  In this work, we combine 3D convolution with late temporal modeling for
action recognition. For this aim, we replace the conventional Temporal Global
Average Pooling (TGAP) layer at the end of 3D convolutional architecture with
the Bidirectional Encoder Representations from Transformers (BERT) layer in
order to better utilize the temporal information with BERT's attention
mechanism. We show that this replacement improves the performances of many
popular 3D convolution architectures for action recognition, including ResNeXt,
I3D, SlowFast and R(2+1)D. Moreover, we provide the-state-of-the-art results on
both HMDB51 and UCF101 datasets with 85.10% and 98.69% top-1 accuracy,
respectively. The code is publicly available.
