The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Semi-Supervised Semantic Segmentation With High- and Low-Level Consistency
Open accessAbstract
The ability to understand visual information from limited labeled data is an important aspect of machine learning. While image-level classification has been extensively studied in a semi-supervised setting, dense pixel-level classification with limited data has only drawn attention recently. In this work, we propose an approach for semi-supervised semantic segmentation that learns from limited pixel-wise annotated samples while exploiting additional annotation-free images. The proposed approach relies on adversarial training with a feature matching loss to learn from unlabeled images. It uses two network branches that link semi-supervised classification with semi-supervised segmentation including self-training. The dual-branch approach reduces both the low-level and the high-level artifacts typical when training with few labels. The approach attains significant improvement over existing methods, especially when trained with very few labeled samples. On several standard benchmarks-PASCAL VOC 2012, PASCAL-Context, and Cityscapes-the approach achieves new state-of-the-art in semi-supervised learning.
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Cites
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Cited by
Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation
Yang, L., Qi, L., Feng, L., Zhang, W., & Shi, Y. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00699 · Cited by 580
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
Extract Free Dense Labels from CLIP
Zhou, C., Loy, C. C., & Dai, B. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19815-1_40 · Cited by 513
Peng, D., Bruzzone, L., Zhang, Y., Guan, H., Ding, H., & Huang, X. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3011913 · Cited by 441
ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised Learning
Olsson, V., Tranheden, W., Pinto, J., & Svensson, L. (2021) · 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv48630.2021.00141 · Cited by 406
Metadata
- Authors
- Mittal, S., Tatarchenko, M., & Brox, T.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2021-04-01
- Volume
- 43
- Issue
- 4
- Pages
- 1369-1379
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2968696051
- Cited by
- 473
- References
- 38
- FWCI
- 18.42
- Type
- journal article
- Language
- en
- Open access
- green
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