Webinar
Using ColPali and Binary Quantization for Efficient Multimodal Retrieval
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In this webinar, we’ll explore the technical details of ColPali, an advanced multimodal retrieval approach that uses Vision Language Models (VLMs) to handle visually complex documents.
We'll explore how ColPali uses multivectors to represent document images, capturing both local and global context.
Key topics:
- Multivectors: Representing documents as multiple embeddings to capture both local and global context, enhancing search accuracy.
- Late Interaction: Performing token-level comparisons between queries and document patches for precise relevance scoring.
- MaxSim Pooling: Aggregating the highest similarity scores from these comparisons to identify the most relevant document sections.
- Binary Quantization: Compressing vector data to optimize memory usage and accelerate search with minimal accuracy loss.
Qdrant Speakers
Atita Arora, Solutions Architect
Sabrina Aquino, Developer Relations
Jenny Sukhodolskaya, Developer Advocate
Join us to learn how these techniques can be applied for efficient multimodal retrieval for complex, visually-rich documents.
If you can't attend the live session, don't worry. Fill out the form and we will email you the recording!