Web-Scale Responsive Visual Search at Bing
Houdong Hu, Yan Wang, Linjun Yang, Pavel Komlev, Li Huang, Xi Chen,, Jiapei Huang, Ye Wu, Meenaz Merchant, Arun Sacheti

TL;DR
This paper presents a scalable, fast visual search system for Bing that handles billions of images with deep learning features, achieving sub-200ms response times through a cascaded learning-to-rank framework on a distributed platform.
Contribution
The paper introduces a novel large-scale visual search system with a cascaded deep learning ranking framework deployed at web scale in Bing.
Findings
Supports tens of billions of images with fast response times
Employs a cascaded learning-to-rank framework for relevance
Demonstrates effectiveness through experiments
Abstract
In this paper, we introduce a web-scale general visual search system deployed in Microsoft Bing. The system accommodates tens of billions of images in the index, with thousands of features for each image, and can respond in less than 200 ms. In order to overcome the challenges in relevance, latency, and scalability in such large scale of data, we employ a cascaded learning-to-rank framework based on various latest deep learning visual features, and deploy in a distributed heterogeneous computing platform. Quantitative and qualitative experiments show that our system is able to support various applications on Bing website and apps.
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