  Data augmentation is vital for deep learning neural networks. By providing
massive training samples, it helps to improve the generalization ability of the
model. Weakly supervised semantic segmentation (WSSS) is a challenging problem
that has been deeply studied in recent years, conventional data augmentation
approaches for WSSS usually employ geometrical transformations, random cropping
and color jittering. However, merely increasing the same contextual semantic
data does not bring much gain to the networks to distinguish the objects, e.g.,
the correct image-level classification of "aeroplane" may be not only due to
the recognition of the object itself, but also its co-occurrence context like
"sky", which will cause the model to focus less on the object features. To this
end, we present a Context Decoupling Augmentation (CDA) method, to change the
inherent context in which the objects appear and thus drive the network to
remove the dependence between object instances and contextual information. To
validate the effectiveness of the proposed method, extensive experiments on
PASCAL VOC 2012 dataset with several alternative network architectures
demonstrate that CDA can boost various popular WSSS methods to the new
state-of-the-art by a large margin.
