Trends, Applications, and Challenges in Human Attention Modelling

• URL: http://arxiv.org/abs/2402.18673v2
• Date: Mon, 22 Apr 2024 17:54:17 GMT
• Title: Trends, Applications, and Challenges in Human Attention Modelling
• Authors: Giuseppe Cartella, Marcella Cornia, Vittorio Cuculo, Alessandro D'Amelio, Dario Zanca, Giuseppe Boccignone, Rita Cucchiara,
• Abstract summary: Human attention modelling has proven to be particularly useful for understanding the cognitive processes underlying visual exploration. It provides 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.
• Score: 65.61554471033844
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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