  This paper describes our solution for the 2$^\text{nd}$ YouTube-8M video
understanding challenge organized by Google AI. Unlike the video recognition
benchmarks, such as Kinetics and Moments, the YouTube-8M challenge provides
pre-extracted visual and audio features instead of raw videos. In this
challenge, the submitted model is restricted to 1GB, which encourages
participants focus on constructing one powerful single model rather than
incorporating of the results from a bunch of models. Our system fuses six
different sub-models into one single computational graph, which are categorized
into three families. More specifically, the most effective family is the model
with non-local operations following the NetVLAD encoding. The other two family
models are Soft-BoF and GRU, respectively. In order to further boost single
models performance, the model parameters of different checkpoints are averaged.
Experimental results demonstrate that our proposed system can effectively
perform the video classification task, achieving 0.88763 on the public test set
and 0.88704 on the private set in terms of GAP@20, respectively. We finally
ranked at the fourth place in the YouTube-8M video understanding challenge.
