W. Liu, G. Lin, T. Zhang and Z. Liu, "Guided Co-Segmentation Network for Fast Video Object Segmentation," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, no. 4, pp. 1607-1617, April 2021, doi: 10.1109/TCSVT.2020.3010293.
Abstract:
Semi-supervised video object segmentation is a task of propagating instance masks given in the first frame to the entire video. It is a challenging task since it usually suffers from heavy occlusions, large deformation, and large variations of objects. To alleviate these problems, many existing works apply time-consuming techniques such as fine-tuning, post-processing, or extracting optical flow, which makes them intractable for online segmentation. In our work, we focus on online semisupervised video object segmentation. We propose a GCSeg (Guided Co-Segmentation) Network which is mainly composed of a Reference Module and a Co-segmentation Module, to simultaneously incorporate the short-term, middle-term, and longterm temporal inter-frame relationships. Moreover, we propose an Adaptive Search Strategy to reduce the risk of propagating inaccurate segmentation results in subsequent frames. Our GCSeg network achieves state-of-the-art performance on online semisupervised video object segmentation on Davis 2016 and Davis 2017 datasets.
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Funding Info:
This research / project is supported by the National Research Foundation, Singapore - AI Singapore Programme
Grant Reference no. : AISG-RP-2018-003
This research / project is supported by the Nanyang Technological University - Start-Up Grant
Grant Reference no. :
This research / project is supported by the Ministry of Education - Tier-1 Research Grants
Grant Reference no. : RG126/17 (S), RG28/18 (S) and RG22/19 (S)