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 of the ongoing research, refer to our dedicated repository available at https://github.com/aimagelab/awesome-human-visual-attention.

Trends, Applications, and Challenges in Human Attention Modelling / G. Cartella, M. Cornia, V. Cuculo, A. D'Amelio, D. Zanca, G. Boccignone, R. Cucchiara - In: Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence[s.l] : International Joint Conferences on Artificial Intelligence, 2024. - ISBN 978-1-956792-04-1. - pp. 7971-7979 (( Intervento presentato al 33. convegno International Joint Conference on Artificial Intelligence tenutosi a Jeju nel 2024 [10.24963/ijcai.2024/882].

Trends, Applications, and Challenges in Human Attention Modelling

V. Cuculo;A. D'Amelio;G. Boccignone
Penultimo
;
2024

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 of the ongoing research, refer to our dedicated repository available at https://github.com/aimagelab/awesome-human-visual-attention.
visual attention; computer vision, artificial intelligence; transformers; deep neural networks
Settore INFO-01/A - Informatica
Settore IINF-05/A - Sistemi di elaborazione delle informazioni
2024
International Joint Conferences on Artifical Intelligence (IJCAI)
https://www.ijcai.org/proceedings/2024/0882.pdf
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1102548
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