  Current methods for video analysis often extract frame-level features using
pre-trained convolutional neural networks (CNNs). Such features are then
aggregated over time e.g., by simple temporal averaging or more sophisticated
recurrent neural networks such as long short-term memory (LSTM) or gated
recurrent units (GRU). In this work we revise existing video representations
and study alternative methods for temporal aggregation. We first explore
clustering-based aggregation layers and propose a two-stream architecture
aggregating audio and visual features. We then introduce a learnable non-linear
unit, named Context Gating, aiming to model interdependencies among network
activations. Our experimental results show the advantage of both improvements
for the task of video classification. In particular, we evaluate our method on
the large-scale multi-modal Youtube-8M v2 dataset and outperform all other
methods in the Youtube 8M Large-Scale Video Understanding challenge.
