The DOI matches its Crossref record; 74 of the 74 references it cites were found in a registry or an index.
Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation
Open accessAbstract
Self-training is a competitive approach in domain adaptive segmentation, which trains the network with the pseudo labels on the target domain. However inevitably, the pseudo labels are noisy and the target features are dispersed due to the discrepancy between source and target domains. In this paper, we rely on representative prototypes, the feature centroids of classes, to address the two issues for unsupervised domain adaptation. In particular, we take one step further and exploit the feature distances from prototypes that provide richer information than mere prototypes. Specifically, we use it to estimate the likelihood of pseudo labels to facilitate online correction in the course of training. Meanwhile, we align the prototypical assignments based on relative feature distances for two different views of the same target, producing a more compact target feature space. Moreover, we find that distilling the already learned knowledge to a self-supervised pretrained model further boosts the performance. Our method shows tremendous performance advantage over state-of-the-art methods. The code is available at https://github.com/microsoft/ProDA.
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Cites
Deep Residual Learning for Image Recognition
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GAN（Generative Adversarial Nets）
(2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,270
Distilling the Knowledge in a Neural Network
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Momentum Contrast for Unsupervised Visual Representation Learning
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The Cityscapes Dataset for Semantic Urban Scene Understanding
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A simple framework for contrastive learning of visual representations
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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
MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
Hoyer, L., Dai, D., Wang, H., & Van Gool, L. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01128 · Cited by 357
Unsupervised Domain Adaptation of Object Detectors: A Survey
Oza, P., Sindagi, V. A., VS, V., & Patel, V. M. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3217046 · Cited by 313
Metadata
- Authors
- Zhang, P., Zhang, B., Zhang, T., Chen, D., Wang, Y., & Wen, F.
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 12409-12419
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3175294391
- Cited by
- 549
- References
- 74
- FWCI
- 51.58
- Type
- conference paper
- Language
- en
- Open access
- green
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- Zhang, P., Zhang, B., Zhang, T., Chen, D., Wang, Y., & Wen, F. (2021). Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr46437.2021.01223
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