Description

Multi-modality elevates the capabilities of neural network models to a whole new level. By leveraging contrastive learning and specialized model architectures, we can create a unified vector space for images and text, enhancing multimodal representations. 

This talk will share insights into building image-text search and Composite Image Retrieval (CIR) using multimodal embeddings and the Milvus open source vector database, demonstrating how multi-modality unlocks new use cases in Retrieval-Augmented Generation (RAG).


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Instructor's Bio

Stefan Webb PhD

Developer Advocate at Zilliz

He advocates for the open-source vector database, Milvus. Prior to this, he spent three years in industry as an Applied ML Researcher at Twitter and Meta, collaborating with product teams to tackle their most complex challenges. Stefan holds a PhD from the University of Oxford and has published papers at leading machine learning conferences such as NeurIPS, ICLR, and ICML. He is passionate about Generative AI and is eager to leverage his deep technical expertise to contribute to the open-source community."

Webinar

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