Cross Modal Data Understanding Based on Visual Language Model

Abstract

With the widespread adoption of multimodal data in artificial intelligence, visual language models that integrate cross-modal information have emerged as a prominent research hotspot. These models are capable of jointly processing and interpreting both image and text information, enabling a range of complex multimodal tasks such as image captioning, visual question answering, cross-modal retrieval, and content summarization. By effectively bridging visual and linguistic modalities, visual language models facilitate more intelligent and context-aware systems that enhance human-computer interaction and decision-making processes. This article provides a comprehensive introduction to visual language models, covering their definitions, fundamental operations, and core methodologies. Key techniques analyzed include visual-language joint embedding, attention mechanisms, graph convolutional networks, and generative adversarial networks, all of which play critical roles in enabling accurate cross-modal understanding and representation. The paper further examines the practical applications of these models in multiple domains, including product labeling and categorization on e-commerce platforms, intelligent home control systems, social media sentiment analysis, and personalized recommendation systems. Through this research, it is evident that the integration of cross-modal data understanding technologies can substantially improve the operational performance and intelligence of systems in complex, real-world scenarios. The ability to accurately interpret and fuse visual and textual information not only enhances system efficiency but also expands the potential for innovative applications. These findings underscore the promising application prospects of visual language models, highlighting their significance for future developments in AI-driven multimodal understanding and intelligent system design.