Exploring Multimodal Generative AI: A Comprehensive Review of Image, Text, and Audio Integration

Balkrishna Rasiklal, Yadav (2024) Exploring Multimodal Generative AI: A Comprehensive Review of Image, Text, and Audio Integration. Innovative: International Multi-disciplinary Journal of Applied Technology, 2 (10). pp. 124-133. ISSN 2995-486X

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Abstract

Multimodal generative artificial intelligence (MGI) is a field that combines text, image, and audio data to produce more comprehensive and richer outputs. It has applications in various industries such as human-computer interaction, entertainment, and healthcare. However, MGI must overcome challenges such as high computational costs, alignment of data types, and ensuring output consistency and coherence. The field's foundations are found in machine learning, audio processing, computer vision, and natural language processing (NLP). Diffusion models, Transformers, VAEs, and GANs are important study fields in MGI. Models like DeViSE, multimodal fusion methods, and shared multimodal embeddings like CLIP and ALIGN are essential for cross-modal learning and representation. Text-to-image models have been created to produce high-resolution pictures from textual descriptions, while models like the Tacotron, VALL-E, and Jukebox have investigated the relationship between text and audio. Applications include virtual assistants, human-computer interaction, and creative material creation. The study aims to investigate the current level of machine learning (MGI) state-of-the-art, examine key image, text, and audio integration methodologies and models, and identify obstacles and possibilities in this rapidly developing area. It also addresses technological challenges such as data alignment, high computational costs, and model consistency, as well as ethical issues like bias, fairness, and privacy. Multimodal generative AI (MGIA) combines the advantages of GANs and VAEs to produce superior quality outputs. Examples of applications include multimodal translation, cross-modal synthesis, VAE-GAN architecture, autoregressive models, self-supervised and contrastive learning models, and truly multimodal models. However, MGIA poses several challenges, such as exorbitant computing costs, dataset biases across modalities, data and privacy concerns, cross-modal alignment, and ethical and social consequences. To address these challenges, more modalities should be integrated, cross-modal learning strategies should be improved, semi-supervised and unsupervised approaches should be investigated for multimodal tasks, scalable and effective training strategies should be developed, and ethical AI frameworks should be created. In conclusion, multimodal AI has the potential to solve ethical issues while reshaping various sectors, improving content creation, and enhancing human-computer connections. More modalities should be integrated, cross-modal learning strategies should be strengthened, semi-supervised and unsupervised approaches should be investigated for multimodal tasks, ethical AI frameworks should be created, and prejudice and false information should be addressed in AI-generated material.

Item Type:	Article
Subjects:	Q Science > Q Science (General)
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	15 Mar 2025 06:18
Last Modified:	15 Mar 2025 06:18
URI:	http://eprints.umsida.ac.id/id/eprint/15830

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