Cited by 604Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot LearningMedical Image Segmentation Techniques

The DOI matches its Crossref record; 98 of the 99 references it cites were found in a registry or an index.

Exploring Cross-Image Pixel Contrast for Semantic Segmentation

Open access

Wang, Wenguan, Zhou, Tianfei, Yu, Fisher, Dai, Jifeng, Konukoglu, Ender, Gool, Luc Van·2021·2021 IEEE/CVF International Conference on Computer Vision (ICCV)

DOI 10.1109/iccv48922.2021.00721

Open this paper in CoChat — free account

Abstract

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.

Integrity signals

Registry

Crossref · 10.1109/iccv48922.2021.00721

Cites

98 of 98 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Squeeze-and-Excitation Networks
Hu, J., Shen, L., & Sun, G. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00745 · Cited by 35,667
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2016) · arXiv · doi:10.48550/arxiv.1606.00915 · Cited by 22,392

Show all 99 references

Sorted by citations

Items 401–500 of 603

Category-Contrastive Fine-Grained Crowd Counting and Beyond
Zhang, M., Chen, M., Li, Q., Chen, Y., Lin, R., Li, X., He, S., & Liu, W. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3521823 · Cited by 5
Boundary semantic interactive aggregation network for scene segmentation
Zhang, F., Lv, Q., Pan, B., & Wang, Y. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126754 · Cited by 5
Cross-attention fusion and edge-guided fully supervised contrastive learning network for rail surface defect detection
Yang, J., & Zhou, W. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06314-7 · Cited by 5
CycFormer: Unsupervised Rain Removal Network Based on CycleGAN and Transformer
Li, J., Yan, S., Liu, X., Zhang, J., & Li, Z. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3548157 · Cited by 5
Pseudo-label Guided Cross-video Pixel Contrast for Robotic Surgical Scene Segmentation with Limited Annotations
Yang, Y., Zhao, Z., Jin, Y., Chen, G., Dou, Q., & Heng, P. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9981798 · Cited by 6
Fuzzy-based cross-image pixel contrastive learning for compact medical image segmentation
Wan, Y., Shao, M., Cheng, Y., & Ding, W. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16611-3 · Cited by 5
A Feature Refinement Module for Light-Weight Semantic Segmentation Network
Wang, Z., Guo, X., Wang, S., Zheng, P., & Qi, L. (2023) · Proceedings - International Conference on Image Processing · doi:10.1109/icip49359.2023.10222851 · Cited by 5
Hierarchical Contrastive Learning for Semantic Segmentation
Jiang, J., He, X., Wang, W., Lu, H., & Liu, J. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3491782 · Cited by 5
Self-citation
Towards Dynamic and Small Objects Refinement for Unsupervised Domain Adaptative Nighttime Semantic Segmentation
Pan, J., Li, S., Chen, Y., Zhu, J., & Wang, L. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801389 · Cited by 5
OutfitAI: shop the outfit with a deep learning-based intelligent expert system
Balloni, E., Pietrini, R., Frontoni, E., Mancini, A., & Paolanti, M. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20753-x · Cited by 7
Weakly supervised segmentation of retinal layers on OCT images with AMD using uncertainty prototype and boundary regression
Liu, X., Zhang, J., Zhang, Y., Chen, L., Luo, L., & Tang, J. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103572 · Cited by 5
Local-Region and Cross-Dataset Contrastive Learning for Retinal Vessel Segmentation
Xu, R., Zhao, J., Ye, X., Wu, P., Wang, Z., Li, H., & Chen, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16434-7_55 · Cited by 5
Efficient real-time semantic segmentation: accelerating accuracy with fast non-local attention
Lan, T., Dou, F., Feng, Z., & Zhang, C. (2023) · The Visual Computer · doi:10.1007/s00371-023-03135-y · Cited by 4
ODDF-Net: Multi-object segmentation in 3D retinal OCTA using optical density and disease features
Yang, C., Fan, J., Bai, Y., Li, Y., Xiao, Q., Li, Z., Li, H., & Li, H. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112704 · Cited by 4
Correlational instance feature embedding for sample specificity learning
Wang, C., Yang, J., & Jiang, G. (2022) · Journal of Electronic Imaging · doi:10.1117/1.jei.31.3.030501 · Cited by 4
Lightweight lane line detection based on learnable cluster segmentation with self‐attention mechanism
Yang, Q., Ma, Y., Li, L., Su, C., Gao, Y., Tao, J., Huang, Z., & Jiang, R. (2022) · IET Intelligent Transport Systems · doi:10.1049/itr2.12277 · Cited by 4
Remote Sensing Image Segmentation Based on a Novel Gaussian Mixture Model and SURF Algorithm
Yin, S., Wang, L., Wang, Q., Yang, J., & Jiang, M. (2023) · International Journal of Swarm Intelligence Research · doi:10.4018/ijsir.322301 · Cited by 4
A Lightweight Transformer With Multigranularity Tokens and Connected Component Loss for Land Cover Classification
Lu, W. S., & Nguyen, M. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3364381 · Cited by 4
Detection and Recognition Methods for Pointer Meters Under Interference Conditions With Improved Ellipse Fitting Methods
Long, G., Liu, X., & Song, W. G. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3500045 · Cited by 4
Unsupervised anomaly detection for tile spalling segmentation using synthetic outlier exposure and contrastive learning
Wang, H.-W., & Wu, R. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105941 · Cited by 4
Revisiting classical DeepLab modernly for semantic segmentation
Lv, J., Huang, Y., Wan, X., & Duan, L. (2024) · International Conference on Image, Vision and Computing (ICIVC) · doi:10.1109/icivc61627.2024.10837090 · Cited by 4
Co‐training semi‐supervised medical image segmentation based on pseudo‐label weight balancing
Zhao, J., Li, Y., Wang, C., Yu, M., Shi, W., Liu, J., & Jiang, Z. (2025) · Medical Physics · doi:10.1002/mp.17712 · Cited by 4
Image Classification and Semantic Segmentation with Deep Learning
Quazi, S., & Musa, S. M. (2021) · IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE) · doi:10.1109/icraie52900.2021.9704014 · Cited by 4
Weakly supervised semantic segmentation of histological tissue via attention accumulation and pixel-level contrast learning
Han, Y., Cheng, L., Huang, G., Zhong, G., Li, J., Yuan, X., Liu, H., Li, J., Zhou, J., & Cai, M. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/acaeee · Cited by 4
Semantic Segmentation in Thermal Videos: A New Benchmark and Multi-Granularity Contrastive Learning-Based Framework
Zheng, Y. H., Zhou, F., Liang, S., Song, W., & Bai, X. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3300038 · Cited by 4
Feature Enhancement with Text-Specific Region Contrast for Scene Text Detection
Sun, X., Lyu, J., Zhang, Y., Zeng, G., Fang, B., Zhou, Y., Xie, E., & Ma, C. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8540-1_1 · Cited by 4
Latent Space Segmentation Model for Visual Surface Defect Inspection
Li, M., Peng, B., & Zhai, D. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3446650 · Cited by 4
Composite Learning for Robust and Effective Dense Predictions
Kanakis, M., Huang, T. E., Brüggemann, D., Yu, F., & Gool, L. V. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00233 · Cited by 4
Self-citation
Multi-level Contrast Network for Wearables-based Joint Activity Segmentation and Recognition
Xia, S., Chu, L., Pei, L., Yu, W., & Qiu, R. C. (2022) · Globecom · doi:10.1109/globecom48099.2022.10001226 · Cited by 4
CEBlock: Balance Factor-Driven Change Enhancement Block via Supervised Contrastive Learning for Remote Sensing Change Detection
Li, X., Lu, J., Hu, Q., Yang, M., Hua, H., & Liu, K. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3654464 · Cited by 3
Interaction and Alignment for Visible-Infrared Person Re-Identification
Gong, J., Zhao, S., & Lam, K. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956505 · Cited by 3
Self-Paced Dual-Axis Attention Fusion Network for Retinal Vessel Segmentation
Shi, Y., Wang, W., Yuan, M., & Wang, X. (2023) · Electronics · doi:10.3390/electronics12092107 · Cited by 3
Self-citation
HC-GCN: hierarchical contrastive graph convolutional network for unsupervised domain adaptation on person re-identification
Chen, S., Xu, B., Zhang, M., Yan, Y., Du, X., Zhuang, W., & Wu, Y. (2023) · Multimedia Systems · doi:10.1007/s00530-023-01147-1 · Cited by 3
Label-Efficient Contrastive Learning-Based Model for Nuclei Detection and Classification in 3D Cardiovascular Immunofluorescent Images
Moradinasab, N., Deaton, R. A., Shankman, L. S., Owens, G. K., & Brown, D. E. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44917-8_3 · Cited by 3
Intelligent Identification of Liquid Aluminum Leakage in Deep Well Casting Production Based on Image Segmentation
Yan, J., Li, X., & Zhou, X. (2024) · Applied Sciences · doi:10.3390/app14135470 · Cited by 3
Remote Sensing Image Coding for Machines on Semantic Segmentation via Contrastive Learning
Zhang, J., Chen, Z., & Liu, S. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3479190 · Cited by 3
Improving spleen segmentation in ultrasound images using a hybrid deep learning framework
Karimi, A., Seraj, J., Sarcheshmeh, F. M., Fazli, K., Seraj, A., Eslami, P., Khanmohamadi, M., Moosavi, H. S., Kashani, H. G., Moosavi, A. S., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-85632-9 · Cited by 3
Hard example learning based on neural collapse for class-imbalanced semantic segmentation
Xie, L., Li, W., & Zhao, Y. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.112755 · Cited by 3
S2L-CM: Scribble-supervised nuclei segmentation in histopathology images using contrastive regularization and pixel-level multiple instance learning
Oh, H.-J., Min, S., & Jeong, W. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110293 · Cited by 3
Semantic knowledge transfer for semi-supervised medical image segmentation
Zhou, S., Zhao, H., Ma, L., & Sun, D. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111235 · Cited by 3
Semantic embedding-guided graph self-attention network for plant stem–leaf separation from 3D point clouds
Yang, A., Yang, J., Wu, H., Li, Z., Bai, B., & Li, G. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.110857 · Cited by 3
Swin-MFA: A Multi-Modal Fusion Attention Network Based on Swin-Transformer for Low-Light Image Human Segmentation
Yi, X., Zhang, H., Wang, Y., Guo, S., Wu, J., & Fan, C. (2022) · Sensors · doi:10.3390/s22166229 · Cited by 3
ABUS tumor segmentation via decouple contrastive knowledge distillation
Pan, P., Li, Y., Chen, H., Sun, J., Li, X., & Cheng, L. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ad1274 · Cited by 3
Text-Vision Relationship Alignment for Referring Image Segmentation
Pu, M., Luo, B., Zhang, C., Xu, L., Xu, F., & Kong, M. (2024) · Neural Processing Letters · doi:10.1007/s11063-024-11487-2 · Cited by 3
Dual Branch Framework Using Positive and Negative Learning for Weakly Supervised Semantic Segmentation
Sang, Y., Ma, T., Liu, Y., Liu, T., & Sun, J. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3391623 · Cited by 3
CoMAL: Contrastive Active Learning for Multi-Label Text Classification
Cheng, P., Wang, H., Chen, K., Shou, L., Yao, C., Wu, R., & Chen, G. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671754 · Cited by 3
Area-wise Augmentation on Segmentation Datasets from 3D Scanned Data Used for Visual Navigation
Wada, M., Ueda, Y., Adachi, M., & Miyamoto, R. (2024) · International Conference on Control, Decision and Information Technologies (CoDIT) · doi:10.1109/codit62066.2024.10708112 · Cited by 3
A Context-Enhanced Full-Resolution Floor Plan Segmentation Network for Topological Semantic Mapping
Cao, Z., Sun, Y., Ma, Z., & Zhou, M. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801649 · Cited by 3
Biomedical Interaction Prediction with Adaptive Line Graph Contrastive Learning
Sun, S., Tian, H., Wang, R., & Zhang, Z. (2023) · Mathematics · doi:10.3390/math11030732 · Cited by 3
Active Domain Adaptation with Multi-level Contrastive Units for Semantic Segmentation
Zhang, H., & Zhang, R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26293-7_27 · Cited by 3
Uncertainty Estimation for 3D Dense Prediction via Cross-Point Embeddings
Cai, K., Lu, C. X., & Huang, X. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3256085 · Cited by 3
Pull and concentrate: improving unsupervised semantic segmentation adaptation with cross- and intra-domain consistencies
Zhang, J., Sun, Y., & Chen, W. R. (2023) · Multimedia Systems · doi:10.1007/s00530-023-01131-9 · Cited by 3
EFECL: Feature encoding enhancement with contrastive learning for indoor 3D object detection
Duan, Y., Yi, R., Gao, Y., Xu, K., & Zhu, C. (2023) · Computational Visual Media · doi:10.1007/s41095-023-0366-0 · Cited by 3
Degraded Image Semantic Segmentation Using Intra-image and Inter-image Contrastive Learning
Dong, L., Li, S., & Zheng, J. G. (2023) · China Automation Congress (CAC) · doi:10.1109/cac59555.2023.10452132 · Cited by 3
DuFNet: Dual Flow Network of Real-Time Semantic Segmentation for Unmanned Driving Application of Internet of Things
Duan, T., Liu, Y., Li, J., Lian, Z., & Li, Q. (2023) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2023.024742 · Cited by 3
Exploring the Robustness of Human Parsers Toward Common Corruptions
Zhang, S., Cao, X., Wang, R., Qi, G.-J., & Zhou, J. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3313493 · Cited by 2
Noise-Aware Regional Contrast Semantic Segmentation of Remote Sensing Images Using Crowdsourcing Labels
Zeng, Z., ZhU, J., Zhang, W., & Hua, Y. (2024) · International Conference on Geology, Mapping and Remote Sensing (ICGMRS) · doi:10.1109/icgmrs62107.2024.10581219 · Cited by 2
Pixel-Wise Contrastive Learning for Multi-Class Instrument Segmentation in Endoscopic Robotic Surgery Videos Using Dataset-Wide Sample Queues
Sun, L., & Chen, X. (2024) · IEEE Access · doi:10.1109/access.2024.3476622 · Cited by 2
Exploring Supervised Contrastive Learning for Skeleton-Based Temporal Action Segmentation
Chen, B., Ji, H., Ma, H., Lin, R., Nie, W., Ren, W., Wang, Z., & Liu, H. (2025) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2025.3532694 · Cited by 2
Camouflaged instance segmentation based on multi-scale feature contour fusion swin transformer
Huang, Y., & Jen, F.-Y. (2025) · Signal Processing Image Communication · doi:10.1016/j.image.2025.117328 · Cited by 2
DistAL: A Domain-Shift Active Learning Framework With Transferable Feature Learning for Lesion Detection
Bai, F., Wei, R., Bai, X., Jin, D., Ye, X., Lü, L., Yan, K., & Meng, M. Q. ‐. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3558861 · Cited by 2
Semi-Supervised Medical Lesion Image Segmentation Based on a Contrast-Guided Diffusion Model
Liu, C., Huang, T., He, Y., Chen, H., Wu, Z., & Yang, Y. (2025) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2025.3560267 · Cited by 2
Unsupervised Domain-Adaptive Semantic Segmentation for Surgical Instruments Leveraging Dropout-Enhanced Dual Heads and Coarse-Grained Classification Branch
Li, Z., Wang, Z., Xu, X., Chen, Y., & Schuller, B. W. (2025) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2025.3561865 · Cited by 2
SED++: A Simple Encoder-Decoder for Improved Open-Vocabulary Semantic Segmentation
Zhu, W., Xie, B., Cao, J., Xie, J., Khan, F. S., & Pang, Y. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3626757 · Cited by 2
Exploiting Diffusion Priors for Generalizable Few-Shot Satellite Image Semantic Segmentation
Li, F., Zhang, Z., Wang, X., Wang, X., & Xu, Y. (2025) · Remote Sensing · doi:10.3390/rs17223706 · Cited by 2
Gated-Dual-Attention-Based Full-Resolution Floor Plan Segmentation Network for Topological Semantic Mapping
Sun, Y., Zhou, M., & Cao, Z. (2026) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2025.3648921 · Cited by 2
NaviFormer: Multimodal scene segmentation for assistive navigation
Bui, L., Phung, S. L., Di, Y., Duong, S. T. M., & Bouzerdoum, A. (2026) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2026.104793 · Cited by 2
A Contrastive Learning Scheme with Transformer Innate Patches
Jyhne, S. R., Andersen, P., Goodwin, M., & Oveland, I. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47994-6_8 · Cited by 2
Aspects of image data preparation to extend a classification scheme for cleaning mechanisms to realistic soils
Golla, C., Boddin, L., Köhler, H., Rüdiger, F., & Fröhlich, J. (2024) · PAMM · doi:10.1002/pamm.202300142 · Cited by 2
Effect of Varied Datasets on Training of a Segmentation Model Used in Visual Navigation
Wada, M., Adachi, M., & Miyamoto, R. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386656 · Cited by 2
Intra- and Inter-Image Causal Intervention for Robust Semantic Segmentation in Remote-Sensing Images
Yu, L., Jin, Q., & Wang, W. (2024) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2024.3383457 · Cited by 2
Self-citation
Prototypical contrastive learning based oriented detector for kitchen waste
Ouyang, L., Fang, L., Tang, Q., Ding, S., Yu, J., Lin, J., & Tang, L. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128239 · Cited by 2
ME-Seg&DLS-Net: A Dataset and a Network for Autonomous Driving Based on Multi-Element Semantic Segmentation of Pavement
Wang, H., Zhang, G., Chen, L., Li, Y., Luo, T., & Cai, Y. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3423437 · Cited by 2
Open-Set Recognition of Pansori Rhythm Patterns Based on Audio Segmentation
You, J., & Lee, J. (2024) · Applied Sciences · doi:10.3390/app14166893 · Cited by 2
A Triplet Multi-Task Learning Network for Semantic Change Detection
Dong, S., Guo, H., & Chen, H. (2024) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss53475.2024.10640566 · Cited by 2
FANet: Feature attention network for semantic segmentation
Lin, Z., Li, L., Tang, M., Niu, W., Xie, J., & Mao, H. (2025) · Signal Processing Image Communication · doi:10.1016/j.image.2025.117330 · Cited by 2
Binary segmentation based on visual attention consistency under background-change
Liu, X., & Li, D. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.108738 · Cited by 2
Focus on hierarchical features: Soft-weighted hierarchical features network
Lin, H., Xin, W., Chang, S., Yang, Q., Miao, Q., Liu, R., & Chang, L. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.09.055 · Cited by 2
Learning the model update with local trusted templates for visual tracking
An, Z., Chen, G., Zhang, X., Wang, Z., Xie, Q., & Zhang, B. (2022) · IET Image Processing · doi:10.1049/ipr2.12654 · Cited by 2
CDMNet: Contrastive Distribution Mapped Network for Infrared Small Target Detection
Lv, C., Guo, J., Yu, J., Zhang, R., & Liu, X. (2023) · Workshop on UAVs in Multimedia: Capturing the World from a New Perspective (UAVM) · doi:10.1145/3607834.3616569 · Cited by 2
Towards End-to-End Unsupervised Saliency Detection with Self-Supervised Top-Down Context
Song, Y., Gao, S., Xing, H., Cheng, Y., Wang, Y., & Zhang, W. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612212 · Cited by 2
A Domain Adaptive Semantic Segmentation Method Using Contrastive Learning and Data Augmentation
Xiang, Y., Tian, L., & Li, C. (2024) · Neural Processing Letters · doi:10.1007/s11063-024-11529-9 · Cited by 2
LHAS: A Lightweight Network Based on Hierarchical Attention for Hyperspectral Image Segmentation
Song, L., Gao, Y., Gui, Y., Jiang, D., Zhang, M., Liu, H., & Li, W. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3547824 · Cited by 2
MSAFed: Generalized Multi-Stage and Adaptive Federated Learning for Test-Time Medical Segmentation
Jin, J., Chen, X., Ma, L., Ying, S., Yang, G., & Zeng, T. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3610103 · Cited by 2
Image Segmentation from Sparse Decomposition with a Pretrained Object-Detection Network
Wu, Y., Lv, C., Ding, B., Chen, L., Zhou, B., & Zhou, H. (2022) · Electronics · doi:10.3390/electronics11040639 · Cited by 2
Fetal Brain MRI Segmentation via Boundary-Aware Voxel-Level Contrastive Learning
Lu, W., Zhang, X., Xu, Q. H., Chen, W., Yang, C., Xing, X., & Xu, X. (2023) · ACIS International Winter Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Winter) · doi:10.1109/snpd-winter57765.2023.10223896 · Cited by 2
CoCa: A Connectivity-Aware Cascade Framework for Histology Gland Segmentation
Bai, Y., Zhang, B., Zhang, Z., Liu, W., Li, J., Gong, X., & Wang, W. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3613779 · Cited by 2
Self-citation
Mining Semantic Correlations Between Mispredictions and Corrections for Interactive Semantic Segmentation
Gao, Y., Lang, C., Liu, F., Foo, C.-S., Cao, Y., Sun, L., & Wei, Y. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3379585 · Cited by 2
Boosting Weakly-Supervised Image Segmentation via Representation, Transform, and Compensator
Wang, C., Zhang, D., & Yan, R. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3413778 · Cited by 2
Contrastive Learning for Self-Supervised Pre-Training of Point Cloud Segmentation Networks With Image Data
Janda, A., Wagstaff, B., Ng, E. G., & Kelly, J. (2023) · Canadian Conference on Computer and Robot Vision · doi:10.1109/crv60082.2023.00026 · Cited by 3
An Algorithm Based on DAF-Net++ Model for Wood Annual Rings Segmentation
Ge, Z., Zhang, Z., Shi, L., Liu, S., Gao, Y., Zhou, Y., & Sun, Q. (2023) · Electronics · doi:10.3390/electronics12143009 · Cited by 1
Self-Supervised Fast Texture Defect Detection Based on Salient Object Detection
Wan, J., Zhou, C., & Zhao, X. (2024) · IEEE International Conference on Real-time Computing and Robotics (RCAR) · doi:10.1109/rcar61438.2024.10671027 · Cited by 1
MENSA: Multi-Dataset Harmonized Pretraining for Semantic Segmentation
Shi, B., Zhang, X., Wang, Y., Dai, W., Zou, J., & Xiong, H. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3521851 · Cited by 1
Asymmetric Dual-Encoder Network With Clustering and Mutual Contrast Loss for the Semantic Segmentation of Remote-Sensing Images
Zhou, W., Li, Y., & Liu, Y. (2025) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3575945 · Cited by 1
Learning Discriminative Representation for Co-Salient Object Detection
Piao, Y., Wang, Z., Liu, T., Yin, J., Zhang, M., & Lu, H. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3567213 · Cited by 1
ProtoMGNet: Memory Guided Network With Instance Prototype Enhancement for Echocardiography Segmentation
Xiao, S., Yang, X., & Chi, J. (2025) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2025.3613382 · Cited by 1
Joint Learning of Confidence Fusion, Semantic Alignment and Group-Guided Reliability: A Novel Semi-Supervised Learning Framework for 3D Medical Image Segmentation
Zhou, X., Wang, G., Cheng, Y., Wang, Z., Wang, X., Wang, G., & Tamura, S. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3605400 · Cited by 1
MEANet: A Novel Multiscale Edge-Aware Network for Building Change Detection in High-Resolution Remote Sensing Images
Chen, T., Huang, L., Zhao, W., Yu, S., Yang, Y., & Plaza, A. (2026) · Remote Sensing · doi:10.3390/rs18020261 · Cited by 1
ISC-Swin: Inter Sample Contrastive Enhancement for Swin-Transformer in Ultrasound Spine Feature Segmentation
Zhang, C., Jia, W., Zheng, Y., & Ling, S. H. (2025) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc58881.2025.11342459 · Cited by 1
Collaborative Dual-Network Training With Region-Based Knowledge Distillation for Semi-Supervised Medical Image Segmentation
Karatas, R., & Kaya, A. (2026) · IEEE Access · doi:10.1109/access.2026.3663067 · Cited by 1

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
ORCID: Wang, Wenguan
Zhou, Tianfei
Yu, Fisher
Dai, Jifeng
Gool, Luc Van
Venue: 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Publisher: IEEE
Published: 2021-10
Pages: 7283-7293
DOI: 10.1109/iccv48922.2021.00721
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
Licence: other-oa
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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 ›

Are you an author, or do you see a problem with this page? Request a review

Exploring Cross-Image Pixel Contrast for Semantic Segmentation

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks