Papers › Dynamic Memory Networks for Visual and Textual Question Answering

Dynamic Memory Networks for Visual and Textual Question Answering

4 Mar 2016arXiv:1603.01417archive 2025-07-28

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

Syntology Ran 7 of 7 code samples harvested from 2 repositories linked to this paper; 0 have no recorded run. Of those that ran: 3 ran · our draft was wrong; 4 ran · fixture could not drive it.

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DongjunLee/dmn-tensorflow mentioned on GitHubtf report
edithal-14/DMN-Novelty mentioned on GitHubpytorch report
imatge-upc/vqa-2016-cvprw mentioned on GitHubtf report
jxz542189/dmn_plus mentioned on GitHubtf report
therne/dmn-tensorflow mentioned on GitHubtf report

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Code Syntology ran Syntology

7 samples harvested; 7 ran; 0 honoured the contract we drafted; 0 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

3ran · our draft was wrong
4ran · fixture could not drive it

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build_vocab dandelin/Dynamic-memory-networks-plus-Pytorch/babi_loader.py community (archive-listed) ran · our draft was wrong no licence file found · pointer only · d99bdbbcd3df541a · report
get_raw_babi dandelin/Dynamic-memory-networks-plus-Pytorch/babi_loader.py community (archive-listed) ran · our draft was wrong no licence file found · pointer only · 5bcdc54c58477426 · report
oversample edithal-14/DMN-Novelty/ste/ste_decom_attn.py community (archive-listed) ran · fixture could not drive it no licence file found · pointer only · 347e5cf7919792fb · report
pad_collate dandelin/Dynamic-memory-networks-plus-Pytorch/babi_loader.py community (archive-listed) ran · our draft was wrong no licence file found · pointer only · c6f6a59b1cb4647f · report
position_encoding dandelin/Dynamic-memory-networks-plus-Pytorch/babi_main.py community (archive-listed) ran · fixture could not drive it no licence file found · pointer only · 9f51aa3fcdd89fcd · report
pretty_size edithal-14/DMN-Novelty/dmn.py community (archive-listed) ran · fixture could not drive it no licence file found · pointer only · 7bdcd24933332cf1 · report
step edithal-14/DMN-Novelty/dmn.py community (archive-listed) ran · fixture could not drive it no licence file found · pointer only · e87ff604cb0fde70 · report

Tasks

Question AnsweringVisual Question AnsweringVisual Question Answering (VQA)

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

Dynamic Memory NetworkGRUMemory NetworkSoftmax

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