Weakly supervised object localization (WSOL) methods utilize the internal feature responses of a classifier trained only on image-level labels. Classifiers tend to focus on the most discriminative part of the target object, instead of considering its full extent. Adversarial erasing (AE) techniques have been proposed to ameliorate this problem. These techniques erase the most discriminative part during training, thereby encouraging the classifiers to learn the less discriminative parts of the object. Despite the success of AE-based methods, we have observed that the hyperparameters fail to generalize across model architectures and datasets. Therefore, new sets of hyperparameters must be determined for each architecture and dataset. The selection of hyperparameters frequently requires strong supervision (e.g., pixel-level annotations or human inspection). Because WSOL is premised on the assumption that such strong supervision is absent, the applicability of AE-based methods is limited. In this paper, we propose the region-based dropout with attention prior (RDAP) algorithm, which features hyperparameter transferability. We combined AE with regional dropout algorithms that provide greater stability against the selection of hyperparameters. We empirically confirmed that the RDAP method achieved state-of-the-art localization accuracy on four architectures, namely VGG-GAP, InceptionV3, ResNet-50 SE, and PreResNet-18, and two datasets, namely CUB-200-2011 and ImageNet-1k, with a single set of hyperparameters.

弱监督对象定位（WSOL）方法利用仅在图像级标签上训练的分类器的内部特征响应。分类器倾向于将重点放在目标对象的最有区别的部分上，而不是考虑其全部范围。已经提出了对抗擦除（AE）技术来改善这个问题。这些技术在训练过程中消除了最有区别的部分，从而鼓励分类器学习对象的较少有区别的部分。尽管基于AE的方法取得了成功，但我们已经观察到超参数无法在模型体系结构和数据集之间推广。因此，必须为每种体系结构和数据集确定新的超参数集。超参数的选择经常需要强有力的监督（例如，像素级注释或人工检查）。由于WSOL的​​前提是缺乏这种强有力的监督，因此基于AE的方法的适用性受到限制。在本文中，我们提出了一种基于区域的先验注意辍学（RDAP）算法，该算法具有超参数可传递性。我们将AE与区域辍学算法结合在一起，针对选择超参数提供了更高的稳定性。我们凭经验证实，RDAP方法在四种架构（即VGG-GAP，InceptionV3，ResNet-50 SE和PreResNet-18）以及两个数据集（即CUB-200-2011和ImageNet）上均达到了最新的定位精度。 -1k，带有一组超参数。