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 this https URL

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

Background Activation Suppression for Weakly Supervised Object  Localization and Semantic Segmentation

Weakly supervised object localization (WSOL) aims to localize objects using
only image-level labels. Recently a new paradigm has emerged by generating a
foreground prediction map (FPM) to achieve localization task. Existing
FPM-based methods use cross-entropy (CE) to evaluate the foreground prediction
map and to guide the learning of generator. We argue for using activation value
to achieve more efficient learning. It is based on the experimental observation
that, for a trained network, CE converges to zero when the foreground mask
covers only part of the object region. While activation value increases until
the mask expands to the object boundary, which indicates that more object areas
can be learned by using activation value. In this paper, we propose a
Background Activation Suppression (BAS) method. Specifically, an Activation Map
Constraint module (AMC) is designed to facilitate the learning of generator by
suppressing the background activation value. Meanwhile, by using the foreground
region guidance and the 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. Code and models
are available at this https URL

本篇论文提出一种 Background Activation Suppression（简称 BAS）的方法来实现弱监督对象定位（WSOL）任务，在该方法中使用的前景预测图（FPM）和 Activation Map Constraint module（AMC）可从多个方面优化定位结果。实验证明，与以往方法相比，BAS 在 CUB-200-2011 和 ILSVRC 数据集上均达到了显著的改进。