Explored seeds generation for weakly supervised semantic segmentation
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-023-09073-z.pdf
Reference44 articles.
1. Dai J, He K, Sun J (2015) Boxsup: exploiting bounding boxes to supervise convolutional networks for semantic segmentation. In: Proceedings of the IEEE international conference on computer vision, pp 1635–1643
2. Lin D, Dai J, Jia J, He K, Sun J (2016) Scribblesup: scribble-supervised convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3159–3167
3. Bearman A, Russakovsky O, Ferrari V, Fei-Fei L (2016) What’s the point: semantic segmentation with point supervision. In: European conference on computer vision, pp 549–565, Springer
4. Kolesnikov A, Lampert CH (2016) Seed, expand and constrain: three principles for weakly-supervised image segmentation. In: European conference on computer vision, pp 695–711, Springer
5. Huang Z, Wang X, Wang J, Liu W, Wang J (2018) Weakly-supervised semantic segmentation network with deep seeded region growing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7014–7023
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