Weakly supervised object localization and semantic segmentation aim to localize objects using only image-level labels. Recently, a new paradigm has emerged by generating a foreground prediction map (FPM) to achieve pixel-level localization. While existing FPM-based methods use cross-entropy to evaluate the foreground prediction map and to guide the learning of the generator, this paper presents two astonishing experimental observations on the object localization learning process: For a trained network, as the foreground mask expands, 1) the cross-entropy converges to zero when the foreground mask covers only part of the object region. 2) The activation value continuously increases until the foreground mask expands to the object boundary. Therefore, to achieve a more effective localization performance, we argue for the usage of activation value to learn more object regions. In this paper, we propose a Background Activation Suppression (BAS) method. Specifically, an Activation Map Constraint (AMC) module is designed to facilitate the learning of generator by suppressing the background activation value. Meanwhile, by using foreground region guidance and area constraint, BAS can learn the whole region of the object. In the inference phase, we consider the prediction maps of different categories together to obtain the final localization results. Extensive experiments show that BAS achieves significant and consistent improvement over the baseline methods on the CUB-200-2011 and ILSVRC datasets. In addition, our method also achieves state-of-the-art weakly supervised semantic segmentation performance on the PASCAL VOC 2012 and MS COCO 2014 datasets. Code and models are available at https://github.com/wpy1999/BAS-Extension.

使用仅有图像级标签的弱监督对象定位和语义分割方法，该论文引入了一个前景预测图（FPM）的新范例，通过生成FPM实现像素级定位；通过观察发现，随着前景掩模的扩大，交叉熵收敛为零（当前景仅覆盖对象部分时），激活值持续增加（直至前景掩模扩展到对象边界），因此，提出了一种背景激活抑制（BAS）方法，通过抑制背景激活值来学习更多对象区域；该方法在CUB-200-2011、ILSVRC、PASCAL VOC 2012和MS COCO 2014数据集上，实验表明相较于基线方法有显著和一致的改进效果，并达到最先进的弱监督语义分割性能。

背景激活抑制用于弱监督物体定位和语义分割