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Video object segmentation (VOS) is a highly challenging problem since the initial mask, defining the target object, is only given at test-time. The main difficulty is to effectively handle appearance changes and similar background objects, while maintaining accurate segmentation. Most previous approaches fine-tune segmentation networks on the first frame, resulting in impractical frame-rates and risk of overfitting. More recent methods integrate generative target appearance models, but eitherdoi:10.1109/cvpr42600.2020.00743 dblp:conf/cvpr/RobinsonLDKF20 fatcat:guuwnmlztjhwdenxvxlchy3pea