  Neural Image Captioning (NIC) or neural caption generation has attracted a
lot of attention over the last few years. Describing an image with a natural
language has been an emerging challenge in both fields of computer vision and
language processing. Therefore a lot of research has focused on driving this
task forward with new creative ideas. So far, the goal has been to maximize
scores on automated metric and to do so, one has to come up with a plurality of
new modules and techniques. Once these add up, the models become complex and
resource-hungry. In this paper, we take a small step backwards in order to
study an architecture with interesting trade-off between performance and
computational complexity. To do so, we tackle every component of a neural
captioning model and propose one or more solution that lightens the model
overall. Our ideas are inspired by two related tasks: Multimodal and Monomodal
Neural Machine Translation.
