Weakly supervised object detection has attracted more and more attention as it only needs image-level annotations for training object detectors. A popular solution to this task is to train a multiple instance detection network (MIDN) which integrates multiple instance learning into a deep convolutional neural network. One major issue of the MIDN is that it is prone to be stuck at local discriminative regions. To address this local optimum issue, we propose a pyramidal MIDN (P-MIDN) comprised of a sequence of multiple MIDNs. In particular, one MIDN performs proposal removal for its subsequent MIDN to reduce the exposure of local discriminative proposal regions to the latter during training. In this manner, it allows our MIDNs to focus on proposals which cover objects more completely. Furthermore, we integrate the P-MIDN into an online instance classifier refinement (OICR) framework. Combined with the P-MIDN, a mask guided self-correction (MGSC) method is proposed to generate high-quality pseudo ground-truths for training the OICR. Experimental results on PASCAL VOC 2007, PASCAL VOC 2010, PASCAL VOC 2012, ILSVRC 2013 DET and MS-COCO benchmarks demonstrate that our approach achieves state-of-the-art performance.

中文翻译：

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具有面罩引导自校正的金字塔形多实例检测网络，用于弱监督对象检测

监督不足的对象检测吸引了越来越多的关注，因为它只需要图像级注释即可训练对象检测器。对此任务的一种流行解决方案是训练多实例检测网络（MIDN），该网络将多实例学习集成到深度卷积神经网络中。MIDN的一个主要问题是它很容易卡在本地区分区域。为了解决这一局部最优问题，我们提出了由多个MIDN序列组成的金字塔MIDN（P-MIDN）。特别是，一个MIDN对其后续的MIDN执行提案删除，以减少培训过程中本地区分性提案区域对后者的暴露。通过这种方式，我们的MIDN可以专注于更全面地涵盖对象的提案。此外，我们将P-MIDN集成到在线实例分类器优化（OICR）框架中。结合P-MIDN，提出了一种掩模导引自校正（MGSC）方法来生成高质量的伪地面真相，用于训练OICR。在PASCAL VOC 2007，PASCAL VOC 2010，PASCAL VOC 2012，ILSVRC 2013 DET和MS-COCO基准测试中的实验结果表明，我们的方法达到了最先进的性能。