  In this report, the method for the iqiyi submission to the task of
ActivityNet 2019 Kinetics-700 challenge is described. Three models are involved
in the model ensemble stage: TSN, HG-NL and StNet. We propose the hierarchical
group-wise non-local (HG-NL) module for frame-level features aggregation for
video classification. The standard non-local (NL) module is effective in
aggregating frame-level features on the task of video classification but
presents low parameters efficiency and high computational cost. The HG-NL
method involves a hierarchical group-wise structure and generates multiple
attention maps to enhance performance. Basing on this hierarchical group-wise
structure, the proposed method has competitive accuracy, fewer parameters and
smaller computational cost than the standard NL. For the task of ActivityNet
2019 Kinetics-700 challenge, after model ensemble, we finally obtain an
averaged top-1 and top-5 error percentage 28.444% on the test set.
