Unsupervised Universal Image Segmentation
Authors: name: Dantong Niu, name: Xudong Wang, name: Xinyang Han, name: Long Lian, name: Roei Herzig, name: Trevor Darrell

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., CutLER), 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 unified 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 +2.6 AP^{box} boost
vs. CutLER 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, surpassing CutLER by +5.0
AP^{mask} 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.