We propose a self-supervised spatio-temporal matching method, coined
Motion-Aware Mask Propagation (MAMP), for video object segmentation. MAMP
leverages the frame reconstruction task for training without the need for
annotations. During inference, MAMP extracts high-resolution features from each
frame to build a memory bank from the features as well as the predicted masks
of selected past frames. MAMP then propagates the masks from the memory bank to
subsequent frames according to our proposed motion-aware spatio-temporal
matching module to handle fast motion and long-term matching scenarios.
Evaluation on DAVIS-2017 and YouTube-VOS datasets show that MAMP achieves
state-of-the-art performance with stronger generalization ability compared to
existing self-supervised methods, i.e., 4.2% higher mean J&F on DAVIS-2017 and
4.85% higher mean J&F on the unseen categories of YouTube-VOS than the nearest
competitor. Moreover, MAMP performs at par with many supervised video object
segmentation methods. Our code is available at:
this https URL

本文提出了一种基于自监督学习的时空匹配方法，称为 Motion-Aware Mask Propagation（MAMP），用于视频对象分割，利用帧重建任务进行训练，而无需进行标注，在 DAVIS-2017 和 YouTube-VOS 数据集上得到了最先进的性能。