  Video classification and analysis is always a popular and challenging field
in computer vision. It is more than just simple image classification due to the
correlation with respect to the semantic contents of subsequent frames brings
difficulties for video analysis. In this literature review, we summarized some
state-of-the-art methods for multi-label video classification. Our goal is
first to experimentally research the current widely used architectures, and
then to develop a method to deal with the sequential data of frames and perform
multi-label classification based on automatic content detection of video.
