  In this report, our approach to tackling the task of ActivityNet 2018
Kinetics-600 challenge is described in detail. Though spatial-temporal
modelling methods, which adopt either such end-to-end framework as I3D
\cite{i3d} or two-stage frameworks (i.e., CNN+RNN), have been proposed in
existing state-of-the-arts for this task, video modelling is far from being
well solved. In this challenge, we propose spatial-temporal network (StNet) for
better joint spatial-temporal modelling and comprehensively video
understanding. Besides, given that multi-modal information is contained in
video source, we manage to integrate both early-fusion and later-fusion
strategy of multi-modal information via our proposed improved temporal Xception
network (iTXN) for video understanding. Our StNet RGB single model achieves
78.99\% top-1 precision in the Kinetics-600 validation set and that of our
improved temporal Xception network which integrates RGB, flow and audio
modalities is up to 82.35\%. After model ensemble, we achieve top-1 precision
as high as 85.0\% on the validation set and rank No.1 among all submissions.
