In recent years, weakly supervised object detection (WSOD) has attracted much attention due to its low labeling cost. The success of recent WSOD models is often ascribed to the two-stage multi-class classification (MCC) task, i.e., multiple instance learning and online classification refinement. Despite achieving non-trivial progresses, these methods overlook potential classification ambiguities between these two MCC tasks and fail to leverage their unique strengths. In this work, we introduce a novel WSOD framework to ameliorate these two issues. For one thing, we propose a self-classification enhancement module that integrates intra-class binary classification (ICBC) to bridge the gap between the two distinct MCC tasks. The ICBC task enhances the network's discrimination between positive and mis-located samples in a class-wise manner and forges a mutually reinforcing relationship with the MCC task. For another, we propose a self-classification correction algorithm during inference, which combines the results of both MCC tasks to effectively reduce the mis-classified predictions. Extensive experiments on the prevalent VOC 2007 & 2012 datasets demonstrate the superior performance of our framework.

翻译：近年来，弱监督目标检测（WSOD）因其标注成本低而备受关注。当前WSOD模型的成功通常归功于两阶段多类别分类（MCC）任务，即多示例学习与在线分类优化。尽管取得了显著进展，这些方法忽视了这两个MCC任务之间潜在分类模糊性，且未能充分利用其各自优势。本文提出一种新颖的WSOD框架以改善这两个问题。一方面，我们设计了自分类增强模块，通过引入类内二分类（ICBC）任务来弥合两个独立MCC任务之间的鸿沟。ICBC任务以逐类方式增强网络对正样本与错位样本的判别能力，并与MCC任务形成相互促进的关系。另一方面，我们在推理阶段提出自分类校正算法，通过融合两个MCC任务的结果有效减少误分类预测。在广泛使用的VOC 2007和2012数据集上的大量实验验证了我们框架的优越性能。