Titlebook: Deep Learning for Video Understanding; Zuxuan Wu,Yu-Gang Jiang Book 2024 The Editor(s) (if applicable) and The Author(s), under exclusive

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書目名稱Deep Learning for Video Understanding影響因子(影響力)




書目名稱Deep Learning for Video Understanding影響因子(影響力)學(xué)科排名




書目名稱Deep Learning for Video Understanding網(wǎng)絡(luò)公開度




書目名稱Deep Learning for Video Understanding網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Deep Learning for Video Understanding被引頻次




書目名稱Deep Learning for Video Understanding被引頻次學(xué)科排名




書目名稱Deep Learning for Video Understanding年度引用




書目名稱Deep Learning for Video Understanding年度引用學(xué)科排名




書目名稱Deep Learning for Video Understanding讀者反饋




書目名稱Deep Learning for Video Understanding讀者反饋學(xué)科排名

有害

Book 2024ng 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

SPASM

the hotbeds of pretext tasks, which refer to network optimization tasks based on surrogate signals without human supervision, facilitating better performance on video-related downstream tasks. In this chapter, we undertake a comprehensive review of UVL, which begins with a preliminary introduction o

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頭腦冷靜

2366-1186 n, video captioning, and more.Introduces cutting-edge and st.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. F

厭煩

厭煩

Angst – Bedingung des Mensch-Seinsirections, e.g., the construction of large-scale video foundation models, the application of large language models (LLMs) in video understanding, etc. By depicting these exciting prospects, we encourage the readers to embark on new endeavors to contribute to the advancement of this field.

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Book 2024ions, 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, th

鋼盔

,I. Führung der eigenen Person,en successively proposed, promoting this large field to becoming more and more mature. In this chapter, we will briefly introduce the above aspects and travel through the corridors of time to systematically review the chronology of this dynamic field.

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Fallstudien ?Führung von Experten“ons of these backbones. By the end of the chapter, readers will have a solid understanding of the basics of deep learning for video understanding and be well-equipped to explore more advanced topics in this exciting field.