Multimodal Transformers vs. Graph Neural Networks in Visual Question Answering Performance

This report synthesises findings from 15 peer-reviewed papers addressing the following research question: How do multimodal transformers performing graph-based relational reasoning compare to dedicated GNNs in terms of alignment scores and computational efficiency on visual question answering tasks. 8 claims were extracted from source literature; 8 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: How do multimodal transformers performing graph-based relational reasoning compare to dedicated GNNs in terms of alignment scores and computational efficiency on visual question answering tasks?

Autonomous literature synthesis. Automated review score: 9.2/10. Full text and citation available at Assignee Research.