Weakly supervised segmentation requires assigning a label to every pixel
based on training instances with partial annotations such as image-level tags,
object bounding boxes, labeled points and scribbles. This task is challenging,
as coarse annotations (tags, boxes) lack precise pixel localization whereas
sparse annotations (points, scribbles) lack broad region coverage. Existing
methods tackle these two types of weak supervision differently: Class
activation maps are used to localize coarse labels and iteratively refine the
segmentation model, whereas conditional random fields are used to propagate
sparse labels to the entire image.
We formulate weakly supervised segmentation as a semi-supervised metric
learning problem, where pixels of the same (different) semantics need to be
mapped to the same (distinctive) features. We propose 4 types of contrastive
relationships between pixels and segments in the feature space, capturing
low-level image similarity, semantic annotation, co-occurrence, and feature
affinity They act as priors; the pixel-wise feature can be learned from
training images with any partial annotations in a data-driven fashion. In
particular, unlabeled pixels in training images participate not only in
data-driven grouping within each image, but also in discriminative feature
learning within and across images. We deliver a universal weakly supervised
segmenter with significant gains on Pascal VOC and DensePose. Our code is
publicly available at this https URL

本研究基于半监督度量学习方法，提出了四种对应关系来捕捉低 - level 图像相似性、语义标注、共现和特征亲和力。这些节点可以从任何部分注释的训练图像中以数据驱动的方式进行学习，因此，该模型不仅适用于弱监督分割中标记的像素，还适用于未标记的像素。