SCOPS: Self-Supervised Co-Part Segmentation

 

Wei-Chih Hung        Varun Jampani        Sifei Liu        Pavlo Molchanov        Ming-Hsuan Yang        Jan Kautz

 

Abstract

Parts provide a good intermediate representation of objects that is robust with respect to the camera, pose and appearance variations. Existing works on part segmentation is dominated by supervised approaches that rely on large amounts of manual annotations and can not generalize to unseen object categories. We propose a self-supervised deep learning approach for part segmentation, where we devise several loss functions that aids in predicting part segments that are geometrically concentrated, robust to object variations and are also semantically consistent across different object instances. Extensive experiments on different types of image collections demonstrate that our approach can produce part segments that adhere to object boundaries and also more semantically consistent across object instances compared to existing self-supervised techniques.


Sample part segmentation obtained by SCOPS on different types of image collections: (left) unaligned faces from CelebA, (middle) birds from CUB and (right) horses from PASCAL VOC dataset images, showing that SCOPS can be robust to appearance, viewpoint and pose variations.

Paper

Please consider citing if you make use of this work and/or the corresponding code:

@inproceedings{hung:CVPR:2019,
	title = {SCOPS: Self-Supervised Co-Part Segmentation},
	author = {Hung, Wei-Chih and Jampani, Varun and Liu, Sifei and Molchanov, Pavlo and Yang, Ming-Hsuan and Kautz, Jan},
	booktitle = {IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
	month = june,
	year = {2019}
}

Code

Coming soon.

Video