Improved RAMEN: Towards Domain Generalization for Visual Question Answering

TL;DR

This paper enhances the RAMEN model for Visual Question Answering by introducing vector operation-based fusion and transformer-based aggregation modules, significantly improving domain generalization across multiple datasets.

Contribution

It proposes two novel improvements to RAMEN's architecture, focusing on fusion and aggregation modules, to better generalize across diverse VQA datasets.

Findings

Up to five VQA datasets show improved performance.

Vector-based fusion strategies enhance feature integration.

Transformer-based aggregation improves domain robustness.

Abstract

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}.