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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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

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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

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,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

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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.

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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

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https://doi.org/10.1007/978-3-031-57679-9action recognition; video captioning; action localization; motion extraction; spatial-temporal feature l