Open-World Instance Segmentation (OWIS) is an emerging research topic that aims to segment class-agnostic object instances from images. The mainstream approaches use a two-stage segmentation framework, which first locates the candidate object bounding boxes and then performs instance segmentation. In this work, we instead promote a single-stage framework for OWIS. We argue that the end-to-end training process in the single-stage framework can be more convenient for directly regularizing the localization of class-agnostic object pixels. Based on the single-stage instance segmentation framework, we propose a regularization model to predict foreground pixels and use its relation to instance segmentation to construct a cross-task consistency loss. We show that such a consistency loss could alleviate the problem of incomplete instance annotation -- a common problem in the existing OWIS datasets. We also show that the proposed loss lends itself to an effective solution to semi-supervised OWIS that could be considered an extreme case that all object annotations are absent for some images. Our extensive experiments demonstrate that the proposed method achieves impressive results in both fully-supervised and semi-supervised settings. Compared to SOTA methods, the proposed method significantly improves the $AP_{100}$ score by 4.75\% in UVO$\rightarrow$UVO setting and 4.05\% in COCO$\rightarrow$UVO setting. In the case of semi-supervised learning, our model learned with only 30\% labeled data, even outperforms its fully-supervised counterpart with 50\% labeled data. The code will be released soon.
翻译:开放世界分解( OWIS) 是一个新兴的研究课题, 目的是从图像中分解类类、 不可知的物体实例。 主流方法使用两阶段分解框架, 首先定位候选对象框, 然后进行实例分解。 在这项工作中, 我们提倡OWIS 的单阶段框架。 我们主张, 单阶段框架中的端到端培训进程可以更方便地直接规范类、 不可知的物体像素的本地化。 在单阶段分解框架的基础上, 我们提议一个正规化模型, 预测地表像素, 并使用它与实例分解的关系, 以构建跨任务一致性损失。 我们表明, 这种一致性损失可以缓解一个不完整的例子说明问题 -- -- 这是现有的 OWIS 数据集中的一个常见问题。 我们还指出, 拟议的亏损有助于半受监督的美元 OWIS 的本地值的本地值 。 在一些图像中, 我们提出的方法可以很快在SO- 105 和 IMO- recional 中实现令人印象深刻的结果, 通过完全的SO- AS- AS- sloveri 30 的系统 数据化方法来大幅改进。