  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 https://github.com/wpy1999/BAS.
