Deep Learning for Video Understanding
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 framew...
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| Autori principali: | , |
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| Ente Autore: | |
| Natura: | Elettronico eBook |
| Lingua: | inglese |
| Pubblicazione: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
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| Edizione: | 1st ed. 2024. |
| Serie: | Wireless Networks,
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| Soggetti: | |
| Accesso online: | https://doi.org/10.1007/978-3-031-57679-9 |
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| 100 | 1 | |a Wu, Zuxuan. |e author. |0 (orcid)0000-0002-8689-5807 |1 https://orcid.org/0000-0002-8689-5807 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Deep Learning for Video Understanding |h [electronic resource] / |c by Zuxuan Wu, Yu-Gang Jiang. |
| 250 | |a 1st ed. 2024. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2024. | |
| 300 | |a IX, 188 p. 99 illus. in color. |b online resource. | ||
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| 490 | 1 | |a Wireless Networks, |x 2366-1445 | |
| 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. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 650 | 0 | |a Telecommunication. | |
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| 650 | 2 | 4 | |a Signal, Speech and Image Processing. |
| 650 | 2 | 4 | |a Computer Vision. |
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| 700 | 1 | |a Jiang, Yu-Gang. |e author. |0 (orcid)0000-0002-1907-8567 |1 https://orcid.org/0000-0002-1907-8567 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
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| 830 | 0 | |a Wireless Networks, |x 2366-1445 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-031-57679-9 |
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