  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.
