Advanced Video Search: Leveraging Twelve Labs and Milvus for Semantic Retrieval

In August 2024, James Le from Twelve Labs presented an insightful talk on advanced video search for semantic retrieval at the Unstructured Data Meetup in San Francisco. He discussed how cutting-edge multimodal models like those developed by Twelve Labs can help machines understand videos as intuitively as humans do, and how integrating these models with efficient vector databases such as Milvus by Zilliz can create exciting applications for semantic retrieval. Video understanding involves analyzing, interpreting, and extracting meaningful information from videos using computer vision and deep learning techniques. Twelve Labs' latest state-of-the-art video foundation model, Marengo 2.6, is capable of performing 'any-to-any' search tasks, significantly enhancing video search efficiency and allowing robust interactions across different modalities. By harnessing the power of advanced multimodal embeddings and integrating it with Milvus, developers can unlock new possibilities in video content analysis by creating applications such as search engines, recommendation systems, and content-based video retrieval.