Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals
Abstract
Being able to learn dense semantic representations of images without supervision is an important problem in computer vision. However, despite its significance, this problem remains rather unexplored, with a few exceptions that considered unsupervised semantic segmentation on small-scale datasets with a narrow visual domain. In this paper, we make a first attempt to tackle the problem on datasets that have been traditionally utilized for the supervised case. To achieve this, we introduce a two-step framework that adopts a predetermined mid-level prior in a contrastive optimization objective to learn pixel embeddings. This marks a large deviation from existing works that relied on proxy tasks or end-to-end clustering. Additionally, we argue about the importance of having a prior that contains information about objects, or their parts, and discuss several possibilities to obtain such a prior in an unsupervised manner.Experimental evaluation shows that our method comes with key advantages over existing works. First, the learned pixel embeddings can be directly clustered in semantic groups using K-Means on PASCAL. Under the fully unsupervised setting, there is no precedent in solving the semantic segmentation task on such a challenging benchmark. Second, our representations can improve over strong baselines when transferred to new datasets, e.g. COCO and DAVIS. The code is available1.
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Some USS methods utilize supervised prior knowledge, making it difficult to evaluate these methods.
“2) Due to the lack of clear problem definition and standardized evaluation, some methods utilize supervised prior knowledge, e.g., supervised pre-trained network weights [46], supervised edge detection [30], and supervised saliency detection [31], [47], [48], making it difficult to evaluate these methods.”
From Large-Scale Unsupervised Semantic Segmentation (2023) · cites this paper as [31]
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Algorithmic fairness in artificial intelligence for medicine and healthcare
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CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation
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Self-Supervised Learning of Object Parts for Semantic Segmentation
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Expanding Low-Density Latent Regions for Open-Set Object Detection
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GeSeNet: A General Semantic-Guided Network With Couple Mask Ensemble for Medical Image Fusion
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Cross-Image Pixel Contrasting for Semantic Segmentation
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Large-Scale Unsupervised Semantic Segmentation
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Towards goal-oriented semantic signal processing: Applications and future challenges
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Unsupervised Object Localization: Observing the Background to Discover Objects
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GrowSP: Unsupervised Semantic Segmentation of 3D Point Clouds
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PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation
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Labels4Free: Unsupervised Segmentation using StyleGAN
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Unsupervised Pre-training for Temporal Action Localization Tasks
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HGFormer: Hierarchical Grouping Transformer for Domain Generalized Semantic Segmentation
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Open-World Semantic Segmentation via Contrasting and Clustering Vision-Language Embedding
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Self-supervised object detection from audio-visual correspondence
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Divide and Contrast: Self-supervised Learning from Uncurated Data
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CLIP-S4: Language-Guided Self-Supervised Semantic Segmentation
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A Robust Shape-Aware Rib Fracture Detection and Segmentation Framework With Contrastive Learning
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Leveraging Hidden Positives for Unsupervised Semantic Segmentation
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Object Discovery and Representation Networks
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Boosting Video Object Segmentation via Space-Time Correspondence Learning
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Self-Supervised Leaf Segmentation under Complex Lighting Conditions
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Overview of Multi-Robot Collaborative SLAM from the Perspective of Data Fusion
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TransFGU: A Top-Down Approach to Fine-Grained Unsupervised Semantic Segmentation
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Perceptual Grouping in Contrastive Vision-Language Models
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ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation
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Revisiting Contrastive Methods for Unsupervised Learning of Visual RepresentationsPreprint
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Self-citationNovel Class Discovery in Semantic Segmentation
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Pixel-level clustering network for unsupervised image segmentation
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Unsupervised Part Discovery from Contrastive ReconstructionPreprint
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Self-citationContrastive Conditional Latent Diffusion for Audio-Visual Segmentation
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Training a deep learning model for single-cell segmentation without manual annotation
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Contextualized Spatio-Temporal Contrastive Learning with Self-Supervision
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Overview of temporal action detection based on deep learning
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Exploiting shape cues for weakly supervised semantic segmentation
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EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised Semantic Segmentation
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Visual Concept Programming: A Visual Analytics Approach to Injecting Human Intelligence At Scale
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ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology Images
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Weakly-Supervised Contrastive Learning for Unsupervised Object Discovery
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HP-Capsule: Unsupervised Face Part Discovery by Hierarchical Parsing Capsule Network
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Deep learning‐based panoptic segmentation: Recent advances and perspectives
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Scaling-up Disentanglement for Image Translation
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Pyramid Geometric Consistency Learning For Semantic Segmentation
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Dense Siamese Network for Dense Unsupervised Learning
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Extracting Semantic Knowledge From GANs With Unsupervised Learning
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PointClustering: Unsupervised Point Cloud Pre-training using Transformation Invariance in Clustering
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Few-Shot Panoptic Segmentation With Foundation Models
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Boundary-aware information maximization for self-supervised medical image segmentation
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U3DS 3 : Unsupervised 3D Semantic Scene Segmentation
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Causal unsupervised semantic segmentation
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MaskSplit: Self-supervised Meta-learning for Few-shot Semantic Segmentation
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Rethinking Self-Supervised Semantic Segmentation: Achieving End-to-End Segmentation
Liu, Y., Zeng, J., Tao, X., & Fang, G. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3432326 · Cited by 17 · see citing works
Exploring the trade-off between performance and annotation complexity in semantic segmentation
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Rethinking Alignment and Uniformity in Unsupervised Image Semantic Segmentation
Zhang, D., Li, C., Li, H., Huang, W., Huang, L., & Zhang, J. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i9.26325 · Cited by 16 · see citing works
He, T., Li, H., Qian, Z., Niu, C., & Huang, R. (2023) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2023.134668 · Cited by 16 · see citing works
Tan, Z., & Gu, X. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109356 · Cited by 16 · see citing works
A Superpixel-Guided Unsupervised Fast Semantic Segmentation Method of Remote Sensing Images
Chen, G., He, C., Wang, T., Zhu, K., Liao, P., & Zhang, X. (2022) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2022.3198065 · Cited by 16 · see citing works
An Active and Contrastive Learning Framework for Fine-Grained Off-Road Semantic Segmentation
Gao, B., Zhao, X., & Zhao, H. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3218403 · Cited by 16 · see citing works
Drive&Segment: Unsupervised Semantic Segmentation of Urban Scenes via Cross-Modal Distillation
Vobecký, A., Hurych, D., Siméoni, O., Gidaris, S., Bursuc, A., Pérez, P., & Šivic, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19839-7_28 · Cited by 15 · see citing works
NamedMask: Distilling Segmenters from Complementary Foundation Models
Shin, G., Xie, W., & Albanie, S. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00524 · Cited by 15 · see citing works
Time Does Tell: Self-Supervised Time-Tuning of Dense Image Representations
Salehi, M., Gavves, E., Snoek, C. G. M., & Asano, Y. M. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01516 · Cited by 15 · see citing works
Kobmann, D., Brack, V., & Wilhelm, T. (2022) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss46834.2022.9884079 · Cited by 14 · see citing works
Learn to Rectify the Bias of CLIP for Unsupervised Semantic Segmentation
Wang, J., & Kang, G. (2024) · 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/cvpr52733.2024.00393 · Cited by 14 · see citing works
FreePoint: Unsupervised Point Cloud Instance Segmentation
Zhang, Z., Ding, J., Jiang, L., Dai, D., & Xia, G.-S. (2024) · 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/cvpr52733.2024.02669 · Cited by 14 · see citing works
Reliable Extraction of Semantic Information and Rate of Innovation Estimation for Graph Signals
Kalfa, M., Yetim, S. Y., Atalık, A., Gok, M., Ge, Y., Li, R., Tong, W., Duman, T. M., & Arıkan, O. (2022) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3221950 · Cited by 14 · see citing works
Semi-supervised medical image segmentation via feature similarity and reliable-region enhancement
Long, J., Yang, C., Ren, Y. X., & Zeng, Z. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107668 · Cited by 13 · see citing works
Rodrigues, C. N., Nunes, I. M., Pereira, M. B., Oliveira, H., & dos Santos, J. A. (2024) · Computers & Graphics · doi:10.1016/j.cag.2024.104014 · Cited by 13 · see citing works
Kuang, X., Cheung, J. P. Y., Wong, K.-Y. K., Lam, W. Y., Lam, C. H., Choy, R. W., Cheng, C., Wu, H., Cao, Y., Wang, K., et al. (2022) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2022.102091 · Cited by 12 · see citing works
Image segmentation for thin structures using a zero-shot learner
Siriborvornratanakul, T. (2024) · International Journal of Information Technology · doi:10.1007/s41870-024-02215-z · Cited by 12 · see citing works
Zhang, Q., Li, Y., Xue, C., Wang, H., & Li, X. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3476176 · Cited by 12 · see citing works
Unseen Object Instance Segmentation with Fully Test-time RGB-D Embeddings Adaptation
Zhang, L., Zhang, S., Xu, Y., Qiao, H., & Liu, Z. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160742 · Cited by 12 · see citing works
- Authors
- Van Gansbeke, W., Vandenhende, S., Georgoulis, S., & Van Gool, L.
- Affiliations
- KU Leuven/ESAT-PSI
- KU Leuven (BE)
- ETH Zurich (CH)
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 10032-10042
- OpenAlex
- W3129110783
- Cited by
- 231
- References
- 107
- Citations by year
- 2026: 12 · 2025: 34 · 2024: 57 · 2023: 69 · 2022: 45 · 2021: 14
- FWCI
- 15.04
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Funders
- KU Leuven
- 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 semantic segmentation, contrastive learning, pixel embedding, semantic clustering, domain transfer, COCO, Davis
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.
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