  Visual Question Answering (VQA) is an increasingly popular topic in deep
learning research, requiring coordination of natural language processing and
computer vision modules into a single architecture. We build upon the model
which placed first in the VQA Challenge by developing thirteen new attention
mechanisms and introducing a simplified classifier. We performed 300 GPU hours
of extensive hyperparameter and architecture searches and were able to achieve
an evaluation score of 64.78%, outperforming the existing state-of-the-art
single model's validation score of 63.15%.
