This paper studies semi-supervised video object segmentation through boosting
intra-frame interaction. Recent memory network-based methods focus on
exploiting inter-frame temporal reference while paying little attention to
intra-frame spatial dependency. Specifically, these segmentation model tends to
be susceptible to interference from unrelated nontarget objects in a certain
frame. To this end, we propose Global Spectral Filter Memory network (GSFM),
which improves intra-frame interaction through learning long-term spatial
dependencies in the spectral domain. The key components of GSFM is 2D (inverse)
discrete Fourier transform for spatial information mixing. Besides, we
empirically find low frequency feature should be enhanced in encoder (backbone)
while high frequency for decoder (segmentation head). We attribute this to
semantic information extracting role for encoder and fine-grained details
highlighting role for decoder. Thus, Low (High) Frequency Module is proposed to
fit this circumstance. Extensive experiments on the popular DAVIS and
YouTube-VOS benchmarks demonstrate that GSFM noticeably outperforms the
baseline method and achieves state-of-the-art performance. Besides, extensive
analysis shows that the proposed modules are reasonable and of great
generalization ability. Our source code is available at
this https URL

本研究提出了 Global Spectral Filter Memory 网络以提高半监督视频对象分割的内框交互，并通过 Low（High）Frequency Module 有效地学习长期空间依赖性，显着优于基准方法并达到了最先进的表现。