# MUREL: Multimodal Relational Reasoning for Visual Question Answering

**Authors:** Remi Cadene, Hedi Ben-younes, Matthieu Cord, Nicolas Thome  
**arXiv:** [1902.09487](https://arxiv.org/abs/1902.09487)  
**Date:** 2019-02-25  
**Summary page:** https://www.summarizepaper.com/en/paper/1902.09487v1/

## Key points

- Multimodal attentional networks are state-of-the-art models for tasks involving real images in Visual Question Answering (VQA)
- Attention mechanisms may not be sufficient to capture complex reasoning features required for VQA and other high-level tasks
- MuRel is a multimodal relational network that learns to reason over real images in an end-to-end manner
- MuRel cell serves as an atomic reasoning primitive, representing interactions between question and image regions using rich vectorial representation and modeling region relations through pairwise combinations
- MuRel network progressively refines visual and question interactions, enabling visualization schemes beyond attention maps
- Ablation studies and comparisons with attention-based approaches on three datasets demonstrate the superiority of MuRel in VQA tasks involving real images
- Final MuRel network competes with or outperforms state-of-the-art results
- Paper presents a novel multimodal relational reasoning approach for VQA tasks involving real images
- Availability of code facilitates reproducibility and future research in this area.

## Summary

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In the field of Visual Question Answering (VQA), multimodal attentional networks have emerged as state-of-the-art models for tasks involving real images. However, while attention mechanisms allow these models to focus on relevant visual content, they may not be sufficient to capture the complex reasoning features required for VQA and other high-level tasks. To address this limitation, the authors propose MuRel, a multimodal relational network that learns to reason over real images in an end-to-end manner. The key contribution of this work is the introduction of the MuRel cell, which serves as an atomic reasoning primitive. This cell represents interactions between question and image regions using a rich vectorial representation and models region relations through pairwise combinations. The authors then incorporate this cell into a full MuRel network, which progressively refines visual and question interactions. Importantly, this network enables the definition of visualization schemes that go beyond mere attention maps. To validate their approach, the authors conduct various ablation studies and compare their method to attention-based approaches on three datasets: VQA 2.0, VQA-CP v2, and TDIUC. The results demonstrate the superiority of MuRel over existing methods in this challenging context. In fact, the final MuRel network either competes with or outperforms state-of-the-art results. Overall, this paper presents a novel multimodal relational reasoning approach for VQA tasks involving real images. By introducing the MuRel cell and incorporating it into a comprehensive network architecture, the authors achieve significant improvements in performance compared to attention-based methods. The availability of their code further facilitates reproducibility and future research in this area.