Large‐Scale Video Understanding with Limited Training Labels
Jingkuan Song, Xu Zhao, Lianli Gao, Liangliang Cao · 2019
This chapter discusses both unsupervised and semi-supervised methods to facilitate video understanding tasks. It considers two general research problems: video retrieval and video annotation/summarization. It covers video retrieval and annotation/summarization, two approaches to fight the consequences of the big video data. The chapter presents the state-of-the-art research in each research field. Content-based video hashing is a promising direction for content-based video retrieval, but it is more challenging than image hashing. Graph-based learning is an efficient approach for modeling data in various machine learning schemes that is unsupervised learning, supervised learning, and semi-supervised learning. The chapter describes two different memory units in context memory model (CMM), and proposes the framework based on them. There are two different memory units in CMM: short-term context memory and long-term context memory units. The chapter also considers applying the video understanding methods to more real-world applications, for example, surveillance, video advertising, and short video recommendations.