The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation
Open accessAbstract
In this work, we revisit the weak-to-strong consistency framework, popularized by FixMatch from semi-supervised classification, where the prediction of a weakly perturbed image serves as supervision for its strongly perturbed version. Intriguingly, we observe that such a simple pipeline already achieves competitive results against recent advanced works, when transferred to our segmentation scenario. Its success heavily relies on the manual design of strong data augmentations, however, which may be limited and inadequate to explore a broader perturbation space. Motivated by this, we propose an auxiliary feature perturbation stream as a supplement, leading to an expanded perturbation space. On the other, to sufficiently probe original image-level augmentations, we present a dual-stream perturbation technique, enabling two strong views to be simultaneously guided by a common weak view. Consequently, our overall Unified Dual-Stream Perturbations approach (UniMatch) surpasses all existing methods significantly across all evaluation protocols on the Pascal, Cityscapes, and COCO benchmarks. Its superiority is also demonstrated in remote sensing interpretation and medical image analysis. We hope our reproduced FixMatch and our results can inspire more future works.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01234-2_49 · Cited by 20,054
Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.660 · Cited by 16,438
Momentum Contrast for Unsupervised Visual Representation Learning
He, K., Fan, H., Wu, Y., Xie, S., & Girshick, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00975 · Cited by 13,347
The Cityscapes Dataset for Semantic Urban Scene Understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.350 · Cited by 12,433
A simple framework for contrastive learning of visual representations
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Research works printed but not linked: 12
- tang, 2021, Humble teachers teach better students for semi -superviscd object detection, CVPR
- liu, 2021, Unbiased teacher for semi-supervised object detection, ICLRE
- bandara, 2022, Revis-iting consistency regularization for semi -supervised change detection in remote sensing images
- berthelot, 2019, Mixmatch: A holistic approach to semi-supervised learning, NeurIPS
- berthelot, 2020, Remix-match: Semi-supervised learning with distribution alignment and augmentation anchoring, ICLRE
- lee, 0, Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks, ICML Workshop
- xu, 2022, Cross-model pseudo-labeling for semi -superviscd action recognition, CVPR
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- grandvalet, 2005, Semi-supervised learning by entropy minimization, NeurIPS
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Cited by
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
Yang, L., Kang, B., Huang, Z., Xu, X., Feng, J., & Zhao, H. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.00987 · Cited by 1,296
Self-citationModel-Heterogeneous Semi-Supervised Federated Learning for Medical Image Segmentation
Ma, Y., Wang, J., Yang, J., & Wang, L. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3348982 · Cited by 55
AnomalyMatch: Discovering Rare Objects of Interest with Semi-supervised and Active Learning
Gómez, P., Ruhberg, L. E., Nardone, M. T., & O'Ryan, D. (2025) · arXiv · doi:10.48550/arxiv.2505.03509
“Variants of FixMatch have been successfully applied to specific domains, such as remote sensing imagery [16], semantic segmentation [21, 22], and graph classification [23].”
Metadata
- Authors
- Yang, L., Qi, L., Feng, L., Zhang, W., & Shi, Y.
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 7236-7246
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386075981
- Cited by
- 580
- References
- 70
- FWCI
- 109.64
- Type
- conference paper
- Language
- en
- Open access
- green
- Funders
- NSFC (62222604,62206052,62192783)
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- Yang, L., Qi, L., Feng, L., Zhang, W., & Shi, Y. (2023). Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr52729.2023.00699
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