MUREL: Multimodal Relational Reasoning for Visual Question Answering
Open accessAbstract
Multimodal attentional networks are currently state-of-the-art models for Visual Question Answering (VQA) tasks involving real images. Although attention allows to focus on the visual content relevant to the question, this simple mechanism is arguably insufficient to model complex reasoning features required for VQA or other high-level tasks. In this paper, we propose MuRel, a multimodal relational network which is learned end-to-end to reason over real images. Our first contribution is the introduction of the MuRel cell, an atomic reasoning primitive representing interactions between question and image regions by a rich vectorial representation, and modeling region relations with pairwise combinations. Secondly, we incorporate the cell into a full MuRel network, which progressively refines visual and question interactions, and can be leveraged to define visualization schemes finer than mere attention maps. We validate the relevance of our approach with various ablation studies, and show its superiority to attention-based methods on three datasets: VQA 2.0, VQA-CP v2 and TDIUC. Our final MuRel network is competitive to or outperforms state-of-the-art results in this challenging context. Our code is available: github.com/Cadene/murel.bootstrap.pytorch
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The most common existing VQA methods use attention and memory mechanisms to learn visual artifacts and the semantic correlations needed to answer questions accurately.
“Currently, the most common existing methods use attention [125, 127, 129] and memory [126] mechanisms to efficiently learn visual artifacts and the semantic correlations that allow questions to be answered accurately.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [125]
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- Authors
- Cadene, R., Ben-younes, H., Cord, M., & Thome, N.
- Affiliations
- Sorbonne université
- Centre National de la Recherche Scientifique (FR)
- Sorbonne Université (FR)
- Conservatoire National des Arts et Métiers (FR)
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 1989-1998
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2916723116
- Cited by
- 330
- References
- 46
- Citations by year
- 2026: 8 · 2025: 22 · 2024: 31 · 2023: 54 · 2022: 64 · 2021: 70 · 2020: 68 · 2019: 13
- FWCI
- 17.52
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- Funders
- Agence Nationale de la Recherche (ANR-16-CE23-0006)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- visual question answering, relational networks, attentional networks
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