  In this paper, we introduce our submissions for the tasks of trimmed activity
recognition (Kinetics) and trimmed event recognition (Moments in Time) for
Activitynet Challenge 2018. In the two tasks, non-local neural networks and
temporal segment networks are implemented as our base models. Multi-modal cues
such as RGB image, optical flow and acoustic signal have also been used in our
method. We also propose new non-local-based models for further improvement on
the recognition accuracy. The final submissions after ensembling the models
achieve 83.5% top-1 accuracy and 96.8% top-5 accuracy on the Kinetics
validation set, 35.81% top-1 accuracy and 62.59% top-5 accuracy on the MIT
validation set.
