Neural Search in Action
A CVPR 2023 tutorial that demonstrates neural search systems in practice, including vector representations, similarity search, and scalable retrieval architectures closely related to vector databases.
About this tool
Neural Search in Action
Type: Tutorial / Conference Session
Event: CVPR 2023 Tutorial
Category: Curated Resource Lists
Tags: tutorials, neural-search, vector-search
Overview
“Neural Search in Action” is a CVPR 2023 tutorial focused on practical neural search systems. It covers how to design and implement search over deep embeddings for large-scale multimodal collections, with emphasis on:
- Million-scale and billion-scale similarity search
- Vector search engines and approximate nearest neighbor (ANN) methods
- Using multimodal encoders (e.g., CLIP) to turn tasks into embedding-and-search problems
- How neural search supports applications like feeding context into large language models (LLMs)
- Designing query languages for real-world neural search applications
Time and Venue
- Event: CVPR 2023 (Computer Vision and Pattern Recognition)
- Format: In-person tutorial session (single-afternoon block)
Schedule & Sessions
-
Opening
- Time: 13:30–13:40
- Presenter: Yusuke Matsui
- Materials: Slides
-
Theory and Applications of Graph-based Search
- Time: 13:40–14:30
- Presenter: Yusuke Matsui
- Focus: Theoretical foundations and practical uses of graph-based approximate nearest neighbor search for neural search systems
- Materials: Slides
-
A Survey on Billion-Scale Approximate Nearest Neighbors
- Time: 14:30–15:20
- Presenter: Martin Aumüller
- Focus: Methods, algorithms, and system design for billion-scale ANN search, including performance and scalability considerations
- Materials: Slides (PDF)
-
Break
- Time: 15:20–15:30
-
Query Language for Neural Search in Practical Applications
- Time: 15:30–16:20
- Presenter: Han Xiao
- Focus: Designing and using query languages tailored to neural search over multimodal data; representing, transforming, and searching embeddings in real-world systems
- Materials: Slides (PDF)
Features
- Focus on deep embedding-based search across large multimodal datasets
- Discussion of foundation models, prompt engineering, and multimodal encoders (e.g., CLIP) as enablers of neural search
- Practical treatment of million-scale and billion-scale similarity search problems
- Coverage of graph-based search algorithms and their applications in vector search engines
- Survey of billion-scale approximate nearest neighbor (ANN) techniques
- Exploration of how vector search engines support real-world pipelines, including providing context for LLMs
- Introduction to query languages for neural search, focusing on representing, composing, and executing complex search queries over embeddings
- Slide decks available for all main sessions (opening and three technical talks)
Organizers / Instructors
- Yusuke Matsui – The University of Tokyo
- Martin Aumüller – IT University of Copenhagen
- Han Xiao – Jina AI
Media
- Main image/teaser: Neural Search in Action teaser graphic
Pricing
- Not applicable (research tutorial; no pricing information provided).
Source
- Official page: https://matsui528.github.io/cvpr2023_tutorial_neural_search/
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