YouTube-VOS: Sequence-to-Sequence Video Object Segmentation

@article{Xu2018YouTubeVOSSV,
  title={YouTube-VOS: Sequence-to-Sequence Video Object Segmentation},
  author={N. Xu and L. Yang and Yuchen Fan and Jianchao Yang and Dingcheng Yue and Yuchen Liang and Brian L. Price and S. Cohen and Thomas S. Huang},
  journal={ArXiv},
  year={2018},
  volume={abs/1809.00461}
}
Learning long-term spatial-temporal features are critical for many video analysis tasks. However, existing video segmentation methods predominantly rely on static image segmentation techniques, and methods capturing temporal dependency for segmentation have to depend on pretrained optical flow models, leading to suboptimal solutions for the problem. End-to-end sequential learning to explore spatial-temporal features for video segmentation is largely limited by the scale of available video… Expand
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