论文标题
重新考虑到弱监督物体本地化的路线
Rethinking the Route Towards Weakly Supervised Object Localization
论文作者
论文摘要
弱监督的对象本地化（WSOL）旨在仅具有图像级标签的对象。以前的方法通常尝试利用特征图和分类权重，以间接使用图像级别注释来定位对象。在本文中，我们证明了弱监督的对象本地化应分为两个部分：类不足的对象定位和对象分类。对于类别不稳定的对象的本地化，我们应该使用类敏锐的方法来生成嘈杂的伪注释，然后在没有类标签的情况下对它们进行边界框回归。我们将伪监督对象定位（PSOL）方法作为解决WSOL的新方法。我们的PSOL模型在不同数据集中具有良好的可传递性，而无需微调。使用产生的伪边界框，我们在ImageNet上实现了58.00％的定位精度，而CUB-200上的定位精度为74.97％，Cub-200，这些精度比以前的型号具有很大的优势。
Weakly supervised object localization (WSOL) aims to localize objects with only image-level labels. Previous methods often try to utilize feature maps and classification weights to localize objects using image level annotations indirectly. In this paper, we demonstrate that weakly supervised object localization should be divided into two parts: class-agnostic object localization and object classification. For class-agnostic object localization, we should use class-agnostic methods to generate noisy pseudo annotations and then perform bounding box regression on them without class labels. We propose the pseudo supervised object localization (PSOL) method as a new way to solve WSOL. Our PSOL models have good transferability across different datasets without fine-tuning. With generated pseudo bounding boxes, we achieve 58.00% localization accuracy on ImageNet and 74.97% localization accuracy on CUB-200, which have a large edge over previous models.
