Autoregressive Unsupervised Image Segmentation
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Existing unsupervised segmentation methods mainly deal with small data with limited number of categories and small diversity, and are not suitable for the LUSS task.
“Due to insufficient data, a few unsupervised segmentation methods [28], [29], [30], [31] mainly deal with small data with limited number of categories and small diversity, thus are not suitable for the LUSS task.”
From Large-Scale Unsupervised Semantic Segmentation (2023) · cites this paper as [28]
Related Works (p. 4) says some GAN-based segmentation methods are limited to two classes, and Experiments (p. 10) describes evaluations using 3, 6, or 15 labels. The paper does not say that unsupervised segmentation methods generally use small or low-diversity datasets because of insufficient data, or that they are unsuitable for LUSS. (LLM, conf=0.82)
Experimental — accuracy not yet measuredExisting USS methods focus on small datasets and a few easy categories, so the advantages of unsupervised learning of rich representations from large-scale data are not explored.
“1) Existing methods focus on small datasets [28], [29], [30], [31] and a few (e.g., 20+) easy categories [28] (e.g., sky and ground). Because of the insufficient data, the advantages of unsupervised learning of rich representations from large-scale data are not explored.”
From Large-Scale Unsupervised Semantic Segmentation (2023) · cites this paper as [28]
The Experiments section (p. 10) reports using 8,550 Potsdam images and a COCO-Stuff subset reduced from 164,000 to 52,000 images, with variants of 3, 6, or 15 labels. This supports the citing sentence’s point about restricted datasets and categories for this method. The paper does not establish that existing USS methods generally focus on small datasets or that insufficient data has prevented exploration of large-scale representation learning. (LLM, conf=0.82)
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Jo, S., Pan, F., Yu, I.-J., & Kim, K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73004-7_14 · Cited by 5 · see citing works
ReCLIP++: Learn to Rectify the Bias of CLIP for Unsupervised Semantic Segmentation
Wang, J., & Kang, G. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02566-5 · Cited by 5 · see citing works
Zhang, Z., Dai, W., Wen, H., & Yang, B. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.00136 · Cited by 4 · see citing works
Kim, J.-Y., Shim, K., Lee, I., & Shim, B. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i3.28056 · Cited by 4 · see citing works
Imbalance-Aware Discriminative Clustering for Unsupervised Semantic Segmentation
Liu, M., Zhang, J., & Tang, W. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02083-x · Cited by 4 · see citing works
Enhancing Unsupervised Semantic Segmentation Through Context-Aware Clustering
Zhuo, W., Wang, Y., Chen, J., Deng, S., Wang, Z., Shen, L., & Zhu, W. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3405648 · Cited by 3 · see citing works
Recent advances on generative models for semantic segmentation: a survey
Bhurtel, M., Rawat, D. B., & Rice, D. O. (2024) · Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications · doi:10.1117/12.3014235 · Cited by 3 · see citing works
GrowSP++: Growing Superpoints and Primitives for Unsupervised 3D Semantic Segmentation
Zhang, Z., Dai, W., Wang, B., Li, B., & Yang, B. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3650165 · Cited by 2 · see citing works
Landmark-aware Self-supervised Eye Semantic Segmentation
Cai, X., Zeng, J., & Shan, S. (2021) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg52635.2021.9667031 · Cited by 2 · see citing works
Li, X., He, P. L., Wu, A., Ranka, S., & Rangarajan, A. (2023) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc57777.2023.10422461 · Cited by 2 · see citing works
Wang, B., Wang, S., Yuan, C., Wu, Z., Li, B., Hu, W., & Xiong, J. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897441 · Cited by 2 · see citing works
SkyCloud: Neural Network-Based Sky and Cloud Segmentation from Natural Images
Gerhardt, C., Weidner, F., & Broll, W. (2023) · International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc58118.2023.10270450 · Cited by 2 · see citing works
Unsupervised Dual Transformer Learning for 3-D Textured Surface Segmentation
Ganapathi, I. I., Dharejo, F. A., Javed, S., Ali, S. S., & Werghi, N. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3365515 · Cited by 2 · see citing works
Correlation Weighted Prototype-Based Self-supervised One-Shot Segmentation of Medical Images
Manna, S., Bhattacharya, S., & Pal, U. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78192-6_2 · Cited by 1 · see citing works
Unsupervised cell segmentation by invariant information clustering
van Nierop, W. L., Schneider, J.-N., de With, P. H. N., & van der Sommen, F. (2024) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.3001442 · Cited by 1 · see citing works
Learning to Count from Pseudo-Labeled Segmentation
Xu, J., Le, H., & Samaras, D. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00848 · Cited by 1 · see citing works
Edge-Cloud Polarization and Collaboration: A Comprehensive SurveyPreprint
Yao, J., Zhang, S., Yang, Y., Wang, F., Ma, J., Zhang, J., Chu, Y., Ji, L., Jia, K., Shen, T., et al. (2021) · arXiv (Cornell University)
Karlsson, R., Hayashi, T., Fujii, K., Carballo, A., Ohtani, K., & Takeda, K. (2021) · arXiv (Cornell University)
Duan, B., Tang, H., Sun, C., Zhu, Y., & Yan, Y. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00055
S$^{{\text{3}}}$Seg: A Three-Stage Unsupervised Foreground and Background Segmentation Network
Yang, Z., Shen, Y., Hou, L., Zhang, W. E., & Chen, T. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3404348
A Novel Unsupervised Approach for Training Convolutional DSSNs on Remote Sensing
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Learning Motion-Scene Disentanglement for Trajectory Prediction From Videos
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Post-MI: unsupervised brain tissue segmentation via post-maximized mutual information
Niu, K., Guo, J., Zhang, M., Guo, Z., Cai, J., Xiao, N., Jiang, B., & Miao, H. (2026) · Frontiers in Medicine · doi:10.3389/fmed.2026.1852159
Label-Free Semantic Segmentation via Soft Superpixel-Graph Coupling
Eliasof, M., Zikri, N. B., & Treister, E. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37657-2_37
- Authors
- Ouali, Y., Hudelot, C., & Tami, M.
- Affiliations
- École Centrale Paris (FR)
- Corresponding author
- Ouali, Yassine
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 142-158
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3043515726
- Cited by
- 75
- References
- 52
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 20 · 2023: 17 · 2022: 16 · 2021: 10
- FWCI
- 3.32
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- École Normale Supérieure
- Université Paris-Saclay
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- unsupervised image segmentation, mutual information maximization, autoregressive generative models, data views, representation learning, clustering, semantic segmentation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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