Image Retrieval in the Wild
A CVPR 2020 tutorial on large-scale image retrieval in unconstrained environments, including methods and system considerations for vector-based image search relevant to vector database and ANN applications.
About this tool
Image Retrieval in the Wild
Type: Conference tutorial
Category: Curated resource lists / Tutorials
Event: CVPR 2020 Tutorial
Format: Slides + recorded videos (remote, via Zoom)
Overview
“Image Retrieval in the Wild” is a CVPR 2020 tutorial focused on building large-scale, practical content-based image retrieval systems for real-world applications. It emphasizes:
- Vector-based image search and approximate nearest neighbor (ANN) algorithms
- System design and deployment for billion-scale visual search
- Cross-modality person re-identification (e.g., low-res, infrared, sketch, text-to-image)
- A live implementation demo of an image search engine using a pre-trained deep model
All presentation slides and videos are accessible from the tutorial schedule.
Features
Core Topics
-
Content-based image retrieval foundations
- Role of image retrieval in interacting with large visual collections
- Limitations of standard benchmarks (e.g., Oxford buildings) for real-world scenarios
-
Approximate Nearest Neighbor (ANN) search
- Review of state-of-the-art ANN algorithms
- Billion-scale approximate nearest neighbor search
- Practical guidance on selecting ANN algorithms for specific tasks and constraints
-
Large-scale visual search systems
- Case study: Online C2C marketplace app (Mercari)
- Handling over one billion listings and ~15M monthly active users
- System design for scalability and high availability
- Deployment on Kubernetes for production visual search
-
Heterogeneous person re-identification
- Focus on inter-modality discrepancies as the main challenge
- Cross-modality scenarios covered:
- Low-resolution (LR)
- Infrared (IR)
- Sketch
- Text
- Organization and overview of available datasets per scenario
- Summary and comparison of representative algorithms and approaches
-
Live-coding demo: image search engine from scratch
- Building a web-based image search system in ~100 lines of Python
- Using a pre-trained deep model for feature extraction
- End-to-end walkthrough from embedding to search interface
Access & Materials
- Tutorial held remotely (Zoom) as part of CVPR 2020
- Slides and videos linked directly from the schedule:
- Opening & context
- Billion-scale ANN search
- Large-scale visual search in a C2C marketplace
- Heterogeneous person re-identification survey
- Live-coding demo of an image search engine
Schedule (High-Level)
- Opening & Billion-scale ANN Search – Yusuke Matsui
- Large-scale Visual Search in the Mercari C2C App – Takuma Yamaguchi
- Break
- Heterogeneous Person Re-identification Survey – Zheng Wang
- Live-coding: Image Search Engine from Scratch – Yusuke Matsui
Tags
- Tutorials
- Multimodal
- Vector search
Link
- Tutorial page: https://matsui528.github.io/cvpr2020_tutorial_retrieval/
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