Transformer架构在多模态学习中的应用与优化
摘 要
随着人工智能技术的发展，多模态学习成为研究热点，旨在融合来自不同感官的信息以实现更全面的理解。Transformer架构凭借其强大的并行处理能力和对长距离依赖的有效捕捉，在自然语言处理领域取得巨大成功，但将其应用于多模态学习面临诸多挑战。本研究聚焦于Transformer架构在多模态学习中的应用与优化，旨在探索如何有效整合文本、图像等多源异构信息。通过引入跨模态注意力机制，设计了新型的多模态Transformer模型，该模型能够在不同模态间建立深层次关联，提升特征表示能力。
关键词：多模态学习 Transformer架构 跨模态注意力机制
Abstract
With the development of artificial intelligence technology, multimodal learning has become a research hotspot, aiming to integrate information from different senses to achieve a more comprehensive understanding. Transformer Architecture has achieved great success in the field of natural language processing with its powerful parallel processing capability and effective capture of the dependence on long distance, but its application to multimodal learning faces many challenges. This study focuses on the application and optimization of Transformer architecture in multimodal learning, aiming to explore how to effectively integrate multi-source heterogeneous information such as text and image. By introducing the cross-modal attention mechanism, a new multimodal Transformer model is designed, which can establish deep correlation between different modes and improve the ability of feature representation.
Keyword:Multimodal Learning Transformer Architecture Cross-modal Attention Mechanism
目 录
1绪论 1
1.1 Transformer架构研究背景与意义 1
1.2多模态学习领域研究现状综述 1
2Transformer在多模态融合中的应用 1
2.1视觉与文本的跨模态表示 2
2.2跨模态注意力机制设计 2
2.3多模态数据对齐与融合策略 3
3Transformer架构优化探索 4
3.1参数高效化改进方案 4
3.2自注意力机制性能优化 4
3.3面向多模态任务的模型压缩 5
4多模态场景下的Transformer创新 6
4.1新兴应用场景分析 6
4.2模型架构创新实践 6
4.3多模态交互式学习框架 7
结论 7
参考文献 9
致谢 10
摘 要
随着人工智能技术的发展，多模态学习成为研究热点，旨在融合来自不同感官的信息以实现更全面的理解。Transformer架构凭借其强大的并行处理能力和对长距离依赖的有效捕捉，在自然语言处理领域取得巨大成功，但将其应用于多模态学习面临诸多挑战。本研究聚焦于Transformer架构在多模态学习中的应用与优化，旨在探索如何有效整合文本、图像等多源异构信息。通过引入跨模态注意力机制，设计了新型的多模态Transformer模型，该模型能够在不同模态间建立深层次关联，提升特征表示能力。
关键词：多模态学习 Transformer架构 跨模态注意力机制
Abstract
With the development of artificial intelligence technology, multimodal learning has become a research hotspot, aiming to integrate information from different senses to achieve a more comprehensive understanding. Transformer Architecture has achieved great success in the field of natural language processing with its powerful parallel processing capability and effective capture of the dependence on long distance, but its application to multimodal learning faces many challenges. This study focuses on the application and optimization of Transformer architecture in multimodal learning, aiming to explore how to effectively integrate multi-source heterogeneous information such as text and image. By introducing the cross-modal attention mechanism, a new multimodal Transformer model is designed, which can establish deep correlation between different modes and improve the ability of feature representation.
Keyword:Multimodal Learning Transformer Architecture Cross-modal Attention Mechanism
目 录
1绪论 1
1.1 Transformer架构研究背景与意义 1
1.2多模态学习领域研究现状综述 1
2Transformer在多模态融合中的应用 1
2.1视觉与文本的跨模态表示 2
2.2跨模态注意力机制设计 2
2.3多模态数据对齐与融合策略 3
3Transformer架构优化探索 4
3.1参数高效化改进方案 4
3.2自注意力机制性能优化 4
3.3面向多模态任务的模型压缩 5
4多模态场景下的Transformer创新 6
4.1新兴应用场景分析 6
4.2模型架构创新实践 6
4.3多模态交互式学习框架 7
结论 7
参考文献 9
致谢 10
