  Semantic segmentation has been continuously investigated in the last ten
years, and majority of the established technologies are based on supervised
models. In recent years, image-level weakly supervised semantic segmentation
(WSSS), including single- and multi-stage process, has attracted large
attention due to data labeling efficiency. In this paper, we propose to embed
affinity learning of multi-stage approaches in a single-stage model. To be
specific, we introduce an adaptive affinity loss to thoroughly learn the local
pairwise affinity. As such, a deep neural network is used to deliver
comprehensive semantic information in the training phase, whilst improving the
performance of the final prediction module. On the other hand, considering the
existence of errors in the pseudo labels, we propose a novel label reassign
loss to mitigate over-fitting. Extensive experiments are conducted on the
PASCAL VOC 2012 dataset to evaluate the effectiveness of our proposed approach
that outperforms other standard single-stage methods and achieves comparable
performance against several multi-stage methods.
