Overview of Multimodal Generative Models in Natural Language Processing and Computer Vision

Multimodal generative models have become essential in the deep learning renaissance, as they provide unparalleled flexibility over a diverse context of applications within Natural Language Processing (NLP) and Computer Vision (CV). In this paper, we systematically review the basic concepts and technical improvements in multimodal generative models by discussing their applications across different modalities such as text, images, audio,and video. These models though augment the strength of AI to comprehend and perform complicated tasks by coalescing data from various modalities. In this paper, we investigate how these principles apply to many of the existing mainstream models (including CLIP, DALL·E, Flamingo), and consider their applications in VQA,text-to-image-synthesis; medical image analysis; edutainment content creation & user research developments. This paper also examines the existing difficulties of such technologies including paucity in data availability, modality fusion effectiveness and constraints on computational resources while suggesting pathways for future research. The paper goes on to state privacy parallels between multi-modal generative models (GGMs) calls for a model of safety over responsibility when it comes to technological innovation.