  In this paper, Gated-ViGAT, an efficient approach for video event
recognition, utilizing bottom-up (object) information, a new frame sampling
policy and a gating mechanism is proposed. Specifically, the frame sampling
policy uses weighted in-degrees (WiDs), derived from the adjacency matrices of
graph attention networks (GATs), and a dissimilarity measure to select the most
salient and at the same time diverse frames representing the event in the
video. Additionally, the proposed gating mechanism fetches the selected frames
sequentially, and commits early-exiting when an adequately confident decision
is achieved. In this way, only a few frames are processed by the
computationally expensive branch of our network that is responsible for the
bottom-up information extraction. The experimental evaluation on two large,
publicly available video datasets (MiniKinetics, ActivityNet) demonstrates that
Gated-ViGAT provides a large computational complexity reduction in comparison
to our previous approach (ViGAT), while maintaining the excellent event
recognition and explainability performance. Gated-ViGAT source code is made
publicly available at https://github.com/bmezaris/Gated-ViGAT
