多模态智能中的跨模态迁移学习策略研究  

Research on Cross-Modal Transfer Learning Strategy in Multi-Modal Intelligence

作　　者：简丽琼[1] JIAN Liqiong(The Blood Center of the Ningxia Hui Autonomous Region,Yinchuan Ningxia 750001,China)

机构地区：[1]宁夏回族自治区血液中心,宁夏银川750001

出　　处：《信息与电脑》2024年第22期180-182,共3页Information & Computer

基　　金：宁夏自然科学基金“基于正交矩的图像压缩研究”(项目编号:2022AAC03704)。

摘　　要：文章深入探讨了多模态智能领域中跨模态迁移学习的策略及其应用。首先,文章回顾了跨模态迁移学习的基本概念和主要方法,然后详细分析了三种主流策略:特征对齐、知识蒸馏和元学习。通过对比实验,文章评估了这些策略在不同多模态任务中的效果,并提出了一种融合多种策略的新方法。实验结果表明,该方法在图像-文本检索、跨语言情感分析等任务中取得了显著的性能提升。最后,文章讨论了跨模态迁移学习面临的挑战及未来研究方向。The paper provides an in-depth discussion of cross-modal transfer learning strategies and their applications in the field of multimodal intelligence.First,the article reviews the basic concepts and main methods of cross-modal transfer learning,and then analyzes three mainstream strategies in detail:feature alignment,knowledge distillation,and meta-learning.Through comparative experiments,the article evaluates the effectiveness of these strategies in different multimodal tasks and proposes a new method that incorporates multiple strategies.The experimental results show that the method achieves significant performance improvements in tasks such as image-text retrieval and cross-lingual sentiment analysis.Finally,the article discusses the challenges and future research directions for cross-modal transfer learning.

关 键 词：多模态智能 跨模态迁移学习 特征对齐 知识蒸馏 元学习 图像-文本检索 跨语言情感分析 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]