  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.
