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We present Temporal Aggregation Network (TAN) which decomposes 3Dconvolutions into spatial and temporal aggregation blocks. By stacking spatialand temporal convolutions repeatedly, TAN forms a deep hierarchicalrepresentation for capturing spatio-temporal information in videos. Since we donot apply 3D convolutions in each layer but only apply temporal aggregationblocks once after each spatial downsampling layer in the network, wesignificantly reduce the model complexity. The use of dilated convolutions atdifferent resolutions of the network helps in aggregating multi-scalespatio-temporal information efficiently. Experiments show that our model iswell suited for dense multi-label action recognition, which is a challengingsub-topic of action recognition that requires predicting multiple action labelsin each frame. We outperform state-of-the-art methods by 5% and 3% on theCharades and Multi-THUMOS dataset respectively.

TAN: Temporal Aggregation Network for Dense Multi-label Action Recognition