Spatially Consistent Representation Learning
Open accessAbstract
Self-supervised learning has been widely used to obtain transferrable representations from unlabeled images. Especially, recent contrastive learning methods have shown impressive performances on downstream image classification tasks. While these contrastive methods mainly focus on generating invariant global representations at the image-level under semantic-preserving transformations, they are prone to overlook spatial consistency of local representations and therefore have a limitation in pretraining for localization tasks such as object detection and instance segmentation. Moreover, aggressively cropped views used in existing contrastive methods can minimize representation distances between the semantically different regions of a single image.In this paper, we propose a spatially consistent representation learning algorithm (SCRL) for multi-object and location-specific tasks. In particular, we devise a novel self-supervised objective that tries to produce coherent spatial representations of a randomly cropped local region according to geometric translations and zooming operations. On various downstream localization tasks with benchmark datasets, the proposed SCRL shows significant performance improvements over the image-level supervised pretraining as well as the state-of-the-art self-supervised learning methods. Code is available at https://github.com/kakaobrain/scrl.
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Pixel-level SSL methods ignore the category-related representation ability required by the LUSS task.
“Despite the good performance for transfer learning, these methods ignore the category-related representation ability required by the LUSS task.”
From Large-Scale Unsupervised Semantic Segmentation (2023) · cites this paper as [94]
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Yin, K., Rao, V. R., Jiang, R., Liu, X., Aarabi, P., & Lindell, D. B. (2024) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52733.2024.00131 · Cited by 8 · see citing works
Teaching Masked Autoencoder With Strong Augmentations
Zhu, R., Bai, Y., Yao, T., Liu, J., Sun, Z., Mei, T., & Chen, C. W. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3419898 · Cited by 3 · see citing works
Semi-weakly Supervised Contrastive Representation Learning for Retinal Fundus ImagesPreprint
Yap, B. P., & Ng, B. K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.02122 · Cited by 3 · see citing works
Contrastive Object-level Pre-training with Spatial Noise Curriculum LearningPreprint
Yang, C., Huang, L., & Crowley, E. J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.13651 · Cited by 3 · see citing works
Self-supervised learning for early detection of neurodegenerative diseases with small data
Jiang, H. (2023) · doi:10.32657/10356/166402 · Cited by 3 · see citing works
Enhancing Representations Through Heterogeneous Self-Supervised Learning
Li, Z.-Y., Yin, B., Liu, Y., Liu, L., & Cheng, M. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3558989 · Cited by 2 · see citing works
EinsPT: Efficient Instance-Aware Pre-Training of Vision Foundation Models
Wang, Z., Tian, Y., Xie, L., Wang, Y., & Ye, Q. (2026) · IEEE Transactions on Image Processing · doi:10.1109/tip.2026.3652371 · Cited by 2 · see citing works
Convolutional Masked Image Modeling for Dense Prediction Tasks on Pathology Images
Yang, Y., Pan, L., Liu, L., & Stone, E. A. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00762 · Cited by 2 · see citing works
Refined self-supervised learning via trident network and cross-hybrid optimization
Zhao, W., Song, Q., Li, W., Tian, Y., Wang, Y., Zhang, W., & Yang, H. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128397 · Cited by 2 · see citing works
Flip Side of Artificial Intelligence Technologies: New Labor-Intensive Industry of the 21st Century
Heo, S., Na, S., Han, S., Shin, Y.-S., & Lee, S. (2021) · Journal of the Computational Structural Engineering Institute of Korea · doi:10.7734/coseik.2021.34.5.327 · Cited by 2 · see citing works
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Label-Free Synthetic Pretraining of Object Detectors
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TDAE: Tensored Deep Autoencoder for Classification of Hyperspectral Images
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MEJIGCLU: More Effective Jigsaw Clustering For Unsupervised Visual Representation Learning
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Space-correlated Contrastive Representation Learning with Multiple Instances
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HoughCL: Finding Better Positive Pairs in Dense Self-supervised LearningPreprint
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Self-citationHypernymization of named entity-rich captions for grounding-based multi-modal pretraining
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View Crafting For Instance-Level Representation from Scene Images
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Unsqueeze [CLS] Bottleneck to Learn Rich Representations
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Contrastive Representation Learning With Mixture-of-Instance-and-Pixel
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Self-supervised learning for object detection in challenging settings: A survey
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DASC-SPT: Towards Self-Supervised Panoramic Semantic Segmentation
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Recent advances of local mechanisms in vision foundation models: A survey and outlook
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Τοwards Εnd-tο-end Ηandwritten Dοcument Recοgnitiοn
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Small target detection using deep learning
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Tracing Copied Pixels and Regularizing Patch Affinity in Copy Detection
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- Authors
- Roh, B., Shin, W., Kim, I., & Kim, S.
- Affiliations
- Kakao Brain (South Korea) (KR)
- ORCID
- Kim, Sungwoong
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 1144-1153
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3174415835
- Cited by
- 85
- References
- 47
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 19 · 2023: 21 · 2022: 23 · 2021: 12
- FWCI
- 8.59
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- self-supervised learning, contrastive learning, image localization, object detection, instance segmentation, geometric transformations, zooming, random cropping
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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