Guided Co-segmentation network for fast video object segmentation

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 semi-supervised 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 long-term 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 semi-supervised video object segmentation on Davis 2016 and Davis 2017 datasets.AI SingaporeMinistry of Education (MOE)National Research Foundation (NRF)Accepted versionThis research is supported by the National Research Foundation Singapore under its AI Singapore Programme (Award Number: AISG-RP-2018-003), the NTU start-up grant, and the MOE Tier-1 research grants: RG126/17 (S), RG28/18 (S) and RG22/19 (S)