Abstract
At present, keyword-based video retrieval is gradually difficult to adapt to the needs of the rapid development of the Internet due to its strong subjectivity and huge workload. As a result, multi-modal video retrieval based on deep learning has appeared. This retrieval method can conduct video retrieval through multiple methods such as text, image, and video, which fully meets the different retrieval needs of different users, and significantly improves the accuracy and effectiveness of video retrieval. Based on this, this article discusses in detail the design of a multi-modal video retrieval system based on deep learning, analyzes and designs each functional module of the system to provide reference for future related work.
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