Error propagation is a general but crucial problem in online semi-supervised video object segmentation. We aim to suppress error propagation through a correction mechanism with high reliability. The key insight is to disentangle the correction from the conventional mask propagation process with reliable cues. We introduce two modulators, propagation and correction modulators, to separately perform channel-wise re-calibration on the target frame embeddings according to local temporal correlations and reliable references respectively. Specifically, we assemble the modulators with a cascaded propagation-correction scheme. This avoids overriding the effects of the reliable correction modulator by the propagation modulator. Although the reference frame with the ground truth label provides reliable cues, it could be very different from the target frame and introduce uncertain or incomplete correlations. We augment the reference cues by supplementing reliable feature patches to a maintained pool, thus offering more comprehensive and expressive object representations to the modulators. In addition, a reliability filter is designed to retrieve reliable patches and pass them in subsequent frames. Our model achieves state-of-the-art performance on YouTube-VOS18/19 and DAVIS17-Val/Test benchmarks. Extensive experiments demonstrate that the correction mechanism provides considerable performance gain by fully utilizing reliable guidance. Code is available at: https://github.com/JerryX1110/RPCMVOS.

本研究提出一种具有高可靠性的纠错机制，包括对目标帧嵌入进行稳健的信道重校准，引入可靠引用补充“根据局部时间相关性和可靠参考”两种调节器，采用级联纠正方案，用于在线半监督视频对象分割中抑制误差传播，并通过在可靠性过滤器中检索可靠补丁，将参考线索增强，最终，在YouTube-VOS18/19和DAVIS17-Val/Test基准上实现最先进的性能表现。

可靠的传播校正调制技术用于视频对象分割