持续多模态知识图谱构建
Continual Multimodal Knowledge Graph Construction
AI总结：
本研究针对持续多模态知识图谱构建中的灾难性遗忘问题，提出MSPT框架，通过平衡稳定性和可塑性，在动态知识环境中实现卓越性能。
摘要：
当前多模态知识图谱构建（MKGC）模型难以应对现实世界中持续涌现的实体和关系，常常遭受灾难性遗忘——即丢失先前获取的知识。本研究引入了基准测试，旨在促进持续MKGC领域的发展。我们进一步提出了MSPT框架，旨在克服现有MKGC方法在多媒体数据处理过程中的不足。MSPT协调了所学知识的保留（稳定性）与新数据的整合（可塑性），超越了现有的持续学习和多模态方法。我们的结果证实了MSPT在不断演变的知识环境中的卓越性能，展示了其在稳定性和可塑性之间保持平衡的能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
