Corpus ID: 13586383

Mix-and-Match Tuning for Self-Supervised Semantic Segmentation

@article{Zhan2018MixandMatchTF,
  title={Mix-and-Match Tuning for Self-Supervised Semantic Segmentation},
  author={Xiaohang Zhan and Z. Liu and Ping Luo and X. Tang and Chen Change Loy},
  journal={ArXiv},
  year={2018},
  volume={abs/1712.00661}
}

Xiaohang Zhan, Z. Liu, +2 authors Chen Change Loy

Published 2018

Computer Science

ArXiv

Deep convolutional networks for semantic image segmentation typically require large-scale labeled data, e.g. ImageNet and MS COCO, for network pre-training. To reduce annotation efforts, self-supervised semantic segmentation is recently proposed to pre-train a network without any human-provided labels. The key of this new form of learning is to design a proxy task (e.g. image colorization), from which a discriminative loss can be formulated on unlabeled data. Many proxy tasks, however, lack the… CONTINUE READING

View PDF on arXiv

Save to Library

Create Alert

Cite

Launch Research Feed