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Deep learning for video understanding

Wu, Zuxuan.

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Deep learning for video understanding

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep learning for video understanding/ by Zuxuan Wu, Yu-Gang Jiang.
Author:	Wu, Zuxuan.
other author:	Jiang, Yu-Gang.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	ix, 188 p. :ill. (chiefly col.), digital ;24 cm.
[NT 15003449]:	Introduction -- Overview of Video Understanding -- Deep Learning Basics for Video Understanding -- Deep Learning for Action Recognition -- Deep Learning for Action Localization -- Deep Learning for Video Captioning -- Unsupervised Feature Learning for Video Understanding -- Efficient Video Understanding -- Future Research Directions -- Conclusion.
Contained By:	Springer Nature eBook
Subject:	Deep learning (Machine learning) -
Online resource:	https://doi.org/10.1007/978-3-031-57679-9
ISBN:	9783031576799

Deep learning for video understanding
Wu, Zuxuan.

Deep learning for video understanding[electronic resource] /by Zuxuan Wu, Yu-Gang Jiang. - Cham :Springer Nature Switzerland :2024. - ix, 188 p. :ill. (chiefly col.), digital ;24 cm. - Wireless networks,2366-1445. - Wireless networks..

Introduction -- Overview of Video Understanding -- Deep Learning Basics for Video Understanding -- Deep Learning for Action Recognition -- Deep Learning for Action Localization -- Deep Learning for Video Captioning -- Unsupervised Feature Learning for Video Understanding -- Efficient Video Understanding -- Future Research Directions -- Conclusion.

This book presents deep learning techniques for video understanding. For deep learning basics, the authors cover machine learning pipelines and notations, 2D and 3D Convolutional Neural Networks for spatial and temporal feature learning. For action recognition, the authors introduce classical frameworks for image classification, and then elaborate both image-based and clip-based 2D/3D CNN networks for action recognition. For action detection, the authors elaborate sliding windows, proposal-based detection methods, single stage and two stage approaches, spatial and temporal action localization, followed by datasets introduction. For video captioning, the authors present language-based models and how to perform sequence to sequence learning for video captioning. For unsupervised feature learning, the authors discuss the necessity of shifting from supervised learning to unsupervised learning and then introduce how to design better surrogate training tasks to learn video representations. Finally, the book introduces recent self-training pipelines like contrastive learning and masked image/video modeling with transformers. The book provides promising directions, with an aim to promote future research outcomes in the field of video understanding with deep learning. Presents an overview of deep learning techniques for video understanding; Covers important topics like action recognition, action localization, video captioning, and more; Introduces cutting-edge and state-of-the-art video understanding techniques.

ISBN: 9783031576799

Standard No.: 10.1007/978-3-031-57679-9doiSubjects--Topical Terms:

3538509
Deep learning (Machine learning)

LC Class. No.: Q325.73

Dewey Class. No.: 006.31

Deep learning for video understanding
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505 0 $a Introduction -- Overview of Video Understanding -- Deep Learning Basics for Video Understanding -- Deep Learning for Action Recognition -- Deep Learning for Action Localization -- Deep Learning for Video Captioning -- Unsupervised Feature Learning for Video Understanding -- Efficient Video Understanding -- Future Research Directions -- Conclusion.
520 $a This book presents deep learning techniques for video understanding. For deep learning basics, the authors cover machine learning pipelines and notations, 2D and 3D Convolutional Neural Networks for spatial and temporal feature learning. For action recognition, the authors introduce classical frameworks for image classification, and then elaborate both image-based and clip-based 2D/3D CNN networks for action recognition. For action detection, the authors elaborate sliding windows, proposal-based detection methods, single stage and two stage approaches, spatial and temporal action localization, followed by datasets introduction. For video captioning, the authors present language-based models and how to perform sequence to sequence learning for video captioning. For unsupervised feature learning, the authors discuss the necessity of shifting from supervised learning to unsupervised learning and then introduce how to design better surrogate training tasks to learn video representations. Finally, the book introduces recent self-training pipelines like contrastive learning and masked image/video modeling with transformers. The book provides promising directions, with an aim to promote future research outcomes in the field of video understanding with deep learning. Presents an overview of deep learning techniques for video understanding; Covers important topics like action recognition, action localization, video captioning, and more; Introduces cutting-edge and state-of-the-art video understanding techniques.
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