Learning object detectors from weak image annotations is an important yet challenging problem. Many weakly supervised approaches formulate the task as a multiple instance learning problem, where each image is represented as a bag of instances. For predicting the score for each object that occurs in an image, existing MIL based approaches tend to select the instance that responds more strongly to a specific class, which, however, overlooks the contextual information. Besides, objects often exhibit dramatic variations such as scaling and transformations, which makes them hard to detect. In this paper, we propose the weakly supervised group mask network (WSGMN), which mainly has two distinctive properties: (i) it exploits the relations among regions to generate community instances, which contain context information and are robust to object variations. (ii) It generates a mask for each label group, and utilizes these masks to dynamically select the feature information of the most useful community instances for recognizing specific objects. Extensive experiments on several benchmark datasets demonstrate the effectiveness of WSGMN on the tasks of weakly supervised object detection.

中文翻译：

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用于目标检测的弱监督组掩码网络

从弱图像注释中学习目标检测器是一个重要但具有挑战性的问题。许多弱监督方法将任务表述为多实例学习问题，其中每个图像都表示为一个实例包。为了预测图像中出现的每个对象的分数，现有的基于 MIL 的方法倾向于选择对特定类响应更强烈的实例，然而，这忽略了上下文信息。此外，对象经常表现出巨大的变化，例如缩放和变换，这使得它们难以检测。在本文中，我们提出了弱监督组掩码网络（WSGMN），它主要具有两个独特的特性：（i）它利用区域之间的关系来生成社区实例，其中包含上下文信息并且对对象变化具有鲁棒性。(ii) 它为每个标签组生成一个掩码，并利用这些掩码动态选择最有用的社区实例的特征信息来识别特定对象。在几个基准数据集上的大量实验证明了 WSGMN 在弱监督对象检测任务上的有效性。