Advancements in Large-Scale Transformer Architectures for Multimodal AI Integration
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V1I2P101Keywords:
Transformer Architecture, Self-Attention, Multimodal AI, Vision Transformer, Cross-Attention, Pretraining, Fine-Tuning, Deep Learning, Neural Networks, Data IntegrationAbstract
Large-scale transformer architectures have revolutionized the field of artificial intelligence (AI), particularly in natural language processing (NLP) and computer vision (CV). The recent advancements in these architectures have enabled the integration of multimodal data, leading to more robust and versatile AI systems. This paper provides a comprehensive overview of the latest developments in large-scale transformer architectures, focusing on their application in multimodal AI integration. We discuss the theoretical foundations, key architectural innovations, and practical applications. Additionally, we present a detailed analysis of the challenges and future directions in this rapidly evolving field. The paper includes empirical evaluations, algorithmic descriptions, and comparative studies to highlight the effectiveness of these models.
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