We introduce a new image segmentation task, termed Entity Segmentation (ES) with the aim to segment all visual entities in an image without considering semantic category labels. It has many practical applications in image manipulation/editing where the segmentation mask quality is typically crucial but category labels are less important. In this setting, all semantically-meaningful segments are equally treated as categoryless entities and there is no thing-stuff distinction. Based on our unified entity representation, we propose a center-based entity segmentation framework with two novel modules to improve mask quality. Experimentally, both our new task and framework demonstrate superior advantages as against existing work. In particular, ES enables the following: (1) merging multiple datasets to form a large training set without the need to resolve label conflicts; (2) any model trained on one dataset can generalize exceptionally well to other datasets with unseen domains. Our code is made publicly available at https://github.com/dvlab-research/Entity.

中文翻译：

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开放世界实体分割

我们引入了一个新的图像分割任务，称为实体分割（ES），目的是在不考虑语义类别标签的情况下分割图像中的所有视觉实体。它在图像处理/编辑中有许多实际应用，其中分割掩码质量通常至关重要，但类别标签不太重要。在这种情况下，所有语义上有意义的段都被同等地视为无类别实体，并且没有事物与事物的区别。基于我们统一的实体表示，我们提出了一个基于中心的实体分割框架，该框架具有两个新颖的模块来提高掩码质量。在实验上，我们的新任务和框架都展示了与现有工作相比的优越优势。特别是，ES 启用了以下功能：(1) 合并多个数据集形成一个大的训练集，无需解决标签冲突；(2) 在一个数据集上训练的任何模型都可以非常好地泛化到其他具有未知域的数据集。我们的代码在 https://github.com/dvlab-research/Entity 上公开提供。