  We present our three branch solutions for International Challenge on Activity
Recognition at CVPR2019. This model seeks to fuse richer information of global
video clip, short human attention and long-term human activity into a unified
model. We have participated in two tasks: Task A, the Kinetics challenge and
Task B, spatio-temporal action localization challenge. For Kinetics, we achieve
21.59% error rate. For the AVA challenge, our final model obtains 32.49% mAP on
the test sets, which outperforms all submissions to the AVA challenge at CVPR
2018 for more than 10% mAP. As the future work, we will introduce human
activity knowledge, which is a new dataset including key information of human
activity.
