Image-text matching is a key multimodal task that aims to model the semantic
association between images and text as a matching relationship. With the advent
of the multimedia information age, image, and text data show explosive growth,
and how to accurately realize the efficient and accurate semantic
correspondence between them has become the core issue of common concern in
academia and industry. In this study, we delve into the limitations of current
multimodal deep learning models in processing image-text pairing tasks.
Therefore, we innovatively design an advanced multimodal deep learning
architecture, which combines the high-level abstract representation ability of
deep neural networks for visual information with the advantages of natural
language processing models for text semantic understanding. By introducing a
novel cross-modal attention mechanism and hierarchical feature fusion strategy,
the model achieves deep fusion and two-way interaction between image and text
feature space. In addition, we also optimize the training objectives and loss
functions to ensure that the model can better map the potential association
structure between images and text during the learning process. Experiments show
that compared with existing image-text matching models, the optimized new model
has significantly improved performance on a series of benchmark data sets. In
addition, the new model also shows excellent generalization and robustness on
large and diverse open scenario datasets and can maintain high matching
performance even in the face of previously unseen complex situations.

利用创新的跨模态关注机制和分层特征融合策略，将深度神经网络的高级抽象表示能力与自然语言处理模型的文本语义理解优势相结合，以实现图像和文本之间的深度融合和双向交互的多模态深度学习架构。实验证明，与现有的图像和文本匹配模型相比，优化后的新模型在一系列基准数据集上表现出显著的性能提升，并且在面对以前未见过的复杂情况时也能保持较高的匹配性能。