Current state-of-the-art video object segmentation models have achieved great
success using supervised learning with massive labeled training datasets.
However, these models are trained using a single source domain and evaluated
using videos sampled from the same source domain. When these models are
evaluated using videos sampled from a different target domain, their
performance degrades significantly due to poor domain generalization, i.e.,
their inability to learn from multi-domain sources simultaneously using
traditional supervised learning. In this paper, We propose a dynamically
growing tree of sub-networks (DGT) to learn effectively from multi-domain
sources. DGT uses a novel lifelong learning technique that allows the model to
continuously and effectively learn from new domains without forgetting the
previously learned domains. Hence, the model can generalize to out-of-domain
videos. The proposed work is evaluated using single-source in-domain
(traditional video object segmentation), multi-source in-domain, and
multi-source out-of-domain video object segmentation. The results of DGT show a
single source in-domain performance gain of 0.2% and 3.5% on the DAVIS16 and
DAVIS17 datasets, respectively. However, when DGT is evaluated using in-domain
multi-sources, the results show superior performance compared to
state-of-the-art video object segmentation and other lifelong learning
techniques with an average performance increase in the F-score of 6.9% with
minimal catastrophic forgetting. Finally, in the out-of-domain experiment, the
performance of DGT is 2.7% and 4% better than state-of-the-art in 1 and
5-shots, respectively.

基于动态增长的子网络（DGT）的多领域视频对象分割方法，在单一领域和多领域源的评估中实现了显著的性能提升并具有最小的领域遗忘。