mlsanityData to 7 Oct 2026

Trends, Applications, and Challenges in Human Attention Modelling

arXiv 2402.18673 ↗First posted 28 Feb 2024cs.CV cs.AIcited by 9

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Abstract (arXiv)

Human attention modelling has proven, in recent years, to be particularly useful not only for understanding the cognitive processes underlying visual exploration, but also for providing support to artificial intelligence models that aim to solve problems in various domains, including image and video processing, vision-and-language applications, and language modelling. This survey offers a reasoned overview of recent efforts to integrate human attention mechanisms into contemporary deep learning models and discusses future research directions and challenges. For a comprehensive overview on the ongoing research refer to our dedicated repository available at https://github.com/aimagelab/awesome-human-visual-attention.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 40 of its 64 references that are in the map. First posted on arXiv 28 Feb 2024.

Builds on

40 of 64 references.

References · 40

  1. 2021Ego4D: Around the World in 3,000 Hours of Egocentric Videocited by 1,530
  2. 2016What do different evaluation metrics tell us about saliency models?cited by 184
  3. 2016Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?cited by 111
  4. 2016Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Modelcited by 91
  5. 2017Predicting the Driver's Focus of Attention: the DR(eye)VE Projectcited by 79
  6. 2017Predicting Driver Attention in Critical Situationscited by 61
  7. 2020Creation and Validation of a Chest X-Ray Dataset with Eye-tracking and Report Dictation for AI Developmentcited by 55
  8. 2020Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attentioncited by 45
  9. 2021TranSalNet: Towards perceptually relevant visual saliency predictioncited by 44
  10. 2020Integrating Human Gaze into Attention for Egocentric Activity Recognitioncited by 42
  11. 2022Follow My Eye: Using Gaze to Supervise Computer-Aided Diagnosiscited by 38
  12. 2023Gazeformer: Scalable, Effective and Fast Prediction of Goal-Directed Human Attentioncited by 37
28 more
  1. 2017Exploring Human-like Attention Supervision in Visual Question Answeringcited by 36
  2. 2021Automatic Gaze Analysis: A Survey of Deep Learning based Approachescited by 33
  3. 2019Looking at the right stuff: Guided semantic-gaze for autonomous drivingcited by 32
  4. 2020Predicting Visual Importance Across Graphic Design Typescited by 30
  5. 2019MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learningcited by 29
  6. 2020AiR: Attention with Reasoning Capabilitycited by 28
  7. 2023Voila-A: Aligning Vision-Language Models with User's Gaze Attentioncited by 28
  8. 2023TempSAL -- Uncovering Temporal Information for Deep Saliency Predictioncited by 25
  9. 2023GazeGNN: A Gaze-Guided Graph Neural Network for Chest X-ray Classificationcited by 25
  10. 2017Learning Visual Importance for Graphic Designs and Data Visualizationscited by 22
  11. 2016Seeing with Humans: Gaze-Assisted Neural Image Captioningcited by 20
  12. 2021Multimodal Integration of Human-Like Attention in Visual Question Answeringcited by 20
  13. 2022Eye-gaze-guided Vision Transformer for Rectifying Shortcut Learningcited by 20
  14. 2022RadioTransformer: A Cascaded Global-Focal Transformer for Visual Attention-guided Disease Classificationcited by 19
  15. 2017Supervising Neural Attention Models for Video Captioning by Human Gaze Datacited by 18
  16. 2021Deep Saliency Prior for Reducing Visual Distractioncited by 17
  17. 2020Generating Image Descriptions via Sequential Cross-Modal Alignment Guided by Human Gazecited by 16
  18. 2021VQA-MHUG: A Gaze Dataset to Study Multimodal Neural Attention in Visual Question Answeringcited by 16
  19. 2023Synthesizing Human Gaze Feedback for Improved NLP Performancecited by 16
  20. 2023Realistic Saliency Guided Image Enhancementcited by 14
  21. 2021Understanding Visual Saliency in Mobile User Interfacescited by 13
  22. 2019Understanding Teacher Gaze Patterns for Robot Learningcited by 12
  23. 2017Paying More Attention to Saliency: Image Captioning with Saliency and Context Attentioncited by 10
  24. 2017Predicting Native Language from Gazecited by 9
  25. 2023Pre-Trained Language Models Augmented with Synthetic Scanpaths for Natural Language Understandingcited by 8
  26. 2023Multi-Modal Gaze Following in Conversational Scenarioscited by 6
  27. 2017Guiding human gaze with convolutional neural networkscited by 5
  28. 2019Nonverbal Robot Feedback for Human Teacherscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 5; 2026: 2.

9 papers.

  1. 2025Where, What, Why: Towards Explainable Driver Attention Predictioncited by 10
  2. 2025Modeling Human Gaze Behavior with Diffusion Models for Unified Scanpath Predictioncited by 7
  3. 2024TPP-Gaze: Modelling Gaze Dynamics in Space and Time with Neural Temporal Point Processescited by 4
  4. 2025Integrating Cognitive Processing Signals into Language Models: A Review of Advances, Applications and Future Directionscited by 2
  5. 2024Caption-Driven Explorations: Aligning Image and Text Embeddings through Human-Inspired Foveated Visioncited by 0
  6. 2025A recurrent vision transformer shows signatures of primate visual attentioncited by 0
  7. 2025A Comparative Study of Scanpath Models in Graph-Based Visualizationcited by 0
  8. 2026EVA: Bridging Performance and Human Alignment in Hard-Attention Vision Models for Image Classificationcited by 0
  9. 2026Object Referring-Guided Scanpath Prediction with Perception-Enhanced Vision-Language Modelscited by 0