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In this paper, we introduce Masked Feature Modelling (MFM), a novel approachfor the unsupervised pre-training of a Graph Attention Network (GAT) block. MFMutilizes a pretrained Visual Tokenizer to reconstruct masked features ofobjects within a video, leveraging the MiniKinetics dataset. We thenincorporate the pre-trained GAT block into a state-of-the-art bottom-upsupervised video-event recognition architecture, ViGAT, to improve the model'sstarting point and overall accuracy. Experimental evaluations on the YLI-MEDdataset demonstrate the effectiveness of MFM in improving event recognitionperformance.

Masked Feature Modelling: Feature Masking for the Unsupervised Pre-training of a Graph Attention Network Block for Bottom-up Video Event Recognition