  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.
