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%.

本文介绍Generic Object Detection（GenOD）系统，是一个可以在微软Bing Visual Search的所有查询中实时检测超过900个类别的最大的物体检测系统之一，可提供以物体为中心的信息，并在多种生产场景中表现出色。作者讨论了收集数据、训练、部署和更新此类大规模物体检测模型所面临的挑战。我们展示了GenOD相对于多个领域特定模型可以提高20%以上的加权平均精度，并通过所提出的不相交探测器训练来将模型更新敏捷度提高近2倍。最后，我们展示了GenOD如何通过显著提高搜索相关性和用户参与度来改善视觉搜索应用。

Microsoft Bing 上的大规模通用目标检测