mlsanityData to 14 Oct 2026

Improved RAMEN: Towards Domain Generalization for Visual Question Answering

arXiv 2109.02370 ↗First posted 6 Sep 2021cs.CV cs.LGcited by 1

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Abstract (arXiv)

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 citeShrestha2019 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 urlhttps://github.com/bhanukaManesha/ramen.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 42 of its 51 references that are in the map. First posted on arXiv 6 Sep 2021.

Builds on

42 of 51 references.

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30 more
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