Existing studies in weakly-supervised semantic segmentation (WSSS) using image-level weak supervision have several limitations: sparse object coverage, inaccurate object boundaries, and co-occurring pixels from non-target objects. To overcome these challenges, we propose a novel framework, namely Explicit Pseudo-pixel Supervision (EPS), which learns from pixel-level feedback by combining two weak supervisions; the image-level label provides the object identity via the localization map and the saliency map from the off-the-shelf saliency detection model offers rich boundaries. We devise a joint training strategy to fully utilize the complementary relationship between both information. Our method can obtain accurate object boundaries and discard co-occurring pixels, thereby significantly improving the quality of pseudo-masks. Experimental results show that the proposed method remarkably outperforms existing methods by resolving key challenges of WSSS and achieves the new state-of-the-art performance on both PASCAL VOC 2012 and MS COCO 2014 datasets.

通过像素级反馈和结合图像级标签和预训练显著性检测模型，提出了一种新的弱监督语义分割框架EPS，可有效解决目标覆盖不全、目标边界不准确、目标不同部分像素分散等问题，并在PASCAL VOC 2012和MS COCO 2014数据集上实现了最新的技术水平表现。

铁路不是火车：显著性作为弱监督语义分割的拟像素监督