Object detection requires plentiful data annotated with bounding boxes for model training. However, in many applications, it is difficult or even impossible to acquire a large set of labeled examples for the target task due to the privacy concern or lack of reliable annotators. On the other hand, due to the high-quality image search engines, such as Flickr and Google , it is relatively easy to obtain resource-rich unlabeled datasets, whose categories are a superset of those of target data. In this article, to improve the target model with cost-effective supervision from source data, we propose a partial transfer learning approach QBox to actively query labels for bounding boxes of source images. Specifically, we design two criteria, i.e., informativeness and transferability, to measure the potential utility of a bounding box for improving the target model. Based on these criteria, QBox actively queries the labels of the most useful boxes from the source domain and, thus, requires fewer training examples to save the labeling cost. Furthermore, the proposed query strategy allows annotators to simply labeling a specific region, instead of the whole image, and, thus, significantly reduces the labeling difficulty. Extensive experiments are performed on various partial transfer benchmarks and a real COVID-19 detection task. The results validate that QBox improves the detection accuracy with lower labeling cost compared to state-of-the-art query strategies for object detection.

中文翻译：

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QBox：通过主动查询进行目标检测的部分迁移学习

对象检测需要大量带有边界框注释的数据来进行模型训练。然而，在许多应用中，由于隐私问题或缺乏可靠的注释器，很难甚至不可能获取目标任务的大量标记示例。另一方面，由于高质量的图像搜索引擎，例如弗利克和谷歌 ，相对容易获得资源丰富的未标记数据集，其类别是目标数据类别的超集。在本文中，为了通过源数据的成本效益监督来改进目标模型，我们提出了一种部分迁移学习方法Q盒子主动查询源图像边界框的标签。具体来说，我们设计了两个标准，即信息性和可转移性，来衡量边界框对于改进目标模型的潜在效用。基于这些标准，Q盒子主动从源域查询最有用的框的标签，因此需要更少的训练示例来节省标签成本。此外，所提出的查询策略允许注释者简单地标记特定区域，而不是整个图像，从而显着降低标记难度。在各种部分传输基准和真实的 COVID-19 检测任务上进行了大量的实验。结果验证Q盒子与最先进的对象检测查询策略相比，以更低的标记成本提高了检测精度。