  Currently nearing human-level performance, Visual Question Answering (VQA) is
an emerging area in artificial intelligence.
  Established as a multi-disciplinary field in machine learning, both computer
vision and natural language processing communities are working together to
achieve state-of-the-art (SOTA) performance.
  However, there is a gap between the SOTA results and real world applications.
  This is due to the lack of model generalisation.
  The RAMEN model \cite{Shrestha2019} aimed to achieve domain generalization by
obtaining the highest score across two main types of VQA datasets.
  This study provides two major improvements to the early/late fusion module
and aggregation module of the RAMEN architecture, with the objective of further
strengthening domain generalization.
  Vector operations based fusion strategies are introduced for the fusion
module and the transformer architecture is introduced for the aggregation
module.
  Improvements of up to five VQA datasets from the experiments conducted are
evident.
  Following the results, this study analyses the effects of both the
improvements on the domain generalization problem.
  The code is available on GitHub though the following link
\url{https://github.com/bhanukaManesha/ramen}.

Computer Vision and Pattern Recognition;Machine Learning