The Integration of NLP and Computer Vision: Advanced Frameworks for Multi-Modal Content Understanding

Abstract

Content understanding systems leveraging Natural Language Processing (NLP) and Computer Vision (CV) have revolutionized how machines interpret and analyze multimodal information across diverse applications. This article explores the technologies driving advancements in content analysis, from text embedding techniques such as BERT to image and video representation methods including CNN-based approaches and Vision Transformers. It examines the challenges of processing diverse languages and regional contexts in a multimodal framework, alongside methodologies for collecting and preparing high-quality training data. The discussion covers various fusion architectures for integrating information across modalities, training approaches for multimodal classifiers, and evaluation frameworks to ensure model effectiveness. As these technologies continue to evolve, the integration of NLP and CV promises to unlock new possibilities for intelligent content understanding in an increasingly complex digital landscape.