Song, Y.,
Ou, Z.,
Semi-Supervised Seq2seq Joint-Stochastic-Approximation Autoencoders
With Applications to Semantic Parsing,
SPLetters(27), 2020, pp. 31-35.
IEEE DOI
2001
Semi-supervised learning, seq2seq, semantic parsing,
joint stochastic approximation, variational auto-encoder
BibRef
Liang, C.B.[Chen-Bin],
Cheng, B.[Bo],
Xiao, B.H.[Bai-Hua],
He, C.Q.[Chenlin-Qiu],
Liu, X.[Xunan],
Jia, N.[Ning],
Chen, J.F.[Jin-Fen],
Semi-/Weakly-Supervised Semantic Segmentation Method and Its
Application for Coastal Aquaculture Areas Based on Multi-Source
Remote Sensing Images: Taking the Fujian Coastal Area (Mainly Sanduo)
as an Example,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link
2104
BibRef
Hu, P.[Ping],
Sclaroff, S.[Stan],
Saenko, K.[Kate],
Leveraging Geometric Structure for Label-Efficient Semi-Supervised
Scene Segmentation,
IP(31), 2022, pp. 6320-6330.
IEEE DOI
2210
Annotations, Training, Labeling, Solid modeling, Task analysis,
Semantics, Semantic segmentation,
geometric structure
BibRef
Cao, C.[Cong],
Lin, T.W.[Tian-Wei],
He, D.L.[Dong-Liang],
Li, F.[Fu],
Yue, H.J.[Huan-Jing],
Yang, J.Y.[Jing-Yu],
Ding, E.[Errui],
Adversarial Dual-Student with Differentiable Spatial Warping for
Semi-Supervised Semantic Segmentation,
CirSysVideo(33), No. 2, February 2023, pp. 793-803.
IEEE DOI
2302
Semantics, Image segmentation, Training, Perturbation methods,
Data models, Context modeling, Task analysis, differentiable spatial warping
BibRef
Kim, S.[Soopil],
Chikontwe, P.[Philip],
An, S.[Sion],
Park, S.H.[Sang Hyun],
Uncertainty-aware semi-supervised few shot segmentation,
PR(137), 2023, pp. 109292.
Elsevier DOI
2302
Few shot segmentation, Meta learning, Uncertainty estimation,
Semi-supervised learning, Prototype
BibRef
Zang, Y.H.[Yu-Hang],
Zhou, K.Y.[Kai-Yang],
Huang, C.[Chen],
Loy, C.C.[Chen Change],
Semi-Supervised and Long-Tailed Object Detection with CascadeMatch,
IJCV(131), No. 1, January 2023, pp. 987-1001.
Springer DOI
2303
BibRef
Chen, J.K.[Jing-Kun],
Zhang, J.G.[Jian-Guo],
Debattista, K.[Kurt],
Han, J.G.[Jun-Gong],
Semi-Supervised Unpaired Medical Image Segmentation Through
Task-Affinity Consistency,
MedImg(42), No. 3, March 2023, pp. 594-605.
IEEE DOI
2303
Image segmentation, Task analysis, Feature extraction, Training,
Semisupervised learning, Medical diagnostic imaging, consistency
BibRef
Wu, F.P.[Fu-Ping],
Zhuang, X.H.[Xia-Hai],
Minimizing Estimated Risks on Unlabeled Data: A New Formulation for
Semi-Supervised Medical Image Segmentation,
PAMI(45), No. 5, May 2023, pp. 6021-6036.
IEEE DOI
2304
Image segmentation, Training, Biomedical imaging, Task analysis,
Data models, Minimization, Risk management,
Semi-supervised learning
BibRef
Wu, L.S.[Lin-Shan],
Fang, L.Y.[Le-Yuan],
He, X.X.[Xing-Xin],
He, M.[Min],
Ma, J.Y.[Jia-Yi],
Zhong, Z.[Zhun],
Querying Labeled for Unlabeled: Cross-Image Semantic Consistency
Guided Semi-Supervised Semantic Segmentation,
PAMI(45), No. 7, July 2023, pp. 8827-8844.
IEEE DOI
2306
Semantics, Semantic segmentation, Reliability, Training,
Task analysis, Annotations, Deep learning, semi-supervised learning
BibRef
Wang, Y.[Ying],
Xuan, Z.W.[Zi-Wei],
Ho, C.[Chiuman],
Qi, G.J.[Guo-Jun],
Adversarial Dense Contrastive Learning for Semi-Supervised Semantic
Segmentation,
IP(32), 2023, pp. 4459-4471.
IEEE DOI
2309
BibRef
Lee, M.[Minhyun],
Lee, S.[Seungho],
Lee, J.[Jongwuk],
Shim, H.J.[Hyun-Jung],
Saliency as Pseudo-Pixel Supervision for Weakly and Semi-Supervised
Semantic Segmentation,
PAMI(45), No. 10, October 2023, pp. 12341-12357.
IEEE DOI
2310
BibRef
Lee, S.[Seungho],
Lee, M.[Minhyun],
Lee, J.[Jongwuk],
Shim, H.J.[Hyun-Jung],
Railroad is not a Train: Saliency as Pseudo-pixel Supervision for
Weakly Supervised Semantic Segmentation,
CVPR21(5491-5501)
IEEE DOI
2111
Location awareness, Training, Image segmentation,
Codes, Semantics, Pattern recognition
BibRef
Hou, Y.Z.[Yun-Zhong],
Gould, S.[Stephen],
Zheng, L.[Liang],
View-coherent correlation consistency for semi-supervised semantic
segmentation,
PR(147), 2024, pp. 110089.
Elsevier DOI
2312
Semi-supervised learning, Semantic segmentation,
Contrastive learning, Data augmentation
BibRef
Chen, Z.J.[Ze-Jian],
Zhuo, W.[Wei],
Wang, T.F.[Tian-Fu],
Cheng, J.[Jun],
Xue, W.F.[Wu-Feng],
Ni, D.[Dong],
Semi-Supervised Representation Learning for Segmentation on Medical
Volumes and Sequences,
MedImg(42), No. 12, December 2023, pp. 3972-3986.
IEEE DOI Code:
WWW Link.
2312
BibRef
Wu, J.W.[Jia-Wei],
Fan, H.[Haoyi],
Li, Z.Y.[Zuo-Yong],
Liu, G.H.[Guang-Hai],
Lin, S.[Shouying],
Information Transfer in Semi-Supervised Semantic Segmentation,
CirSysVideo(34), No. 2, February 2024, pp. 1174-1185.
IEEE DOI
2402
Semantic segmentation, Training, Task analysis, Semantics, Bars,
Semisupervised learning, Entropy, Semi-supervised learning,
information transfer
BibRef
Wang, Y.[Yooseung],
Jang, J.[Jaehyuk],
Kim, C.[Changick],
Subdivided Mask Dispersion Framework for semi-supervised semantic
segmentation,
PRL(179), 2024, pp. 58-64.
Elsevier DOI
2403
Semi-supervised semantic segmentation,
Semi-supervised learning, Cutmix, Transformer
BibRef
Miao, J.Z.[Ju-Zheng],
Zhou, S.P.[Si-Ping],
Zhou, G.Q.[Guang-Quan],
Wang, K.N.[Kai-Ni],
Yang, M.[Meng],
Zhou, S.[Shoujun],
Chen, Y.[Yang],
SC-SSL: Self-Correcting Collaborative and Contrastive Co-Training
Model for Semi-Supervised Medical Image Segmentation,
MedImg(43), No. 4, April 2024, pp. 1347-1364.
IEEE DOI
2404
Image segmentation, Biomedical imaging, Reliability, Task analysis,
Training, Semisupervised learning, Semantics, Self-correcting,
structure constraint
BibRef
Ma, J.[Jie],
Wang, C.[Chuan],
Liu, Y.[Yang],
Lin, L.[Liang],
Li, G.B.[Guan-Bin],
Enhanced Soft Label for Semi-Supervised Semantic Segmentation,
ICCV23(1185-1195)
IEEE DOI
2401
BibRef
Dong, R.[Runmin],
Mou, L.C.[Li-Chao],
Chen, M.X.[Meng-Xuan],
Li, W.J.[Wei-Jia],
Tong, X.Y.[Xin-Yi],
Yuan, S.[Shuai],
Zhang, L.X.[Li-Xian],
Zheng, J.[Juepeng],
Zhu, X.X.[Xiao Xiang],
Fu, H.[Haohuan],
Large-Scale Land Cover Mapping with Fine-Grained Classes via
Class-Aware Semi-Supervised Semantic Segmentation,
ICCV23(16737-16747)
IEEE DOI
2401
BibRef
Liang, C.[Chen],
Wang, W.G.[Wen-Guan],
Miao, J.X.[Jia-Xu],
Yang, Y.[Yi],
Logic-induced Diagnostic Reasoning for Semi-supervised Semantic
Segmentation,
ICCV23(16151-16162)
IEEE DOI
2401
BibRef
Zhou, Y.F.[Yan-Feng],
Huang, J.X.[Jia-Xing],
Wang, C.[Chenlong],
Song, L.[Le],
Yang, G.[Ge],
XNet: Wavelet-Based Low and High Frequency Fusion Networks for Fully-
and Semi-Supervised Semantic Segmentation of Biomedical Images,
ICCV23(21028-21039)
IEEE DOI Code:
WWW Link.
2401
BibRef
Wang, Z.Y.[Zi-Yang],
Ma, C.Y.[Cong-Ying],
Dual-Contrastive Dual-Consistency Dual-Transformer:
A Semi-Supervised Approach to Medical Image Segmentation,
NIVT23(870-879)
IEEE DOI Code:
WWW Link.
2401
BibRef
Li, Y.J.[Yi-Jiang],
Wang, X.[Xinjiang],
Yang, L.[Lihe],
Feng, L.[Litong],
Zhang, W.[Wayne],
Gao, Y.[Ying],
Diverse Cotraining Makes Strong Semi-Supervised Segmentor,
ICCV23(16009-16021)
IEEE DOI
2401
BibRef
Fang, Y.[Yan],
Zhu, F.[Feng],
Cheng, B.[Bowen],
Liu, L.Q.[Luo-Qi],
Zhao, Y.[Yao],
Wei, Y.C.[Yun-Chao],
Locating Noise is Halfway Denoising for Semi-Supervised Segmentation,
ICCV23(16566-16576)
IEEE DOI
2401
BibRef
Wang, C.Q.[Chang-Qi],
Xie, H.Y.[Hao-Yu],
Yuan, Y.H.[Yu-Hui],
Fu, C.[Chong],
Yue, X.Y.[Xiang-Yu],
Space Engage: Collaborative Space Supervision for Contrastive-based
Semi-Supervised Semantic Segmentation,
ICCV23(931-942)
IEEE DOI
2401
BibRef
Zhang, R.T.[Run-Tong],
Zhu, H.Y.[Hong-Yuan],
Zhang, H.W.[Han-Wang],
Gong, C.[Chen],
Zhou, J.T.Y.[Joey Tian-Yi],
Meng, F.M.[Fan-Man],
Semi-Supervised Few-Shot Segmentation with Noisy Support Images,
ICIP23(1550-1554)
IEEE DOI
2312
BibRef
Kar, P.[Purbayan],
Chudasama, V.[Vishal],
Onoe, N.[Naoyuki],
Wasnik, P.[Pankaj],
Revisiting Class Imbalance for End-to-end Semi-Supervised Object
Detection,
ECV23(4570-4579)
IEEE DOI
2309
BibRef
Lao, J.W.[Jiang-Wei],
Hong, W.X.[Wei-Xiang],
Guo, X.[Xin],
Zhang, Y.Y.[Ying-Ying],
Wang, J.[Jian],
Chen, J.D.[Jing-Dong],
Chu, W.[Wei],
Simultaneously Short- and Long-Term Temporal Modeling for
Semi-Supervised Video Semantic Segmentation,
CVPR23(14763-14772)
IEEE DOI
2309
BibRef
Qiao, P.C.[Peng-Chong],
Wei, Z.D.[Zhi-Dan],
Wang, Y.[Yu],
Wang, Z.N.[Zhen-Nan],
Song, G.[Guoli],
Xu, F.[Fan],
Ji, X.Y.[Xiang-Yang],
Liu, C.[Chang],
Chen, J.[Jie],
Fuzzy Positive Learning for Semi-Supervised Semantic Segmentation,
CVPR23(15465-15474)
IEEE DOI
2309
BibRef
Wang, Z.C.[Zi-Cheng],
Zhao, Z.[Zhen],
Xing, X.X.[Xiao-Xia],
Xu, D.[Dong],
Kong, X.Y.[Xiang-Yu],
Zhou, L.P.[Lu-Ping],
Conflict-Based Cross-View Consistency for Semi-Supervised Semantic
Segmentation,
CVPR23(19585-19595)
IEEE DOI
2309
BibRef
Yang, L.[Lihe],
Qi, L.[Lei],
Feng, L.[Litong],
Zhang, W.[Wayne],
Shi, Y.[Yinghuan],
Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic
Segmentation,
CVPR23(7236-7246)
IEEE DOI
2309
BibRef
Wang, X.Y.[Xiao-Yang],
Zhang, B.F.[Bing-Feng],
Yu, L.M.[Li-Min],
Xiao, J.[Jimin],
Hunting Sparsity: Density-Guided Contrastive Learning for
Semi-Supervised Semantic Segmentation,
CVPR23(3114-3123)
IEEE DOI
2309
BibRef
Zhang, Y.Y.[Yun-Yang],
Gong, Z.Q.[Zhi-Qiang],
Zhao, X.Y.[Xiao-Yu],
Zheng, X.H.[Xiao-Hu],
Yao, W.[Wen],
Semi-supervised Semantic Segmentation with Uncertainty-guided Self
Cross Supervision,
ACCV22(VII:327-343).
Springer DOI
2307
BibRef
Rangnekar, A.[Aneesh],
Kanan, C.[Christopher],
Hoffman, M.[Matthew],
Semantic Segmentation with Active Semi-Supervised Learning,
WACV23(5955-5966)
IEEE DOI
2302
Training, Deep learning, Head, Costs, Annotations,
Semantic segmentation, Training data,
visual reasoning
BibRef
Kong, H.[Heejo],
Lee, G.H.[Gun-Hee],
Kim, S.[Suneung],
Lee, S.W.[Seong-Whan],
Pruning-Guided Curriculum Learning for Semi-Supervised Semantic
Segmentation,
WACV23(5903-5912)
IEEE DOI
2302
Training, Knowledge engineering, Semantic segmentation,
Benchmark testing, Feature extraction, Noise measurement
BibRef
Chai, L.[Lu],
Liu, Q.[Qinyuan],
Semi-Supervised Semantic Segmentation of Class-Imbalanced Images: A
Hierarchical Self-Attention Generative Adversarial Network,
ICIVC22(398-404)
IEEE DOI
2301
Image segmentation, Image synthesis, Computational modeling,
Biological system modeling, Semantics, Pipelines, biomedical images
BibRef
Teh, E.W.[Eu Wern],
de Vries, T.[Terrance],
Duke, B.[Brendan],
Jiang, R.[Ruowei],
Aarabi, P.[Parham],
Taylor, G.W.[Graham W.],
The GIST and RIST of Iterative Self-Training for Semi-Supervised
Segmentation,
CRV22(58-66)
IEEE DOI
2301
Greedy Iterative Self-Training (GIST) and
Random Iterative Self-Training (RIST).
Training, Degradation, Semantics, Semisupervised learning,
Behavioral sciences, Iterative methods, Task analysis, self-training
BibRef
Pauletto, L.[Loïc],
Amini, M.R.[Massih-Reza],
Winckler, N.[Nicolas],
Se2NAS: Self-Semi-Supervised architecture optimization for Semantic
Segmentation,
ICPR22(54-60)
IEEE DOI
2212
Semantic segmentation, Training data, Artificial neural networks,
Self-supervised learning, Semisupervised learning, Routing
BibRef
Chen, Y.[Ying],
Ouyang, X.[Xu],
Zhu, K.Y.[Kai-Yue],
Agam, G.[Gady],
Semi-supervised Dual-Domain Adaptation for Semantic Segmentation,
ICPR22(230-237)
IEEE DOI
2212
Deep learning, Annotations, Semantic segmentation,
Supervised learning, Semisupervised learning, Benchmark testing
BibRef
Qin, J.[Jie],
Wu, J.[Jie],
Li, M.[Ming],
Xiao, X.F.[Xue-Feng],
Zheng, M.[Min],
Wang, X.G.[Xin-Gang],
Multi-granularity Distillation Scheme Towards Lightweight
Semi-supervised Semantic Segmentation,
ECCV22(XXX:481-498).
Springer DOI
2211
BibRef
Kwon, D.[Donghyeon],
Kwak, S.[Suha],
Semi-supervised Semantic Segmentation with Error Localization Network,
CVPR22(9947-9957)
IEEE DOI
2210
Training, Location awareness, Degradation, Heart, Image resolution,
Semisupervised learning, Pattern recognition,
grouping and shape analysis
BibRef
Wang, Y.C.[Yu-Chao],
Wang, H.C.[Hao-Chen],
Shen, Y.J.[Yu-Jun],
Fei, J.J.[Jing-Jing],
Li, W.[Wei],
Jin, G.Q.[Guo-Qiang],
Wu, L.W.[Li-Wei],
Zhao, R.[Rui],
Le, X.[Xinyi],
Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels,
CVPR22(4238-4247)
IEEE DOI
2210
Training, Visualization, Shape, Semantics, Predictive models,
Semisupervised learning, Solids, Segmentation,
Self- semi- meta- unsupervised learning
BibRef
Zatsarynna, O.[Olga],
Sawatzky, J.[Johann],
Gall, J.[Juergen],
Discovering Latent Classes for Semi-supervised Semantic Segmentation,
GCPR20(202-217).
Springer DOI
2110
BibRef
Luo, W.F.[Wen-Feng],
Yang, M.[Meng],
Semi-supervised Semantic Segmentation via Strong-weak Dual-branch
Network,
ECCV20(V:784-800).
Springer DOI
2011
BibRef
Stekovic, S.,
Fraundorfer, F.,
Lepetit, V.[Vincent],
Casting Geometric Constraints in Semantic Segmentation as
Semi-Supervised Learning,
WACV20(1843-1852)
IEEE DOI
2006
Image segmentation, Semantics, Predictive models,
Semisupervised learning, Task analysis, Training,
BibRef
Kalluri, T.,
Varma, G.,
Chandraker, M.,
Jawahar, C.V.,
Universal Semi-Supervised Semantic Segmentation,
ICCV19(5258-5269)
IEEE DOI
2004
entropy, image segmentation, unsupervised learning,
cross-domain unsupervised losses, segmentation datasets, Roads
BibRef
Wei, Y.,
Xiao, H.,
Shi, H.,
Jie, Z.,
Feng, J.,
Huang, T.S.,
Revisiting Dilated Convolution: A Simple Approach for Weakly- and
Semi-Supervised Semantic Segmentation,
CVPR18(7268-7277)
IEEE DOI
1812
Image segmentation, Semantics, Convolution, Training, Kernel,
Standards, Head
BibRef
Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Efficient Semantic Segmentation, Real-Time Segmentation .