標(biāo)題: Titlebook: Deep Learning for Video Understanding; Zuxuan Wu,Yu-Gang Jiang Book 2024 The Editor(s) (if applicable) and The Author(s), under exclusive [打印本頁] 作者: digestive-tract 時(shí)間: 2025-3-21 20:09
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
作者: 有害 時(shí)間: 2025-3-21 21:14
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 作者: SPASM 時(shí)間: 2025-3-22 01:57
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作者: ATP861 時(shí)間: 2025-3-22 08:19 作者: 頭腦冷靜 時(shí)間: 2025-3-22 09:48
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作者: 厭煩 時(shí)間: 2025-3-22 15:10 作者: 厭煩 時(shí)間: 2025-3-22 18:20
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.作者: left-ventricle 時(shí)間: 2025-3-22 23:24
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作者: 鋼盔 時(shí)間: 2025-3-23 02:50
,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.作者: transient-pain 時(shí)間: 2025-3-23 09:34
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.作者: Ballerina 時(shí)間: 2025-3-23 10:49 作者: 種族被根除 時(shí)間: 2025-3-23 16:25
temporal video grounding. Action localization aims to find the video segments that contain potential actions and predict the action classes, while temporal video grounding aims to localize video moments that best match given natural language. We present an overview of existing approaches and benchmarks used for evalution.作者: somnambulism 時(shí)間: 2025-3-23 18:41 作者: 車床 時(shí)間: 2025-3-24 01:01 作者: 記憶法 時(shí)間: 2025-3-24 03:37
Deep Learning Basics for Video Understanding,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.作者: 行業(yè) 時(shí)間: 2025-3-24 08:11 作者: ADORN 時(shí)間: 2025-3-24 13:18 作者: Ambiguous 時(shí)間: 2025-3-24 17:09
Efficient Video Understanding,mic inference techniques that adaptively allocate computation resources to different video frames to further accelerate video analysis without sacrificing performance. Through this chapter, we aim to provide a comprehensive overview of efficient deep learning methods for video understanding.作者: 疲憊的老馬 時(shí)間: 2025-3-24 19:25 作者: prostatitis 時(shí)間: 2025-3-25 01:22 作者: ORBIT 時(shí)間: 2025-3-25 03:53
Overview of Video Understanding,tal media, video owns the unique charm of conveying rich and vivid information, making it more and more popular on various social platforms. At the same time, video understanding techniques, which aim to recognize the objects and actions within videos and analyze their temporal evolution, are gainin作者: discord 時(shí)間: 2025-3-25 08:04 作者: 令人悲傷 時(shí)間: 2025-3-25 14:26 作者: 血友病 時(shí)間: 2025-3-25 16:32
Deep Learning for Video Localization,wever, video recognition is limited in understanding the overall event that exists in a video, without a fine-grained analysis of video segments. To compensate for the limitations of video recognition, video localization provides an accurate and comprehensive understanding of videos by predicting wh作者: Ancestor 時(shí)間: 2025-3-25 23:24 作者: BLANC 時(shí)間: 2025-3-26 00:46
Unsupervised Feature Learning for Video Understanding,of large-scale training datasets. Vast amounts of annotated data have led to the growth in the performance of supervised learning; nevertheless, manual collection and annotation are demanding of time and labor. Subsequently, research interests have been aroused in unsupervised feature learning that 作者: epidermis 時(shí)間: 2025-3-26 06:37
Efficient Video Understanding,a result, the development of efficient deep video models and training strategies is necessary for practical video understanding applications. In this chapter, we will delve into the design choices for creating compact video understanding models, such as CNNs and Transformers. Furthermore, we will ex作者: 偽造 時(shí)間: 2025-3-26 09:42
Conclusion and Future Directions,hapters. Furthermore, this chapter will also look into the future of deep-learning-based video understanding by briefly discussing several promising directions, e.g., the construction of large-scale video foundation models, the application of large language models (LLMs) in video understanding, etc.作者: alcohol-abuse 時(shí)間: 2025-3-26 12:48 作者: 商業(yè)上 時(shí)間: 2025-3-26 19:16 作者: 運(yùn)動性 時(shí)間: 2025-3-26 22:27 作者: 敬禮 時(shí)間: 2025-3-27 03:12
Rafaela Kraus,Tanja Kreitenweisaches to automated analysis strategies based on deep neural networks. In this chapter, we review the evolution of deep learning methods for video content classification, which includes categorizing human activities and complex events in videos. We provide a detailed discussion on Convolutional Neura作者: 慌張 時(shí)間: 2025-3-27 05:59
wever, video recognition is limited in understanding the overall event that exists in a video, without a fine-grained analysis of video segments. To compensate for the limitations of video recognition, video localization provides an accurate and comprehensive understanding of videos by predicting wh作者: 側(cè)面左右 時(shí)間: 2025-3-27 11:34
,Koh?renz?– Macht und Ver?nderung verstehen,onnection between vision and language. The goal of video captioning is to automatically generate a natural language to describe the visual content of a video. This can have a significant impact on video indexing and retrieval, applicable in helping visually impaired people. In this chapter, we intro作者: Optic-Disk 時(shí)間: 2025-3-27 13:44 作者: ALT 時(shí)間: 2025-3-27 18:27 作者: Brochure 時(shí)間: 2025-3-28 00:38
Angst – Bedingung des Mensch-Seinshapters. Furthermore, this chapter will also look into the future of deep-learning-based video understanding by briefly discussing several promising directions, e.g., the construction of large-scale video foundation models, the application of large language models (LLMs) in video understanding, etc.作者: sterilization 時(shí)間: 2025-3-28 05:32
Zuxuan Wu,Yu-Gang JiangPresents 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 st作者: Pamphlet 時(shí)間: 2025-3-28 06:55
Wireless Networkshttp://image.papertrans.cn/e/image/284501.jpg作者: 字的誤用 時(shí)間: 2025-3-28 10:30
https://doi.org/10.1007/978-3-031-57679-9action recognition; video captioning; action localization; motion extraction; spatial-temporal feature l作者: 我就不公正 時(shí)間: 2025-3-28 15:45 作者: PSA-velocity 時(shí)間: 2025-3-28 21:50
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