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Visual Question Answering with Memory-Augmented Networks

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Chao Ma, Chunhua Shen, Anthony Dick, Qi Wu, Peng Wang, Anton van den Hengel, Ian Reid

Computer Vision and Pattern Recognition · Submitted 17 Jul 2017

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Experimental results on two large-scale benchmark datasets show the favorable performance of the proposed algorithm with a comparison to state of the art.

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arXiv ID1707.04968 (version 2)
Pith IDBAFQNIQR, Pith's permanent record of this paper and its review
CitationAnthony Dick, Anton van den Hengel, Chao Ma, Chunhua Shen, Ian Reid, Peng Wang, Qi Wu. "Visual Question Answering with Memory-Augmented Networks." arXiv:1707.04968 (2017). https://pith.science/paper/BAFQNIQR
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,605 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.07 · human participants 0.04 · controlled comparison 0.59 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.93 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

In this paper, we exploit a memory-augmented neural network to predict accurate answers to visual questions, even when those answers occur rarely in the training set. The memory network incorporates both internal and external memory blocks and selectively pays attention to each training exemplar. We show that memory-augmented neural networks are able to maintain a relatively long-term memory of scarce training exemplars, which is important for visual question answering due to the heavy-tailed distribution of answers in a general VQA setting. Experimental results on two large-scale benchmark datasets show the favorable performance of the proposed algorithm with a comparison to state of the art.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 In this paper, we exploit a memory-augmented neural network to predict accurate answers to visual questions, even when those answers occur rarely in the training set. contribution 0.96 0.78 0.26 asserted 0.97
s1 The memory network incorporates both internal and external memory blocks and selectively pays attention to each training exemplar. method 1.00 0.63 0.04 asserted 1.00
s2 We show that memory-augmented neural networks are able to maintain a relatively long-term memory of scarce training exemplars, which is important for visual question answering due to the heavy-tailed distribution of answers in a general VQA setting. interpretation 0.50 0.85 0.42 asserted 0.99
s3 Experimental results on two large-scale benchmark datasets show the favorable performance of the proposed algorithm with a comparison to state of the art. result 0.99 0.93 0.36 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.96.

What was examined, and what was not

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  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

In this paper, we exploit a memory-augmented neural network to predict accurate answers to visual questions, even when those answers occur rarely in the training set. The memory network incorporates both internal and external memory blocks and selectively pays attention to each training exemplar. We show that memory-augmented neural networks are able to maintain a relatively long-term memory of scarce training exemplars, which is important for visual question answering due to the heavy-tailed distribution of answers in a general VQA setting. Experimental results on two large-scale benchmark datasets show the favorable performance of the proposed algorithm with a comparison to state of the art.

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How the literature uses it

5 arXiv papers cite this in 6 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it · 4 sentences without a confident label

Compares with it 1 paper

What it cites · 5 sentences without a confident label

Cites as background 6 papers
  • Previous memory networks [ref] rely on a learnable gate to write the new input data in specific memory addresses.

    End-To-End Memory Networks · Memory Augmented Network · 0.83

  • Previous memory networks [ref] rely on a learnable gate to write the new input data in specific memory addresses.

    Recurrent Neural Networks with External Memory for Language Understanding · Memory Augmented Network · 0.83

  • Numerous efforts have been made to improve the effectiveness of joint embedding, such as exploring external knowledge [ref] , multi-model compact pooling [ref] , multimodel residual learning [ref] , or fixed language embedding using bag of words [ref] .

    Simple Baseline for Visual Question Answering · Related Work · 0.94

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