ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation
Open accessAbstract
Large-scale data is of crucial importance for learning semantic segmentation models, but annotating per-pixel masks is a tedious and inefficient procedure. We note that for the topic of interactive image segmentation, scribbles are very widely used in academic research and commercial software, and are recognized as one of the most userfriendly ways of interacting. In this paper, we propose to use scribbles to annotate images, and develop an algorithm to train convolutional networks for semantic segmentation supervised by scribbles. Our algorithm is based on a graphical model that jointly propagates information from scribbles to unmarked pixels and learns network parameters. We present competitive object semantic segmentation results on the PASCAL VOC dataset by using scribbles as annotations. Scribbles are also favored for annotating stuff (e.g., water, sky, grass) that has no well-defined shape, and our method shows excellent results on the PASCALCONTEXT dataset thanks to extra inexpensive scribble annotations. Our scribble annotations on PASCAL VOC are available at http://research.microsoft.com/en-us/um/ people/jifdai/downloads/scribble_sup.
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These deep CNN interactive or semi-automatic methods are not fully interactive for testing since they do not accept further interactions for refinement.
“However, these methods are not fully interactive for testing since they do not accept further interactions for refinement.”
From DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation (2019) · cites this paper as [39]
ScribbleSup trains CNNs for semantic segmentation supervised by scribbles.
“ScribbleSup [39] also trains CNNs for semantic segmentation supervised by scribbles.”
From DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation (2019) · cites this paper as [39]
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Cites
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The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes
Neuhold, G., Ollmann, T., Bulo, S. R., & Kontschieder, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.534 · Cited by 1,434 · see citing works
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Self-citationInteractive medical image segmentation using deep learning with image-specific fine tuning
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“ScribbleSup [39] also trains CNNs for semantic segmentation supervised by scribbles.”
BackgroundCausal Intervention for Weakly-Supervised Semantic Segmentation
Zhang, D., Zhang, H., Tang, J., Hua, X., & Sun, Q. (2020) · arXiv · doi:10.48550/arxiv.2009.12547 · Cited by 353 · see citing works
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Van Gansbeke, W., Vandenhende, S., Georgoulis, S., & Van Gool, L. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00990 · Cited by 231 · see citing works
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Fang, H.-S., Sun, J., Wang, R., Gou, M., Li, Y.-L., & Lu, C. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00077 · Cited by 228 · see citing works
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Gao, B., Pan, Y., Li, C., Geng, S., & Zhao, H. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3076844 · Cited by 105 · see citing works
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Dung, H. A., Chen, B., & Chin, T.-J. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00229 · Cited by 69 · see citing works
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- Authors
- Lin, D., Dai, J., Jia, J., He, K., & Sun, J.
- Affiliations
- Chinese University of Hong Kong (HK)
- Microsoft (United States) (US)
- Venue
- 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2016-06
- Pages
- 3159-3167
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2337429362
- Cited by
- 1,129
- References
- 2
- Citations by year
- 2026: 53 · 2025: 115 · 2024: 139 · 2023: 178 · 2022: 161 · 2021: 165 · 2020: 111 · 2019: 96 · 2018: 60 · 2017: 44 · 2016: 6 · 2015: 1
- FWCI
- 23.82
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Microsoft Research
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- semantic segmentation, scribble annotations, interactive segmentation, convolutional neural networks, probabilistic graphical models, object segmentation, Pascal VOC, Pascal Context
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