Web-Scale Generic Object Detection at Microsoft Bing

• URL: http://arxiv.org/abs/2107.01814v1
• Date: Mon, 5 Jul 2021 06:46:09 GMT
• Title: Web-Scale Generic Object Detection at Microsoft Bing
• Authors: Stephen Xi Chen, Saurajit Mukherjee, Unmesh Phadke, Tingting Wang, Junwon Park, Ravi Theja Yada
• Abstract summary: We present Generic Object Detection (GenOD), one of the largest object detection systems deployed to a web-scale general visual search engine. It can detect over 900 categories for all Microsoft Bing Visual Search queries in near real-time.
• Score: 4.350999432264304
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In this paper, we present Generic Object Detection (GenOD), one of the largest object detection systems deployed to a web-scale general visual search engine that can detect over 900 categories for all Microsoft Bing Visual Search queries in near real-time. It acts as a fundamental visual query understanding service that provides object-centric information and shows gains in multiple production scenarios, improving upon domain-specific models. We discuss the challenges of collecting data, training, deploying and updating such a large-scale object detection model with multiple dependencies. We discuss a data collection pipeline that reduces per-bounding box labeling cost by 81.5% and latency by 61.2% while improving on annotation quality. We show that GenOD can improve weighted average precision by over 20% compared to multiple domain-specific models. We also improve the model update agility by nearly 2 times with the proposed disjoint detector training compared to joint fine-tuning. Finally we demonstrate how GenOD benefits visual search applications by significantly improving object-level search relevance by 54.9% and user engagement by 59.9%.

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