  In this paper, we address the problem of visual question answering by
proposing a novel model, called VIBIKNet. Our model is based on integrating
Kernelized Convolutional Neural Networks and Long-Short Term Memory units to
generate an answer given a question about an image. We prove that VIBIKNet is
an optimal trade-off between accuracy and computational load, in terms of
memory and time consumption. We validate our method on the VQA challenge
dataset and compare it to the top performing methods in order to illustrate its
performance and speed.
