  Nowadays, the videos on the Internet are prevailing. The precise and in-depth
understanding of the videos is a difficult but valuable problem for both
platforms and researchers. The existing video understand models do well in
object recognition tasks but currently still cannot understand the abstract and
contextual features like highlight humor frames in comedy videos. The current
industrial works are also mainly focused on the basic category classification
task based on the appearances of objects. The feature detection methods for the
abstract category remains blank. A data structure that includes the information
of video frames, audio spectrum and texts provide a new direction to explore.
The multimodal models are proposed to make this in-depth video understanding
mission possible. In this paper, we analyze the difficulties in abstract
understanding of videos and propose a multimodal structure to obtain
state-of-the-art performance in this field. Then we select several benchmarks
for multimodal video understanding and apply the most suitable model to find
the best performance. At last, we evaluate the overall spotlights and drawbacks
of the models and methods in this paper and point out the possible directions
for further improvements.

Computer Vision and Pattern Recognition;Artificial Intelligence;Computation and Language