Papers › Dynamic Memory Networks for Visual and Textual Question Answering
Dynamic Memory Networks for Visual and Textual Question Answering
Caiming Xiong, Stephen Merity, Richard Socher
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.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Visual Question Answering (VQA) | COCO Visual Question Answering (VQA) real images 1.0 open ended | DMN+ [xiong2016dynamic] | Percentage correct | 60.4 | #8 of 14 | Archive leaderboard | report |
| Visual Question Answering (VQA) | VQA v1 test-dev | DMN+ | Accuracy | 60.3 | #6 of 7 | Archive leaderboard | report |
| Visual Question Answering (VQA) | VQA v1 test-std | DMN+ | Accuracy | 60.4 | #4 of 6 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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