Exploring Cross-Image Pixel Contrast for Semantic Segmentation
Open accessAbstract
Current semantic segmentation methods focus only on mining "local" context, i.e., dependencies between pixels within individual images, by context-aggregation modules (e.g., dilated convolution, neural attention) or structure-aware optimization criteria (e.g., IoU-like loss). However, they ignore "global" context of the training data, i.e., rich semantic relations between pixels across different images. Inspired by recent advance in unsupervised contrastive representation learning, we propose a pixel-wise contrastive algorithm for semantic segmentation in the fully supervised setting. The core idea is to enforce pixel embeddings belonging to a same semantic class to be more similar than embeddings from different classes. It raises a pixel-wise metric learning paradigm for semantic segmentation, by explicitly exploring the structures of labeled pixels, which were rarely explored before. Our method can be effortlessly incorporated into existing segmentation frameworks without extra overhead during testing. We experimentally show that, with famous segmentation models (i.e., DeepLabV3, HRNet, OCR) and backbones (i.e., ResNet, HRNet), our method brings performance improvements across diverse datasets (i.e., Cityscapes, PASCAL-Context, COCO-Stuff, CamVid). We expect this work will encourage our community to rethink the current de facto training paradigm in semantic segmentation.
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SCL can eliminate task-independent information by constructing positive data pairs.
“Secondly, SCL can eliminate the task-independent information by constructing positive data pairs.”
From Dual Contrastive Learning for General Face Forgery Detection (2022) · cites this paper as Wang et al. 2021
Hard negative pair selection is crucial for contrastive learning.
“Robinson et al. (2021); Wang et al. (2021) found that the hard negative pair selection is crucial for contrastive learning.”
From Dual Contrastive Learning for General Face Forgery Detection (2022) · cites this paper as Wang et al. (2021)
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CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation With Transformers
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Self-citationCross-Image Relational Knowledge Distillation for Semantic Segmentation
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Self-citationCoANet: Connectivity Attention Network for Road Extraction From Satellite Imagery
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Contrastive Boundary Learning for Point Cloud Segmentation
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Pseudo-Label Guided Contrastive Learning for Semi-Supervised Medical Image Segmentation
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Regional Semantic Contrast and Aggregation for Weakly Supervised Semantic Segmentation
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Self-citationDeep learning ensemble 2D CNN approach towards the detection of lung cancer
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Dual Contrastive Learning for General Face Forgery Detection
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“Robinson et al. (2021); Wang et al. (2021) found that the hard negative pair selection is crucial for contrastive learning.”
BackgroundPixel Contrastive-Consistent Semi-Supervised Semantic Segmentation
Zhong, Y., Yuan, B., Wu, H. Q., Yuan, Z., Peng, J., & Wang, Y.-X. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00718 · Cited by 208 · see citing works
Weakly Supervised Semantic Segmentation by Pixel-to-Prototype Contrast
Du, Y., Fu, Z., Liu, Q., & Wang, Y. (2022) · 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/cvpr52688.2022.00428 · Cited by 202 · see citing works
Deep Hierarchical Semantic Segmentation
Li, L., Zhou, T., Wang, W., Li, J., & Yang, Y. (2022) · 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/cvpr52688.2022.00131 · Cited by 199 · see citing works
Self-citationRGB-T Semantic Segmentation With Location, Activation, and Sharpening
Li, G., Wang, Y., Liu, Z., Zhang, X., & Zeng, D. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3208833 · Cited by 196 · see citing works
Contrastive Learning for Label Efficient Semantic Segmentation
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Category Contrast for Unsupervised Domain Adaptation in Visual Tasks
Huang, J., Guan, D., Xiao, A., Lu, S., & Shao, L. (2022) · 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/cvpr52688.2022.00127 · Cited by 160 · see citing works
Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning
Yuan, X., Cheng, G., Yan, K., Zeng, Q., & Han, J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00581 · Cited by 159 · see citing works
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Learning Non-target Knowledge for Few-shot Semantic Segmentation
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Duplex Contextual Relation Network For Polyp Segmentation
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Detail-Preserving Transformer for Light Field Image Super-resolution
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Region-aware Contrastive Learning for Semantic Segmentation
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Group-Wise Semantic Mining for Weakly Supervised Semantic Segmentation
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Self-citationCTNet: Contrastive Transformer Network for Polyp Segmentation
Xiao, B., Hu, J., Li, W., Pun, C., & Bi, X. (2024) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2024.3368154 · Cited by 131 · see citing works
Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
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RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation
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Effective lung nodule detection using deep CNN with dual attention mechanisms
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Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation
Li, R., Li, S., He, C., Zhang, Y., Xu, J., & Lei Zhang, A. (2022) · 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/cvpr52688.2022.01130 · Cited by 110 · see citing works
Bootstrapping Semantic Segmentation with Regional ContrastPreprint
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Group-Wise Learning for Weakly Supervised Semantic Segmentation
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Self-citationWhen Does Contrastive Visual Representation Learning Work?
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Vision-Based UAV Self-Positioning in Low-Altitude Urban Environments
Dai, M., Zheng, E., Feng, Z., Qi, L., Zhuang, J., & Yang, W. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3346279 · Cited by 105 · see citing works
Context-Guided Spatial Feature Reconstruction for Efficient Semantic Segmentation
Ni, Z.-L., Chen, X., Zhai, Y., Tang, Y., & Wang, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72943-0_14 · Cited by 103 · see citing works
Looking Beyond Single Images for Weakly Supervised Semantic Segmentation Learning
Wang, W.-G., Sun, G.-L., & van Gool, L. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3168530 · Cited by 96 · see citing works
Self-citationProposalContrast: Unsupervised Pre-training for LiDAR-Based 3D Object Detection
Yin, J., Zhou, D., Zhang, L., Fang, J., Xu, C., Shen, J., & Wang, W. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19842-7_2 · Cited by 98 · see citing works
Self-citationGao, H., Guo, J., Wang, G., & Zhang, Q. (2022) · 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/cvpr52688.2022.00968 · Cited by 96 · see citing works
Tree Energy Loss: Towards Sparsely Annotated Semantic Segmentation
Liang, Z., Wang, T., Zhang, X., Sun, J., & Shen, J. (2022) · 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/cvpr52688.2022.01640 · Cited by 96 · see citing works
Expanding Low-Density Latent Regions for Open-Set Object Detection
Han, J., Ren, Y., Ding, J., Pan, X., Yan, K., & Xia, G.-S. (2022) · 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/cvpr52688.2022.00937 · Cited by 95 · see citing works
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Cross-Image Pixel Contrasting for Semantic Segmentation
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Self-citationTowards Data-And Knowledge-Driven AI: A Survey on Neuro-Symbolic Computing
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Self-citationChen, Y., Song, P., Liu, H., Dai, L., Zhang, X., Ding, R., & Li, S. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.01.053 · Cited by 91 · see citing works
Uncertainty-Aware Contrastive Distillation for Incremental Semantic Segmentation
Yang, G., Fini, E., Xu, D., Rota, P., Ding, M., Nabi, M., Alameda-Pineda, X., & Ricci, E. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3163806 · Cited by 88 · see citing works
Prototype-Based Semantic Segmentation
Zhou, T., & Wang, W. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3387116 · Cited by 87 · see citing works
Self-citationSemi-supervised 3D Object Detection with Proficient Teachers
Yin, J., Fang, J., Zhou, D., Zhang, L., Xu, C., Shen, J., & Wang, W. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19839-7_42 · Cited by 85 · see citing works
Self-citationNunes, L., Marcuzzi, R., Chen, X., Behley, J., & Stachniss, C. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3142440 · Cited by 81 · see citing works
Transformer Fusion and Pixel-Level Contrastive Learning for RGB-D Salient Object Detection
Wu, J., Hao, F., Liang, W., & Xu, J. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3275308 · Cited by 80 · see citing works
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Multi-Task Learning With Multi-Query Transformer for Dense Prediction
Xu, Y., Li, X., Yuan, H., Yang, Y., & Zhang, L. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3292995 · Cited by 79 · see citing works
Core: Cooperative Reconstruction for Multi-Agent Perception
Wang, B., Zhang, L., Wang, Z., Zhao, Y., & Zhou, T. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00800 · Cited by 79 · see citing works
Self-citationCFL-Net: Image Forgery Localization Using Contrastive Learning
Niloy, F. F., Bhaumik, K. K., & Woo, S. S. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00462 · Cited by 78 · see citing works
Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview
Ren, W., Tang, Y. S., Sun, Q., Zhao, C., & Han, Q. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123207 · Cited by 78 · see citing works
FsaNet: Frequency Self-Attention for Semantic Segmentation
Zhang, F., Panahi, A., & Gao, G. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3305090 · Cited by 78 · see citing works
Dual Branch Multi-Level Semantic Learning for Few-Shot Segmentation
Chen, Y., Jiang, R., Zheng, Y., Sheng, B., Yang, Z.-X., & Wu, E. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3364056 · Cited by 78 · see citing works
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Tripartite Feature Enhanced Pyramid Network for Dense Prediction
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Self-citationMapping seasonal changes of street greenery using multi-temporal street-view images
Han, Y., Zhong, T., Yeh, A. G. O., Zhong, X., Chen, M., & Lü, G. (2023) · Sustainable Cities and Society · doi:10.1016/j.scs.2023.104498 · Cited by 75 · see citing works
Large-scale Video Panoptic Segmentation in the Wild: A Benchmark
Miao, J., Wang, X., Wu, Y., Li, W., Zhang, X., Wei, Y., & Yang, Y. (2022) · 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/cvpr52688.2022.02036 · Cited by 73 · see citing works
Enabling Resource-Efficient AIoT System With Cross-Level Optimization: A Survey
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Semi-supervised medical image segmentation via hard positives oriented contrastive learning
Tang, C., Zeng, X., Zhou, L., Zhou, Q., Wang, P., Wu, X., Ren, H., Zhou, J., & Wang, Y. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.110020 · Cited by 73 · see citing works
FedSeg: Class-Heterogeneous Federated Learning for Semantic Segmentation
Miao, J., Yang, Z., Fan, L., & Yang, Y. (2023) · 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/cvpr52729.2023.00777 · Cited by 72 · see citing works
PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation
Chen, M., Zheng, Z., Yang, Y., & Chua, T.-S. (2022) · ACM Multimedia · doi:10.1145/3581783.3611708 · Cited by 98 · see citing works
Enhanced Soft Label for Semi-Supervised Semantic Segmentation
Ma, J., Wang, C. X., Liu, Y., Lin, L., & Li, G. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00115 · Cited by 70 · see citing works
Compound Domain Generalization via Meta-Knowledge Encoding
Chen, C., Li, J., Han, X., Liu, X., & Yu, Y. (2022) · 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/cvpr52688.2022.00698 · Cited by 64 · see citing works
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VLPD: Context-Aware Pedestrian Detection via Vision-Language Semantic Self-Supervision
Liu, M., Jiang, J., Zhu, C., & Yin, X.-C. (2023) · 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/cvpr52729.2023.00644 · Cited by 61 · see citing works
Logic-induced Diagnostic Reasoning for Semi-supervised Semantic Segmentation
Chen, L., Wang, W., Miao, J., & Yang, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01484 · Cited by 61 · see citing works
Self-citationCross-Difference Semantic Consistency Network for Semantic Change Detection
Wang, Q., Jing, W. P., Chi, K., & Yuan, Y. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3386334 · Cited by 61 · see citing works
Uncertainty-Guided Pixel Contrastive Learning for Semi-Supervised Medical Image Segmentation
Wang, T., Lu, J., Lai, Z., Wen, J., & Kong, H. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/201 · Cited by 60 · see citing works
Exploring Denoised Cross-video Contrast for Weakly-supervised Temporal Action Localization
Li, J., Yang, T., Ji, W., Wang, J., & Cheng, L. (2022) · 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/cvpr52688.2022.01929 · Cited by 58 · see citing works
Self-Attention Memory-Augmented Wavelet-CNN for Anomaly Detection
Wu, K., Zhu, L., Shi, W., Wang, W., & Wu, J. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3211839 · Cited by 58 · see citing works
Self-citationEdge Perception Camouflaged Object Detection Under Frequency Domain Reconstruction
Liu, Z., Deng, X., Jiang, P., Lv, C., Min, G., & Wang, X. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3404005 · Cited by 58 · see citing works
Style Projected Clustering for Domain Generalized Semantic Segmentation
Huang, W., Chen, C., Li, Y., Li, J., Li, C., Song, F., Yan, Y., & Xiong, Z. (2023) · 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/cvpr52729.2023.00299 · Cited by 57 · see citing works
CACFNet: Cross-Modal Attention Cascaded Fusion Network for RGB-T Urban Scene Parsing
Zhou, W., Dong, S., Fang, M., & Yu, L. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3314527 · Cited by 57 · see citing works
Next-Gen Medical Imaging: U-Net Evolution and the Rise of Transformers
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Zhou, T. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110282 · Cited by 56 · see citing works
Self-citationHyperbolic Contrastive Learning for Visual Representations beyond Objects
Ge, S., Mishra, S. K., Kornblith, S., Li, C., & Jacobs, D. W. (2023) · 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/cvpr52729.2023.00661 · Cited by 55 · see citing works
A Robust Shape-Aware Rib Fracture Detection and Segmentation Framework With Contrastive Learning
Cao, Z., Xu, L., Chen, D. Z., Gao, H., & Wu, J. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3263074 · Cited by 53 · see citing works
Exploring Multi-View Pixel Contrast for General and Robust Image Forgery Localization
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Few-Shot Segmentation With Optimal Transport Matching and Message Flow
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Attention-induced semantic and boundary interaction network for camouflaged object detection
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Cross-modal attention fusion network for RGB-D semantic segmentation
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Residual spatial fusion network for RGB-thermal semantic segmentation
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Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation
Jin, Y., Yu, Y., Chen, C., Zhao, Z., Heng, P., & Stoyanov, D. V. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3177077 · Cited by 49 · see citing works
Target-Aware Object Discovery and Association for Unsupervised Video Multi-Object Segmentation
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Self-citationBoosting Video Object Segmentation via Space-Time Correspondence Learning
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Self-citationLogicSeg: Parsing Visual Semantics with Neural Logic Learning and Reasoning
Li, L., Wang, W., & Yang, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00381 · Cited by 48 · see citing works
Self-citationMask Grounding for Referring Image Segmentation
Chng, Y. X., Zheng, H. W., Han, Y., Qiu, X., & Huang, G. M. (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/cvpr52733.2024.02509 · Cited by 48 · see citing works
Contextrast: Contextual Contrastive Learning for Semantic Segmentation
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Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation
Liu, Y., Ding, C., Tian, Y., Pang, G., Belagiannis, V., Reid, I. D., & Carneiro, G. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00112 · Cited by 47 · see citing works
Cross-Image Dependency Modeling for Breast Ultrasound Segmentation
Wu, H., Huang, X., Guo, X., Wen, Z., & Qin, J. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3233648 · Cited by 46 · see citing works
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- Authors
- Wang, W., Zhou, T., Yu, F., Dai, J., Konukoglu, E., & Gool, L. V.
- Affiliations
- ETH Zurich (CH)
- Sensetime (China) (CN)
- ETH Zurich,Computer Vision Lab
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 7283-7293
- OpenAlex
- W3122412340
- Cited by
- 604
- References
- 99
- Citations by year
- 2026: 48 · 2025: 85 · 2024: 145 · 2023: 182 · 2022: 110 · 2021: 34
- FWCI
- 38.51
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Repository for Publications and Research Data (ETH Zurich) (acceptedVersion, other-oa)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, other-oa)
- ETH Zürich Research Collection
- 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
- semantic segmentation, unsupervised contrastive learning, metric learning, global context, pixel embedding, training paradigms
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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