摘要
面对多媒体数据的爆炸式增长,跨模态语义理解已成为人工智能领域的核心挑战与前沿方向。Transformer架构凭借在自然语言处理中展现出的卓越能力,为解决这一难题提供了关键范式。文章系统性地研究了基于Transformer的跨模态语义理解方法,重点探讨了该架构在语义对齐、信息融合与深层理解3个关键环节的创新应用。
In the face of the explosive growth of multimedia data,cross-modal semantic understanding has become a core challenge and frontier direction in the field of artificial intelligence.The Transformer architecture offers a key paradigm for addressing this challenge,thanks to its outstanding capabilities demonstrated in natural language processing.This paper systematically studies the Transformer-based cross-modal semantic understanding method,and focuses on the innovative application of this architecture in three key links:semantic alignment,information fusion and deep understanding.
作者
蒋毅
JIANG Yi(Electromechanical and Information Engineering Department,Changde Vocational Technical College,Changde,Hunan 415000,China)
