Unsupervised Part Segmentation through Disentangling Appearance and Shape

@article{Liu2021UnsupervisedPS,
  title={Unsupervised Part Segmentation through Disentangling Appearance and Shape},
  author={Shilong Liu and Lei Zhang and X. Yang and Hang Su and Jun Zhu},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021},
  pages={8351-8360}
}
  • Shilong Liu, Lei Zhang, +2 authors Jun Zhu
  • Published 26 May 2021
  • Computer Science
  • 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
We study the problem, of unsupervised discovery and segmentation of object parts, which, as an intermediate local representation, are capable of finding intrinsic object structure and providing more explainable recognition results. Recent unsupervised methods have greatly relaxed the dependency on annotated data which are costly to obtain, but still rely on additional information such as object segmentation mask or saliency map. To remove such a dependency and further improve the part… Expand
GANSeg: Learning to Segment by Unsupervised Hierarchical Image Generation
  • Xingzhe He, Bastian Wandt, Helge Rhodin
  • Computer Science
  • 2021
Segmenting an image into its parts is a common preprocess for high-level vision tasks such as image editing. However, annotating masks for supervised training is expensive. Weakly-supervised andExpand

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