Due to the shortcomings of the weakly supervised and fully supervised object detection (i.e., unsatisfactory performance and expensive annotations, respectively), leveraging partially labeled images in a cost-effective way to train an object detector has attracted much attention. In this paper, we formulate this challenging task as a missing bounding-boxes’ object detection problem. Specifically, we develop a pseudo ground truth mining procedure to automatically find the missing bounding boxes for the unlabeled instances, called pseudo ground truths here, in the training data, and then combine the mined pseudo ground truths and the labeled annotations to train a fully supervised object detector. Furthermore, we propose an incremental learning framework to gradually incorporate the results of the trained fully supervised detector to improve the performance of the missing bounding-boxes’ object detection. More importantly, we find an effective way to label the massive images with limited labors and funds, which is crucial when building a large-scale weakly/webly labeled dataset for object detection. The extensive experiments on the PASCAL VOC and COCO benchmarks demonstrate that our proposed method can narrow the gap between the fully supervised and weakly supervised object detectors, and outperform the previous state-of-the-art weakly supervised detectors by a large margin (more than 3% mAP absolutely) when the missing rate equals 0.9. Moreover, our proposed method with 30% missing bounding-box annotations can achieve comparable performance to some fully supervised detectors.

中文翻译：

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超越弱监督：用于缺失边界框对象检测的伪地面真相挖掘

由于弱监督和全监督对象检测的缺点（即分别表现不令人满意和昂贵的注释），以具有成本效益的方式利用部分标记的图像来训练对象检测器已经引起了很多关注。在本文中，我们将这个具有挑战性的任务表述为缺少边界框的对象检测问题。具体来说，我们开发了一个伪地面实况挖掘程序，以在训练数据中自动找到未标记实例的缺失边界框，这里称为伪地面实况，然后将挖掘的伪地面实况和标记的注释结合起来训练一个完全监督的物体检测器。此外，我们提出了一个增量学习框架，以逐渐结合训练有素的全监督检测器的结果，以提高丢失边界框的目标检测性能。更重要的是，我们找到了一种用有限的人力和资金来标记海量图像的有效方法，这在构建大规模弱/网络标记数据集用于对象检测时至关重要。在 PASCAL VOC 和 COCO 基准测试上的大量实验表明，我们提出的方法可以缩小全监督和弱监督目标检测器之间的差距，并大大优于以前最先进的弱监督检测器（超过3% mAP 绝对）当缺失率等于 0.9 时。而且，