  Weakly supervised semantic segmentation (WSSS) methods are often built on
pixel-level localization maps obtained from a classifier. However, training on
class labels only, classifiers suffer from the spurious correlation between
foreground and background cues (e.g. train and rail), fundamentally bounding
the performance of WSSS. There have been previous endeavors to address this
issue with additional supervision. We propose a novel source of information to
distinguish foreground from the background: Out-of-Distribution (OoD) data, or
images devoid of foreground object classes. In particular, we utilize the hard
OoDs that the classifier is likely to make false-positive predictions. These
samples typically carry key visual features on the background (e.g. rail) that
the classifiers often confuse as foreground (e.g. train), so these cues let
classifiers correctly suppress spurious background cues. Acquiring such hard
OoDs does not require an extensive amount of annotation efforts; it only incurs
a few additional image-level labeling costs on top of the original efforts to
collect class labels. We propose a method, W-OoD, for utilizing the hard OoDs.
W-OoD achieves state-of-the-art performance on Pascal VOC 2012.

Computer Vision and Pattern Recognition