  This paper introduces the system we developed for the Youtube-8M Video
Understanding Challenge, in which a large-scale benchmark dataset was used for
multi-label video classification. The proposed framework contains hierarchical
deep architecture, including the frame-level sequence modeling part and the
video-level classification part. In the frame-level sequence modelling part, we
explore a set of methods including Pooling-LSTM (PLSTM), Hierarchical-LSTM
(HLSTM), Random-LSTM (RLSTM) in order to address the problem of large amount of
frames in a video. We also introduce two attention pooling methods, single
attention pooling (ATT) and multiply attention pooling (Multi-ATT) so that we
can pay more attention to the informative frames in a video and ignore the
useless frames. In the video-level classification part, two methods are
proposed to increase the classification performance, i.e.
Hierarchical-Mixture-of-Experts (HMoE) and Classifier Chains (CC). Our final
submission is an ensemble consisting of 18 sub-models. In terms of the official
evaluation metric Global Average Precision (GAP) at 20, our best submission
achieves 0.84346 on the public 50% of test dataset and 0.84333 on the private
50% of test data.
