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Current methods for video analysis often extract frame-level features usingpre-trained convolutional neural networks (CNNs). Such features are thenaggregated over time e.g., by simple temporal averaging or more sophisticatedrecurrent neural networks such as long short-term memory (LSTM) or gatedrecurrent units (GRU). In this work we revise existing video representationsand study alternative methods for temporal aggregation. We first exploreclustering-based aggregation layers and propose a two-stream architectureaggregating audio and visual features. We then introduce a learnable non-linearunit, named Context Gating, aiming to model interdependencies among networkactivations. Our experimental results show the advantage of both improvementsfor the task of video classification. In particular, we evaluate our method onthe large-scale multi-modal Youtube-8M v2 dataset and outperform all othermethods in the Youtube 8M Large-Scale Video Understanding challenge.

Learnable pooling with Context Gating for video classification