Online Adaptation of Convolutional Neural Networks for Video Object Segmentation

@article{Voigtlaender2017OnlineAO,
  title={Online Adaptation of Convolutional Neural Networks for Video Object Segmentation},
  author={Paul Voigtlaender and Bastian Leibe},
  journal={CoRR},
  year={2017},
  volume={abs/1706.09364}
}
We tackle the task of semi-supervised video object segmentation, i.e. segmenting the pixels belonging to an object in a video using the ground truth pixel mask for the first frame. We build on the recently introduced one-shot video object segmentation (OSVOS) approach which uses a pretrained network and fine-tunes it on the first frame. While achieving impressive performance, at test time OSVOS uses the fine-tuned network in unchanged form and is not able to adapt to large changes in object… CONTINUE READING
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