Video object segmentation (VOS) is a highly challenging problem, since the
target object is only defined during inference with a given first-frame
reference mask. The problem of how to capture and utilize this limited target
information remains a fundamental research question. We address this by
introducing an end-to-end trainable VOS architecture that integrates a
differentiable few-shot learning module. This internal learner is designed to
predict a powerful parametric model of the target by minimizing a segmentation
error in the first frame. We further go beyond standard few-shot learning
techniques by learning what the few-shot learner should learn. This allows us
to achieve a rich internal representation of the target in the current frame,
significantly increasing the segmentation accuracy of our approach. We perform
extensive experiments on multiple benchmarks. Our approach sets a new
state-of-the-art on the large-scale YouTube-VOS 2018 dataset by achieving an
overall score of 81.5, corresponding to a 2.6% relative improvement over the
previous best result.

该研究提出了一种新的、自动化的视频物体分割方法，通过集成一种可区分的少量样本学习模块，提高了分割精度，实现了当前 YouTube-VOS 2018 数据集的最新最佳分数，并使相对改进率达到了 2.6％以上。