Semi-supervised video object segmentation aims to separate a target object
from a video sequence, given the mask in the first frame. Most of current
prevailing methods utilize information from additional modules trained in other
domains like optical flow and instance segmentation, and as a result they do
not compete with other methods on common ground. To address this issue, we
propose a simple yet strong transductive method, in which additional modules,
datasets, and dedicated architectural designs are not needed. Our method takes
a label propagation approach where pixel labels are passed forward based on
feature similarity in an embedding space. Different from other propagation
methods, ours diffuses temporal information in a holistic manner which take
accounts of long-term object appearance. In addition, our method requires few
additional computational overhead, and runs at a fast $\sim$37 fps speed. Our
single model with a vanilla ResNet50 backbone achieves an overall score of 72.3
on the DAVIS 2017 validation set and 63.1 on the test set. This simple yet high
performing and efficient method can serve as a solid baseline that facilitates
future research. Code and models are available at
https://github.com/microsoft/transductive-vos.pytorch.

本篇论文提出了一种基于标签传播和特征相似性的转导方法，用于半监督视频目标分割，其中考虑了长期目标出现特征。该方法无需额外的模块、数据集或架构设计，使用普通的 ResNet50 作为主干，在 DAVIS 2017 评估集上达到了 72.3 的分数，有着高性能和高效率的优点。