Unsupervised Universal Image Segmentation

• URL: http://arxiv.org/abs/2312.17243v1
• Date: Thu, 28 Dec 2023 18:59:04 GMT
• Title: Unsupervised Universal Image Segmentation
• Authors: Dantong Niu, Xudong Wang, Xinyang Han, Long Lian, Roei Herzig, Trevor Darrell
• Abstract summary: We propose an Unsupervised Universal model (U2Seg) adept at performing various image segmentation tasks. U2Seg generates pseudo semantic labels for these segmentation tasks via leveraging self-supervised models. We then self-train the model on these pseudo semantic labels, yielding substantial performance gains.
• Score: 59.0383635597103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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., 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$^{\text{box}}$ 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$^{\text{mask}}$ 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.

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