Crafting Better Contrastive Views for Siamese Representation Learning
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Recent self-supervised contrastive learning methods greatly benefit from the Siamese structure that aims at minimizing distances between positive pairs. For high performance Siamese representation learning, one of the keys is to design good contrastive pairs. Most previous works simply apply random sampling to make different crops of the same image, which overlooks the semantic information that may degrade the quality of views. In this work, we propose ContrastiveCrop, which could effectively generate better crops for Siamese representation learning. Firstly, a semantic-aware object localization strategy is proposed within the training process in a fully unsupervised manner. This guides us to generate contrastive views which could avoid most false positives (i.e., object vs. background). Moreover, we empirically find that views with similar appearances are trivial for the Siamese model training. Thus, a center-suppressed sampling is further designed to enlarge the variance of crops. Remarkably, our method takes a careful consideration of positive pairs for contrastive learning with negligible extra training overhead. As a plug-and-play and framework-agnostic module, ContrastiveCrop consistently improves SimCLR, MoCo, BYOL, SimSiam by 0.4% ∼ 2.0% classification accuracy on CIFAR-10, CIFAR-100, Tiny ImageNet and STL-10. Superior results are also achieved on downstream detection and segmentation tasks when pre-trained on ImageNet-1K.
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Recent work has demonstrated the benefit of using learned visual word masks or spatially constrained crops to encourage representational invariance with semantically more similar regions of an image.
“Recent work has demonstrated the benefit of using learned visual word masks98,99 or spatially constrained crops100,101 to encourage representational invariance with semantically more similar regions of an image.”
From Self-supervised learning for medical image classification: a systematic review and implementation guidelines (2023) · cites this paper as [101]
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- Authors
- Peng, X., Wang, K., Zhu, Z., Wang, M., & You, Y.
- Affiliations
- National University of Singapore (SG)
- Tsinghua University (CN)
- Alibaba Group (China) (CN)
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 16010-16019
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4313056800
- Cited by
- 122
- References
- 54
- Citations by year
- 2026: 9 · 2025: 33 · 2024: 25 · 2023: 39 · 2022: 16
- FWCI
- 15.62
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- 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)
- arXiv (Cornell University) (cc-by)
- Funders
- National Research Foundation, Singapore
- 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
- contrastive learning, SimCLR, MoCo, BYOL, SimSiam, self-supervised pre-training, image classification, object detection, semantic segmentation
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