# Scaling Cross-Domain Content-Based Image Retrieval for E-commerce Snap and Search Application

arXiv: https://arxiv.org/abs/2204.11593 · HTML: https://mlsanity.com/paper/2204.11593/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 5 references that are in the map.
- First posted on arXiv 13 Apr 2022.

## Builds on (most cited first, 3 of 3)

- [Visual Search at Alibaba](https://mlsanity.com/paper/2102.04674/) (2021)
- [Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce](https://mlsanity.com/paper/1703.02344/) (2017)
- [SIR: Similar Image Retrieval for Product Search in E-Commerce](https://mlsanity.com/paper/2009.13836/) (2020)

## Abstract (arXiv)

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.