Semi-supervised video object segmentation (VOS) is a task that involves
predicting a target object in a video when the ground truth segmentation mask
of the target object is given in the first frame. Recently, space-time memory
networks (STM) have received significant attention as a promising solution for
semi-supervised VOS. However, an important point is overlooked when applying
STM to VOS. The solution (STM) is non-local, but the problem (VOS) is
predominantly local. To solve the mismatch between STM and VOS, we propose a
kernelized memory network (KMN). Before being trained on real videos, our KMN
is pre-trained on static images, as in previous works. Unlike in previous
works, we use the Hide-and-Seek strategy in pre-training to obtain the best
possible results in handling occlusions and segment boundary extraction. The
proposed KMN surpasses the state-of-the-art on standard benchmarks by a
significant margin (+5% on DAVIS 2017 test-dev set). In addition, the runtime
of KMN is 0.12 seconds per frame on the DAVIS 2016 validation set, and the KMN
rarely requires extra computation, when compared with STM.

提出了一种基于核化记忆网络的半监督视频目标分割算法，采用了找茬策略来处理遮挡和边界提取等问题，并在标准基准测试上超过了目前最先进的算法 5% 的性能表现。