Weakly-supervised semantic segmentation is a challenging task as no
pixel-wise label information is provided for training. Recent methods have
exploited classification networks to localize objects by selecting regions with
strong response. While such response map provides sparse information, however,
there exist strong pairwise relations between pixels in natural images, which
can be utilized to propagate the sparse map to a much denser one. In this
paper, we propose an iterative algorithm to learn such pairwise relations,
which consists of two branches, a unary segmentation network which learns the
label probabilities for each pixel, and a pairwise affinity network which
learns affinity matrix and refines the probability map generated from the unary
network. The refined results by the pairwise network are then used as
supervision to train the unary network, and the procedures are conducted
iteratively to obtain better segmentation progressively. To learn reliable
pixel affinity without accurate annotation, we also propose to mine confident
regions. We show that iteratively training this framework is equivalent to
optimizing an energy function with convergence to a local minimum. Experimental
results on the PASCAL VOC 2012 and COCO datasets demonstrate that the proposed
algorithm performs favorably against the state-of-the-art methods.

该研究提出了一种基于迭代算法的学习方法，利用像素点之间的强关联性来生成密集分割地图，并通过信任区域来确保像素关联的可靠性以在没有准确标记的情况下学习亲和力矩阵，实验表明该方法在 PASCAL VOC 2012 和 COCO 数据集上性能优越。