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.

本文提出了将多阶段方法的关联学习嵌入到单阶段模型中，引入自适应关联损失深度学习网络，考虑到伪标签中可能存在的错误，提出了一种新的标签重新分配损失函数，本方法在 PASCAL VOC 2012 数据集上实验表明，优于其他标准单阶段方法，并达到了与多阶段方法相当的性能。