UNIVERSAL WEAKLY SUPERVISED SEGMENTATION BY PIXEL-TO-SEGMENT CONTRASTIVE LEARNING

Abstract

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 https://github.com/twke18/SPML.

1. INTRODUCTION

Consider the task of learning a semantic segmenter given sparsely labeled training images (Fig. 1 ): Each body part is labeled with a single seed pixel and the task is to segment out the entire person 



Figure1: Our task learns a segmenter given partially labeled training images and applies it to test images. A common baseline is to propagate labels within an image based on feature similarity. We model it as semi-supervised metric learning and learn the pixel-wise feature by contrasting it within and across images. Our results are fuller and more accurate, approaching the ground-truth.

