Research of Event Reconstruct Based on Multi-View Contrastive Learning (Student Abstract)

Abstract

The proliferation of social media exacerbates information fragmentation, posing challenges to understanding public events. We address the problem of event reconstruction with a novel Multi-view Contrast Event Reconstruction (MCER) model. MCER maximizes feature dissimilarity between different views of the same event using contrastive learning, while minimizing mutual information between distinct events. This aggregates fragmented views to reconstruct comprehensive event representations. MCER employs momentum and weight-sharing encoders in a three-tower architecture with supervised contrastive loss for multi-view representation learning. Due to the scarcity of multi-view public datasets, we construct a new Mul-view-data benchmark.Experiments demonstrate MCER’s superior performance on public data and our Mul-view-data, significantly outperforming selfsupervised methods by incorporating supervised contrastive techniques. MCER advances multi-view representation learning to counter information fragmentation and enable robust event understanding.