Knowledge Integration Networks for Action Recognition

Understand

In this work, we propose Knowledge Integration Networks (referred as KINet) for video action recognition.

• KINet is capable of aggregating meaningful context features which are of great importance to identifying an action, such as human information and scene context.
• We design a three-branch architecture consisting of a main branch for action recognition, and two auxiliary branches for human parsing and scene recognition which allow the model to encode the knowledge of human and scene for action recognition.
• We explore two pre-trained models as teacher networks to distill the knowledge of human and scene for training the auxiliary tasks of KINet.