Multi-model learning by sequential reading of untrimmed videos for
  action recognition
Kodai Kamiya Toru Tamaki
We propose a new method for learning videos by aggregating multiple models by
sequentially extracting video clips from untrimmed video. The proposed method
reduces the correlation between clips by feeding clips to multiple models in
turn and synchronizes these models through federated learning. Experimental
results show that the proposed method improves the performance compared to the
no synchronization.