多模态学习在图像与文本融合中的应用研究
摘 要
随着信息技术的迅猛发展，图像与文本信息在互联网环境中呈现爆发式增长，多模态学习成为处理和理解这类复合型数据的关键技术。本研究旨在探讨多模态学习在图像与文本融合中的应用，通过构建深度神经网络模型实现跨模态信息的有效整合。研究采用预训练语言模型与卷积神经网络相结合的方法，提出了一种基于自注意力机制的多模态特征对齐框架，能够自动提取并融合图像和文本的语义特征。实验结果表明，该方法在多个公开数据集上取得了显著优于传统方法的表现，在跨模态检索任务中实现了更高的准确率和召回率。特别是针对中文环境下的图文匹配任务，模型展现出了更强的泛化能力和鲁棒性。
关键词：多模态学习 自注意力机制 跨模态信息整合
Abstract
With the rapid development of information technology, image and text information show an explosive growth in the Internet environment, and multi-modal learning has become the key technology to process and understand this kind of compound data. This study aims to explore the application of multimodal learning in image and text fusion, and realize the effective integration of cross-modal information by constructing deep neural network models. By combining pre-trained language model and convolutional neural network, we propose a multi-modal feature alignment fr amework based on self-attention mechanism, which can automatically extract and fuse semantic features of images and text. Experimental results show that the proposed method significantly outperforms conventional methods on multiple publicly available datasets, achieving higher accuracy and recall in cross-modal retrieval tasks. Especially for the task of graphic matching in Chinese environment, the model shows stronger generalization ability and robustness.
Keyword: Multimodal learning self-attention mechanism cross-modal information integration
目 录
1绪论 1
1.1 研究背景及意义 1
1.2 国内外研究现状 1
1.3 研究方法概述 2
2多模态学习理论基础 2
2.1 多模态数据特性分析 2
2.2 图像与文本的表征学习 3
2.3 融合模型架构设计 3
3图像与文本特征提取 4
3.1 图像特征提取技术 4
3.2 文本特征表示方法 4
3.3 特征对齐与映射策略 5
4融合模型构建与优化 6
4.1 模型选择与构建原则 6
4.2 融合算法性能评估 7
4.3 模型优化与改进措施 7
5结论 8
参考文献 9
致谢 10
摘 要
随着信息技术的迅猛发展，图像与文本信息在互联网环境中呈现爆发式增长，多模态学习成为处理和理解这类复合型数据的关键技术。本研究旨在探讨多模态学习在图像与文本融合中的应用，通过构建深度神经网络模型实现跨模态信息的有效整合。研究采用预训练语言模型与卷积神经网络相结合的方法，提出了一种基于自注意力机制的多模态特征对齐框架，能够自动提取并融合图像和文本的语义特征。实验结果表明，该方法在多个公开数据集上取得了显著优于传统方法的表现，在跨模态检索任务中实现了更高的准确率和召回率。特别是针对中文环境下的图文匹配任务，模型展现出了更强的泛化能力和鲁棒性。
关键词：多模态学习 自注意力机制 跨模态信息整合
Abstract
With the rapid development of information technology, image and text information show an explosive growth in the Internet environment, and multi-modal learning has become the key technology to process and understand this kind of compound data. This study aims to explore the application of multimodal learning in image and text fusion, and realize the effective integration of cross-modal information by constructing deep neural network models. By combining pre-trained language model and convolutional neural network, we propose a multi-modal feature alignment fr amework based on self-attention mechanism, which can automatically extract and fuse semantic features of images and text. Experimental results show that the proposed method significantly outperforms conventional methods on multiple publicly available datasets, achieving higher accuracy and recall in cross-modal retrieval tasks. Especially for the task of graphic matching in Chinese environment, the model shows stronger generalization ability and robustness.
Keyword: Multimodal learning self-attention mechanism cross-modal information integration
目 录
1绪论 1
1.1 研究背景及意义 1
1.2 国内外研究现状 1
1.3 研究方法概述 2
2多模态学习理论基础 2
2.1 多模态数据特性分析 2
2.2 图像与文本的表征学习 3
2.3 融合模型架构设计 3
3图像与文本特征提取 4
3.1 图像特征提取技术 4
3.2 文本特征表示方法 4
3.3 特征对齐与映射策略 5
4融合模型构建与优化 6
4.1 模型选择与构建原则 6
4.2 融合算法性能评估 7
4.3 模型优化与改进措施 7
5结论 8
参考文献 9
致谢 10
