  The introduction of Transformer model has led to tremendous advancements in
sequence modeling, especially in text domain. However, the use of
attention-based models for video understanding is still relatively unexplored.
In this paper, we introduce Gated Adversarial Transformer (GAT) to enhance the
applicability of attention-based models to videos. GAT uses a multi-level
attention gate to model the relevance of a frame based on local and global
contexts. This enables the model to understand the video at various
granularities. Further, GAT uses adversarial training to improve model
generalization. We propose temporal attention regularization scheme to improve
the robustness of attention modules to adversarial examples. We illustrate the
performance of GAT on the large-scale YoutTube-8M data set on the task of video
categorization. We further show ablation studies along with quantitative and
qualitative analysis to showcase the improvement.
