论文标题
在半监督视频对象细分中研究循环机制
Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation
论文作者
论文摘要
在本文中，我们解决了当前视频对象分割管道的几个不足之处。首先，将循环机理纳入标准的半监督过程，以产生更强大的表示。通过依靠起始框架中的准确参考掩码，我们表明可以缓解错误传播问题。接下来，我们引入了一个简单的梯度校正模块，该模块将离线管道扩展到在线方法，同时保持前者的效率。最后，我们基于梯度校正来开发循环有效的接受场（Cycle-ERF），以提供分析特定对象特异性区域的新观点。我们对Davis17和YouTube-VOS的具有挑战性的基准进行了全面的实验，表明环状机制对分割质量有益。
In this paper, we address several inadequacies of current video object segmentation pipelines. Firstly, a cyclic mechanism is incorporated to the standard semi-supervised process to produce more robust representations. By relying on the accurate reference mask in the starting frame, we show that the error propagation problem can be mitigated. Next, we introduce a simple gradient correction module, which extends the offline pipeline to an online method while maintaining the efficiency of the former. Finally we develop cycle effective receptive field (cycle-ERF) based on gradient correction to provide a new perspective into analyzing object-specific regions of interests. We conduct comprehensive experiments on challenging benchmarks of DAVIS17 and Youtube-VOS, demonstrating that the cyclic mechanism is beneficial to segmentation quality.
