Segmentation is a core computer vision competency, with applications spanning a broad range of scientifically and economically valuable domains. To date, however, the prohibitive cost of annotation has limited the deployment of flexible segmentation models. In this work, we propose Zero-shot Unsupervised Transfer Instance Segmentation (ZUTIS), a framework that aims to meet this challenge. The key strengths of ZUTIS are: (i) no requirement for instance-level or pixel-level annotations; (ii) an ability of zero-shot transfer, i.e., no assumption on access to a target data distribution; (iii) a unified framework for semantic and instance segmentations with solid performance on both tasks compared to state-of-the-art unsupervised methods. While comparing to previous work, we show ZUTIS achieves a gain of 2.2 mask AP on COCO-20K and 14.5 mIoU on ImageNet-S with 919 categories for instance and semantic segmentations, respectively. The code is made publicly available.
@article{arxiv.2304.14376,
title = {Zero-shot Unsupervised Transfer Instance Segmentation},
author = {Gyungin Shin and Samuel Albanie and Weidi Xie},
journal= {arXiv preprint arXiv:2304.14376},
year = {2023}
}
Comments
Accepted to CVPRW 2023. Code: https://github.com/NoelShin/zutis