  Image-level weakly supervised semantic segmentation (WSSS) is a fundamental
yet challenging computer vision task facilitating scene understanding and
automatic driving. Most existing methods resort to classification-based Class
Activation Maps (CAMs) to play as the initial pseudo labels, which tend to
focus on the discriminative image regions and lack customized characteristics
for the segmentation task. To alleviate this issue, we propose a novel
activation modulation and recalibration (AMR) scheme, which leverages a
spotlight branch and a compensation branch to obtain weighted CAMs that can
provide recalibration supervision and task-specific concepts. Specifically, an
attention modulation module (AMM) is employed to rearrange the distribution of
feature importance from the channel-spatial sequential perspective, which helps
to explicitly model channel-wise interdependencies and spatial encodings to
adaptively modulate segmentation-oriented activation responses. Furthermore, we
introduce a cross pseudo supervision for dual branches, which can be regarded
as a semantic similar regularization to mutually refine two branches. Extensive
experiments show that AMR establishes a new state-of-the-art performance on the
PASCAL VOC 2012 dataset, surpassing not only current methods trained with the
image-level of supervision but also some methods relying on stronger
supervision, such as saliency label. Experiments also reveal that our scheme is
plug-and-play and can be incorporated with other approaches to boost their
performance.
