Bridging Multimedia Modalities: Enhanced Multimodal AI Understanding and Intelligent Agents

Abstract

With the increasing availability of multimodal data, especially in the sports and medical domains, there is growing interest in developing Artificial Intelligence (AI) models capable of comprehending the world in a more holistic manner. Nevertheless, various challenges exist in multimodal understanding, including the integration of multiple modalities and the resolution of semantic gaps between them. The proposed research aims to leverage multiple input modalities for the multimodal understanding of AI models, enhancing their reasoning, generation, and intelligent behavior. The research objectives focus on developing novel methods for multimodal AI, integrating them into conversational agents with optimizations for domain-specific requirements. The research methodology encompasses literature review, data curation, model development and implementation, evaluation and performance analysis, domain-specific applications, and documentation and reporting. Ethical considerations will be thoroughly addressed, and a comprehensive research plan is outlined to provide guidance. The research contributes to the field of multimodal AI understanding and the advancement of sophisticated AI systems by experimenting with multimodal data to enhance the performance of state-of-the-art neural networks.