Papers › MM-GATBT: Enriching Multimodal Representation Using Graph Attention Network
MM-GATBT: Enriching Multimodal Representation Using Graph Attention Network
Seung Byum Seo, Hyoungwook Nam, Payam Delgosha
While there have been advances in Natural Language Processing (NLP), their success is mainly gained by applying a self-attention mechanism into single or multi-modalities. While this approach has brought significant improvements in multiple downstream tasks, it fails to capture the interaction between different entities. Therefore, we propose MM-GATBT, a multimodal graph representation learning model that captures not only the relational semantics within one modality but also the interactions between different modalities. Specifically, the proposed method constructs image-based node embedding which contains relational semantics of entities. Our empirical results show that MM-GATBT achieves state-of-the-art results among all published papers on the MM-IMDb dataset.
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