Unsupervised Universal Image Segmentation

Abstract

Several unsupervised image segmentation approaches have been proposed which eliminate the need for dense manually-annotated segmentation masks; current models separately handle either semantic segmentation (e.g., STEGO) or class-agnostic instance segmentation (e.g., Cut-LER), but not both (i.e., panoptic segmentation). We propose an Unsupervised Universal Segmentation model (U2Seg) adept at performing various image segmentation tasks-instance, semantic and panoptic-using a novel uni-fied framework. U2Seg generates pseudo semantic labels for these segmentation tasks via leveraging self-supervised models followed by clustering; each cluster represents different semantic and/or instance membership of pixels. We then self-train the model on these pseudo semantic labels, yielding substantial performance gains over specialized methods tailored to each task: a+2.6APbox boost (vs. Cut-LER) in unsupervised instance segmentation on COCO and a + 7.0 PixelAcc increase (vs. STEGO) in unsupervised semantic segmentation on COCOStuff. Moreover, our method sets up a new baseline for unsupervised panoptic segmentation, which has not been previously explored. U2Seg is also a strong pretrained model for few-shot segmentation, sur-passing CutLERby+3.0APmask when trained on a low-data regime, e.g., only 1 % COCO labels. We hope our simple yet effective method can inspire more research on unsupervised universal image segmentation.