Li, R.[Rui],
Cao, W.M.[Wen-Ming],
Jiao, Q.F.[Qian-Fen],
Wu, S.[Si],
Wong, H.S.[Hau-San],
Simplified unsupervised image translation for semantic segmentation
adaptation,
PR(105), 2020, pp. 107343.
Elsevier DOI
2006
Domain adaptation, Image segmentation, Image translation
BibRef
Xu, H.Q.[Han-Qing],
Yang, M.[Ming],
Deng, L.Y.[Liu-Yuan],
Qian, Y.Q.[Ye-Qiang],
Wang, C.X.[Chun-Xiang],
Neutral Cross-Entropy Loss Based Unsupervised Domain Adaptation for
Semantic Segmentation,
IP(30), 2021, pp. 4516-4525.
IEEE DOI
2105
Entropy, Semantics, Image segmentation, Minimization, Training,
Perturbation methods, Optimization, Semantic segmentation,
gradient neutralization
BibRef
Yan, W.H.[Wei-Hao],
Qian, Y.Q.[Ye-Qiang],
Wang, C.X.[Chun-Xiang],
Yang, M.[Ming],
Threshold-Adaptive Unsupervised Focal Loss for Domain Adaptation of
Semantic Segmentation,
ITS(24), No. 1, January 2023, pp. 752-763.
IEEE DOI
2301
Training, Entropy, Semantics, Adaptation models, Real-time systems,
Urban areas, Predictive models, Semantic segmentation, focal loss
BibRef
Shenaj, D.[Donald],
Barbato, F.[Francesco],
Michieli, U.[Umberto],
Zanuttigh, P.[Pietro],
Continual coarse-to-fine domain adaptation in semantic segmentation,
IVC(121), 2022, pp. 104426.
Elsevier DOI
2205
Coarse-to-fine learning, Unsupervised domain adaptation,
Semantic segmentation, Continual learning, Deep learning
BibRef
Nguyen-Meidine, L.T.[Le Thanh],
Kiran, M.[Madhu],
Pedersoli, M.[Marco],
Dolz, J.[Jose],
Blais-Morin, L.A.[Louis-Antoine],
Granger, E.[Eric],
Incremental multi-target domain adaptation for object detection with
efficient domain transfer,
PR(129), 2022, pp. 108771.
Elsevier DOI
2206
Deep learning, Convolutional NNs, Object detection,
Unsupervised domain adaptation, Incremental learning
BibRef
Chen, X.[Xu],
Kuang, T.S.[Tian-Shu],
Deng, H.[Hannah],
Fung, S.H.[Steve H.],
Gateno, J.[Jaime],
Xia, J.J.[James J.],
Yap, P.T.[Pew-Thian],
Dual Adversarial Attention Mechanism for Unsupervised Domain Adaptive
Medical Image Segmentation,
MedImg(41), No. 11, November 2022, pp. 3445-3453.
IEEE DOI
2211
Image segmentation, Annotations, Semantics, Feature extraction,
Task analysis, Medical diagnostic imaging, Adaptation models,
medical image segmentation
BibRef
Wu, X.[Xinyi],
Wu, Z.Y.[Zhen-Yao],
Ju, L.[Lili],
Wang, S.[Song],
A One-Stage Domain Adaptation Network With Image Alignment for
Unsupervised Nighttime Semantic Segmentation,
PAMI(45), No. 1, January 2023, pp. 58-72.
IEEE DOI
2212
Semantics, Image segmentation, Training, Adaptation models, Lighting,
Meteorology, Computational modeling, Domain adaptation,
image alignment
BibRef
Wu, X.[Xinyi],
Wu, Z.Y.[Zhen-Yao],
Guo, H.[Hao],
Ju, L.[Lili],
Wang, S.[Song],
DANNet: A One-Stage Domain Adaptation Network for Unsupervised
Nighttime Semantic Segmentation,
CVPR21(15764-15773)
IEEE DOI
2111
Training, Bridges, Image segmentation, Annotations, Semantics, Neural networks
BibRef
Wang, S.[Shuang],
Zhao, D.[Dong],
Zhang, C.[Chi],
Guo, Y.W.[Yu-Wei],
Zang, Q.[Qi],
Gu, Y.[Yu],
Li, Y.[Yi],
Jiao, L.C.[Li-Cheng],
Cluster Alignment With Target Knowledge Mining for Unsupervised
Domain Adaptation Semantic Segmentation,
IP(31), 2022, pp. 7403-7418.
IEEE DOI
2212
Adaptation models, Correlation, Semantic segmentation,
Clustering methods, Transforms, Benchmark testing, Task analysis, graph cut
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
Zhao, Z.Y.[Zi-Yuan],
Zhou, F.C.[Fang-Cheng],
Xu, K.X.[Kai-Xin],
Zeng, Z.[Zeng],
Guan, C.T.[Cun-Tai],
Zhou, S.K.[S. Kevin],
LE-UDA: Label-Efficient Unsupervised Domain Adaptation for Medical
Image Segmentation,
MedImg(42), No. 3, March 2023, pp. 633-646.
IEEE DOI
2303
Image segmentation, Adaptation models, Biomedical imaging,
Annotations, Adversarial machine learning, adversarial learning
BibRef
Marnissi, M.A.[Mohamed Amine],
Fradi, H.[Hajer],
Sahbani, A.[Anis],
Essoukri-Ben Amara, N.[Najoua],
Improved domain adaptive object detector via adversarial feature
learning,
CVIU(230), 2023, pp. 103660.
Elsevier DOI
2303
BibRef
And:
Corrigendum:
CVIU(237), 2023, pp. 103685.
Elsevier DOI
2311
Unsupervised domain adaptation, Object detection,
Adversarial learning, Feature alignment, Discriminator, Transferability
BibRef
Wang, Z.J.[Zhi-Jie],
Liu, X.[Xing],
Suganuma, M.[Masanori],
Okatani, T.[Takayuki],
Unsupervised domain adaptation for semantic segmentation via
cross-region alignment,
CVIU(234), 2023, pp. 103743.
Elsevier DOI
2307
Domain adaptation, Semantic segmentation, CNN
BibRef
Wang, Z.J.[Zhi-Jie],
Suganuma, M.[Masanori],
Okatani, T.[Takayuki],
Rethinking unsupervised domain adaptation for semantic segmentation,
PRL(186), 2024, pp. 119-125.
Elsevier DOI
2412
Domain adaptation, Semantic segmentation, Unsupervised learning
BibRef
Cao, Y.H.[Yi-Hong],
Zhang, H.[Hui],
Lu, X.[Xiao],
Chen, Y.R.[Yu-Rong],
Xiao, Z.[Zheng],
Wang, Y.N.[Yao-Nan],
Adaptive Refining-Aggregation-Separation Framework for Unsupervised
Domain Adaptation Semantic Segmentation,
CirSysVideo(33), No. 8, August 2023, pp. 3822-3832.
IEEE DOI
2308
Semantic segmentation, Training, Semantics, Prototypes,
Task analysis, Robots, Refining, Unsupervised domain adaptation,
clustering technique
BibRef
Li, L.Y.[Lu-Yang],
Ma, T.[Tai],
Lu, Y.[Yue],
Li, Q.L.[Qing-Li],
He, L.H.[Liang-Hua],
Wen, Y.[Ying],
A multi-grained unsupervised domain adaptation approach for semantic
segmentation,
PR(144), 2023, pp. 109841.
Elsevier DOI
2310
Domain adaptation, Unsupervised semantic segmentation, Neural network
BibRef
Dong, Z.[Zihao],
Niu, S.J.[Si-Jie],
Gao, X.Z.[Xi-Zhan],
Shao, X.L.[Xiu-Li],
Coarse-to-fine online latent representations matching for one-stage
domain adaptive semantic segmentation,
PR(146), 2024, pp. 110019.
Elsevier DOI
2311
Unsupervised domain adaptation, Semantic segmentation,
Coarse-to-fine matching, Latent representations, Adversarial structure
BibRef
Shao, J.[Jie],
Wu, J.C.[Jia-Cheng],
Shen, W.Z.[Wen-Zhong],
Yang, C.[Cheng],
A Pairwise DomMix Attentive Adversarial Network for Unsupervised
Domain Adaptive Object Detection,
SPLetters(30), 2023, pp. 1667-1671.
IEEE DOI
2311
BibRef
Wang, Y.[Yan],
Cheng, J.[Jian],
Chen, Y.X.[Yi-Xin],
Shao, S.[Shuai],
Zhu, L.Y.[Lan-Yun],
Wu, Z.Z.[Zhen-Zhou],
Liu, T.[Tao],
Zhu, H.G.[Hao-Gang],
FVP: Fourier Visual Prompting for Source-Free Unsupervised Domain
Adaptation of Medical Image Segmentation,
MedImg(42), No. 12, December 2023, pp. 3738-3751.
IEEE DOI
2312
BibRef
Xie, Q.S.[Qing-Song],
Li, Y.X.[Yue-Xiang],
He, N.[Nanjun],
Ning, M.[Munan],
Ma, K.[Kai],
Wang, G.X.[Guo-Xing],
Lian, Y.[Yong],
Zheng, Y.F.[Ye-Feng],
Unsupervised Domain Adaptation for Medical Image Segmentation by
Disentanglement Learning and Self-Training,
MedImg(43), No. 1, January 2024, pp. 4-14.
IEEE DOI
2401
BibRef
Kim, Y.E.[Young-Eun],
Lee, Y.W.[Yu-Won],
Lee, S.W.[Seong-Whan],
LC-MSM: Language-Conditioned Masked Segmentation Model for
unsupervised domain adaptation,
PR(148), 2024, pp. 110201.
Elsevier DOI
2402
Unsupervised domain adaptation, Semantic segmentation, Text-image correlation
BibRef
Ma, H.Y.[Hao-Yu],
Lin, X.R.[Xiang-Ru],
Yu, Y.Z.[Yi-Zhou],
I2F: A Unified Image-to-Feature Approach for Domain Adaptive Semantic
Segmentation,
PAMI(46), No. 3, March 2024, pp. 1695-1710.
IEEE DOI
2402
Semantic segmentation, Manifolds, Adaptation models, Task analysis,
Pipelines, Semantics, Benchmark testing, Category triplet loss,
unsupervised domain adaptation
BibRef
Pei, C.H.[Chen-Hao],
Wu, F.[Fuping],
Yang, M.J.[Ming-Jing],
Pan, L.[Lin],
Ding, W.[Wangbin],
Dong, J.[Jinwei],
Huang, L.Q.[Li-Qin],
Zhuang, X.[Xiahai],
Multi-Source Domain Adaptation for Medical Image Segmentation,
MedImg(43), No. 4, April 2024, pp. 1640-1651.
IEEE DOI
2404
Semantic segmentation, Adaptation models, Training, Task analysis,
Predictive models, Feature extraction, Shape, Domain adaptation,
unsupervised learning
BibRef
Tian, Y.T.[Yun-Tong],
Li, J.X.[Jia-Xi],
Fu, H.Z.[Hua-Zhu],
Zhu, L.[Lei],
Yu, L.Q.[Le-Quan],
Wan, L.[Liang],
Self-Mining the Confident Prototypes for Source-Free Unsupervised
Domain Adaptation in Image Segmentation,
MultMed(26), 2024, pp. 7709-7720.
IEEE DOI
2405
Reliability, Image segmentation, Adaptation models, Data models,
Prototypes, Federated learning, Predictive models,
source-free unsupervised domain adaptation
BibRef
Chen, L.[Lang],
Bian, Y.[Yun],
Zeng, J.B.[Jian-Bin],
Meng, Q.Q.[Qing-Quan],
Zhu, W.F.[Wei-Fang],
Shi, F.[Fei],
Shao, C.W.[Cheng-Wei],
Chen, X.J.[Xin-Jian],
Xiang, D.[Dehui],
Style Consistency Unsupervised Domain Adaptation Medical Image
Segmentation,
IP(33), 2024, pp. 4882-4895.
IEEE DOI
2409
Image segmentation, Biomedical imaging, Feature extraction,
Pancreas, Generative adversarial networks, Training, entropy consistency
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
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
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
Bao, D.[Dong],
Zhou, J.[Jun],
Tuxworth, G.[Gervase],
Zhang, J.[Jue],
Gao, Y.S.[Yong-Sheng],
Hierarchical Context Learning of object components for unsupervised
semantic segmentation,
PR(167), 2025, pp. 111713.
Elsevier DOI Code:
WWW Link.
2506
Unsupervised semantic segmentation, Context learning, Self-supervised learning
BibRef
Chen, J.M.[Jiang-Ming],
Liu, L.[Li],
Deng, W.X.[Wan-Xia],
Liu, Z.[Zhen],
Liu, Y.[Yu],
Wei, Y.M.[Ying-Mei],
Liu, Y.X.[Yong-Xiang],
Refining Pseudo Labeling via Multi-Granularity Confidence Alignment
for Unsupervised Cross Domain Object Detection,
IP(34), 2025, pp. 279-294.
IEEE DOI
2501
Object detection, Detectors, Uncertainty, Location awareness, Layout,
Accuracy, Labeling, Deep learning, Supervised learning, Noise, mean teacher
BibRef
Chen, L.[Liang],
Han, J.H.[Jian-Hong],
Wang, Y.P.[Yu-Pei],
DATR: Unsupervised Domain Adaptive Detection Transformer With
Dataset-Level Adaptation and Prototypical Alignment,
IP(34), 2025, pp. 982-994.
IEEE DOI Code:
WWW Link.
2502
Feature extraction, Detectors, Prototypes, Decoding, Transformers,
Object detection, Memory modules, Contrastive learning
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
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
Brorsson, E.[Erik],
Åkesson, K.[Knut],
Svensson, L.[Lennart],
Bengtsson, K.[Kristofer],
ECAP: Extensive Cut-and-Paste Augmentation for Unsupervised Domain
Adaptive Semantic Segmentation,
ICIP24(610-616)
IEEE DOI Code:
WWW Link.
2411
Training, Microwave integrated circuits, Adaptation models, Codes,
Accuracy, Semantic segmentation, Semantics, Semantic Segmentation,
Self-training
BibRef
Liu, K.[Kebin],
Zhu, C.[Chuang],
Unsupervised Domain Adaptive Semantic Segmentation Based on
Clip-Guided Prototypical Contrastive Learning,
ICIP24(291-297)
IEEE DOI Code:
WWW Link.
2411
Training, Visualization, Adaptation models, Codes, Shape,
Image color analysis, Semantic segmentation, Domain adaptation, CLIP
BibRef
Pan, F.[Fei],
Yin, X.[Xu],
Lee, S.[Seokju],
Niu, A.[Axi],
Yoon, S.[Sungeui],
Kweon, I.S.[In So],
MoDA: Leveraging Motion Priors from Videos for Advancing Unsupervised
Domain Adaptation in Semantic Segmentation,
L3D-IVU24(2649-2658)
IEEE DOI
2410
Bridges, Adaptive systems, Navigation, Semantic segmentation,
Motion segmentation, Semantics
BibRef
Pan, F.,
Shin, I.,
Rameau, F.,
Lee, S.,
Kweon, I.S.,
Unsupervised Intra-Domain Adaptation for Semantic Segmentation
Through Self-Supervision,
CVPR20(3763-3772)
IEEE DOI
2008
Adaptation models, Entropy, Image segmentation, Semantics,
Generators, Data models, Task analysis
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
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
Kennerley, M.[Mikhail],
Wang, J.G.[Jian-Gang],
Veeravalli, B.[Bharadwaj],
Tan, R.T.[Robby T.],
2PCNet: Two-Phase Consistency Training for Day-to-Night Unsupervised
Domain Adaptive Object Detection,
CVPR23(11484-11493)
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
Li, T.Y.[Tian-Yu],
Roy, S.[Subhankar],
Zhou, H.Y.[Hua-Yi],
Lu, H.T.[Hong-Tao],
Lathuilière, S.[Stéphane],
Contrast, Stylize and Adapt: Unsupervised Contrastive Learning
Framework for Domain Adaptive Semantic Segmentation,
L3D-IVU23(4869-4879)
IEEE DOI
2309
BibRef
Zheng, X.[Xu],
Pan, T.[Tianbo],
Luo, Y.H.[Yun-Hao],
Wang, L.[Lin],
Look at the Neighbor: Distortion-aware Unsupervised Domain Adaptation
for Panoramic Semantic Segmentation,
ICCV23(18641-18652)
IEEE DOI
2401
BibRef
Zheng, X.[Xu],
Zhu, J.J.[Jin-Jing],
Liu, Y.X.[Ye-Xin],
Cao, Z.D.[Zi-Dong],
Fu, C.[Chong],
Wang, L.[Lin],
Both Style and Distortion Matter: Dual-Path Unsupervised Domain
Adaptation for Panoramic Semantic Segmentation,
CVPR23(1285-1295)
IEEE DOI
2309
BibRef
Mattolin, G.[Giulio],
Zanella, L.[Luca],
Ricci, E.[Elisa],
Wang, Y.M.[Yi-Ming],
ConfMix: Unsupervised Domain Adaptation for Object Detection via
Confidence-based Mixing,
WACV23(423-433)
IEEE DOI
2302
Measurement, Training, Adaptation models, Uncertainty, Codes,
Object detection, Algorithms: Machine learning architectures, visual reasoning
BibRef
Chung, I.[Inseop],
Yoo, J.[Jayeon],
Kwak, N.[Nojun],
Exploiting Inter-pixel Correlations in Unsupervised Domain Adaptation
for Semantic Segmentation,
RealWorld23(12-21)
IEEE DOI
2302
Knowledge engineering, Correlation, Semantic segmentation,
Heuristic algorithms, Conferences, Benchmark testing
BibRef
Piva, F.J.[Fabrizio J.],
de Geus, D.[Daan],
Dubbelman, G.[Gijs],
Empirical Generalization Study: Unsupervised Domain Adaptation vs.
Domain Generalization Methods for Semantic Segmentation in the Wild,
WACV23(499-508)
IEEE DOI
2302
Training, Adaptation models, Protocols, Semantic segmentation,
Computational modeling, Training data, Applications: Robotics,
visual reasoning.
BibRef
Hegde, D.[Deepti],
Patel, V.M.[Vishal M.],
Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D
Object Detection,
WACV24(3054-3064)
IEEE DOI
2404
Training, Noise, Memory management, Prototypes, Object detection,
Detectors, Algorithms, 3D computer vision, Algorithms,
and algorithms
BibRef
Vibashan, V.S.,
Oza, P.[Poojan],
Patel, V.M.[Vishal M.],
Instance Relation Graph Guided Source-Free Domain Adaptive Object
Detection,
CVPR23(3520-3530)
IEEE DOI
2309
BibRef
Vibashan, V.S.,
Oza, P.[Poojan],
Sindagi, V.A.[Vishwanath A.],
Patel, V.M.[Vishal M.],
Mixture of Teacher Experts for Source-Free Domain Adaptive Object
Detection,
ICIP22(3606-3610)
IEEE DOI
2211
Training, Adaptation models, Uncertainty, Monte Carlo methods, Law,
Object detection, Object detection, Domain adaptation, Pseudo-labels
BibRef
Scherer, S.[Sebastian],
Brehm, S.[Stephan],
Lienhart, R.[Rainer],
Consistency Regularization for Unsupervised Domain Adaptation in
Semantic Segmentation,
CIAP22(I:500-511).
Springer DOI
2205
BibRef
Guizilini, V.[Vitor],
Li, J.[Jie],
Ambrus, R.[Rares],
Gaidon, A.[Adrien],
Geometric Unsupervised Domain Adaptation for Semantic Segmentation,
ICCV21(8517-8527)
IEEE DOI
2203
Bridges, Visualization, Semantics, Estimation, Benchmark testing,
Multitasking, Transfer/Low-shot/Semi/Unsupervised Learning,
Vision for robotics and autonomous vehicles
BibRef
Kuznietsov, Y.[Yevhen],
Proesmans, M.[Marc],
Van Gool, L.J.[Luc J.],
Towards Unsupervised Online Domain Adaptation for Semantic
Segmentation,
Novelty22(261-271)
IEEE DOI
2202
Adaptation models, Conferences, Semantics,
Pipelines, Training data, Predictive models
BibRef
Klingner, M.[Marvin],
Termöhlen, J.A.[Jan-Aike],
Ritterbach, J.[Jacob],
Fingscheidt, T.[Tim],
Unsupervised BatchNorm Adaptation (UBNA): A Domain Adaptation Method
for Semantic Segmentation Without Using Source Domain Representations,
Novelty22(210-220)
IEEE DOI
2202
Training, Knowledge engineering, Adaptation models,
Image segmentation, Semantics, Network architecture
BibRef
Cardace, A.[Adriano],
Ramirez, P.Z.[Pierluigi Zama],
Salti, S.[Samuele],
di Stefano, L.[Luigi],
Shallow Features Guide Unsupervised Domain Adaptation for Semantic
Segmentation at Class Boundaries,
WACV22(2010-2020)
IEEE DOI
2202
Deep learning, Semantics, Neural networks,
Task analysis, Transfer, Few-shot,
Grouping and Shape
BibRef
Chung, I.[Inseop],
Kim, D.[Daesik],
Kwak, N.[Nojun],
Maximizing Cosine Similarity Between Spatial Features for
Unsupervised Domain Adaptation in Semantic Segmentation,
WACV22(1979-1988)
IEEE DOI
2202
Image segmentation, Head, Dictionaries, Semantics,
Feature extraction, Task analysis, Segmentation, Scene Understanding
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
Vu, T.,
Jain, H.,
Bucher, M.,
Cord, M.,
Pérez, P.P.,
DADA: Depth-Aware Domain Adaptation in Semantic Segmentation,
ICCV19(7363-7372)
IEEE DOI
2004
data models, image segmentation, DADA,
depth-aware domain adaptation, unsupervised domain adaptation,
Benchmark testing
BibRef
Luo, Y.,
Liu, P.,
Guan, T.,
Yu, J.,
Yang, Y.,
Significance-Aware Information Bottleneck for Domain Adaptive
Semantic Segmentation,
ICCV19(6777-6786)
IEEE DOI
2004
feature extraction, image classification, image segmentation,
neural nets, unsupervised learning,
Data mining
BibRef
Zou, Y.[Yang],
Yu, Z.D.[Zhi-Ding],
Kumar, B.V.K.V.[B. V. K. Vijaya],
Wang, J.S.[Jin-Song],
Unsupervised Domain Adaptation for Semantic Segmentation via
Class-Balanced Self-training,
ECCV18(III: 297-313).
Springer DOI
1810
BibRef
Chapter on 2-D Region Segmentation Techniques, Snakes, Active Contours continues in
Weakly Supervised, Self Supervised Semantic Segmentation .