Trends, Applications, and Challenges in Human Attention Modelling

Giuseppe Cartella, Marcella Cornia, Vittorio Cuculo, Alessandro D'Amelio, Dario Zanca +2 more
2/28/2024
cs.CVcs.AI

Official code: aimagelab/awesome-human-visual-attention (66 stars, last commit 2025-05-09).

Abstract

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.

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aimagelab/awesome-human-visual-attentionOfficial100%

This repository contains a curated list of research papers and resources focusing on saliency and scanpath prediction, human attention, human visual search.

665Jan 31, 20241 years ago

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Cite this paper

@article{cartella2024trends,
  title  = {Trends, Applications, and Challenges in Human Attention Modelling},
  author = {Giuseppe Cartella and Marcella Cornia and Vittorio Cuculo and Alessandro D'Amelio and Dario Zanca and Giuseppe Boccignone and Rita Cucchiara},
  year   = {2024},
  eprint = {2402.18673},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.18673v2}
}

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