Trends, Applications, and Challenges in Human Attention Modelling

TL;DR

This survey reviews recent developments in human attention modelling, highlighting its integration into deep learning for various AI applications, and discusses future challenges and directions.

Contribution

It provides a comprehensive overview of recent efforts to incorporate human attention mechanisms into deep learning models and outlines future research challenges.

Findings

Human attention modelling enhances AI interpretability and performance.

Integration of attention mechanisms improves image, video, and language processing.

Future challenges include modeling complex attention dynamics and real-world applicability.

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.