Deep Learning for Video Understanding

Name: Deep Learning for Video Understanding
Brand: Springer, Berlin
SKU: 9783031576782
Price: 147.07 EUR
Availability: InStock

Автор: Zuxuan Wu, Yu-Gang Jiang

Език:

Корици: С твърди корици

Издател: Springer, Berlin

Наличност: Външен склад

Изпращаме след 10-13 дни

147.07 € 287.64 лв

This book presents deep learning techniques for video understanding. For deep learning basics, the a...

Информация за книгата

Автор

Zuxuan Wu, Yu-Gang Jiang

Език

Английски език

Корици

Книга - С твърди корици

Издадена

2024

страници

170

EAN

9783031576782

Enbook ID

45209535

Издател

Springer, Berlin

Теглоt

414

Размери

155 x 235

Пълно описание

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.

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Deep Learning for Video Understanding

Информация за книгата

Пълно описание

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