Li, Y.[Yi],
Guo, Y.Q.[Yan-Qing],
Kao, Y.Y.[Yue-Ying],
He, R.[Ran],
Image Piece Learning for Weakly Supervised Semantic Segmentation,
SMCS(47), No. 4, April 2017, pp. 648-659.
IEEE DOI
1704
Correlation
BibRef
Shimoda, W.[Wataru],
Yanai, K.[Keiji],
Weakly supervised semantic segmentation using distinct class specific
saliency maps,
CVIU(191), 2020, pp. 102712.
Elsevier DOI
2002
BibRef
Earlier:
Distinct Class-Specific Saliency Maps for Weakly Supervised Semantic
Segmentation,
ECCV16(IV: 218-234).
Springer DOI
1611
Semantic segmentation, Weakly supervised learning,
Weakly supervised segmentation, Visualization, Deep learning
BibRef
Shimoda, W.[Wataru],
Yanai, K.[Keiji],
Self-Supervised Difference Detection for Weakly-Supervised Semantic
Segmentation,
ICCV19(5207-5216)
IEEE DOI
2004
estimation theory, image denoising, image segmentation,
iterative methods, learning (artificial intelligence),
Predictive models
BibRef
Chen, Y.C.[Yun-Chun],
Lin, Y.Y.[Yen-Yu],
Yang, M.H.[Ming-Hsuan],
Huang, J.B.[Jia-Bin],
Show, Match and Segment: Joint Weakly Supervised Learning of Semantic
Matching and Object Co-Segmentation,
PAMI(43), No. 10, October 2021, pp. 3632-3647.
IEEE DOI
2109
Semantics, Task analysis, Image segmentation, Training, Clutter,
Proposals, Pattern matching, Semantic matching,
weakly-supervised learning
BibRef
Zhou, T.F.[Tian-Fei],
Li, L.L.[Liu-Lei],
Li, X.Y.[Xue-Yi],
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Li, J.W.[Jian-Wu],
Shao, L.[Ling],
Group-Wise Learning for Weakly Supervised Semantic Segmentation,
IP(31), 2022, pp. 799-811.
IEEE DOI
2201
Semantics, Image segmentation, Training, Location awareness,
Cognition, Task analysis, Graph neural networks,
neural attention
BibRef
Zhou, T.F.[Tian-Fei],
Zhang, M.J.[Mei-Jie],
Zhao, F.[Fang],
Li, J.W.[Jian-Wu],
Regional Semantic Contrast and Aggregation for Weakly Supervised
Semantic Segmentation,
CVPR22(4289-4299)
IEEE DOI
2210
Training, Location awareness, Annotations, Semantics, Training data,
Benchmark testing, Segmentation, grouping and shape analysis, Scene analysis and understanding
BibRef
Chen, H.J.[Hong-Jun],
Wang, J.B.[Jin-Bao],
Chen, H.C.[Hong Cai],
Zhen, X.T.[Xian-Tong],
Zheng, F.[Feng],
Ji, R.R.[Rong-Rong],
Shao, L.[Ling],
Seminar Learning for Click-Level Weakly Supervised Semantic
Segmentation,
ICCV21(6900-6909)
IEEE DOI
2203
Seminars, Training, Knowledge engineering, Bridges, Costs, Annotations,
Segmentation, grouping and shape, Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Zhang, R.M.[Rui-Mao],
Lin, L.[Liang],
Wang, G.R.[Guang-Run],
Wang, M.[Meng],
Zuo, W.M.[Wang-Meng],
Hierarchical Scene Parsing by Weakly Supervised Learning with Image
Descriptions,
PAMI(41), No. 3, March 2019, pp. 596-610.
IEEE DOI
1902
Semantics, Labeling, Training, Neural networks, Task analysis,
Predictive models, Image segmentation, Scene parsing,
recursive structured prediction
BibRef
Qu, M.X.[Meng-Xue],
Wu, Y.[Yu],
Wei, Y.C.[Yun-Chao],
Liu, W.[Wu],
Liang, X.D.[Xiao-Dan],
Zhao, Y.[Yao],
Learning to Segment Every Referring Object Point by Point,
CVPR23(3021-3030)
IEEE DOI
2309
BibRef
Lin, L.[Liang],
Wang, G.R.[Guang-Run],
Zhang, R.[Rui],
Zhang, R.M.[Rui-Mao],
Liang, X.D.[Xiao-Dan],
Zuo, W.M.[Wang-Meng],
Deep Structured Scene Parsing by Learning with Image Descriptions,
CVPR16(2276-2284)
IEEE DOI
1612
BibRef
Wang, X.A.[Xi-Ang],
Liu, S.F.[Si-Fei],
Ma, H.M.[Hui-Min],
Yang, M.H.[Ming-Hsuan],
Weakly-Supervised Semantic Segmentation by Iterative Affinity Learning,
IJCV(128), No. 6, June 2020, pp. 1736-1749.
Springer DOI
2006
BibRef
Chan, L.[Lyndon],
Hosseini, M.S.[Mahdi S.],
Plataniotis, K.N.[Konstantinos N.],
A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in
Different Image Domains,
IJCV(129), No. 2, February 2021, pp. 361-384.
Springer DOI
2102
BibRef
Krešo, I.[Ivan],
Krapac, J.[Josip],
Šegvic, S.[Siniša],
Efficient Ladder-Style DenseNets for Semantic Segmentation of Large
Images,
ITS(22), No. 8, August 2021, pp. 4951-4961.
IEEE DOI
2108
BibRef
Earlier: A2, A1, A3:
Ladder-Style DenseNets for Semantic Segmentation of Large Natural
Images,
CVRoads17(238-245)
IEEE DOI
1802
BibRef
Earlier: A2, A3, Only:
Weakly-Supervised Semantic Segmentation by Redistributing Region Scores
Back to the Pixels,
GCPR16(377-388).
Springer DOI
1611
Semantics, Feature extraction, Image segmentation,
Computational modeling, Spatial resolution, Checkpointing,
road transportation.
Convolution, Tensile stress, Training
BibRef
Grcic, M.[Matej],
Šegvic, S.[Siniša],
Hybrid Open-Set Segmentation With Synthetic Negative Data,
PAMI(46), No. 10, October 2024, pp. 6748-6760.
IEEE DOI
2409
Training, Detectors, Data models, Anomaly detection, Semantics,
Predictive models, Semantic segmentation, Open-set segmentation,
hybrid models
BibRef
Krešo, I.[Ivan],
Cauševic, D.[Denis],
Krapac, J.[Josip],
Šegvic, S.[Siniša],
Convolutional Scale Invariance for Semantic Segmentation,
GCPR16(64-75).
Springer DOI
1611
BibRef
Zhang, B.F.[Bing-Feng],
Xiao, J.[Jimin],
Wei, Y.C.[Yun-Chao],
Huang, K.[Kaizhu],
Luo, S.[Shan],
Zhao, Y.[Yao],
End-to-end weakly supervised semantic segmentation with reliable
region mining,
PR(128), 2022, pp. 108663.
Elsevier DOI
2205
Weakly supervised, Semantic segmentation, End-to-end, Attention
BibRef
Jiang, P.T.[Peng-Tao],
Han, L.H.[Ling-Hao],
Hou, Q.B.[Qi-Bin],
Cheng, M.M.[Ming-Ming],
Wei, Y.C.[Yun-Chao],
Online Attention Accumulation for Weakly Supervised Semantic
Segmentation,
PAMI(44), No. 10, October 2022, pp. 7062-7077.
IEEE DOI
2209
Training, Semantics, Cats, Image segmentation, Visualization,
Task analysis, Location awareness,
pixel-level supervision
BibRef
Zhang, B.F.[Bing-Feng],
Xiao, J.[Jimin],
Jiao, J.B.[Jian-Bo],
Wei, Y.C.[Yun-Chao],
Zhao, Y.[Yao],
Affinity Attention Graph Neural Network for Weakly Supervised
Semantic Segmentation,
PAMI(44), No. 11, November 2022, pp. 8082-8096.
IEEE DOI
2210
Semantics, Task analysis, Image edge detection, Image segmentation,
Reliability, Proposals, Graph neural networks, Weakly supervised,
graph neural network
BibRef
Zhang, B.F.[Bing-Feng],
Xiao, J.M.[Ji-Min],
Wei, Y.C.[Yun-Chao],
Zhao, Y.[Yao],
Credible Dual-Expert Learning for Weakly Supervised Semantic
Segmentation,
IJCV(131), No. 8, August 2023, pp. 1892-1908.
Springer DOI
2307
BibRef
Xia, R.Y.[Rui-Yang],
Li, G.Q.[Guo-Quan],
Huang, Z.W.[Zheng-Wen],
Meng, H.Y.[Hong-Ying],
Pang, Y.[Yu],
CBASH: Combined Backbone and Advanced Selection Heads With Object
Semantic Proposals for Weakly Supervised Object Detection,
CirSysVideo(32), No. 10, October 2022, pp. 6502-6514.
IEEE DOI
2210
Proposals, Semantics, Feature extraction, Object detection, Training,
Location awareness, Head, Weakly supervised object detection,
object semantic proposals
BibRef
Zhou, H.J.[Hua-Jun],
Yang, L.X.[Ling-Xiao],
Xie, X.H.[Xiao-Hua],
Lai, J.H.[Jian-Huang],
Selective Intra-Image Similarity for Personalized Fixation-Based
Object Segmentation,
CirSysVideo(32), No. 11, November 2022, pp. 7910-7923.
IEEE DOI
2211
Image segmentation, Task analysis, Object segmentation, Observers,
Image edge detection, Object detection, Measurement, evaluation metric
BibRef
Chen, Q.[Qi],
Yang, L.X.[Ling-Xiao],
Lai, J.H.[Jian-Huang],
Xie, X.H.[Xiao-Hua],
Self-supervised Image-specific Prototype Exploration for Weakly
Supervised Semantic Segmentation,
CVPR22(4278-4288)
IEEE DOI
2210
Location awareness, Weight measurement, Image segmentation, Costs,
Shape, Semantics, Prototypes, Segmentation,
Self- semi- meta- unsupervised learning
BibRef
Fasana, C.[Corrado],
Pasini, S.[Samuele],
Milani, F.[Federico],
Fraternali, P.[Piero],
Weakly Supervised Object Detection for Remote Sensing Images:
A Survey,
RS(14), No. 21, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Li, Y.J.[Yu-Jie],
Sun, J.X.[Jia-Xing],
Li, Y.[Yun],
Weakly-Supervised Semantic Segmentation Network With Iterative dCRF,
ITS(23), No. 12, December 2022, pp. 25419-25426.
IEEE DOI
2212
Semantics, Cams, Image segmentation, Convolution, Annotations,
Feature extraction, Training, Weakly-supervised, image-level annotations
BibRef
Cheng, L.[Lin],
Fang, P.F.[Peng-Fei],
Yan, Y.[Yan],
Lu, Y.[Yang],
Wang, H.Z.[Han-Zi],
TRL: Transformer based refinement learning for hybrid-supervised
semantic segmentation,
PRL(164), 2022, pp. 239-245.
Elsevier DOI
2212
Hybrid-supervised semantic segmentation,
Simi-supervised semantic segmentation, Pseudo label
BibRef
Feng, J.[Jiapei],
Wang, X.G.[Xing-Gang],
Li, T.[Te],
Ji, S.S.[Shan-Shan],
Liu, W.Y.[Wen-Yu],
Weakly-supervised semantic segmentation via online pseudo-mask
correcting,
PRL(165), 2023, pp. 33-38.
Elsevier DOI
2301
Weakly-supervised learning, Semantic segmentation, Noisy label learning
BibRef
Min, H.[Hai],
Zhang, Y.M.[Ye-Mao],
Zhao, Y.[Yang],
Jia, W.[Wei],
Lei, Y.K.[Ying-Ke],
Fan, C.X.[Chun-Xiao],
Hybrid feature enhancement network for few-shot semantic segmentation,
PR(137), 2023, pp. 109291.
Elsevier DOI
2302
Semantic segmentation, Few-shot segmentation, Few-shot learning
BibRef
Yuan, K.H.[Kun-Hao],
Schaefer, G.[Gerald],
Lai, Y.K.[Yu-Kun],
Wang, Y.F.[Yi-Fan],
Liu, X.[Xiyao],
Guan, L.[Lin],
Fang, H.[Hui],
A multi-strategy contrastive learning framework for weakly supervised
semantic segmentation,
PR(137), 2023, pp. 109298.
Elsevier DOI
2302
Weakly supervised learning, Representation learning,
Contrastive learning, Semantic segmentation
BibRef
Li, R.[Ruiwen],
Mai, Z.[Zheda],
Zhang, Z.B.[Zhi-Bo],
Jang, J.[Jongseong],
Sanner, S.[Scott],
TransCAM: Transformer attention-based CAM refinement for Weakly
supervised semantic segmentation,
JVCIR(92), 2023, pp. 103800.
Elsevier DOI
2303
Weakly supervised learning, Semantic segmentation, Vision transformer
BibRef
Fan, J.S.[Jun-Song],
Zhang, Z.X.[Zhao-Xiang],
Memory-Based Cross-Image Contexts for Weakly Supervised Semantic
Segmentation,
PAMI(45), No. 5, May 2023, pp. 6006-6020.
IEEE DOI
2304
Image segmentation, Semantics, Training, Heating systems,
Context modeling, Task analysis, Computational modeling, cross-image context
BibRef
Liu, W.[Weide],
Kong, X.F.[Xiang-Fei],
Hung, T.Y.[Tzu-Yi],
Lin, G.S.[Guo-Sheng],
Cross-Image Region Mining With Region Prototypical Network for Weakly
Supervised Segmentation,
MultMed(25), 2023, pp. 1148-1160.
IEEE DOI
2305
Prototypes, Training, Semantics, Image segmentation, Task analysis,
Robustness, Annotations, Cross-image, weakly-supervised, segmentation
BibRef
Chen, T.[Tao],
Yao, Y.Z.[Ya-Zhou],
Tang, J.H.[Jin-Hui],
Multi-Granularity Denoising and Bidirectional Alignment for Weakly
Supervised Semantic Segmentation,
IP(32), 2023, pp. 2960-2971.
IEEE DOI
2306
Training, Noise measurement, Task analysis, Cams,
Semantic segmentation, Filtering, Adversarial machine learning, noisy label
BibRef
Li, J.L.[Jin-Long],
Jie, Z.Q.[Ze-Qun],
Wang, X.[Xu],
Zhou, Y.[Yu],
Wei, X.L.[Xiao-Lin],
Ma, L.[Lin],
Weakly Supervised Semantic Segmentation via Progressive Patch
Learning,
MultMed(25), 2023, pp. 1686-1699.
IEEE DOI
2306
Training, Semantics, Image segmentation, Task analysis, Sensitivity,
Location awareness, Annotations, Weakly supervised learning,
progressive learning
BibRef
Chen, T.[Tao],
Yao, Y.Z.[Ya-Zhou],
Zhang, L.[Lei],
Wang, Q.[Qiong],
Xie, G.S.[Guo-Sen],
Shen, F.M.[Fu-Min],
Saliency Guided Inter- and Intra-Class Relation Constraints for
Weakly Supervised Semantic Segmentation,
MultMed(25), 2023, pp. 1727-1737.
IEEE DOI
2306
Cams, Image segmentation, Semantics, Task analysis, Training,
Prototypes, Location awareness, Semantic segmentation,
relation constraint
BibRef
Chen, T.[Tao],
Yao, Y.Z.[Ya-Zhou],
Huang, X.[Xingguo],
Li, Z.C.[Ze-Chao],
Nie, L.Q.[Li-Qiang],
Tang, J.H.[Jin-Hui],
Spatial Structure Constraints for Weakly Supervised Semantic
Segmentation,
IP(33), 2024, pp. 1136-1148.
IEEE DOI Code:
WWW Link.
2402
Cams, Semantic segmentation, Image reconstruction, Training,
Task analysis, Annotations, Semantics, Semantic segmentation,
spatial structure constraints
BibRef
Lei, J.[Jie],
Yang, G.Y.[Guo-Yu],
Wang, S.W.[Shuai-Wei],
Feng, Z.L.[Zun-Lei],
Liang, R.H.[Rong-Hua],
DCAM: Disturbed class activation maps for weakly supervised semantic
segmentation,
JVCIR(94), 2023, pp. 103852.
Elsevier DOI
2306
Weakly supervised semantic segmentation, Class activation map,
Image-level class label, Disturbance injection
BibRef
Zhu, M.[Meilu],
Chen, Z.[Zhen],
Yuan, Y.X.[Yi-Xuan],
FedDM: Federated Weakly Supervised Segmentation via Annotation
Calibration and Gradient De-Conflicting,
MedImg(42), No. 6, June 2023, pp. 1632-1643.
IEEE DOI
2306
Annotations, Training, Noise measurement, Data models,
Image segmentation, Servers, Adaptation models, Federated learning,
medical image segmentation
BibRef
Yin, Y.F.[Yu-Fei],
Deng, J.J.[Jia-Jun],
Zhou, W.G.[Wen-Gang],
Li, L.[Li],
Li, H.Q.[Hou-Qiang],
FI-WSOD: Foreground Information Guided Weakly Supervised Object
Detection,
MultMed(25), 2023, pp. 1890-1902.
IEEE DOI
2306
Task analysis, Proposals, Detectors, Object detection, Training,
Feature extraction, Noise measurement, Object detection,
weakly supervised learning
BibRef
Gama, P.H.T.[Pedro H. T.],
Oliveira, H.[Hugo],
Marcato-Junior, J.[José],
dos Santos, J.A.[Jefersson A.],
Weakly Supervised Few-Shot Segmentation via Meta-Learning,
MultMed(25), 2023, pp. 1784-1797.
IEEE DOI
2306
Image segmentation, Task analysis, Semantics, Annotations, Prototypes,
Biomedical imaging, Training, Agriculture, few-shot, weakly supervised
BibRef
Oliveira, H.[Hugo],
Gama, P.H.T.[Pedro H.T.],
Bloch, I.[Isabelle],
Cesar, R.M.[Roberto Marcondes],
Meta-learners for few-shot weakly-supervised medical image
segmentation,
PR(153), 2024, pp. 110471.
Elsevier DOI
2405
Meta-learning, Weakly supervised segmentation,
Few-shot learning, Medical images, Domain generalization
BibRef
Pu, Y.N.[Yan-Nan],
Sun, J.[Jian],
Tang, N.S.[Nian-Sheng],
Xu, Z.B.[Zong-Ben],
Deep expectation-maximization network for unsupervised image
segmentation and clustering,
IVC(135), 2023, pp. 104717.
Elsevier DOI
2306
Deep clustering, EM algorithm, Image clustering,
Representation learning, Unsupervised image segmentation
BibRef
Shen, W.[Wei],
Peng, Z.L.[Ze-Lin],
Wang, X.[Xuehui],
Wang, H.Y.[Hua-Yu],
Cen, J.Z.[Jia-Zhong],
Jiang, D.S.[Dong-Sheng],
Xie, L.X.[Ling-Xi],
Yang, X.K.[Xiao-Kang],
Tian, Q.[Qi],
A Survey on Label-Efficient Deep Image Segmentation:
Bridging the Gap Between Weak Supervision and Dense Prediction,
PAMI(45), No. 8, August 2023, pp. 9284-9305.
IEEE DOI
2307
Semantic segmentation, Semantics, Training, Annotations,
Task analysis, Taxonomy, Sports equipment, Instance segmentation,
weakly-supervised learning
BibRef
Zhu, Y.[Yueyi],
Zhang, Y.Q.[Yong-Qiang],
Ding, M.L.[Ming-Li],
Zuo, W.M.[Wang-Meng],
Uncertainty-Aware Graph-Guided Weakly Supervised Object Detection,
CirSysVideo(33), No. 7, July 2023, pp. 3257-3269.
IEEE DOI
2307
Task analysis, Proposals, Uncertainty, Object detection,
Location awareness, Training, Annotations,
graph convolution network
BibRef
Li, Y.Q.[Yu-Qiang],
Wu, Y.[Ying],
Liu, C.[Chun],
Wu, X.[Xinyi],
IAC-ReCAM: Two-dimensional attention modulation and category label
guidance for weakly supervised semantic segmentation,
IVC(136), 2023, pp. 104738.
Elsevier DOI
2308
Semantic segmentation, Weakly supervised learning, Attention modulation
BibRef
Chen, J.L.[Jun-Liang],
Lu, W.Z.[Wei-Zeng],
Li, Y.X.[Yue-Xiang],
Shen, L.L.[Lin-Lin],
Duan, J.M.[Jin-Ming],
Adversarial Learning of Object-Aware Activation Map for
Weakly-Supervised Semantic Segmentation,
CirSysVideo(33), No. 8, August 2023, pp. 3935-3946.
IEEE DOI
2308
Cams, Semantic segmentation, Training, Task analysis, Streaming media,
Semantics, Object segmentation, object-aware activation map
BibRef
Huang, Z.[Zuxian],
Wu, G.S.[Gang-Shan],
Wang, L.M.[Li-Min],
Webly-supervised semantic segmentation via curriculum learning,
CVIU(236), 2023, pp. 103810.
Elsevier DOI
2310
Image Segmentation, Webly supervised learning, Weakly supervised learning
BibRef
Song, C.F.[Chun-Feng],
Ouyang, W.L.[Wan-Li],
Zhang, Z.X.[Zhao-Xiang],
Weakly Supervised Semantic Segmentation via Box-Driven Masking and
Filling Rate Shifting,
PAMI(45), No. 12, December 2023, pp. 15996-16012.
IEEE DOI
2311
BibRef
Song, C.F.[Chun-Feng],
Huang, Y.[Yan],
Ouyang, W.L.[Wan-Li],
Wang, L.[Liang],
Box-Driven Class-Wise Region Masking and Filling Rate Guided Loss for
Weakly Supervised Semantic Segmentation,
CVPR19(3131-3140).
IEEE DOI
2002
BibRef
Xu, L.[Lian],
Bennamoun, M.[Mohammed],
Boussaid, F.[Farid],
Laga, H.[Hamid],
Ouyang, W.L.[Wan-Li],
Xu, D.[Dan],
MCTformer+: Multi-Class Token Transformer for Weakly Supervised
Semantic Segmentation,
PAMI(46), No. 12, December 2024, pp. 8380-8395.
IEEE DOI
2411
BibRef
Earlier: A1, A5, A2, A3, A6, Only:
Learning Multi-Modal Class-Specific Tokens for Weakly Supervised
Dense Object Localization,
CVPR23(19596-19605)
IEEE DOI
2309
BibRef
Earlier: A1, A5, A2, A3, A6, Only:
Multi-class Token Transformer for Weakly Supervised Semantic
Segmentation,
CVPR22(4300-4309)
IEEE DOI
2210
Transformers, Location awareness, Semantic segmentation, Semantics,
Prototypes, Training, Task analysis, Weakly supervised learning,
object localization.
Shape, Object detection, Transformers, Segmentation,
grouping and shape analysis
BibRef
Fan, J.S.[Jun-Song],
Zhang, Z.X.[Zhao-Xiang],
Toward Practical Weakly Supervised Semantic Segmentation via
Point-Level Supervision,
IJCV(131), No. 12, December 2023, pp. 3252-3271.
Springer DOI
2311
BibRef
Chen, M.[Man],
Xu, K.[Kun],
Chen, E.[Enping],
Zhang, Y.[Yao],
Xie, Y.F.[Yi-Fei],
Hu, Y.[Yahao],
Pan, Z.S.[Zhi-Song],
Semantic Attention and Structured Model for Weakly Supervised
Instance Segmentation in Optical and SAR Remote Sensing Imagery,
RS(15), No. 21, 2023, pp. 5201.
DOI Link
2311
BibRef
Zhang, M.[Man],
Zhou, Y.[Yong],
Liu, B.[Bing],
Zhao, J.Q.[Jia-Qi],
Yao, R.[Rui],
Shao, Z.W.[Zhi-Wen],
Zhu, H.C.[Han-Cheng],
Weakly Supervised Few-Shot Semantic Segmentation via Pseudo Mask
Enhancement and Meta Learning,
MultMed(25), 2023, pp. 7980-7991.
IEEE DOI
2312
BibRef
Zhang, Y.H.[Yu-Hang],
Tian, S.[Shishun],
Liao, M.[Muxin],
Zhang, Z.Y.[Zheng-Yu],
Zou, W.B.[Wen-Bin],
Xu, C.[Chen],
Fine-Grained Self-Supervision for Generalizable Semantic Segmentation,
CirSysVideo(34), No. 1, January 2024, pp. 371-383.
IEEE DOI
2401
BibRef
Yaganapu, A.[Avinash],
Kang, M.[Mingon],
Multi-layered self-attention mechanism for weakly supervised semantic
segmentation,
CVIU(239), 2024, pp. 103886.
Elsevier DOI
2402
Weakly supervised semantic segmentation, Segmentation,
Self-attention, Image-level labels
BibRef
Wang, W.G.[Wen-Guan],
Sun, G.L.[Guo-Lei],
Van Gool, L.J.[Luc J.],
Looking Beyond Single Images for Weakly Supervised Semantic
Segmentation Learning,
PAMI(46), No. 3, March 2024, pp. 1635-1649.
IEEE DOI
2402
Semantics, Image segmentation, Location awareness, Training, Birds,
Training data, Noise measurement, Semantic segmentation,
cross-image semantic relation
BibRef
Zhou, T.F.[Tian-Fei],
Wang, W.G.[Wen-Guan],
Prototype-Based Semantic Segmentation,
PAMI(46), No. 10, October 2024, pp. 6858-6872.
IEEE DOI
2409
Prototypes, Measurement, Semantic segmentation, Image segmentation,
Vectors, Semantics, Transformers, Semantic segmentation, prototype,
online clustering
BibRef
Sun, G.L.[Guo-Lei],
Wang, W.G.[Wen-Guan],
Dai, J.F.[Ji-Feng],
Van Gool, L.J.[Luc J.],
Mining Cross-Image Semantics for Weakly Supervised Semantic
Segmentation,
ECCV20(II:347-365).
Springer DOI
2011
BibRef
Xu, R.T.[Rong-Tao],
Wang, C.W.[Chang-Wei],
Xu, S.B.[Shi-Biao],
Meng, W.L.[Wei-Liang],
Zhang, X.P.[Xiao-Peng],
Wave-Like Class Activation Map With Representation Fusion for
Weakly-Supervised Semantic Segmentation,
MultMed(26), 2024, pp. 581-592.
IEEE DOI
2402
Semantics, Semantic segmentation, Adaptation models,
Wave functions, Training, Task analysis, Fuses, Class activation map,
weakly supervised semantic segmentation
BibRef
Li, J.X.[Jun-Xia],
Shi, D.[Deshuo],
Cui, Y.[Ying],
Guo, D.Y.[Dong-Yan],
Liu, Q.S.[Qing-Shan],
Adaptive Activation Network for Weakly Supervised Semantic
Segmentation,
MultMed(26), 2024, pp. 6078-6089.
IEEE DOI
2404
Semantic segmentation, Task analysis, Semantics, Adaptive systems,
Training, Noise reduction, Annotations,
scale adaptation
BibRef
Zheng, S.D.[Shang-Dong],
Wu, Z.B.[Ze-Bin],
Xu, Y.[Yang],
Wei, Z.H.[Zhi-Hui],
Weakly Supervised Object Detection for Remote Sensing Images via
Progressive Image-Level and Instance-Level Feature Refinement,
RS(16), No. 7, 2024, pp. 1203.
DOI Link
2404
BibRef
Kim, H.[Hyoseo],
Choe, J.[Junsuk],
Weakly-Supervised Incremental Learning for Semantic Segmentation with
Class Hierarchy,
PRL(182), 2024, pp. 31-38.
Elsevier DOI
2405
Machine learning, Deep learning, Semantic segmentation,
Weak supervision, Incremental learning
BibRef
Hwang, D.J.[Dong-Jun],
Kim, H.[Hyoseo],
Baek, D.[Doyeol],
Kim, H.[Hyunbin],
Kye, I.[Inhye],
Choe, J.[Junsuk],
Curriculum learning with class-label composition for weakly
supervised semantic segmentation,
PRL(188), 2025, pp. 171-177.
Elsevier DOI
2502
Deep learning, Weakly-supervised learning, Computer vision,
Semantic segmentation, Curriculum learning
BibRef
Qian, X.L.[Xiao-Liang],
Lin, C.Y.[Chen-Yang],
Chen, Z.W.[Zhi-Wu],
Wang, W.[Wei],
SAM-Induced Pseudo Fully Supervised Learning for Weakly Supervised
Object Detection in Remote Sensing Images,
RS(16), No. 9, 2024, pp. 1532.
DOI Link
2405
BibRef
Sang, Y.[Yu],
Ma, T.J.[Tian-Jiao],
Liu, Y.N.[Yu-Nan],
Liu, T.[Tong],
Sun, J.G.[Jin-Guang],
Dual Branch Framework Using Positive and Negative Learning for Weakly
Supervised Semantic Segmentation,
SPLetters(31), 2024, pp. 1384-1388.
IEEE DOI
2405
Noise measurement, Training, Semantics, Indexes, Annotations, Noise,
Semantic segmentation,
positive and negative learning
BibRef
Fu, Y.[Yun],
Wang, W.W.[Wen-Wu],
Zhu, L.[Lei],
Ye, X.Y.[Xin-Yue],
Yue, H.G.[Hua-Gang],
Weakly supervised semantic segmentation based on superpixel affinity,
JVCIR(101), 2024, pp. 104168.
Elsevier DOI
2406
Weakly supervised learning, Semantic segmentation,
Feature reorganization, Superpixel affinity
BibRef
David, L.[Lucas],
Pedrini, H.[Helio],
Dias, Z.[Zanoni],
P-NOC: Adversarial training of CAM generating networks for robust
weakly supervised semantic segmentation priors,
JVCIR(102), 2024, pp. 104187.
Elsevier DOI
2407
Semantic Segmentation, Saliency Detection, Weak Supervision,
Adversarial Erasing, Adversarial training, CAM
BibRef
Zhang, B.F.[Bing-Feng],
Gao, X.[Xuru],
Yu, S.Y.[Si-Yue],
Liu, W.F.[Wei-Feng],
Enhanced online CAM: Single-stage weakly supervised semantic
segmentation via collaborative guidance,
PR(156), 2024, pp. 110787.
Elsevier DOI Code:
WWW Link.
2408
Semantic segmentation, Weakly supervised learning, CAM, Single-stage
BibRef
Lee, S.[Sanghuk],
Mun, C.[Cheolhyun],
Uh, Y.J.[Young-Jung],
Choe, J.[Junsuk],
Byun, H.R.[Hye-Ran],
Discovering an inference recipe for weakly-supervised object
localization,
PR(156), 2024, pp. 110838.
Elsevier DOI
2408
Object localization, Weakly-supervised learning,
Semantic segmentation, Test-time processing, Binarization
BibRef
Qin, Z.[Zhen],
Chen, Y.J.[Yu-Jie],
Zhu, G.S.[Guo-Song],
Zhou, E.[Erqiang],
Zhou, Y.J.[Ying-Jie],
Zhou, Y.C.[Yi-Cong],
Zhu, C.[Ce],
Enhanced Pseudo-Label Generation With Self-Supervised Training for
Weakly- Supervised Semantic Segmentation,
CirSysVideo(34), No. 8, August 2024, pp. 7017-7028.
IEEE DOI
2408
Cams, Semantics, Semantic segmentation, Training, Feature extraction,
Convolution, Task analysis, Semantic segmentation,
class attention/activation maps
BibRef
Ren, Q.H.[Qing-Hua],
Lu, S.J.[Shi-Jian],
Mao, Q.[Qirong],
Dong, M.[Ming],
Exploring Prototype-Anchor Contrast for Semantic Segmentation,
CirSysVideo(34), No. 8, August 2024, pp. 7106-7120.
IEEE DOI
2408
Prototypes, Semantics, Semantic segmentation,
Self-supervised learning, Training, Task analysis, Optimization,
prototypical contrast
BibRef
Lu, X.Q.[Xiao-Qiang],
Jiao, L.C.[Li-Cheng],
Li, L.L.[Ling-Ling],
Liu, F.[Fang],
Liu, X.[Xu],
Yang, S.Y.[Shu-Yuan],
Self Pseudo Entropy Knowledge Distillation for Semi-Supervised
Semantic Segmentation,
CirSysVideo(34), No. 8, August 2024, pp. 7359-7372.
IEEE DOI Code:
WWW Link.
2408
Perturbation methods, Training, Data augmentation,
Semantic segmentation, Entropy, Optimization, Semantics,
self knowledge distillation
BibRef
Du, Y.[Ye],
Fu, Z.[Zehua],
Liu, Q.J.[Qing-Jie],
Pixel-Level Domain Adaptation: A New Perspective for Enhancing Weakly
Supervised Semantic Segmentation,
IP(33), 2024, pp. 4654-4669.
IEEE DOI
2409
Cams, Semantic segmentation, Training, Feature extraction, Adaptation models,
Task analysis, Semantics, Semantic segmentation, pseudo-labeling
BibRef
Du, Y.[Ye],
Fu, Z.[Zehua],
Liu, Q.J.[Qing-Jie],
Wang, Y.H.[Yun-Hong],
Weakly Supervised Semantic Segmentation by Pixel-to-Prototype
Contrast,
CVPR22(4310-4319)
IEEE DOI
2210
Image segmentation, Shape, Semantics, Prototypes, Estimation,
Segmentation, grouping and shape analysis
BibRef
Han, W.[Woojung],
Kang, S.[Seil],
Choo, K.[Kyobin],
Hwang, S.J.[Seong Jae],
Complementary branch fusing class and semantic knowledge for robust
weakly supervised semantic segmentation,
PR(157), 2025, pp. 110922.
Elsevier DOI Code:
WWW Link.
2409
Weakly supervised semantic segmentation,
Representation learning, Contrastive learning
BibRef
Wang, W.Z.[Wei-Zheng],
Zhou, L.[Lei],
Wang, H.[Haonan],
Multi-scale feature correspondence and pseudo label retraining
strategy for weakly supervised semantic segmentation,
IVC(150), 2024, pp. 105215.
Elsevier DOI
2409
Weakly supervised semantic segmentation, Vision transformer,
Multi-scale feature correspondence, Pseudo label retraining strategy
BibRef
Guermazi, B.[Boujemaa],
Ksantini, R.[Riadh],
Khan, N.[Naimul],
DynaSeg: A deep dynamic fusion method for unsupervised image
segmentation incorporating feature similarity and spatial continuity,
IVC(150), 2024, pp. 105206.
Elsevier DOI Code:
WWW Link.
2409
Unsupervised learning, Image segmentation
BibRef
Zhang, Y.[Yi],
Zhu, X.T.[Xiao-Tian],
Attention-Based Layer Fusion and Token Masking for Weakly Supervised
Semantic Segmentation,
CirSysVideo(34), No. 9, September 2024, pp. 7912-7921.
IEEE DOI
2410
Transformers, Semantics, Training, Aggregates, Visualization,
Semantic segmentation, Head, over-smoothing
BibRef
Huang, Y.[Ye],
Kang, D.[Di],
Gao, S.H.[Sheng-Hua],
Li, W.[Wen],
Duan, L.X.[Li-Xin],
High-Level Feature Guided Decoding for Semantic Segmentation,
CirSysVideo(34), No. 9, September 2024, pp. 8281-8291.
IEEE DOI
2410
Training, Decoding, Semantic segmentation, Feature extraction,
Task analysis, Spatial resolution, cityscapes
BibRef
Zhuo, W.[Wei],
Wang, Y.[Yuan],
Chen, J.L.[Jun-Liang],
Deng, S.[Songhe],
Wang, Z.[Zhi],
Shen, L.L.[Lin-Lin],
Zhu, W.W.[Wen-Wu],
Enhancing Unsupervised Semantic Segmentation Through Context-Aware
Clustering,
MultMed(26), 2024, pp. 10081-10093.
IEEE DOI
2410
Semantic segmentation, Semantics, Training, Annotations,
Unsupervised learning, Convolutional neural networks,
context-aware embedding
BibRef
Li, S.[Shan],
Yang, L.[Lu],
Cao, P.[Pu],
Li, L.[Liulei],
Ma, H.D.[Hua-Dong],
Frequency-Based Matcher for Long-Tailed Semantic Segmentation,
MultMed(26), 2024, pp. 10395-10405.
IEEE DOI
2410
Semantic segmentation, Task analysis, Transformers,
Image segmentation, Semantics, Tail, Decoding, frequency-based matcher
BibRef
Liu, M.U.[Mingy-Uan],
Zhang, J.C.[Ji-Cong],
Tang, W.[Wei],
Imbalance-Aware Discriminative Clustering for Unsupervised Semantic
Segmentation,
IJCV(132), No. 10, October 2024, pp. 4362-4378.
Springer DOI
2410
BibRef
Liu, Y.[Yue],
Zeng, J.[Jun],
Tao, X.Z.[Xing-Zhen],
Fang, G.[Gang],
Rethinking Self-Supervised Semantic Segmentation: Achieving
End-to-End Segmentation,
PAMI(46), No. 12, December 2024, pp. 10036-10046.
IEEE DOI
2411
Semantics, Semantic segmentation, Image segmentation, Prototypes,
Training, Task analysis, Representation learning, semantic segmentation
BibRef
Xie, Z.Z.[Zhao-Zhi],
Jiang, W.H.[Wei-Hao],
Yang, Y.[Yuwen],
Lu, H.T.[Hong-Tao],
Superpixel Guided Network for Weakly Supervised Semantic Segmentation,
SPLetters(31), 2024, pp. 2885-2889.
IEEE DOI
2411
Semantic segmentation, Image reconstruction, Feature extraction, Annotations,
Image color analysis, Training, Task analysis, long-range dependency
BibRef
Wang, J.[Jian],
Dai, T.H.[Tian-Hong],
Zhao, X.[Xinqiao],
García-Fernández, Á.F.[Ángel F.],
Lim, E.G.[Eng Gee],
Xiao, J.[Jimin],
Class Activation Map Calibration for Weakly Supervised Semantic
Segmentation,
CirSysVideo(34), No. 11, November 2024, pp. 11668-11681.
IEEE DOI
2412
Cams, Prototypes, Feature extraction, Calibration,
Semantic segmentation, Noise,
class activation maps calibration
BibRef
Yin, X.[Xu],
Im, W.B.[Woo-Bin],
Min, D.B.[Dong-Bo],
Huo, Y.[Yuchi],
Pan, F.[Fei],
Yoon, S.E.[Sung-Eui],
Fine-Grained Background Representation for Weakly Supervised Semantic
Segmentation,
CirSysVideo(34), No. 11, November 2024, pp. 11739-11750.
IEEE DOI
2412
Semantics, Semantic segmentation, Prototypes, Task analysis,
Training, Loss measurement,
weakly supervised image segmentation
BibRef
Hao, X.[Xuze],
Jiang, X.[Xuhao],
Ni, W.Q.[Wen-Qian],
Tan, W.M.[Wei-Min],
Yan, B.[Bo],
Prompt-Guided Semantic-Aware Distillation for Weakly Supervised
Incremental Semantic Segmentation,
CirSysVideo(34), No. 11, November 2024, pp. 10632-10645.
IEEE DOI Code:
WWW Link.
2412
Semantics, Semantic segmentation, Automobiles, Cows, Annotations,
Horses, Task analysis, prompt tuning
BibRef
Wang, C.Y.[Chun-Yan],
Zhang, D.[Dong],
Yan, R.[Rui],
Boosting Weakly-Supervised Image Segmentation via Representation,
Transform, and Compensator,
CirSysVideo(34), No. 11, November 2024, pp. 11013-11025.
IEEE DOI Code:
WWW Link.
2412
Cams, Training, Task analysis, Prototypes, Accuracy,
Semantic segmentation, Weakly-supervised learning, contrastive learning
BibRef
Cai, Z.W.[Zhi-Wen],
Xu, B.D.[Bao-Dong],
Yu, Q.[Qiangyi],
Zhang, X.Y.[Xin-Yu],
Yang, J.Y.[Jing-Ya],
Wei, H.D.[Hao-Dong],
Li, S.Q.[Shi-Qi],
Song, Q.[Qian],
Xiong, H.[Hang],
Wu, H.[Hao],
Wu, W.B.[Wen-Bin],
Shi, Z.H.[Zhi-Hua],
Hu, Q.[Qiong],
A cost-effective and robust mapping method for diverse crop types
using weakly supervised semantic segmentation with sparse point
samples,
PandRS(218), 2024, pp. 260-276.
Elsevier DOI Code:
WWW Link.
2412
Crop type mapping, Limited sparse point annotations,
Weakly supervised semantic segmentation, multi-temporal Sentinel-2 images
BibRef
Zhang, S.W.[Shi-Wei],
Wang, Z.Z.[Zheng-Zheng],
Ke, W.[Wei],
One point is all you need for weakly supervised object detection,
PR(159), 2025, pp. 111087.
Elsevier DOI
2412
Object detection, Weak annotation, Similarity-based learning
BibRef
Cheng, K.Y.[Ke-Yang],
Tang, J.F.[Jing-Feng],
Gu, H.J.[Hong-Jian],
Wan, H.[Hao],
Li, M.[Maozhen],
Cross-Block Sparse Class Token Contrast for Weakly Supervised
Semantic Segmentation,
CirSysVideo(34), No. 12, December 2024, pp. 13004-13015.
IEEE DOI Code:
WWW Link.
2501
Semantics, Semantic segmentation, Task analysis, Transformers, Noise,
Feature extraction, Training, Weakly supervised, dynamic sparse
BibRef
Saeed, S.U.[Shaheer U.],
Huang, S.Q.[Shi-Qi],
Ramalhinho, J.[Joăo],
Gayo, I.J.M.B.[Iani J.M.B.],
Montańa-Brown, N.[Nina],
Bonmati, E.[Ester],
Pereira, S.P.[Stephen P.],
Davidson, B.[Brian],
Barratt, D.C.[Dean C.],
Clarkson, M.J.[Matthew J.],
Hu, Y.P.[Yi-Peng],
Competing for Pixels: A Self-Play Algorithm for Weakly-Supervised
Semantic Segmentation,
PAMI(47), No. 2, February 2025, pp. 825-839.
IEEE DOI
2501
Games, Detectors, Annotations, Training, Sports, Semantic segmentation,
Reinforcement learning, Biomedical imaging, Sensitivity,
weak supervision
BibRef
Wang, W.Z.[Wei-Zheng],
Zeng, C.[Chao],
Wang, H.[Haonan],
Zhou, L.[Lei],
Local optimization cropping and boundary enhancement for end-to-end
weakly-supervised segmentation network,
CVIU(251), 2025, pp. 104260.
Elsevier DOI Code:
WWW Link.
2501
Deep learning, Weakly-supervised semantic segmentation,
Single-stage, Boundary enhancement, Local optimization cropping
BibRef
Gu, X.F.[Xian-Fan],
Hu, Y.D.[Ying-Dong],
Wen, C.[Chuan],
Gao, Y.[Yang],
Self-supervised vision transformers for semantic segmentation,
CVIU(251), 2025, pp. 104272.
Elsevier DOI
2501
Self-supervised representation learning,
Semantic segmentation, Vision transformer
BibRef
Zhu, L.H.[Liang-Hui],
Wang, X.G.[Xing-Gang],
Feng, J.P.[Jia-Pei],
Cheng, T.H.[Tian-Heng],
Li, Y.Y.[Ying-Yue],
Jiang, B.[Bo],
Zhang, D.W.[Ding-Wen],
Han, J.W.[Jun-Wei],
WeakCLIP: Adapting CLIP for Weakly-Supervised Semantic Segmentation,
IJCV(133), No. 3, March 2025, pp. 1085-1105.
Springer DOI
2502
BibRef
Moraes, D.[Daniel],
Campagnolo, M.L.[Manuel L.],
Caetano, M.[Mário],
A Weakly Supervised and Self-Supervised Learning Approach for
Semantic Segmentation of Land Cover in Satellite Images with National
Forest Inventory Data,
RS(17), No. 4, 2025, pp. 711.
DOI Link
2502
BibRef
Zhou, L.[Lei],
Wang, J.S.[Jia-Song],
Luo, J.[Jing],
Guo, Y.H.[Yu-Heng],
Li, X.X.[Xiao-Xiao],
Optimizing Multi-Task Network with Learned Prototypes for Weakly
Supervised Semantic Segmentation,
SP:IC(134), 2025, pp. 117272.
Elsevier DOI
2503
Weakly supervised semantic segmentation, Multi-task network, Prototype learning
BibRef
Yang, Z.Q.[Zi-Qian],
Zhao, X.Q.[Xin-Qiao],
Yao, C.[Chao],
Zhang, Q.[Quan],
Xiao, J.[Jimin],
M-SEE: A multi-scale encoder enhancement framework for end-to-end
Weakly Supervised Semantic Segmentation,
PR(162), 2025, pp. 111348.
Elsevier DOI
2503
Weakly Supervised Semantic Segmentation, End-to-end framework,
Knowledge distillation, Image-level labels
BibRef
Fujimori, I.[Izumi],
Oono, M.[Masaki],
Shishibori, M.[Masami],
BAO: Background-aware activation map optimization for weakly
supervised semantic segmentation without background threshold,
JVCIR(107), 2025, pp. 104404.
Elsevier DOI Code:
WWW Link.
2503
Weakly supervised learning, Semantic segmentation,
Class activation map, Deep learning
BibRef
Pang, B.[Bo],
Wei, Z.Y.[Zhen-Yu],
Lin, J.L.[Jing-Li],
Lu, C.W.[Ce-Wu],
Auto-Pairing Positives Through Implicit Relation Circulation for
Discriminative Self-Learning,
PAMI(47), No. 4, April 2025, pp. 2739-2753.
IEEE DOI
2503
Contrastive learning, Semantics, Visualization,
Representation learning, Semantic segmentation, Robustness, auto pairing
BibRef
Zhang, B.F.[Bing-Feng],
Yu, S.Y.[Si-Yue],
Xiao, J.[Jimin],
Wei, Y.C.[Yun-Chao],
Zhao, Y.[Yao],
Frozen CLIP-DINO: A Strong Backbone for Weakly Supervised Semantic
Segmentation,
PAMI(47), No. 5, May 2025, pp. 4198-4214.
IEEE DOI
2504
BibRef
Earlier: A1, A2, A4, A5, A3:
Frozen CLIP: A Strong Backbone for Weakly Supervised Semantic
Segmentation,
CVPR24(3796-3806)
IEEE DOI Code:
WWW Link.
2410
Training, Semantics, Decoding, Semantic segmentation,
Feature extraction, Dogs, Transformers, Costs, Pipelines, Hands, DINO.
Costs, Pipelines, Predictive models, CLIP, Weakly-supervised
BibRef
Shi, H.Y.[Han-Yu],
Liu, F.[Fayao],
Wu, Z.H.[Zhong-Hua],
Xu, Y.[Yi],
Lin, G.S.[Guo-Sheng],
Weakly Supervised Segmentation on Outdoor 4D Point Clouds With
Progressive 4D Grouping,
PAMI(47), No. 5, May 2025, pp. 3487-3499.
IEEE DOI
2504
Annotations, Point cloud compression, Training, Contrastive learning,
Labeling, Solid modeling, Surveys, weakly supervised semantic segmentation
BibRef
Zhang, B.F.[Bing-Feng],
Xiao, J.[Jimin],
Zhao, Y.[Yao],
Dynamic feature regularized loss for weakly supervised semantic
segmentation,
PR(164), 2025, pp. 111540.
Elsevier DOI Code:
WWW Link.
2504
Weakly supervised, Semantic segmentation, Scribble annotation, Regularized loss
BibRef
Liu, P.[Peng],
Huang, B.[Boxue],
Jin, T.T.[Ting-Ting],
Long, H.[Hui],
Low-Shot Weakly Supervised Object Detection for Remote Sensing Images
via Part Domination-Based Active Learning and Enhanced Fine-Tuning,
RS(17), No. 7, 2025, pp. 1155.
DOI Link
2504
BibRef
Choi, J.[Junsu],
Lee, J.S.[Jin-Seop],
Kim, N.R.[Noo-Ri],
Yoon, S.[SuHyun],
Lee, J.H.[Jee-Hyong],
Feature-Level and Spatial-Level Activation Expansion for
Weakly-Supervised Semantic Segmentation,
WACV25(8712-8722)
IEEE DOI Code:
WWW Link.
2505
Representation learning, Accuracy, Semantic segmentation,
Perturbation methods, Benchmark testing, Feature extraction, Cams,
semantic segmentation
BibRef
Lin, C.S.[Ci-Siang],
Wang, C.Y.[Chien-Yi],
Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Chen, M.H.[Min-Hung],
Semantic Prompt Learning for Weakly-Supervised Semantic Segmentation,
WACV25(8764-8774)
IEEE DOI Code:
WWW Link.
2505
Training, Heating systems, Visualization, Semantic segmentation,
Semantics, Refining, Benchmark testing, Data models, Standards
BibRef
Lee, Y.H.[Yuan-Hao],
Yang, F.E.[Fu-En],
Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
A Pixel-Level Meta-Learner for Weakly Supervised Few-Shot Semantic
Segmentation,
WACV22(1607-1617)
IEEE DOI
2202
Training, Image segmentation,
Computational modeling, Semantics, Benchmark testing,
Semi- and Un- supervised Learning
BibRef
Yang, X.Y.[Xin-Yu],
Rahmani, H.[Hossein],
Black, S.[Sue],
Williams, B.M.[Bryan M.],
Weakly Supervised Co-training with Swapping Assignments for Semantic
Segmentation,
ECCV24(LVI: 459-478).
Springer DOI
2412
BibRef
Jo, S.[Sanghyun],
Pan, F.[Fei],
Yu, I.J.[In-Jae],
Kim, K.[Kyungsu],
DHR: Dual Features-driven Hierarchical Rebalancing in Inter- and
Intra-class Regions for Weakly-supervised Semantic Segmentation,
ECCV24(LXXXI: 231-248).
Springer DOI
2412
BibRef
Jang, S.[Soojin],
Yun, J.[Jungmin],
Kwon, J.[Junehyoung],
Lee, E.[Eunju],
Kim, Y.[Youngbin],
Dial: Dense Image-text Alignment for Weakly Supervised Semantic
Segmentation,
ECCV24(LXIX: 248-266).
Springer DOI
2412
BibRef
Seong, H.S.[Hyun Seok],
Moon, W.[WonJun],
Lee, S.[SuBeen],
Heo, J.P.[Jae-Pil],
Progressive Proxy Anchor Propagation for Unsupervised Semantic
Segmentation,
ECCV24(XLIX: 472-490).
Springer DOI
2412
BibRef
Yoon, S.H.[Sung-Hoon],
Kwon, H.[Hoyong],
Jeong, J.[Jaeseok],
Park, D.[Daehee],
Yoon, K.J.[Kuk-Jin],
Diffusion-guided Weakly Supervised Semantic Segmentation,
ECCV24(XLVIII: 393-411).
Springer DOI
2412
BibRef
Chen, T.[Tao],
Jiang, X.[Xiruo],
Pei, G.[Gensheng],
Sun, Z.[Zeren],
Wang, Y.C.[Yu-Cheng],
Yao, Y.Z.[Ya-Zhou],
Knowledge Transfer with Simulated Inter-image Erasing for Weakly
Supervised Semantic Segmentation,
ECCV24(XLII: 441-458).
Springer DOI
2412
BibRef
Cho, H.[Hoonhee],
Yoon, S.H.[Sung-Hoon],
Kweon, H.[Hyeokjun],
Yoon, K.J.[Kuk-Jin],
Finding Meaning in Points: Weakly Supervised Semantic Segmentation for
Event Cameras,
ECCV24(XL: 266-286).
Springer DOI
2412
BibRef
Si, C.J.[Chong-Jie],
Wang, X.H.[Xue-Hui],
Yang, X.K.[Xiao-Kang],
Shen, W.[Wei],
Tendency-driven Mutual Exclusivity for Weakly Supervised Incremental
Semantic Segmentation,
ECCV24(XXXV: 37-54).
Springer DOI
2412
BibRef
Kwon, H.[Hoyong],
Jeong, J.[Jaeseok],
Yoon, S.H.[Sung-Hoon],
Yoon, K.J.[Kuk-Jin],
Phase Concentration and Shortcut Suppression for Weakly Supervised
Semantic Segmentation,
ECCV24(XXXII: 293-312).
Springer DOI
2412
BibRef
Yoshihashi, R.[Ryota],
Otsuka, Y.[Yuya],
Doi, K.[Kenji],
Tanaka, T.[Tomohiro],
Kataoka, H.[Hirokatsu],
Exploring Limits of Diffusion-synthetic Training with Weakly Supervised
Semantic Segmentation,
ACCV24(V: 167-186).
Springer DOI
2412
BibRef
Xu, X.X.[Xiao-Xu],
Yuan, Y.T.[Yi-Tian],
Li, J.L.[Jin-Long],
Zhang, Q.[Qiudan],
Jie, Z.Q.[Ze-Qun],
Ma, L.[Lin],
Tang, H.[Hao],
Sebe, N.[Nicu],
Wang, X.[Xu],
3d Weakly Supervised Semantic Segmentation with 2d Vision-language
Guidance,
ECCV24(LXXIII: 87-104).
Springer DOI
2412
BibRef
Kamra, C.G.[Chanda Grover],
Mastan, I.D.[Indra Deep],
Kumar, N.[Nitin],
Gupta, D.[Debayan],
Simsam: Simple Siamese Representations Based Semantic Affinity Matrix
for Unsupervised Image Segmentation,
ICIP24(1172-1178)
IEEE DOI Code:
WWW Link.
2411
Correlation, Codes, Annotations, Semantic segmentation, Semantics,
Self-supervised learning, Object segmentation,
Siamese network
BibRef
Nandama, S.[Srinivasa],
Atitoa, S.[Sara],
Fengb, Z.H.[Zhen-Hua],
Kittlerb, J.V.[Josef V.],
Awaisa, M.[Muhammad],
Investigating Self-Supervised Methods for Label-Efficient Learning,
ICIP24(1193-1199)
IEEE DOI
2411
Analytical models, Semantic segmentation, Semantics,
Contrastive learning, Performance gain, Transformers,
Deep Learning
BibRef
Kim, C.[Chanyoung],
Han, W.[Woojung],
Ju, D.[Dayun],
Hwang, S.J.[Seong Jae],
EAGLE: Eigen Aggregation Learning for Object-Centric Unsupervised
Semantic Segmentation,
CVPR24(3523-3533)
IEEE DOI
2410
Representation learning, Accuracy, Laplace equations,
Semantic segmentation, Semantics, Prototypes, Graph Theory
BibRef
Niu, D.[Dantong],
Wang, X.D.[Xu-Dong],
Han, X.Y.[Xin-Yang],
Lian, L.[Long],
Herzig, R.[Roei],
Darrell, T.J.[Trevor J.],
Unsupervised Universal Image Segmentation,
CVPR24(22744-22754)
IEEE DOI
2410
Instance segmentation, Semantic segmentation,
Computational modeling, Semantics, Manuals, Performance gain
BibRef
Wang, J.Y.[Jing-Yun],
Kang, G.L.[Guo-Liang],
Learn to Rectify the Bias of CLIP for Unsupervised Semantic
Segmentation,
CVPR24(4102-4112)
IEEE DOI Code:
WWW Link.
2410
Visualization, Annotations, Semantic segmentation,
Benchmark testing, Transformers,
unsupervised semantic segmentation
BibRef
Sick, L.[Leon],
Engel, D.[Dominik],
Hermosilla, P.[Pedro],
Ropinski, T.[Timo],
Unsupervised Semantic Segmentation Through Depth-Guided Feature
Correlation and Sampling,
CVPR24(3637-3646)
IEEE DOI
2410
Training, Correlation, Semantic segmentation, Semantics,
Neural networks, Unsupervised Semantic Segmentation,
Semantic Segmentation
BibRef
Yang, Z.W.[Zhi-Wei],
Fu, K.[Kexue],
Duan, M.[Minghong],
Qu, L.[Linhao],
Wang, S.[Shuo],
Song, Z.J.[Zhi-Jian],
Separate and Conquer: Decoupling Co-occurrence via Decomposition and
Representation for Weakly Supervised Semantic Segmentation,
CVPR24(3606-3615)
IEEE DOI
2410
Couplings, Codes, Semantic segmentation, Semantics, Pipelines,
Noise reduction, Weakly supervised semantic segmentation,
Contrastive learning
BibRef
Zhao, X.[Xinqiao],
Yang, Z.Q.[Zi-Qian],
Dai, T.H.[Tian-Hong],
Zhang, B.F.[Bing-Feng],
Xiao, J.[Jimin],
PSDPM: Prototype-based Secondary Discriminative Pixels Mining for
Weakly Supervised Semantic Segmentation,
CVPR24(3437-3446)
IEEE DOI Code:
WWW Link.
2410
Costs, Correlation, Codes, Semantic segmentation,
Computational modeling, Prototypes, Weakly Supervised, Semantic Segmentation
BibRef
Wu, Y.[Yuanchen],
Ye, X.C.[Xi-Chen],
Yang, K.[Kequan],
Li, J.[Jide],
Li, X.Q.[Xiao-Qiang],
DuPL: Dual Student with Trustworthy Progressive Learning for Robust
Weakly Supervised Semantic Segmentation,
CVPR24(3534-3543)
IEEE DOI Code:
WWW Link.
2410
Training, Codes, Filtering, Semantic segmentation, Noise, Pipelines
BibRef
Tang, F.L.[Fei-Long],
Xu, Z.X.[Zhong-Xing],
Qu, Z.J.[Zhao-Jun],
Feng, W.[Wei],
Jiang, X.J.[Xing-Jian],
Ge, Z.Y.[Zong-Yuan],
Hunting Attributes: Context Prototype-Aware Learning for Weakly
Supervised Semantic Segmentation,
CVPR24(3324-3334)
IEEE DOI Code:
WWW Link.
2410
Training, Semantic segmentation, Semantics, Prototypes,
Weakly Supervised Semantic Segmentation, Attributes
BibRef
Kim, B.[Beomyoung],
Yoo, Y.J.[Young-Joon],
Rhee, C.E.[Chae Eun],
Kim, J.[Junmo],
Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance
Segmentation via Semantic Knowledge Transfer and Self-Refinement,
CVPR22(4268-4277)
IEEE DOI
2210
Training, Location awareness, Image segmentation, Shape, Semantics,
Proposals, Segmentation,
Self- semi- meta- unsupervised learning
BibRef
Hong, S.[Seungbum],
Yoon, J.[Jihun],
Choi, M.K.[Min-Kook],
Kim, J.[Junmo],
Self-Supervised Knowledge Transfer via Loosely Supervised Auxiliary
Tasks,
WACV22(2947-2956)
IEEE DOI
2202
Training, Knowledge engineering, Codes,
Transfer learning, Heterogeneous networks,
Semi- and Un- supervised Learning
BibRef
Yoon, S.H.[Sung-Hoon],
Kwon, H.[Hoyong],
Kim, H.[Hyeonseong],
Yoon, K.J.[Kuk-Jin],
Class Tokens Infusion for Weakly Supervised Semantic Segmentation,
CVPR24(3595-3605)
IEEE DOI Code:
WWW Link.
2410
Codes, Image color analysis, Deformation, Semantic segmentation,
Transformers, Cams, Weakly Supervised Semantic Segmentation
BibRef
Tay, C.P.[Chiat-Pin],
Subbaraju, V.[Vigneshwaran],
Kandappu, T.[Thivya],
PrivObfNet: A Weakly Supervised Semantic Segmentation Model for Data
Protection,
WACV24(2410-2420)
IEEE DOI
2404
Privacy, Correlation, Social networking (online),
Semantic segmentation, Computational modeling, Urban areas,
Vision + language and/or other modalities
BibRef
Jonnarth, A.[Arvi],
Zhang, Y.S.[Yu-Shan],
Felsberg, M.[Michael],
High-fidelity Pseudo-labels for Boosting Weakly-Supervised
Segmentation,
WACV24(999-1008)
IEEE DOI Code:
WWW Link.
2404
Training, Image segmentation, Monte Carlo methods,
Image color analysis, Semantic segmentation
BibRef
Kwon, J.[JuneHyoung],
Lee, E.[Eunju],
Cho, Y.[Yunsung],
Kim, Y.[YoungBin],
Learning to Detour: Shortcut Mitigating Augmentation for Weakly
Supervised Semantic Segmentation,
WACV24(808-817)
IEEE DOI
2404
Location awareness, Training, Measurement, Costs, Correlation,
Semantic segmentation, Algorithms
BibRef
Yang, X.B.[Xiao-Bo],
Gong, X.J.[Xiao-Jin],
Foundation Model Assisted Weakly Supervised Semantic Segmentation,
WACV24(512-521)
IEEE DOI
2404
Training, Codes, Semantic segmentation, Task analysis,
Image classification, Algorithms,
Vision + language and/or other modalities
BibRef
Mun, C.[Cheolhyun],
Lee, S.[Sanghuk],
Uh, Y.J.[Young-Jung],
Choe, J.[Junsuk],
Byun, H.R.[Hye-Ran],
Small Objects Matters in Weakly-supervised Semantic Segmentation,
WACV24(413-422)
IEEE DOI
2404
Training, Measurement, Semantic segmentation, Task analysis,
Algorithms, Image recognition and understanding, Algorithms, Datasets and evaluations
BibRef
Murugesan, B.[Balamurali],
Hussain, R.[Rukhshanda],
Bhattacharya, R.[Rajarshi],
Ben Ayed, I.[Ismail],
Dolz, J.[Jose],
Prompting classes: Exploring the Power of Prompt Class Learning in
Weakly Supervised Semantic Segmentation,
WACV24(290-301)
IEEE DOI
2404
Adaptation models, Codes, Semantic segmentation, Focusing,
Benchmark testing, Performance gain, Algorithms,
Vision + language and/or other modalities
BibRef
Wu, F.W.[Fang-Wen],
He, J.X.[Jing-Xuan],
Yin, Y.F.[Yu-Fei],
Hao, Y.B.[Yan-Bin],
Huang, G.[Gang],
Cheng, L.[Lechao],
Masked Collaborative Contrast for Weakly Supervised Semantic
Segmentation,
WACV24(851-860)
IEEE DOI Code:
WWW Link.
2404
Semantic segmentation, Source coding, Computational modeling,
Semantics, Collaboration, Self-supervised learning, Algorithms
BibRef
Yin, Y.F.[Yu-Fei],
Deng, J.J.[Jia-Jun],
Zhou, W.G.[Wen-Gang],
Li, L.[Li],
Li, H.Q.[Hou-Qiang],
Cyclic-Bootstrap Labeling for Weakly Supervised Object Detection,
ICCV23(6985-6995)
IEEE DOI Code:
WWW Link.
2401
BibRef
Chen, L.[Liyi],
Lei, C.Y.[Chen-Yang],
Li, R.H.[Rui-Huang],
Li, S.[Shuai],
Zhang, Z.X.[Zhao-Xiang],
Zhang, L.[Lei],
FPR: False Positive Rectification for Weakly Supervised Semantic
Segmentation,
ICCV23(1108-1118)
IEEE DOI Code:
WWW Link.
2401
BibRef
Shinoda, R.[Risa],
Hayamizu, R.[Ryo],
Nakashima, K.[Kodai],
Inoue, N.[Nakamasa],
Yokota, R.[Rio],
Kataoka, H.[Hirokatsu],
SegRCDB: Semantic Segmentation via Formula-Driven Supervised Learning,
ICCV23(19997-20006)
IEEE DOI Code:
WWW Link.
2401
BibRef
Peng, Z.L.[Ze-Lin],
Wang, G.C.[Guan-Chun],
Xie, L.X.[Ling-Xi],
Jiang, D.S.[Dong-Sheng],
Shen, W.[Wei],
Tian, Q.[Qi],
USAGE: A Unified Seed Area Generation Paradigm for Weakly Supervised
Semantic Segmentation,
ICCV23(624-634)
IEEE DOI
2401
BibRef
Karimijafarbigloo, S.[Sanaz],
Azad, R.[Reza],
Kazerouni, A.[Amirhossein],
Velichko, Y.[Yury],
Bagci, U.[Ulas],
Merhof, D.[Dorit],
Self-supervised Semantic Segmentation:
Consistency over Transformation,
CVAMD23(2646-2655)
IEEE DOI
2401
BibRef
Wang, C.W.[Chang-Wei],
Xu, R.T.[Rong-Tao],
Xu, S.B.[Shi-Biao],
Meng, W.L.[Wei-Liang],
Zhang, X.P.[Xiao-Peng],
Treating Pseudo-labels Generation as Image Matting for Weakly
Supervised Semantic Segmentation,
ICCV23(755-765)
IEEE DOI
2401
BibRef
Jo, S.[Sanghyun],
Yu, I.J.[In-Jae],
Kim, K.[Kyungsu],
MARS: Model-agnostic Biased Object Removal without Additional
Supervision for Weakly-Supervised Semantic Segmentation,
ICCV23(614-623)
IEEE DOI Code:
WWW Link.
2401
BibRef
Deng, Z.J.[Zhi-Jie],
Luo, Y.[Yucen],
Learning Neural Eigenfunctions for Unsupervised Semantic Segmentation,
ICCV23(551-561)
IEEE DOI Code:
WWW Link.
2401
BibRef
Sun, W.X.[Wei-Xuan],
Zhang, Y.H.[Yan-Hao],
Qin, Z.[Zhen],
Liu, Z.Y.[Zhe-Yuan],
Cheng, L.[Lin],
Wang, F.[Fanyi],
Zhong, Y.[Yiran],
Barnes, N.M.[Nick M.],
All-Pairs Consistency Learning for Weakly Supervised Semantic
Segmentation,
NIVT23(826-837)
IEEE DOI
2401
BibRef
Liu, Y.[Yanan],
Zhang, L.[Libao],
Mutually Supervised Learning via Interactive Consistency for
Geographic Object Segmentation from Weakly Labeled Remote Sensing
Imagery,
ICIP23(2985-2989)
IEEE DOI
2312
BibRef
Zhang, H.[Hao],
Li, F.[Feng],
Xu, H.Z.[Huai-Zhe],
Huang, S.J.[Shi-Jia],
Liu, S.L.[Shi-Long],
Ni, L.M.[Lionel M.],
Zhang, L.[Lei],
MP-Former: Mask-Piloted Transformer for Image Segmentation,
CVPR23(18074-18083)
IEEE DOI
2309
BibRef
Reiß, S.[Simon],
Seibold, C.[Constantin],
Freytag, A.[Alexander],
Rodner, E.[Erik],
Stiefelhagen, R.[Rainer],
Decoupled Semantic Prototypes enable learning from diverse annotation
types for semi-weakly segmentation in expert-driven domains,
CVPR23(15495-15506)
IEEE DOI
2309
BibRef
Chen, Z.Z.[Zhao-Zheng],
Sun, Q.R.[Qian-Ru],
Extracting Class Activation Maps from Non-Discriminative Features as
well,
CVPR23(3135-3144)
IEEE DOI
2309
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Wu, L.S.[Lin-Shan],
Zhong, Z.[Zhun],
Fang, L.Y.[Le-Yuan],
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Liu, Q.[Qiang],
Ma, J.Y.[Jia-Yi],
Chen, H.[Hao],
Sparsely Annotated Semantic Segmentation with Adaptive Gaussian
Mixtures,
CVPR23(15454-15464)
IEEE DOI
2309
BibRef
Feng, Q.L.[Qian-Li],
Gadde, R.[Raghudeep],
Liao, W.[Wentong],
Ramon, E.[Eduard],
Martinez, A.[Aleix],
Network-Free, Unsupervised Semantic Segmentation with Synthetic
Images,
CVPR23(23602-23610)
IEEE DOI
2309
BibRef
Yu, C.H.[Chao-Hui],
Zhou, Q.[Qiang],
Li, J.L.[Jing-Liang],
Yuan, J.L.[Jian-Long],
Wang, Z.B.[Zhi-Bin],
Wang, F.[Fan],
Foundation Model Drives Weakly Incremental Learning for Semantic
Segmentation,
CVPR23(23685-23694)
IEEE DOI
2309
BibRef
Cheng, Z.[Zesen],
Qiao, P.C.[Peng-Chong],
Li, K.[Kehan],
Li, S.H.[Si-Heng],
Wei, P.X.[Peng-Xu],
Ji, X.Y.[Xiang-Yang],
Yuan, L.[Li],
Liu, C.[Chang],
Chen, J.[Jie],
Out-of-Candidate Rectification for Weakly Supervised Semantic
Segmentation,
CVPR23(23673-23684)
IEEE DOI
2309
BibRef
Hanna, J.[Joëlle],
Mommert, M.[Michael],
Borth, D.[Damian],
Sparse Multimodal Vision Transformer for Weakly Supervised Semantic
Segmentation,
EarthVision23(2145-2154)
IEEE DOI
2309
BibRef
Cheng, T.H.[Tian-Heng],
Wang, X.G.[Xing-Gang],
Chen, S.Y.[Shao-Yu],
Zhang, Q.[Qian],
Liu, W.Y.[Wen-Yu],
BoxTeacher: Exploring High-Quality Pseudo Labels for Weakly
Supervised Instance Segmentation,
CVPR23(3145-3154)
IEEE DOI
2309
BibRef
Kim, B.[Beomyoung],
Jeong, J.[Joonhyun],
Han, D.Y.[Dong-Yoon],
Hwang, S.J.[Sung Ju],
The Devil is in the Points: Weakly Semi-Supervised Instance
Segmentation via Point-Guided Mask Representation,
CVPR23(11360-11370)
IEEE DOI
2309
BibRef
Wang, Y.C.[Yu-Chao],
Fei, J.J.[Jing-Jing],
Wang, H.C.[Hao-Chen],
Li, W.[Wei],
Bao, T.P.[Tian-Peng],
Wu, L.W.[Li-Wei],
Zhao, R.[Rui],
Shen, Y.J.[Yu-Jun],
Balancing Logit Variation for Long-Tailed Semantic Segmentation,
CVPR23(19561-19573)
IEEE DOI
2309
BibRef
Zhong, Z.S.[Zhi-Sheng],
Cui, J.[Jiequan],
Yang, Y.[Yibo],
Wu, X.Y.[Xiao-Yang],
Qi, X.J.[Xiao-Juan],
Zhang, X.Y.[Xiang-Yu],
Jia, J.Y.[Jia-Ya],
Understanding Imbalanced Semantic Segmentation Through Neural
Collapse,
CVPR23(19550-19559)
IEEE DOI
2309
BibRef
Seong, H.S.[Hyun Seok],
Moon, W.[WonJun],
Lee, S.[SuBeen],
Heo, J.P.[Jae-Pil],
Leveraging Hidden Positives for Unsupervised Semantic Segmentation,
CVPR23(19540-19549)
IEEE DOI
2309
BibRef
Li, K.[Kehan],
Wang, Z.[Zhennan],
Cheng, Z.[Zesen],
Yu, R.[Runyi],
Zhao, Y.F.[Yi-Fan],
Song, G.[Guoli],
Liu, C.[Chang],
Yuan, L.[Li],
Chen, J.[Jie],
ACSeg: Adaptive Conceptualization for Unsupervised Semantic
Segmentation,
CVPR23(7162-7172)
IEEE DOI
2309
BibRef
Yang, Y.[Yong],
Chen, Q.[Qiong],
Feng, Y.[Yuan],
Huang, T.[Tianlin],
MIANet: Aggregating Unbiased Instance and General Information for
Few-Shot Semantic Segmentation,
CVPR23(7131-7140)
IEEE DOI
2309
BibRef
Ru, L.X.[Li-Xiang],
Zheng, H.L.[He-Liang],
Zhan, Y.B.[Yi-Bing],
Du, B.[Bo],
Token Contrast for Weakly-Supervised Semantic Segmentation,
CVPR23(3093-3102)
IEEE DOI
2309
BibRef
Zhang, H.R.[Hong-Run],
Burrows, L.[Liam],
Meng, Y.[Yanda],
Sculthorpe, D.[Declan],
Mukherjee, A.[Abhik],
Coupland, S.E.[Sarah E],
Chen, K.[Ke],
Zheng, Y.L.[Ya-Lin],
Weakly Supervised Segmentation with Point Annotations for
Histopathology Images via Contrast-Based Variational Model,
CVPR23(15630-15640)
IEEE DOI
2309
BibRef
Akiva, P.[Peri],
Dana, K.[Kristin],
Single Stage Weakly Supervised Semantic Segmentation of Complex
Scenes,
WACV23(5943-5954)
IEEE DOI
2302
Training, Annotations, Semantic segmentation, Semantics, Focusing,
Benchmark testing, Applications: Agriculture, Remote Sensing
BibRef
Cheng, W.L.[Wen-Li],
Jiao, J.J.[Jia-Jia],
CAU: A Consensus Model of Augmented Unlabeled Data for Medical Image
Segmentation,
ICIVC22(368-374)
IEEE DOI
2301
Training, Image segmentation, Machine learning algorithms,
Semisupervised learning, Prediction algorithms, Data models, Data Augmentation
BibRef
Fan, Z.K.[Zhen-Kun],
Sun, X.[Xin],
Dong, J.Y.[Jun-Yu],
Average Activation Network for Weakly Supervised Semantic
Segmentation,
ICPR22(657-662)
IEEE DOI
2212
Training, Limiting, Semantic segmentation, Pattern recognition
BibRef
Wang, L.T.[Lu-Ting],
Li, X.J.[Xiao-Jie],
Liao, Y.[Yue],
Jiang, Z.[Zeren],
Wu, J.L.[Jian-Long],
Wang, F.[Fei],
Qian, C.[Chen],
Liu, S.[Si],
HEAD: HEtero-Assists Distillation for Heterogeneous Object Detectors,
ECCV22(IX:314-331).
Springer DOI
2211
BibRef
Chen, Z.Z.[Zhao-Zheng],
Wang, T.[Tan],
Wu, X.W.[Xiong-Wei],
Hua, X.S.[Xian-Sheng],
Zhang, H.W.[Han-Wang],
Sun, Q.[Qianru],
Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation,
CVPR22(959-968)
IEEE DOI
2210
Codes, Shape, Computational modeling, Semantics, Benchmark testing,
Feature extraction, Recognition: detection, categorization,
grouping and shape analysis
BibRef
Xie, J.H.[Jin-Heng],
Hou, X.X.[Xian-Xu],
Ye, K.[Kai],
Shen, L.L.[Lin-Lin],
CLIMS: Cross Language Image Matching for Weakly Supervised Semantic
Segmentation,
CVPR22(4473-4482)
IEEE DOI
2210
Image segmentation, Codes, Shape, Image matching, Semantics,
Natural languages, Segmentation, grouping and shape analysis, Vision + language
BibRef
Chen, Z.[Zhang],
Tian, Z.Q.[Zhi-Qiang],
Zhu, J.[Jihua],
Li, C.[Ce],
Du, S.Y.[Shao-Yi],
C-CAM: Causal CAM for Weakly Supervised Semantic Segmentation on
Medical Image,
CVPR22(11666-11675)
IEEE DOI
2210
Training, Chaos, Image segmentation, Codes, Semantics,
Anatomical structure, Segmentation, grouping and shape analysis
BibRef
Li, J.[Jing],
Fan, J.S.[Jun-Song],
Zhang, Z.X.[Zhao-Xiang],
Towards Noiseless Object Contours for Weakly Supervised Semantic
Segmentation,
CVPR22(16835-16844)
IEEE DOI
2210
Training, Location awareness, Image segmentation, Semantics,
Refining, Predictive models, Scene analysis and understanding,
grouping and shape analysis
BibRef
Xie, J.H.[Jin-Heng],
Xiang, J.F.[Jian-Feng],
Chen, J.L.[Jun-Liang],
Hou, X.X.[Xian-Xu],
Zhao, X.D.[Xiao-Dong],
Shen, L.L.[Lin-Lin],
C2 AM: Contrastive learning of Class-agnostic Activation Map for
Weakly Supervised Object Localization and Semantic Segmentation,
CVPR22(979-988)
IEEE DOI
2210
Location awareness, Image segmentation, Codes, Shape, Semantics, Force,
Recognition: detection, categorization, retrieval, Segmentation,
Self- semi- meta- unsupervised learning
BibRef
Sun, W.X.[Wei-Xuan],
Zhang, J.[Jing],
Barnes, N.M.[Nick M.],
Inferring the Class Conditional Response Map for Weakly Supervised
Semantic Segmentation,
WACV22(2653-2662)
IEEE DOI
2202
Training, Image segmentation, Semantics, Pipelines,
Inference algorithms, Cams, Segmentation, Grouping and Shape
BibRef
Wu, T.[Tong],
Huang, J.[Junshi],
Gao, G.Y.[Guang-Yu],
Wei, X.M.[Xiao-Ming],
Wei, X.L.[Xiao-Lin],
Luo, X.[Xuan],
Liu, C.H.[Chi Harold],
Embedded Discriminative Attention Mechanism for Weakly Supervised
Semantic Segmentation,
CVPR21(16760-16769)
IEEE DOI
2111
Image segmentation, Codes, Annotations, Semantics,
Collaboration, Feature extraction
BibRef
Oh, Y.[Youngmin],
Kim, B.[Beomjun],
Ham, B.[Bumsub],
Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised
Semantic Segmentation,
CVPR21(6909-6918)
IEEE DOI
2111
Training, Image segmentation, Annotations,
Computational modeling, Semantics, Feature extraction
BibRef
Liu, Z.Z.[Zheng-Zhe],
Qi, X.J.[Xiao-Juan],
Fu, C.W.[Chi-Wing],
One Thing One Click: A Self-Training Approach for Weakly Supervised
3D Semantic Segmentation,
CVPR21(1726-1736)
IEEE DOI
2111
Training, Annotations, Semantics, Training data, Prototypes
BibRef
Neven, R.[Robby],
Neven, D.[Davy],
de Brabandere, B.[Bert],
Proesmans, M.[Marc],
Goedemé, T.[Toon],
Weakly-Supervised Semantic Segmentation by Learning Label Uncertainty,
ILDAV21(1678-1686)
IEEE DOI
2112
Training, Deep learning, Image segmentation,
Uncertainty, Semantics, Neural networks
BibRef
Yin, J.H.[Jun-Hui],
Zhang, S.Q.[Si-Qing],
Chang, D.L.[Dong-Liang],
Ma, Z.Y.[Zhan-Yu],
Guo, J.[Jun],
Dual-attention Guided Dropblock Module for Weakly Supervised Object
Localization,
ICPR21(4229-4236)
IEEE DOI
2105
weakly supervised object localization.
Location awareness, Training, Deep learning, Adaptation models,
Visualization, Automobiles
BibRef
Cao, T.Y.[Tian-Yue],
Du, L.Y.[Lian-Yu],
Zhang, X.Y.[Xiao-Yun],
Chen, S.H.[Si-Heng],
Zhang, Y.[Ya],
Wang, Y.F.[Yan-Feng],
CaT: Weakly Supervised Object Detection with Category Transfer,
ICCV21(3050-3059)
IEEE DOI
2203
Convolutional codes, Bridges, Semantics, Object detection, Detectors,
Knowledge transfer, Detection and localization in 2D and 3D,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Yamazaki, M.[Masaki],
Peng, X.C.[Xing-Chao],
Saito, K.[Kuniaki],
Hu, P.[Ping],
Saenko, K.[Kate],
Taniguchi, Y.[Yasuhiro],
Weakly Supervised Domain Adaptation using Super-pixel labeling for
Semantic Segmentation,
MVA21(1-5)
DOI Link
2109
Deep learning, Image segmentation, Adaptation models, Annotations,
Semantics, Object segmentation, Data models
BibRef
Watanabe, K.[Kohei],
Saito, K.[Kuniaki],
Ushiku, Y.[Yoshitaka],
Harada, T.[Tatsuya],
Multichannel Semantic Segmentation with Unsupervised Domain Adaptation,
AutoNUE18(V:600-616).
Springer DOI
1905
BibRef
Zhang, T.Y.[Tian-Yi],
Lin, G.S.[Guo-Sheng],
Liu, W.D.[Wei-De],
Cai, J.F.[Jian-Fei],
Kot, A.[Alex],
Splitting Vs. Merging: Mining Object Regions with Discrepancy and
Intersection Loss for Weakly Supervised Semantic Segmentation,
ECCV20(XXII:663-679).
Springer DOI
2011
BibRef
Fan, J.S.[Jun-Song],
Zhang, Z.X.[Zhao-Xiang],
Tan, T.N.[Tie-Niu],
Employing Multi-estimations for Weakly-supervised Semantic Segmentation,
ECCV20(XVII:332-348).
Springer DOI
2011
BibRef
Chang, Y.,
Wang, Q.,
Hung, W.,
Piramuthu, R.,
Tsai, Y.,
Yang, M.,
Weakly-Supervised Semantic Segmentation via Sub-Category Exploration,
CVPR20(8988-8997)
IEEE DOI
2008
Task analysis, Feature extraction, Semantics, Training,
Image segmentation, Computational modeling
BibRef
Fan, J.,
Zhang, Z.,
Song, C.,
Tan, T.,
Learning Integral Objects With Intra-Class Discriminator for
Weakly-Supervised Semantic Segmentation,
CVPR20(4282-4291)
IEEE DOI
2008
Image segmentation, Semantics, Training, Task analysis, Manifolds,
Estimation, Benchmark testing
BibRef
Zareian, A.,
Karaman, S.,
Chang, S.,
Weakly Supervised Visual Semantic Parsing,
CVPR20(3733-3742)
IEEE DOI
2008
Semantics, Visualization, Proposals, Image edge detection,
Message passing, Task analysis
BibRef
Yu, Z.,
Zhuge, Y.,
Lu, H.,
Zhang, L.,
Joint Learning of Saliency Detection and Weakly Supervised Semantic
Segmentation,
ICCV19(7222-7232)
IEEE DOI
2004
image classification, image coding, image recognition,
image segmentation, object detection, supervised learning, WSSS,
Computational modeling
BibRef
Marin, D.[Dmitrii],
Boykov, Y.Y.[Yuri Y.],
Robust Trust Region for Weakly Supervised Segmentation,
ICCV21(6588-6598)
IEEE DOI
2203
Training, Deep learning, Image segmentation, Semantics,
Neural networks, Training data, Vision applications and systems
BibRef
Shen, Y.H.[Yun-Hang],
Cao, L.J.[Liu-Juan],
Chen, Z.W.[Zhi-Wei],
Zhang, B.C.[Bao-Chang],
Su, C.[Chi],
Wu, Y.J.[Yong-Jian],
Huang, F.Y.[Fei-Yue],
Ji, R.R.[Rong-Rong],
Parallel Detection-and-Segmentation Learning for Weakly Supervised
Instance Segmentation,
ICCV21(8178-8188)
IEEE DOI
2203
Training, Image segmentation, Correlation, Pipelines, Coherence,
Object detection, Transfer/Low-shot/Semi/Unsupervised Learning,
grouping and shape
BibRef
Shen, Y.H.[Yun-Hang],
Ji, R.R.[Rong-Rong],
Wang, Y.[Yan],
Wu, Y.J.[Yong-Jian],
Cao, L.J.[Liu-Juan],
Cyclic Guidance for Weakly Supervised Joint Detection and Segmentation,
CVPR19(697-707).
IEEE DOI
2002
BibRef
Vernaza, P.,
Chandraker, M.,
Learning Random-Walk Label Propagation for Weakly-Supervised Semantic
Segmentation,
CVPR17(2953-2961)
IEEE DOI
1711
Image edge detection, Image segmentation, Labeling, Semantics,
Training, Uncertainty
BibRef
Xing, F.Z.,
Cambria, E.,
Huang, W.B.,
Xu, Y.,
Weakly supervised semantic segmentation with superpixel embedding,
ICIP16(1269-1273)
IEEE DOI
1610
Context
BibRef
Pourian, N.,
Karthikeyan, S.,
Manjunath, B.S.,
Weakly Supervised Graph Based Semantic Segmentation by Learning
Communities of Image-Parts,
ICCV15(1359-1367)
IEEE DOI
1602
Correlation
BibRef
Zhang, W.[Wei],
Zeng, S.[Sheng],
Wang, D.[Dequan],
Xue, X.Y.[Xiang-Yang],
Weakly supervised semantic segmentation for social images,
CVPR15(2718-2726)
IEEE DOI
1510
BibRef
Ying, P.[Peng],
Liu, J.[Jing],
Lu, H.Q.[Han-Qing],
Dictionary learning based superpixels clustering for
weakly-supervised semantic segmentation,
ICIP15(4258-4262)
IEEE DOI
1512
Weak supervision;dictionary learning;semantic segmentation
BibRef
Liu, Y.[Yang],
Liu, J.[Jing],
Li, Z.C.[Ze-Chao],
Tang, J.H.[Jin-Hui],
Lu, H.Q.[Han-Qing],
Weakly-Supervised Dual Clustering for Image Semantic Segmentation,
CVPR13(2075-2082)
IEEE DOI
1309
Image Semantic Segmentation; Weakly-Supervised
BibRef
Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Semi-Supervised Semantic Segmentation .