@article{Chen2023, 
author = {Yadang Chen and Duolin Wang and Zhiguo Chen and Zhi-Xin Yang and Enhua Wu},
title = {Global video object segmentation with spatial constraint module},
year = {2023},
journal = {Computational Visual Media},
volume = {9},
number = {2},
pages = {385-400},
keywords = {video object segmentation, semantic segmen-tation, global context (GC) module, spatial constraint},
url = {https://www.sciopen.com/article/10.1007/s41095-022-0282-8},
doi = {10.1007/s41095-022-0282-8},
abstract = {We present a lightweight and efficient semi-supervised video object segmentation network based on the space-time memory framework. To some extent, our method solves the two difficulties encountered in traditional video object segmentation: one is that the single frame calculation time is too long, and the other is that the current frame’s segmentation should use more information from past frames. The algorithm uses a global context (GC) module to achieve high-performance, real-time segmentation. The GC module can effectively integrate multi-frame image information without increased memory and can process each frame in real time. Moreover, the prediction mask of the previous frame is helpful for the segmentation of the current frame, so we input it into a spatial constraint module (SCM), which constrains the areas of segments in the current frame. The SCM effectively alleviates mismatching of similar targets yet consumes few additional resources. We added a refinement module to the decoder to improve boundary segmentation. Our model achieves state-of-the-art results on various datasets, scoring  80.1% on YouTube-VOS 2018 and a  𝒥&ℱ score of  78.0% on DAVIS 2017, while taking 0.05 s per frame on the DAVIS 2016 validation dataset.}
}