Learning From Pixel-Level Label Noise: A New Perspective for Semi-Supervised Semantic Segmentation
Learning From Pixel-Level Label Noise: A New Perspective for Semi-Supervised Semantic Segmentation
This paper addresses semi-supervised semantic segmentation by exploiting a small set of images with pixel-level annotations (strong supervisions) and a large set of images with only image-level annotations (weak supervisions). Most existing approaches aim to generate accurate pixel-level labels from weak supervisions. However, we observe that those generated labels still …