Recently, video object segmentation (VOS) networks typically use memory-based
methods: for each query frame, the mask is predicted by space-time matching to
memory frames. Despite these methods having superior performance, they suffer
from two issues: 1) Challenging data can destroy the space-time coherence
between adjacent video frames. 2) Pixel-level matching will lead to undesired
mismatching caused by the noises or distractors. To address the aforementioned
issues, we first propose to generate an auxiliary frame between adjacent
frames, serving as an implicit short-temporal reference for the query one.
Next, we learn a prototype for each video object and prototype-level matching
can be implemented between the query and memory. The experiment demonstrated
that our network outperforms the state-of-the-art method on the DAVIS 2017,
achieving a J&F score of 86.4%, and attains a competitive result 85.0% on
YouTube VOS 2018. In addition, our network exhibits a high inference speed of
32+ FPS.

最近，视频对象分割（VOS）网络通常使用基于记忆的方法：对于每个查询帧，通过空间 - 时间匹配预测掩码以与记忆帧相匹配。尽管这些方法具有卓越的性能，但存在两个问题：1）具有挑战性的数据可以破坏相邻视频帧之间的空间 - 时间一致性。2）像素级匹配会由噪音或干扰引起不希望的不匹配。为解决上述问题，我们首先提出在相邻帧之间生成一个辅助帧，作为查询帧的隐式短时间参考。随后，我们为每个视频对象学习一个原型，并在查询帧和记忆帧之间实施原型级匹配。实验证明，在 DAVIS 2017 上我们的网络优于最先进的方法，达到了 86.4% 的 J&F 分数，并且在 YouTube VOS 2018 上获得了 85.0% 的竞争结果。此外，我们的网络的推理速度为 32+ FPS。