This paper presents a two-stage approach, track and then segment, to perform semi-supervised video object segmentation (VOS) with only bounding box annotations. The proposed reverse optimization for VOS (ROVOS) which leverages a fully convolutional Siamese network performs tracking and segmentation in the tracker. The segmentation cues are able to reversely optimize the location of the tracker and the object segmentation masks are produced by the two-branch system online. The experimental results on DAVIS 2016 and DAVIS 2017 demonstrate significant improvements of the proposed algorithm over the state-of-the-art methods.

中文翻译：

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带框注释的视频对象跟踪和分段

本文提出了一种跟踪和分段的两阶段方法，以仅使用边界框注释执行半监督视频对象分段（VOS）。提议的VOS反向优化（ROVOS）利用了完全卷积的暹罗网络，可在跟踪器中执行跟踪和分段。分割提示能够反向优化跟踪器的位置，并且对象分割蒙版由在线双分支系统生成。在DAVIS 2016和DAVIS 2017上的实验结果证明了所提出算法相对于最新方法的显着改进。