mlsanityData to 14 Oct 2026

High-Order Attention Models for Visual Question Answering

arXiv 1711.04323 ↗First posted 12 Nov 2017cs.CV cs.AI cs.LGcited by 22

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Abstract (arXiv)

The quest for algorithms that enable cognitive abilities is an important part of machine learning. A common trait in many recently investigated cognitive-like tasks is that they take into account different data modalities, such as visual and textual input. In this paper we propose a novel and generally applicable form of attention mechanism that learns high-order correlations between various data modalities. We show that high-order correlations effectively direct the appropriate attention to the relevant elements in the different data modalities that are required to solve the joint task. We demonstrate the effectiveness of our high-order attention mechanism on the task of visual question answering (VQA), where we achieve state-of-the-art performance on the standard VQA dataset.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Nov 2017.

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

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  1. 2021ZeroCap: Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmeticcited by 150
  2. 2024Natural Language Understanding and Inference with MLLM in Visual Question Answering: A Surveycited by 76
  3. 2024MeaCap: Memory-Augmented Zero-shot Image Captioningcited by 35
  4. 2023ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based Polishingcited by 33
  5. 2023Multi-modal Machine Learning in Engineering Design: A Review and Future Directionscited by 27
  6. 2022Zero-Shot Video Captioning with Evolving Pseudo-Tokenscited by 25
  7. 2021Dealing with Missing Modalities in the Visual Question Answer-Difference Prediction Task through Knowledge Distillationcited by 14
  8. 2021Latent Space Explanation by Interventioncited by 13
  9. 2022What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputscited by 11
  10. 2021Video and Text Matching with Conditioned Embeddingscited by 10
  11. 2024A Comprehensive Survey of 3D Dense Captioning: Localizing and Describing Objects in 3D Scenescited by 10
  12. 2024A Comprehensive Survey on Visual Question Answering Datasets and Algorithmscited by 8
10 more
  1. 2021Ordered Attention for Coherent Visual Storytellingcited by 6
  2. 2022QLEVR: A Diagnostic Dataset for Quantificational Language and Elementary Visual Reasoningcited by 5
  3. 2022AlignVE: Visual Entailment Recognition Based on Alignment Relationscited by 5
  4. 2025Tensor Sketch: Fast and Scalable Polynomial Kernel Approximationcited by 4
  5. 2022Dual Decomposition of Convex Optimization Layers for Consistent Attention in Medical Imagescited by 2
  6. 2021Efficient Human Pose Estimation by Maximizing Fusion and High-Level Spatial Attentioncited by 0
  7. 2023Semantic Segmentation Enhanced Transformer Model for Human Attention Predictioncited by 0
  8. 2023Monotone deep Boltzmann machinescited by 0
  9. 2023From text to multimodal: a survey of adversarial example generation in question answering systemscited by 0
  10. 2024Social Media Ready Caption Generation for Brandscited by 0