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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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“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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Xu, J., Le, H., & Samaras, D. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00848 · Cited by 1
Repurposing SAM for User-Defined Semantics Aware Segmentation
Kundu, R., Paul, S., Dutta, A., & Roy–Chowdhury, A. K. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00347 · Cited by 1
Alekhina, A. E., Gurenko, A., & Dorrer, M. G. (2022) · Studies in systems, decision and control · doi:10.1007/978-3-030-95116-0_17 · Cited by 1
CYBORGS: Contrastively Bootstrapping Object Representations by Grounding in Segmentation
Wang, R., Zhao, H., & Gao, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19821-2_15 · Cited by 1
Two-Stage Segmentation of Lung Cancer Metastasis Lesions by Fusion of Multi-Resolution Features
Zhao, J., Wang, X., She, Y., & Wang, S. (2023) · Health · doi:10.4236/health.2023.155029 · Cited by 1
Абрамов, В. Г., Alekhina, A. E., Dorrer, M. G., Георгиевич, Д. М., & Tutsenko, X. (2021) · Siberian Scientific Workshop on Data Analysis Technologies with Applications (SibDATA) · doi:10.47813/sibdata-2-2021-16
Unsupervised Semantic Segmentation with Feature Enhancement for Few-shot Image Classification
Li, X., Xu, Z., Xu, Q., & Tang, Y. (2022) · International Conference on Advanced Cloud and Big Data · doi:10.1109/cbd58033.2022.00027
Metadata
- 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
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