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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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.

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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.

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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.

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