  We address the highly challenging problem of video object segmentation. Given
only the initial mask, the task is to segment the target in the subsequent
frames. In order to effectively handle appearance changes and similar
background objects, a robust representation of the target is required. Previous
approaches either rely on fine-tuning a segmentation network on the first
frame, or employ generative appearance models. Although partially successful,
these methods often suffer from impractically low frame rates or unsatisfactory
robustness.
  We propose a novel approach, based on a dedicated target appearance model
that is exclusively learned online to discriminate between the target and
background image regions. Importantly, we design a specialized loss and
customized optimization techniques to enable highly efficient online training.
Our light-weight target model is integrated into a carefully designed
segmentation network, trained offline to enhance the predictions generated by
the target model. Extensive experiments are performed on three datasets. Our
approach achieves an overall score of over 70 on YouTube-VOS, while operating
at 25 frames per second.
