Gated-ViGAT：使用新的帧选择策略和门控机制的高效自自底向上的事件识别和解释
Gated-ViGAT: Efficient Bottom-Up Event Recognition and Explanation Using a New Frame Selection Policy and Gating Mechanism
摘要：
Gated-ViGAT是一种有效的视频事件识别方法，它利用自自底向上的【形】的（对象）信息，提出了一种新的帧采样策略和门控机制。具体地，帧采样策略使用从图注意力网络（GAT）的邻接矩阵导出的加权入度（WiD）和相异性度量来选择最显著的并且同时表示视频中的事件的不同帧。此外，所提出的门控机制获取选定的帧顺序，并提交提前退出时，达到足够的信心决定。通过这种方式，我们的网络中负责自自底向上的【形】信息抽取的计算成本很高的分支只处理了几帧。两个大型的，公开的视频数据集（MiniKinetics，ActivityNet）的实验评估表明，Gated-ViGAT提供了一个大的计算复杂性减少相比，我们以前的方法（ViGAT），同时保持了出色的事件识别和解释性能。Gated-ViGAT源代码可在https://github.com/bmezaris/Gated-ViGAT上公开获取
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
