  Weakly supervised semantic segmentation (WSSS) aims to produce pixel-wise
class predictions with only image-level labels for training. To this end,
previous methods adopt the common pipeline: they generate pseudo masks from
class activation maps (CAMs) and use such masks to supervise segmentation
networks. However, it is challenging to derive comprehensive pseudo masks that
cover the whole extent of objects due to the local property of CAMs, i.e., they
tend to focus solely on small discriminative object parts. In this paper, we
associate the locality of CAMs with the texture-biased property of
convolutional neural networks (CNNs). Accordingly, we propose to exploit shape
information to supplement the texture-biased CNN features, thereby encouraging
mask predictions to be not only comprehensive but also well-aligned with object
boundaries. We further refine the predictions in an online fashion with a novel
refinement method that takes into account both the class and the color
affinities, in order to generate reliable pseudo masks to supervise the model.
Importantly, our model is end-to-end trained within a single-stage framework
and therefore efficient in terms of the training cost. Through extensive
experiments on PASCAL VOC 2012, we validate the effectiveness of our method in
producing precise and shape-aligned segmentation results. Specifically, our
model surpasses the existing state-of-the-art single-stage approaches by large
margins. What is more, it also achieves a new state-of-the-art performance over
multi-stage approaches, when adopted in a simple two-stage pipeline without
bells and whistles.
