From Human Attention to Computational Attention: A Multidisciplinary Approach - Rilegato

Rilegato

ISBN 10: 3031842995 ISBN 13: 9783031842993

Casa editrice: Springer Nature, 2025

Riassunto

The new edition of this popular book introduces the study of attention, focusing on attention modeling, and addressing such themes as saliency models, signal detection, and different types of signals, including real-life applications. The first edition was written at a moment when the Deep Learning Neural Network (DNNs) techniques were just at their beginnings in terms of attention.  Deep learning has recently become a key factor in attention prediction on images and video, and attention mechanisms have become key factors in deep learning models. The second edition tackles the arrival of DNNs for attention computing in images and video, and also discusses the attention mechanisms within DNNs (attention modules, transformers, grad-cam-based saliency maps, etc.).  From Human Attention to Computational Attention 2nd Edition  also explores the parallels between the brain structures and the DNN architectures to reveal how biomimetics can improve the model designs. The book is truly multi-disciplinary, collating work from psychology, neuroscience, engineering, and computer science.

 

Su questo libro

Informazioni sull?autore

Matei Mancas, PhD, is Senior Researcher, Numediart Institute for Creative Technologies, University of Mons, Mons, Belgium. 

Vincent P. Ferrara, PhD, is Professor, Department of Neuroscience, Zuckerman Institute, Columbia University, New York, New York

Antoine Coutrot, PhD, is Tenured Researcher, Centre National de la Recherche Scientifique, Laboratoire d’InfoRmatique en Image et Systèmes d’information, Lyon, France.

Dalla quarta di copertina

The new edition of this popular book introduces the study of attention, focusing on attention modeling, and addressing such themes as saliency models, signal detection, and different types of signals, including real-life applications. The first edition was written at a moment when the Deep Learning Neural Network (DNNs) techniques were just at their beginnings in terms of attention.  Deep learning has recently become a key factor in attention prediction on images and video, and attention mechanisms have become key factors in deep learning models. The second edition tackles the arrival of DNNs for attention computing in images and video, and also discusses the attention mechanisms within DNNs (attention modules, transformers, grad-cam-based saliency maps, etc.).  From Human Attention to Computational Attention 2nd Edition  also explores the parallels between the brain structures and the DNN architectures to reveal how biomimetics can improve the model designs. The book is truly multi-disciplinary, collating work from psychology, neuroscience, engineering, and computer science.

Dettagli dell'edizione

• Editore: Springer Nature
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 10: 3031842995
• ISBN 13: 9783031842993
• Rilegatura: Copertina rigida
• Numero edizione: 2
• Numero di pagine: 348
• Redattore: Mancas Matei, Ferrera Vincent P., Coutrot Antoine
• Contatto del produttore: Elevation; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; 22391; Germania