The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning
Open accessAbstract
The state of the art in semantic segmentation is steadily increasing in performance, resulting in more precise and reliable segmentations in many different applications. However, progress is limited by the cost of generating labels for training, which sometimes requires hours of manual labor for a single image. Because of this, semi-supervised methods have been applied to this task, with varying degrees of success. A key challenge is that common augmentations used in semi-supervised classification are less effective for semantic segmentation. We propose a novel data augmentation mechanism called ClassMix, which generates augmentations by mixing unlabelled samples, by leveraging on the network's predictions for respecting object boundaries. We evaluate this augmentation technique on two common semi-supervised semantic segmentation benchmarks, showing that it attains state-of-the-art results. Lastly, we also provide extensive ablation studies comparing different design decisions and training regimes.
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Cites
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Fully convolutional networks for semantic segmentation
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The Cityscapes Dataset for Semantic Urban Scene Understanding
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CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features
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Cited by
Hoyer, L., Dai, D., & Van Gool, L. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00969 · Cited by 622
ST++: Make Self-trainingWork Better for Semi-supervised Semantic Segmentation
Yang, L., Zhuo, W., Qi, L., Shi, Y., & Gao, Y. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00423 · Cited by 518
Denoising Pretraining for Semantic Segmentation
Brempong, E. A., Kornblith, S., Chen, T., Parmar, N., Minderer, M., & Norouzi, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00462 · Cited by 120
Metadata
- Authors
- Olsson, V., Tranheden, W., Pinto, J., & Svensson, L.
- Venue
- 2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
- Publisher
- IEEE
- Published
- 2021-01
- Pages
- 1368-1377
- ISSN
- 2472-6737 (ISSN-L)
- OpenAlex
- W3043044432
- Cited by
- 406
- References
- 40
- FWCI
- 33.93
- Type
- conference paper
- Language
- en
- Open access
- green
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- Olsson, V., Tranheden, W., Pinto, J., & Svensson, L. (2021). ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning. 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). https://doi.org/10.1109/wacv48630.2021.00141
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