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：弱监督目标检测的提案集群学习


弱监督对象检测（WSOD）仅使用图像级注释来训练对象检测器，在对象识别中变得越来越重要。在本文中，我们提出了一种新颖的 WSOD 深度网络。与之前使用多实例学习（MIL）将对象检测问题转移到图像分类问题的网络不同，我们的策略生成提案集群，通过迭代过程学习细化的实例分类器。同一簇中的提案在空间上相邻并且与同一对象相关联。这可以防止网络过度关注对象的一部分而不是整个对象。我们首先证明可以根据建议簇直接为实例分配对象或背景标签以进行实例分类器细化，然后表明将每个簇视为一个小的新包比直接分配标签方法产生的歧义更少。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，其他网络是由前一个网络监督的实例分类器细化。在 PASCAL VOC、ImageNet 检测和 WSOD 的 MS-COCO 基准上进行了实验。结果表明，我们的方法显着优于以前的最先进技术。