Weakly-supervised semantic segmentation aims at tackling the dense labeling task using weak supervision so as to reduce human annotation efforts. For weakly-supervised semantic segmentation using only image-level annotation, we propose a novel model of Learning with Saliency and Incremental Supervision Updating (LSISU), in which both the guidances of saliency prior and class information are jointly used and the segmentation supervision is dynamically updated. In the proposed LSISU, we present an image saliency objective complementary to classification loss, by which the trained weakly-supervised deep network can effectively deal with object co-occurrence problem. Meanwhile, we make full use of the class-wise pooling strategy to generate initial mask estimation of high quality. Given an initial annotation, a segmentation network is learned along with incremental supervision updating, which plays a role of region expansion and corrects the falsely estimated supervision for training images. The incremental supervision updating is performed on the fly and involves repeated usage of a fully connected conditional random field algorithm. LSISU achieves superior segmentation performance in terms of mIoU metric on benchmark datasets, which are 62.5% on the PASCAL VOC 2012 test set and 30.1% on the COCO val set.

弱监督语义分割旨在利用弱监督来解决密集的标注任务，从而减少人工标注的工作量。对于仅使用图像级注释的弱监督语义分割，我们提出了一种基于显着性和增量监督更新的学习模型（LSISU），在该模型中，显着先验和类别信息的指导被共同使用，并且分段监督是动态的更新。在提出的LSISU中，我们提出了一种与分类损失互补的图像显着性目标，通过该目标，训练有素的弱监督深度网络可以有效地处理对象共现问题。同时，我们充分利用分类池策略来生成高质量的初始掩码估计。给定一个初始注释，结合增量监督更新学习分割网络，该网络起到区域扩展的作用，并纠正训练图像的错误估计监督。增量监管更新是即时执行的，并且涉及重复使用完全连接的条件随机字段算法。LSISU在基准数据集的mIoU度量方面实现了出色的分割性能，在PASCAL VOC 2012上为62.5％测试集和COCO val集的30.1％。