Abstract
This paper proposes a novel weakly-supervised semantic segmentation method using image-level label only. The class-specific activation maps from the well-trained classifiers are used as cues to train a segmentation network. The well-known defects of these cues are coarseness and incompleteness. We use super-pixel to refine them, and fuse the cues extracted from both a color image trained classifier and a gray image trained classifier to compensate for their incompleteness. The conditional random field is adapted to regulate the training process and to refine the outputs further. Besides initializing the segmentation network, the previously trained classifier is also used in the testing phase to suppress the non-existing classes. Experimental results on the PASCAL VOC 2012 dataset illustrate the effectiveness of our method.
Abstract (translated)
提出了一种仅使用图像级标签的弱监督语义分割方法。利用训练良好的分类器中的类特定激活图作为训练分割网络的线索。这些线索的主要缺陷是粗糙性和不完全性。我们使用超级像素对其进行细化，并融合从彩色图像训练分类器和灰色图像训练分类器中提取的线索，以弥补其不完整性。条件随机域用于调节训练过程并进一步优化输出。除了初始化分割网络外，在测试阶段还使用了以前训练过的分类器来抑制不存在的类。PascalVOC 2012数据集的实验结果说明了该方法的有效性。
URL
https://arxiv.org/abs/1904.01749
