  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.
