Continual Multimodal Knowledge Graph Construction
arxiv(2023)
摘要
Current Multimodal Knowledge Graph Construction (MKGC) models struggle with
the real-world dynamism of continuously emerging entities and relations, often
succumbing to catastrophic forgetting-loss of previously acquired knowledge.
This study introduces benchmarks aimed at fostering the development of the
continual MKGC domain. We further introduce MSPT framework, designed to
surmount the shortcomings of existing MKGC approaches during multimedia data
processing. MSPT harmonizes the retention of learned knowledge (stability) and
the integration of new data (plasticity), outperforming current continual
learning and multimodal methods. Our results confirm MSPT's superior
performance in evolving knowledge environments, showcasing its capacity to
navigate balance between stability and plasticity.
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