Self-Supervised Video Object Segmentation by Motion-Aware Mask Propagation
Date
2022
Authors
Miao, B.
Bennamoun, M.
Gao, Y.
Mian, A.
Editors
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Conference paper
Citation
Proceedings / IEEE International Conference on Multimedia and Expo. IEEE International Conference on Multimedia and Expo, 2022, vol.2022-July, pp.1-6
Statement of Responsibility
Bo Miao, Mohammed Bennamoun, Yongsheng Gao, Ajmal Mian
Conference Name
IEEE International Conference on Multimedia and Expo (ICME) (18 Jul 2022 - 22 Jul 2022 : Taipei, Taiwan)
Abstract
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 builds a dynamic memory bank and propagates masks according to our proposed motion-aware spatio-temporal matching module, which is able 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: https://github.com/bo-miao/MAMP.
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