摘 要
随着多媒体数据的爆炸性增长，跨模态检索成为人工智能领域的研究热点之一，旨在通过深度学习技术实现异构数据间的高效语义关联。本研究聚焦于深度学习在跨模态检索中的关键技术，提出了一种融合多模态特征表示与统一语义空间映射的新方法。具体而言，通过构建基于注意力机制的深度神经网络模型，有效提取图像、文本等不同模态数据的高层次语义特征，并设计了优化的联合嵌入策略以减少模态间的信息鸿沟。实验结果表明，所提方法在多个公开数据集上显著提升了检索精度与效率，特别是在处理复杂场景和噪声数据时表现出更强的鲁棒性。本研究的主要贡献在于创新性地引入了跨模态对齐与增强机制，解决了传统方法中语义鸿沟及模态偏差的问题，为跨模态信息检索的实际应用提供了新思路和技术支持。关键词：跨模态检索; 深度学习; 语义鸿沟; 注意力机制; 联合嵌入策略
Abstract
With the explosive growth of multimedia data, cross-modal retrieval has become one of the research hotspots in the field of artificial intelligence, aiming to achieve efficient semantic associations between heterogeneous data through deep learning technologies. This study focuses on key techniques of deep learning in cross-modal retrieval and proposes a novel method that integrates multi-modal feature representation with unified semantic space mapping. Specifically, by constructing a deep neural network model based on attention mechanisms, high-level semantic features of different modalities such as images and texts are effectively extracted, and an optimized joint embedding strategy is designed to reduce the information gap between modalities. Experimental results demonstrate that the proposed method significantly improves retrieval accuracy and efficiency on multiple public datasets, particularly exhibiting stronger robustness when dealing with complex scenarios and noisy data. The main contribution of this study lies in innovatively introducing cross-modal alignment and enhancement mechanisms, which address the problems of semantic gaps and modality biases in traditional methods, providing new insights and technical support for the practical application of cross-modal information retrieval.Key words:Cross-Modal Retrieval; Deep Learning; Semantic Gap; Attention Mechanism; Joint Embedding Strategy
目 录
摘 要 I
Abstract II
引 言 1
第1章、跨模态检索基础与挑战 3
1.1、跨模态数据特性分析 3
1.2、深度学习技术概述 3
1.3、跨模态检索主要挑战 4
第2章、特征表示与对齐方法研究 5
2.1、多模态特征提取技术 5
2.2、深度嵌入空间构建方法 5
2.3、特征对齐优化策略 6
第3章、跨模态相似性度量技术 7
3.1、相似性度量模型设计 7
3.2、深度度量学习方法研究 7
3.3、度量性能评估与改进 8
第4章、跨模态检索系统应用与优化 9
4.1、实时检索算法设计 9
4.2、数据增强与噪声处理 9
4.3、系统性能优化策略 10
结 论 11
参考文献 12
