论文标题
ScribbleBox：视频对象分割的交互式注释框架
ScribbleBox: Interactive Annotation Framework for Video Object Segmentation
论文作者
论文摘要
手动为分割任务的视频数据集标记非常耗时。在本文中，我们介绍了Scribblebox，这是一个新颖的交互式框架，用于用视频中的口罩注释对象实例。特别是，我们将注释分为两个步骤：带有跟踪框的注释对象，并在这些轨道中标记口罩。我们以这两个步骤介绍自动化和互动。通过使用参数曲线近似具有少数控制点的参数曲线，可以有效地注释框轨道，以交互式纠正该曲线。我们的方法可以容忍框放置中的噪音，因此通常只需要几下即可注释跟踪的框以获得足够的精度。分割掩模是通过涂鸦进行有效传播的涂鸦校正的。在过去的工作中，我们在注释效率方面表现出了显着的性能。我们证明，我们的ScribbleBox方法在Davis2017上达到88.92％的J＆F，每个盒子轨道键9.14个点击，4帧scribble注释。
Manually labeling video datasets for segmentation tasks is extremely time consuming. In this paper, we introduce ScribbleBox, a novel interactive framework for annotating object instances with masks in videos. In particular, we split annotation into two steps: annotating objects with tracked boxes, and labeling masks inside these tracks. We introduce automation and interaction in both steps. Box tracks are annotated efficiently by approximating the trajectory using a parametric curve with a small number of control points which the annotator can interactively correct. Our approach tolerates a modest amount of noise in the box placements, thus typically only a few clicks are needed to annotate tracked boxes to a sufficient accuracy. Segmentation masks are corrected via scribbles which are efficiently propagated through time. We show significant performance gains in annotation efficiency over past work. We show that our ScribbleBox approach reaches 88.92% J&F on DAVIS2017 with 9.14 clicks per box track, and 4 frames of scribble annotation.
