Video object segmentation (VOS) is a highly challenging problem since the
initial mask, defining the target object, is only given at test-time. The main
difficulty is to effectively handle appearance changes and similar background
objects, while maintaining accurate segmentation. Most previous approaches
fine-tune segmentation networks on the first frame, resulting in impractical
frame-rates and risk of overfitting. More recent methods integrate generative
target appearance models, but either achieve limited robustness or require
large amounts of training data.
We propose a novel VOS architecture consisting of two network components. The
target appearance model consists of a light-weight module, which is learned
during the inference stage using fast optimization techniques to predict a
coarse but robust target segmentation. The segmentation model is exclusively
trained offline, designed to process the coarse scores into high quality
segmentation masks. Our method is fast, easily trainable and remains highly
effective in cases of limited training data. We perform extensive experiments
on the challenging YouTube-VOS and DAVIS datasets. Our network achieves
favorable performance, while operating at higher frame-rates compared to
state-of-the-art. Code and trained models are available at
this https URL

该研究提出了一种新的视频对象分割方式，它由轻量级模块和分割模型两部分组成。在推理阶段使用快速优化技术学习目标外观模型，以预测出粗略但鲁棒的目标分割，然后将粗略的分数转化成高质量的分割掩模，该模式具有快速、易于训练、在有限的训练数据情况下仍然高效等优点。在挑战性的 YouTube-VOS 和 DAVIS 数据集上进行了广泛实验，其结果与现有技术相比具有更高的帧率和优异的性能。