  This paper describes our solution for the video recognition task of
ActivityNet Kinetics challenge that ranked the 1st place. Most of existing
state-of-the-art video recognition approaches are in favor of an end-to-end
pipeline. One exception is the framework of DevNet. The merit of DevNet is that
they first use the video data to learn a network (i.e. fine-tuning or training
from scratch). Instead of directly using the end-to-end classification scores
(e.g. softmax scores), they extract the features from the learned network and
then fed them into the off-the-shelf machine learning models to conduct video
classification. However, the effectiveness of this line work has long-term been
ignored and underestimated. In this submission, we extensively use this
strategy. Particularly, we investigate four temporal modeling approaches using
the learned features: Multi-group Shifting Attention Network, Temporal Xception
Network, Multi-stream sequence Model and Fast-Forward Sequence Model.
Experiment results on the challenging Kinetics dataset demonstrate that our
proposed temporal modeling approaches can significantly improve existing
approaches in the large-scale video recognition tasks. Most remarkably, our
best single Multi-group Shifting Attention Network can achieve 77.7% in term of
top-1 accuracy and 93.2% in term of top-5 accuracy on the validation set.
