mlsanityData to 7 Oct 2026

Visual Question Answering with Memory-Augmented Networks

arXiv 1707.04968 ↗First posted 17 Jul 2017cs.CV cs.CLcited by 25

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jul 2017.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 5; 2022: 11; 2023: 6; 2024: 2; 2025: 1.

25 papers.

Sort:

By area:

  1. 2022Remember Intentions: Retrospective-Memory-based Trajectory Predictioncited by 86
  2. 2024Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Surveycited by 74
  3. 2021Bridge to Answer: Structure-aware Graph Interaction Network for Video Question Answeringcited by 56
  4. 2021HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detectioncited by 47
  5. 2022Memory-Guided Semantic Learning Network for Temporal Sentence Groundingcited by 46
  6. 2022SMEMO: Social Memory for Trajectory Forecastingcited by 26
  7. 2021Cross Attentional Audio-Visual Fusion for Dimensional Emotion Recognitioncited by 19
  8. 2022Visual Perturbation-aware Collaborative Learning for Overcoming the Language Prior Problemcited by 18
  9. 2022Audio-Visual Fusion for Emotion Recognition in the Valence-Arousal Space Using Joint Cross-Attentioncited by 18
  10. 2022MetAug: Contrastive Learning via Meta Feature Augmentationcited by 15
  11. 2022VQA and Visual Reasoning: An Overview of Recent Datasets, Methods and Challengescited by 14
  12. 2023S3C: Semi-Supervised VQA Natural Language Explanation via Self-Critical Learningcited by 10
13 more
  1. 2025Hadamard product in deep learning: Introduction, Advances and Challengescited by 9
  2. 2023Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Ordercited by 6
  3. 2023Towards More Faithful Natural Language Explanation Using Multi-Level Contrastive Learning in VQAcited by 5
  4. 2021Memory and attention in deep learningcited by 4
  5. 2023Audio-Visual Speaker Verification via Joint Cross-Attentioncited by 4
  6. 2022Relation Regularized Scene Graph Generationcited by 3
  7. 2022From Shallow to Deep: Compositional Reasoning over Graphs for Visual Question Answeringcited by 3
  8. 2023Multi-Clue Reasoning with Memory Augmentation for Knowledge-based Visual Question Answeringcited by 2
  9. 2021Memory Wrap: a Data-Efficient and Interpretable Extension to Image Classification Modelscited by 1
  10. 2022Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting Systemcited by 1
  11. 2022Text-Aware Dual Routing Network for Visual Question Answeringcited by 0
  12. 2023Cross-Modal Reasoning with Event Correlation for Video Question Answeringcited by 0
  13. 2024Retrieval-Augmented Natural Language Reasoning for Explainable Visual Question Answeringcited by 0