Weakly Supervised Object Detection (WSOD), using only image-level annotations to train object detectors, is of growing importance in object recognition. In this paper, we propose a novel deep network for WSOD. Unlike previous networks that transfer the object detection problem to an image classification problem using Multiple Instance Learning (MIL), our strategy generates proposal clusters to learn refined instance classifiers by an iterative process. The proposals in the same cluster are spatially adjacent and associated with the same object. This prevents the network from concentrating too much on parts of objects instead of whole objects. We first show that instances can be assigned object or background labels directly based on proposal clusters for instance classifier refinement, and then show that treating each cluster as a small new bag yields fewer ambiguities than the directly assigning label method. The iterative instance classifier refinement is implemented online using multiple streams in convolutional neural networks, where the first is an MIL network and the others are for instance classifier refinement supervised by the preceding one. Experiments are conducted on the PASCAL VOC, ImageNet detection, and MS-COCO benchmarks for WSOD. Results show that our method outperforms the previous state of the art significantly.

中文翻译：

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PCL：用于弱监督对象检测的提案聚类学习。

仅使用图像级注释来训练对象检测器的弱监督对象检测（WSOD）在对象识别中变得越来越重要。在本文中，我们为WSOD提出了一种新颖的深度网络。与以前的使用多实例学习（MIL）将对象检测问题转换为图像分类问题的网络不同，我们的策略生成提案集群，以通过迭代过程学习经过精炼的实例分类器。同一集群中的投标在空间上相邻并且与同一对象相关联。这样可以防止网络将注意力集中在对象的一部分而不是整个对象上。我们首先展示了可以直接基于提案集群为实例分配对象或背景标签，以完善实例分类器，然后表明，将每个类视为一个小的新袋子，与直接分配标签方法相比，产生的歧义更少。迭代实例分类器细化是使用卷积神经网络中的多个流在线实现的，其中第一个是MIL网络，而其他实例是由前一个监督的分类器细化。实验针对WSOD进行了PASCAL VOC，ImageNet检测和MS-COCO基准测试。结果表明，我们的方法明显优于现有技术。其中第一个是MIL网络，其他则是由前一个监督的分类器细化。实验针对WSOD进行了PASCAL VOC，ImageNet检测和MS-COCO基准测试。结果表明，我们的方法明显优于现有技术。其中第一个是MIL网络，其他则是由前一个监督的分类器细化。实验针对WSOD进行了PASCAL VOC，ImageNet检测和MS-COCO基准测试。结果表明，我们的方法明显优于现有技术。