment. We aim to tackle this issue for the specific task of
Visual Question Answering (VQA). A Convolutional Neural Network (CNN) is an
integral part of the visual processing pipeline of a VQA model (assuming the
CNN is trained along with entire VQA model). In this project, we propose an
efficient and modular neural architecture for the VQA task with focus on the
CNN module. Our experiments demonstrate that a sparsely activated CNN based VQA
model achieves comparable performance to a standard CNN based VQA model
architecture.
