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

arXiv 2204.11593 ↗First posted 13 Apr 2022cs.IR cs.CVcited by 0

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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.

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.

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3 of 5 references.

References · 3

  1. 2021Visual Search at Alibabacited by 27
  2. 2017Deep Learning based Large Scale Visual Recommendation and Search for E-Commercecited by 16
  3. 2020SIR: Similar Image Retrieval for Product Search in E-Commercecited by 2

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