  This paper describes our solution for the video recognition task of the
Google Cloud and YouTube-8M Video Understanding Challenge that ranked the 3rd
place. Because the challenge provides pre-extracted visual and audio features
instead of the raw videos, we mainly investigate various temporal modeling
approaches to aggregate the frame-level features for multi-label video
recognition. Our system contains three major components: two-stream sequence
model, fast-forward sequence model and temporal residual neural networks.
Experiment results on the challenging Youtube-8M dataset demonstrate that our
proposed temporal modeling approaches can significantly improve existing
temporal modeling approaches in the large-scale video recognition tasks. To be
noted, our fast-forward LSTM with a depth of 7 layers achieves 82.75% in term
of GAP@20 on the Kaggle Public test set.
