Research on Cross-Modal Interaction Techniques between Natural Language Processing and Computer Vision

Abstract

With the penetration of artificial intelligence technologies into multi-scenario applications, single-modality technologies are no longer able to meet the demands of complex tasks. While NLP can parse text semantics, it lacks the intuitiveness of visual information; while CV can process image pixel features, it struggles to understand the abstract instructions conveyed by text. Against this backdrop, cross-modal interaction techniques between NLP and CV have become a key approach to overcoming these bottlenecks. This paper examines the core logic of cross-modal interaction, first clarifying the essential characteristics and interaction goals of modal heterogeneity. It then analyzes key techniques for extracting modal representations, and then explores implementation paths for cross-modal alignment (semantic matching and spatial mapping) and fusion (at the feature, semantic, and decision levels). The effectiveness of these techniques is validated using real-world application scenarios such as visual question answering (VQA) and image captioning. Finally, the paper summarizes current challenges, such as modality imbalance and insufficient robustness, and proposes optimization strategies that combine knowledge graphs with lightweight models. Research indicates that efficient cross-modal interaction requires "precise alignment" as its foundation and "deep fusion" as its core. The implementation of these technologies can significantly enhance the perception and decision-making capabilities of AI systems in complex environments, providing technical support for fields such as intelligent human-computer interaction and autonomous driving.