Design and Implementation of AI-Based Multi-Modal Video Content Processing

Da Xu

doi:10.71222/1qgkce65

Authors

Da Xu Video Infra, Meta, Menlo Park, CA, 94025, USA Author

DOI:

https://doi.org/10.71222/1qgkce65

Keywords:

multimodal fusion, video comprehension, deep learning, artificial intelligence framework

Abstract

Multimodal information interaction is gradually becoming an important direction for intelligent video content understanding. In videos, image, voice, and text collaboratively form a semantic system, which goes beyond the capabilities of single-modal information analysis. Efficient extraction and fusion of multi-source information has become a key challenge in artificial intelligence applications for various tasks such as classification, summarization, and content monitoring. Current research tends to focus on single-task or single-modal processing, and there is still a lack of universal fusion frameworks. In this context, establishing a universal, highly integrated, and well scalable AI multimodal video processing framework not only conforms to the trend of technological development, but also provides reliable technical support for intelligent communication, social services, educational innovation, and more.

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