# Dynamic Memory Networks for Visual and Textual Question Answering

arXiv: https://arxiv.org/abs/1603.01417 · HTML: https://mlsanity.com/paper/1603.01417/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2016.

## Built on by (most cited first, 30 of 62)

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- [From Image to Language: A Critical Analysis of Visual Question Answering (VQA) Approaches, Challenges, and Opportunities](https://mlsanity.com/paper/2311.00308/) (2023)
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- [Cognitive Visual Commonsense Reasoning Using Dynamic Working Memory](https://mlsanity.com/paper/2107.01671/) (2021)
- [VQA and Visual Reasoning: An Overview of Recent Datasets, Methods and Challenges](https://mlsanity.com/paper/2212.13296/) (2022)
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- [Simple Baselines for Interactive Video Retrieval with Questions and Answers](https://mlsanity.com/paper/2308.10402/) (2023)
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- [Endowing Language Models with Multimodal Knowledge Graph Representations](https://mlsanity.com/paper/2206.13163/) (2022)
- [Multimodal Graph Transformer for Multimodal Question Answering](https://mlsanity.com/paper/2305.00581/) (2023)
- [Answer Mining from a Pool of Images: Towards Retrieval-Based Visual Question Answering](https://mlsanity.com/paper/2306.16713/) (2023)
- [S3C: Semi-Supervised VQA Natural Language Explanation via Self-Critical Learning](https://mlsanity.com/paper/2309.02155/) (2023)
- [Improving Visual Question Answering Models through Robustness Analysis and In-Context Learning with a Chain of Basic Questions](https://mlsanity.com/paper/2304.03147/) (2023)
- [Hadamard product in deep learning: Introduction, Advances and Challenges](https://mlsanity.com/paper/2504.13112/) (2025)

## Abstract (arXiv)

Neural network architectures with memory and attention mechanisms exhibit certain reasoning capabilities required for question answering. One such architecture, the dynamic memory network (DMN), obtained high accuracy on a variety of language tasks. However, it was not shown whether the architecture achieves strong results for question answering when supporting facts are not marked during training or whether it could be applied to other modalities such as images. Based on an analysis of the DMN, we propose several improvements to its memory and input modules. Together with these changes we introduce a novel input module for images in order to be able to answer visual questions. Our new DMN+ model improves the state of the art on both the Visual Question Answering dataset and the babi-10k text question-answering dataset without supporting fact supervision.