In this paper, we introduce Masked Feature Modelling (MFM), a novel approach for the unsupervised pre-training of a Graph Attention Network (GAT) block. MFM utilizes a pretrained Visual Tokenizer to reconstruct masked features of objects within a video, leveraging the MiniKinetics dataset. We then incorporate the pre-trained GAT block into a state-of-the-art bottom-up supervised video-event recognition architecture, ViGAT, to improve the model's starting point and overall accuracy. Experimental evaluations on the YLI-MED dataset demonstrate the effectiveness of MFM in improving event recognition performance.

翻译：本文提出了一种名为掩码特征建模（MFM）的新方法，用于图注意力网络（GAT）模块的无监督预训练。MFM利用预训练的视觉分词器，基于MiniKinetics数据集重构视频中物体的掩码特征。随后，我们将预训练的GAT模块集成到最先进的监督自底向上视频事件识别架构ViGAT中，以优化模型的初始状态并提升整体准确率。在YLI-MED数据集上的实验评估表明，MFM能有效提升事件识别性能。