零样本无监督迁移实例分割
Zero-shot Unsupervised Transfer Instance Segmentation
AI总结：
本文提出ZUTIS框架，无需实例级或像素级标注且不访问目标数据分布，即可零样本统一完成语义与实例分割，并在COCO-20K和ImageNet-S上显著超越现有无监督方法。
摘要：
分割是计算机视觉的一项核心能力，其应用横跨具有广泛科学和经济价值的众多领域。然而，迄今为止，高昂的标注成本限制了灵活分割模型的部署。在本工作中，我们提出了零样本无监督迁移实例分割（Zero-shot Unsupervised Transfer Instance Segmentation，ZUTIS），这是一个旨在应对该挑战的框架。ZUTIS的关键优势在于：（i）不需要实例级或像素级标注；（ii）具备零样本迁移能力，即不假设能够访问目标数据分布；（iii）提供一个统一的语义分割与实例分割框架，并且与最先进的无监督方法相比，在两项任务上均有可靠性能。在与先前工作比较时，我们表明ZUTIS在用于实例分割的COCO-20K上取得了2.2 mask AP的提升，并在包含919个类别的ImageNet-S上用于语义分割时取得了14.5 mIoU的提升。代码已公开发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
