  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.
