IEEE Trans Cybern. 2017 May;47(5):1313-1324. doi: 10.1109/TCYB.2017.2647965. Epub 2017 Jan 23.
In this paper, we aim at irregular-shape object localization under weak supervision. With over-segmentation, this task can be transformed into multiple-instance context. However, most multiple-instance learning methods only emphasize single most positive instance in a positive bag to optimize bag-level classification, and leads to imprecise or incomplete localization. To address this issue, we propose a scheme for instance annotation, where all of the positive instances are detected by labeling each instance in each positive bag. Inspired by the successful application of bag-of-words (BoW) to feature representation, we leverage it at instance-level to model the distributions of the positive class and negative class, and then incorporate the BoW learning and instance labeling in a single optimization formulation. We also demonstrate that the scheme is well suited to weakly supervised object localization of irregular-shape. Experimental results validate the effectiveness both for the problem of generic instance annotation and for the application of weakly supervised object localization compared to some existing methods.
在本文中，我们旨在进行弱监督的不规则形状物体定位。通过过分割，这个任务可以转化为多实例上下文。然而，大多数多实例学习方法仅强调正例袋中单个最正例来优化袋级分类，导致定位不准确或不完整。为了解决这个问题，我们提出了一种实例标注方案，通过对每个正例袋中的每个实例进行标注，来检测所有正例。受词袋（BoW）在特征表示中的成功应用的启发，我们在实例级别上利用它来对正类和负类的分布进行建模，然后将 BoW 学习和实例标注整合到单个优化公式中。我们还证明了该方案非常适合弱监督的不规则形状物体定位。实验结果验证了该方案在通用实例标注问题上以及在弱监督物体定位应用上的有效性，与一些现有方法相比具有优势。
