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%.
@article{arxiv.1803.07724,
title = {Attention on Attention: Architectures for Visual Question Answering (VQA)},
author = {Jasdeep Singh and Vincent Ying and Alex Nutkiewicz},
journal= {arXiv preprint arXiv:1803.07724},
year = {2018}
}