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arXiv · 2304.14376

Zero-shot Unsupervised Transfer Instance Segmentation

Gyungin Shin·Samuel Albanie·Weidi Xie
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Abstract

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.

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Gyungin Shin, Samuel Albanie, Weidi Xie. 2023-04-27. Zero-shot Unsupervised Transfer Instance Segmentation. https://arxiv.org/abs/2304.14376

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