Semi-supervised video object segmentation is a task of propagating instance masks given in the first frame to the entire video. It is a challenging task since it usually suffers from heavy occlusions, large deformation, and large variations of objects. To alleviate these problems, many existing works apply time-consuming techniques such as fine-tuning, post-processing, or extracting optical flow, which makes them intractable for online segmentation. In our work, we focus on online semi-supervised video object segmentation. We propose a GCSeg (Guided Co-Segmentation) Network which is mainly composed of a Reference Module and a Co-segmentation Module, to simultaneously incorporate the short-term, middle-term, and long-term temporal inter-frame relationships. Moreover, we propose an Adaptive Search Strategy to reduce the risk of propagating inaccurate segmentation results in subsequent frames. Our GCSeg network achieves state-of-the-art performance on online semi-supervised video object segmentation on Davis 2016 and Davis 2017 datasets.

中文翻译：

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引导式协同分段网络，用于快速视频对象分段

半监督视频对象分割是将第一帧中给出的实例蒙版传播到整个视频的任务。这是一项具有挑战性的任务，因为它通常会遇到严重的咬合，变形大以及物体变化大的问题。为了减轻这些问题，许多现有的作品都采用了耗时的技术，例如微调，后处理或提取光流，这使得它们对于在线分割来说很棘手。在我们的工作中，我们专注于在线半监督视频对象分割。我们提出了一个GCSeg（有指导的共分段）网络，该网络主要由参考模块和共分段模块组成，以同时合并短期，中期和长期的时间帧间关系。而且，我们提出了一种自适应搜索策略，以降低在后续帧中传播不正确的细分结果的风险。我们的GCSeg网络在Davis 2016和Davis 2017数据集上的在线半监督视频对象分割方面实现了最先进的性能。