Scaling Cross-Domain Content-Based Image Retrieval for E-commerce Snap and Search Application
This work addresses the problem of efficient image retrieval for e-commerce users, but it appears incremental as it builds on existing visual search and classification capabilities.
The paper tackles the challenge of large-scale cross-domain content-based image retrieval for e-commerce snap-and-search applications, presenting a cascade method that combines visual search and classification to handle data scale and cross-domain queries, resulting in improved ranking and latency performance.
In this industry talk at ECIR 2022, we illustrate how we approach the main challenges from large scale cross-domain content-based image retrieval using a cascade method and a combination of our visual search and classification capabilities. Specifically, we present a system that is able to handle the scale of the data for e-commerce usage and the cross-domain nature of the query and gallery image pools. We showcase the approach applied in real-world e-commerce snap and search use case and its impact on ranking and latency performance.