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
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
Li, G.R.[Guang-Rui],
Kang, G.L.[Guo-Liang],
Liu, W.[Wu],
Wei, Y.C.[Yun-Chao],
Yang, Y.[Yi],
Content-consistent Matching for Domain Adaptive Semantic Segmentation,
ECCV20(XIV:440-456).
Springer DOI
2011
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
Bucher, M.[Maxime],
Vu, T.H.[Tuan-Hung],
Cord, M.[Matthieu],
Pérez, P.[Patrick],
Handling new target classes in semantic segmentation with domain
adaptation,
CVIU(212), 2021, pp. 103258.
Elsevier DOI
2110
Boundless unsupervised domain adaptation,
Generalized zero-shot learning, Self-training, Semantic segmentation
BibRef
Tian, H.T.[Hai-Tao],
Qu, S.[Shiru],
Payeur, P.[Pierre],
A Prototypical Knowledge Oriented Adaptation Framework for Semantic
Segmentation,
IP(31), 2022, pp. 149-163.
IEEE DOI
2112
Image segmentation, Semantics, Task analysis, Adaptation models,
Training, Visualization, Image classification, Domain adaptation,
transfer learning
BibRef
Jian, Y.R.[Yi-Ren],
Gao, C.Y.[Chong-Yang],
MetaPix: Domain transfer for semantic segmentation by meta pixel
weighting,
IVC(116), 2021, pp. 104334.
Elsevier DOI
2112
Meta learning, Domain transfer learning, Semantic segmentation,
Street view semantic segmentation
BibRef
Guo, X.Q.[Xiao-Qing],
Liu, J.[Jie],
Yuan, Y.X.[Yi-Xuan],
Semantic-Oriented Labeled-to-Unlabeled Distribution Translation for
Image Segmentation,
MedImg(41), No. 2, February 2022, pp. 434-445.
IEEE DOI
2202
Code, Segmentation.
WWW Link. Image segmentation, Semantics, Feature extraction, Data models,
Task analysis, Semisupervised learning, few sample segmentation
BibRef
Guo, X.Q.[Xiao-Qing],
Yang, C.[Chen],
Li, B.[Baopu],
Yuan, Y.X.[Yi-Xuan],
MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised
Domain Adaptation in Semantic Segmentation,
CVPR21(3926-3935)
IEEE DOI
2111
Bridges, Adaptation models,
Computational modeling, Semantics, Estimation, Metadata
BibRef
Shah, K.[Kunjal],
Bhat, G.[Gururaj],
Exploring semantic segmentation of related subclasses from a superset
of classes,
PR(124), 2022, pp. 108509.
Elsevier DOI
2203
Image segmentation, Stuff classes, Deeplab
BibRef
Zhang, B.[Bo],
Chen, T.[Tao],
Wang, B.[Bin],
Wu, X.F.[Xiao-Feng],
Zhang, L.M.[Li-Ming],
Fan, J.Y.[Jia-Yuan],
Densely Semantic Enhancement for Domain Adaptive Region-Free
Detectors,
CirSysVideo(32), No. 3, March 2022, pp. 1339-1352.
IEEE DOI
2203
Detectors, Feature extraction, Semantics, Object detection,
Proposals, Adaptation models, Task analysis,
adaptive region-free detectors
BibRef
Yuan, B.[Bo],
Zhao, D.[Danpei],
Shao, S.[Shuai],
Yuan, Z.H.[Ze-Huan],
Wang, C.H.[Chang-Hu],
Birds of a Feather Flock Together:
Category-Divergence Guidance for Domain Adaptive Segmentation,
IP(31), No. 2022, pp. 2878-2892.
IEEE DOI
2204
Image segmentation, Semantics, Adaptation models,
Feature extraction, Training, Generative adversarial networks,
intra-class aggregation
BibRef
Rao, X.[Xiya],
Lu, T.[Tao],
Wang, Z.Y.[Zhong-Yuan],
Zhang, Y.D.[Yan-Duo],
Few-Shot Semantic Segmentation via Frequency Guided Neural Network,
SPLetters(29), 2022, pp. 1092-1096.
IEEE DOI
2205
Semantics, Prototypes, Feature extraction, Image segmentation,
Training, Testing, Merging, Semantic segmentation,
frequency separation
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
Tian, Y.J.[Ying-Jie],
Zhu, S.[Siyu],
Partial Domain Adaptation on Semantic Segmentation,
CirSysVideo(32), No. 6, June 2022, pp. 3798-3809.
IEEE DOI
2206
Semantics, Image segmentation, Task analysis, Training, Annotations,
Adaptation models, Feature extraction, Semantic segmentation,
partial adaptation
BibRef
Zhou, Q.Y.[Qian-Yu],
Feng, Z.Y.[Zheng-Yang],
Gu, Q.Q.[Qi-Qi],
Cheng, G.L.[Guang-Liang],
Lu, X.Q.[Xue-Quan],
Shi, J.P.[Jian-Ping],
Ma, L.Z.[Li-Zhuang],
Uncertainty-aware consistency regularization for cross-domain
semantic segmentation,
CVIU(221), 2022, pp. 103448.
Elsevier DOI
2206
Domain adaptation, Semantic segmentation, Transfer learning,
Consistency regularization
BibRef
Wu, A.[Aming],
Han, Y.[Yahong],
Zhu, L.C.[Lin-Chao],
Yang, Y.[Yi],
Instance-Invariant Domain Adaptive Object Detection Via Progressive
Disentanglement,
PAMI(44), No. 8, August 2022, pp. 4178-4193.
IEEE DOI
2207
Feature extraction, Object detection, Training, Task analysis,
Optimization, Proposals, Adaptation models,
instance-invariant features
BibRef
Wu, A.[Aming],
Liu, R.[Rui],
Han, Y.[Yahong],
Zhu, L.C.[Lin-Chao],
Yang, Y.[Yi],
Vector-Decomposed Disentanglement for Domain-Invariant Object
Detection,
ICCV21(9322-9331)
IEEE DOI
2203
Object detection, Detectors, Performance gain, Feature extraction,
Proposals, Compounds,
Detection and localization in 2D and 3D
BibRef
Zhang, X.H.[Xiao-Hong],
Chen, Y.[Yi],
Shen, Z.[Ziyi],
Shen, Y.M.[Yu-Ming],
Zhang, H.F.[Hao-Feng],
Zhang, Y.D.[Yu-Dong],
Confidence-and-Refinement Adaptation Model for Cross-Domain Semantic
Segmentation,
ITS(23), No. 7, July 2022, pp. 9529-9542.
IEEE DOI
2207
Semantics, Adaptation models, Image segmentation, Entropy, Training,
Annotations, Data models, Semantic segmentation,
confidence-aware entropy alignment
BibRef
Iqbal, J.[Javed],
Rawal, H.[Hamza],
Hafiz, R.[Rehan],
Chi, Y.T.[Yu-Tseh],
Ali, M.[Mohsen],
Distribution regularized self-supervised learning for domain
adaptation of semantic segmentation,
IVC(124), 2022, pp. 104504.
Elsevier DOI
2208
Semantic segmentation, Self-supervised learning,
Domain adaptation, Multi-modal distribution learning
BibRef
Zheng, Z.D.[Zhe-Dong],
Yang, Y.[Yi],
Adaptive Boosting for Domain Adaptation:
Toward Robust Predictions in Scene Segmentation,
IP(31), 2022, pp. 5371-5382.
IEEE DOI
2208
Adaptation models, Data models, Training, Predictive models,
Computational modeling, Semantics, Benchmark testing,
scene segmentation
BibRef
Zhang, B.[Bo],
Chen, T.[Tao],
Wang, B.[Bin],
Li, R.Y.[Ruo-Yao],
Joint Distribution Alignment via Adversarial Learning for Domain
Adaptive Object Detection,
MultMed(24), 2022, pp. 4102-4112.
IEEE DOI
2208
Feature extraction, Detectors, Object detection,
Adversarial machine learning, Task analysis, Training,
class-wise transferability
BibRef
An, S.M.[Shu-Min],
Liao, Q.M.[Qing-Min],
Lu, Z.Q.[Zong-Qing],
Xue, J.H.[Jing-Hao],
Efficient Semantic Segmentation via Self-Attention and
Self-Distillation,
ITS(23), No. 9, September 2022, pp. 15256-15266.
IEEE DOI
2209
Semantics, Context modeling, Knowledge engineering, Correlation,
Convolution, Adaptation models, Transforms, Semantic segmentation,
layer-wise context distillation
BibRef
Zhao, Y.Y.[Yu-Yang],
Zhong, Z.[Zhun],
Luo, Z.M.[Zhi-Ming],
Lee, G.H.[Gim Hee],
Sebe, N.[Nicu],
Source-Free Open Compound Domain Adaptation in Semantic Segmentation,
CirSysVideo(32), No. 10, October 2022, pp. 7019-7032.
IEEE DOI
2210
Adaptation models, Compounds, Training, Semantics, Data models,
Image segmentation, Transfer learning, Semantic segmentation,
source-free domain adaptation
BibRef
Li, G.[Guofa],
Ji, Z.F.[Ze-Feng],
Qu, X.D.[Xing-Da],
Stepwise Domain Adaptation (SDA) for Object Detection in Autonomous
Vehicles Using an Adaptive CenterNet,
ITS(23), No. 10, October 2022, pp. 17729-17743.
IEEE DOI
2210
Object detection, Detectors, Training, Deep learning,
Feature extraction, Adaptive systems, Task analysis,
adversarial learning
BibRef
Chen, X.[Xu],
Kuang, T.[Tianshu],
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
Klingner, M.[Marvin],
Ayache, M.[Mouadh],
Fingscheidt, T.[Tim],
Continual BatchNorm Adaptation (CBNA) for Semantic Segmentation,
ITS(23), No. 11, November 2022, pp. 20899-20911.
IEEE DOI
2212
Semantics, Adaptation models, Task analysis, Data models,
Image segmentation, Neural networks, Delays, Domain adaptation,
batch normalization
BibRef
Bou, X.[Xavier],
A Study of RobustNet, a Domain Generalization Method for Semantic
Segmentation,
IPOL(12), 2022, pp. 469-479.
DOI Link
2212
Code, Domain Generalization.
Code, Semantic Segmentation.
See also RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening.
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
Chen, J.Z.[Jun-Zhang],
Liu, Z.[Zichao],
Jin, D.[Darui],
Wang, Y.Y.[Yuan-Yuan],
Yang, F.[Fan],
Bai, X.Z.[Xiang-Zhi],
Light Transport Induced Domain Adaptation for Semantic Segmentation
in Thermal Infrared Urban Scenes,
ITS(23), No. 12, December 2022, pp. 23194-23211.
IEEE DOI
2212
Semantics, Image segmentation, Task analysis, Feature extraction,
Cameras, Voltage control, Lighting, Urban scenes, semantic segmentation
BibRef
Zhu, S.[Siyu],
Tian, Y.J.[Ying-Jie],
Shape robustness in style enhanced cross domain semantic segmentation,
PR(135), 2023, pp. 109143.
Elsevier DOI
2212
Domain adaptation, Semantic segmentation, Transfer learning
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
Liu, F.[Feng],
Zhang, X.S.[Xiao-Song],
Wan, F.[Fang],
Ji, X.Y.[Xiang-Yang],
Ye, Q.X.[Qi-Xiang],
Domain Contrast for Domain Adaptive Object Detection,
CirSysVideo(32), No. 12, December 2022, pp. 8227-8237.
IEEE DOI
2212
Detectors, Adaptation models, Object detection, Feature extraction,
Minimization, Transfer learning, Task analysis, Domain adaptation,
transfer learning
BibRef
Zhang, Y.[You],
Chen, H.[Houjin],
Li, Y.F.[Yan-Feng],
Zhou, J.Q.[Jun-Qi],
ImDeeplabV3plus with instance selective whitening loss in domain
generalization semantic segmentation,
IET-ITS(17), No. 1, 2023, pp. 180-192.
DOI Link
2301
BibRef
Gu, R.[Ran],
Zhang, J.Y.[Jing-Yang],
Wang, G.[Guotai],
Lei, W.H.[Wen-Hui],
Song, T.[Tao],
Zhang, X.F.[Xiao-Fan],
Li, K.[Kang],
Zhang, S.T.[Shao-Ting],
Contrastive Semi-Supervised Learning for Domain Adaptive Segmentation
Across Similar Anatomical Structures,
MedImg(42), No. 1, January 2023, pp. 245-256.
IEEE DOI
2301
Image segmentation, Annotations, Adaptation models, Training,
Biomedical imaging, Anatomical structure, Task analysis, contrastive learning
BibRef
Fang, Z.Y.[Zhi-Yuan],
Gao, G.Y.[Guang-Yu],
Zhang, Z.[Zekang],
Zhang, A.[Anqi],
Hierarchical context-agnostic network with contrastive feature
diversity for one-shot semantic segmentation,
JVCIR(90), 2023, pp. 103754.
Elsevier DOI
2301
Semantic segmentation, Few-shot learning,
Unsupervised clustering, Hierarchical pyramid, Background exclusion
BibRef
Zhou, Q.Y.[Qian-Yu],
Feng, Z.Y.[Zheng-Yang],
Gu, Q.Q.[Qi-Qi],
Pang, J.M.[Jiang-Miao],
Cheng, G.L.[Guang-Liang],
Lu, X.Q.[Xue-Quan],
Shi, J.P.[Jian-Ping],
Ma, L.Z.[Li-Zhuang],
Context-Aware Mixup for Domain Adaptive Semantic Segmentation,
CirSysVideo(33), No. 2, February 2023, pp. 804-817.
IEEE DOI
2302
Semantics, Image segmentation, Adaptation models, Uncertainty,
Task analysis, Degradation, Domain adaptation, scene understanding
BibRef
Xu, S.[Shan],
Zhang, H.D.[Huai-Dong],
Xu, X.M.[Xue-Miao],
Hu, X.W.[Xiao-Wei],
Xu, Y.Y.[Yang-Yang],
Dai, L.G.[Lian-Gui],
Choi, K.S.[Kup-Sze],
Heng, P.A.[Pheng-Ann],
Representative Feature Alignment for Adaptive Object Detection,
CirSysVideo(33), No. 2, February 2023, pp. 689-700.
IEEE DOI
2302
Feature extraction, Detectors, Proposals, Adaptation models,
Object detection, Semantics, Prototypes, Domain adaptation,
object detection
BibRef
Chen, C.Q.[Chao-Qi],
Li, J.C.[Jiong-Cheng],
Zhou, H.Y.[Hong-Yu],
Han, X.G.[Xiao-Guang],
Huang, Y.[Yue],
Ding, X.H.[Xing-Hao],
Yu, Y.Z.[Yi-Zhou],
Relation Matters: Foreground-Aware Graph-Based Relational Reasoning
for Domain Adaptive Object Detection,
PAMI(45), No. 3, March 2023, pp. 3677-3694.
IEEE DOI
2302
Cognition, Feature extraction, Semantics, Object detection, Training,
Task analysis, Knowledge transfer,
intra- and inter-domain
BibRef
Qiu, Y.Q.[Yi-Qiao],
Shen, Y.X.[Yi-Xing],
Sun, Z.[Zhuohao],
Zheng, Y.[Yanchong],
Chang, X.B.[Xia-Bin],
Zheng, W.S.[Wei-Shi],
Wang, R.X.[Rui-Xuan],
SATS: Self-attention transfer for continual semantic segmentation,
PR(138), 2023, pp. 109383.
Elsevier DOI
2303
Continual learning, Semantic segmentation,
Self-attention transfer, Class-specific region pooling
BibRef
Zhou, H.Y.[Hua-Yi],
Jiang, F.[Fei],
Lu, H.T.[Hong-Tao],
SSDA-YOLO: Semi-supervised domain adaptive YOLO for cross-domain
object detection,
CVIU(229), 2023, pp. 103649.
Elsevier DOI
2303
Domain adaptation, Knowledge distillation, Semi-supervised, YOLO
BibRef
Zhao, Z.Y.[Zi-Yuan],
Zhou, F.[Fangcheng],
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
Unsupervised domain adaptation, Object detection,
Adversarial learning, Feature alignment, Discriminator, Transferability
BibRef
Ouyang, C.[Cheng],
Chen, C.[Chen],
Li, S.[Surui],
Li, Z.[Zeju],
Qin, C.[Chen],
Bai, W.J.[Wen-Jia],
Rueckert, D.[Daniel],
Causality-Inspired Single-Source Domain Generalization for Medical
Image Segmentation,
MedImg(42), No. 4, April 2023, pp. 1095-1106.
IEEE DOI
2304
Image segmentation, Training, Biomedical imaging, Correlation,
Robustness, Data models, Training data, Domain generalization,
data augmentation
BibRef
Liu, D.N.[Dong-Nan],
Zhang, C.Y.[Chao-Yi],
Song, Y.[Yang],
Huang, H.[Heng],
Wang, C.Y.[Chen-Yu],
Barnett, M.[Michael],
Cai, W.D.[Wei-Dong],
Decompose to Adapt: Cross-Domain Object Detection Via Feature
Disentanglement,
MultMed(25), 2023, pp. 1333-1344.
IEEE DOI
2305
Feature extraction, Object detection, Task analysis, Training,
Visualization, Minimization, Data mining, Automatic drive, object detection
BibRef
Jiao, Y.F.[Yi-Fan],
Yao, H.T.[Han-Tao],
Xu, C.S.[Chang-Sheng],
Dual Instance-Consistent Network for Cross-Domain Object Detection,
PAMI(45), No. 6, June 2023, pp. 7338-7352.
IEEE DOI
2305
Feature extraction, Object detection, Detectors, Visualization,
Proposals, Head, Task analysis, Cross-domain object detection,
dual instance-consistent network
BibRef
Li, W.Y.[Wu-Yang],
Liu, X.Y.[Xin-Yu],
Yuan, Y.X.[Yi-Xuan],
SIGMA++: Improved Semantic-Complete Graph Matching for Domain
Adaptive Object Detection,
PAMI(45), No. 7, July 2023, pp. 9022-9040.
IEEE DOI
2306
BibRef
Earlier:
SIGMA: Semantic-complete Graph Matching for Domain Adaptive Object
Detection,
CVPR22(5281-5290)
IEEE DOI
2210
Semantics, Adaptation models, Object detection, Prototypes,
Image edge detection, Detectors, Feature extraction.
Training, GSM, Visualization, Semantics,
Self- semi- meta- Visual reasoning
BibRef
Zhou, Q.Y.[Qian-Yu],
Gu, Q.Q.[Qi-Qi],
Pang, J.M.[Jiang-Miao],
Lu, X.Q.[Xue-Quan],
Ma, L.Z.[Li-Zhuang],
Self-Adversarial Disentangling for Specific Domain Adaptation,
PAMI(45), No. 7, July 2023, pp. 8954-8968.
IEEE DOI
2306
Object detection, Semantic segmentation, Adaptation models,
Meteorology, Computational modeling, Training, Task analysis,
scene understanding
BibRef
Xie, B.H.[Bin-Hui],
Li, S.[Shuang],
Li, M.J.[Ming-Jia],
Liu, C.H.[Chi Harold],
Huang, G.[Gao],
Wang, G.[Guoren],
SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic
Segmentation,
PAMI(45), No. 7, July 2023, pp. 9004-9021.
IEEE DOI
2306
Semantics, Training, Task analysis, Semantic segmentation, Adaptation models,
Prototypes, Visualization, Domain adaptation, self-training
BibRef
Cheng, Y.T.[Yi-Ting],
Wei, F.Y.[Fang-Yun],
Bao, J.M.[Jian-Min],
Chen, D.[Dong],
Zhang, W.Q.[Wen-Qiang],
ADPL: Adaptive Dual Path Learning for Domain Adaptation of Semantic
Segmentation,
PAMI(45), No. 8, August 2023, pp. 9339-9356.
IEEE DOI
2307
Visualization, Adaptation models, Training, Semantic segmentation,
Pipelines, Synthetic data, Data models, Domain adaptation,
self-supervised learning
BibRef
Cheng, Y.T.[Yi-Ting],
Wei, F.Y.[Fang-Yun],
Bao, J.M.[Jian-Min],
Chen, D.[Dong],
Wen, F.[Fang],
Zhang, W.Q.[Wen-Qiang],
Dual Path Learning for Domain Adaptation of Semantic Segmentation,
ICCV21(9062-9071)
IEEE DOI
2203
Image segmentation, Adaptation models, Visualization, Codes,
Annotations, Computational modeling,
grouping and shape
BibRef
Guo, X.Q.[Xiao-Qing],
Liu, J.[Jie],
Liu, T.L.[Tong-Liang],
Yuan, Y.X.[Yi-Xuan],
Handling Open-Set Noise and Novel Target Recognition in Domain
Adaptive Semantic Segmentation,
PAMI(45), No. 8, August 2023, pp. 9846-9861.
IEEE DOI
2307
BibRef
Earlier:
SimT: Handling Open-set Noise for Domain Adaptive Semantic
Segmentation,
CVPR22(7022-7031)
IEEE DOI
2210
Semantics, Noise measurement, Target recognition,
Semantic segmentation, Data models, Adaptation models,
simplex noise transition matrix.
Representation learning, Adaptation models, Solid modeling,
Computational geometry, Computational modeling,
grouping and shape analysis
BibRef
Gao, K.L.[Kui-Liang],
Yu, A.[Anzhu],
You, X.[Xiong],
Qiu, C.P.[Chun-Ping],
Liu, B.[Bing],
Zhang, F.B.[Fu-Bing],
Cross-Domain Multi-Prototypes with Contradictory Structure Learning
for Semi-Supervised Domain Adaptation Segmentation of Remote Sensing
Images,
RS(15), No. 13, 2023, pp. 3398.
DOI Link
2307
BibRef
Ma, L.F.[Ling-Feng],
Xie, H.T.[Hong-Tao],
Liu, C.B.[Chuan-Bin],
Zhang, Y.D.[Yong-Dong],
Learning Cross-Channel Representations for Semantic Segmentation,
MultMed(25), 2023, pp. 2774-2787.
IEEE DOI
2307
Feature extraction, Semantics, Cognition, Convolution, Image segmentation,
Data mining, Context modeling, inter-channel relationship
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
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
Wang, P.[Ping],
Peng, J.[Jizong],
Pedersoli, M.[Marco],
Zhou, Y.F.[Yuan-Feng],
Zhang, C.M.[Cai-Ming],
Desrosiers, C.[Christian],
Shape-Aware Joint Distribution Alignment for Cross-Domain Image
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MedImg(42), No. 8, August 2023, pp. 2338-2347.
IEEE DOI
2308
Image segmentation, Entropy, Software,
Adversarial machine learning, Training, Task analysis,
domain adaptation
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Hospedales, T.M.[Timothy M.],
Uncertainty-Aware Source-Free Domain Adaptive Semantic Segmentation,
IP(32), 2023, pp. 4664-4676.
IEEE DOI
2309
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Nakamura, Y.[Yuzuru],
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Yamashita, T.[Takayoshi],
Few-shot Adaptive Object Detection with Cross-domain Cutmix,
ACCV22(VI:746-763).
Springer DOI
2307
BibRef
VS, V.[Vibashan],
Oza, P.[Poojan],
Patel, V.M.[Vishal M.],
Towards Online Domain Adaptive Object Detection,
WACV23(478-488)
IEEE DOI
2302
Training, Representation learning, Adaptation models,
Visualization, Source coding, Object detection, Detectors
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
Agarwal, S.[Sharat],
Anand, S.[Saket],
Arora, C.[Chetan],
Reducing Annotation Effort by Identifying and Labeling Contextually
Diverse Classes for Semantic Segmentation Under Domain Shift,
WACV23(5893-5902)
IEEE DOI
2302
Training, Costs, Uncertainty, Annotations, Semantic segmentation,
Measurement uncertainty,
Visualization
BibRef
Goswami, D.[Dipam],
Schuster, R.[René],
van de Weijer, J.[Joost],
Stricker, D.[Didier],
Attribution-aware Weight Transfer: A Warm-Start Initialization for
Class-Incremental Semantic Segmentation,
WACV23(3194-3203)
IEEE DOI
2302
Semantic segmentation, Semantics, Deep architecture,
Algorithms: Machine learning architectures, formulations,
visual reasoning)
BibRef
Chung, I.[Inseop],
Yoo, J.[Jayeon],
Kwak, N.[Nojun],
Exploiting Inter-pixel Correlations in Unsupervised Domain Adaptation
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RealWorld23(12-21)
IEEE DOI
2302
Knowledge engineering, Correlation, Semantic segmentation,
Heuristic algorithms, Conferences, Benchmark testing
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Piérard, S.[Sébastien],
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Halin, A.[Anaïs],
Vandeghen, R.[Renaud],
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Mahmoudi, S.[Saïd],
van Droogenbroeck, M.[Marc],
Mixture Domain Adaptation to Improve Semantic Segmentation in
Real-World Surveillance,
RealWorld23(1-10)
IEEE DOI
2302
Adaptation models, Machine learning algorithms, Surveillance,
Semantic segmentation, Computational modeling, Machine learning,
Probabilistic logic
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Maurya, J.[Jitender],
Ranipa, K.R.[Keyur R.],
Yamaguchi, O.[Osamu],
Shibata, T.[Tomoyuki],
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Domain Adaptation using Self-Training with Mixup for One-Stage Object
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WACV23(4178-4187)
IEEE DOI
2302
Adaptation models, Detectors, Object detection, Benchmark testing,
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Piva, F.J.[Fabrizio J.],
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Dubbelman, G.[Gijs],
Empirical Generalization Study: Unsupervised Domain Adaptation vs.
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WACV23(499-508)
IEEE DOI
2302
Training, Adaptation models, Protocols, Semantic segmentation,
Computational modeling, Training data, Applications: Robotics,
visual reasoning.
BibRef
Liang, J.M.[Jian-Ming],
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Unifying Visual Perception by Dispersible Points Learning,
ECCV22(IX:439-456).
Springer DOI
2211
WWW Link.
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ECCV22(XIX:701-719).
Springer DOI
2211
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MTTrans: Cross-domain Object Detection with Mean Teacher Transformer,
ECCV22(IX:629-645).
Springer DOI
2211
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Wu, Z.Y.[Zhen-Yao],
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SiamDoGe:
Domain Generalizable Semantic Segmentation Using Siamese Network,
ECCV22(XXXVIII:603-620).
Springer DOI
2211
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HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic
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ECCV22(XXX:372-391).
Springer DOI
2211
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DecoupleNet: Decoupled Network for Domain Adaptive Semantic
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ECCV22(XXXIII:369-387).
Springer DOI
2211
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A Similarity Distillation Guided Feature Refinement Network for
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ICIP22(666-670)
IEEE DOI
2211
Visualization, Prototypes, Benchmark testing, Task analysis,
Feature consistency, knowledge distillation, feature refinement,
few-shot semantic segmentation
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Mixture of Teacher Experts for Source-Free Domain Adaptive Object
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ICIP22(3606-3610)
IEEE DOI
2211
Training, Adaptation models, Uncertainty, Monte Carlo methods, Law,
Object detection, Object detection, Domain adaptation, Pseudo-labels
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Park, J.[Jinyoung],
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DAT: Domain Adaptive Transformer for Domain Adaptive Semantic
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ICIP22(4183-4187)
IEEE DOI
2211
Training, Adaptive systems, Annotations, Benchmark testing,
Transformers, Reliability, Noise measurement,
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Gong, R.[Rui],
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TACS: Taxonomy Adaptive Cross-Domain Semantic Segmentation,
ECCV22(XXXIV:19-35).
Springer DOI
2211
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ECCV22(XXXIV:36-54).
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2211
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ECCV22(XXXIV:55-72).
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2211
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Online Domain Adaptation for Semantic Segmentation in Ever-Changing
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ECCV22(XXXIV:128-146).
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2211
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Bi-directional Contrastive Learning for Domain Adaptive Semantic
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ECCV22(XXX:38-55).
Springer DOI
2211
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Cross-Domain Few-Shot Semantic Segmentation,
ECCV22(XXX:73-90).
Springer DOI
2211
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Huang, K.C.[Kuan-Chih],
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D2ADA: Dynamic Density-Aware Active Domain Adaptation for Semantic
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ECCV22(XXIX:449-467).
Springer DOI
2211
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Style-Hallucinated Dual Consistency Learning for Domain Generalized
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ECCV22(XXVIII:535-552).
Springer DOI
2211
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Kim, J.[Junsik],
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ML-BPM: Multi-teacher Learning with Bidirectional Photometric Mixing
for Open Compound Domain Adaptation in Semantic Segmentation,
ECCV22(XXXIV:236-251).
Springer DOI
2211
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Volpi, R.[Riccardo],
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Larlus, D.[Diane],
Csurka, G.[Gabriela],
On the Road to Online Adaptation for Semantic Image Segmentation,
CVPR22(19162-19173)
IEEE DOI
2210
Representation learning, Image segmentation, Adaptation models,
Protocols, Roads, Semantics, Representation learning,
Transfer/low-shot/long-tail learning
BibRef
Liu, X.Y.[Xin-Yu],
Li, W.Y.[Wu-Yang],
Yang, Q.S.[Qiu-Shi],
Li, B.[Baopu],
Yuan, Y.X.[Yi-Xuan],
Towards Robust Adaptive Object Detection under Noisy Annotations,
CVPR22(14187-14196)
IEEE DOI
2210
Training, Adaptation models, Visualization, Upper bound, Annotations,
Semantics, Object detection, Recognition: detection,
Visual reasoning
BibRef
Zhang, M.[Miao],
Singh, H.[Harvineet],
Chok, L.[Lazarus],
Chunara, R.[Rumi],
Segmenting across places:
The need for fair transfer learning with satellite imagery,
FaDE-TCV22(2915-2924)
IEEE DOI
2210
Measurement, Adaptation models, Image segmentation, Satellites,
Systematics, Transfer learning, Urban areas
BibRef
Vasconcelos, C.[Cristina],
Birodkar, V.[Vighnesh],
Dumoulin, V.[Vincent],
Proper Reuse of Image Classification Features Improves Object
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CVPR22(13618-13627)
IEEE DOI
2210
Training, Schedules, Computational modeling, Transfer learning,
Memory management, Object detection, Feature extraction,
Transfer/low-shot/long-tail learning
BibRef
Li, B.H.[Bang-Huai],
Adaptive Hierarchical Representation Learning for Long-Tailed Object
Detection,
CVPR22(2303-2312)
IEEE DOI
2210
Representation learning, Measurement, Training, Adaptation models,
Machine vision, Design methodology, Object detection,
Transfer/low-shot/long-tail learning
BibRef
Lee, M.[Minhyun],
Kim, D.[Dongseob],
Shim, H.J.[Hyun-Jung],
Threshold Matters in WSSS: Manipulating the Activation for the Robust
and Accurate Segmentation Model Against Thresholds,
CVPR22(4320-4329)
IEEE DOI
2210
Image segmentation, Image resolution, Codes, Semantics, Robustness,
Pattern recognition, Segmentation, grouping and shape analysis,
Self- semi- meta- Transfer/low-shot/long-tail learning
BibRef
Kalluri, T.[Tarun],
Chandraker, M.[Manmohan],
Cluster-to-adapt: Few Shot Domain Adaptation for Semantic
Segmentation across Disjoint Labels,
L3D-IVU22(4120-4130)
IEEE DOI
2210
Training, Image segmentation, Semantics, Minimization, Pattern recognition
BibRef
Zhao, L.[Liang],
Wang, L.M.[Li-Min],
Task-specific Inconsistency Alignment for Domain Adaptive Object
Detection,
CVPR22(14197-14206)
IEEE DOI
2210
Location awareness, Degradation, Codes, Computational modeling,
Detectors, Object detection, Recognition: detection,
Self- semi- meta- Transfer/low-shot/long-tail learning
BibRef
He, M.Z.[Meng-Zhe],
Wang, Y.[Yali],
Wu, J.X.[Jia-Xi],
Wang, Y.[Yiru],
Li, H.Q.[Han-Qing],
Li, B.[Bo],
Gan, W.H.[Wei-Hao],
Wu, W.[Wei],
Qiao, Y.[Yu],
Cross Domain Object Detection by Target-Perceived Dual Branch
Distillation,
CVPR22(9560-9570)
IEEE DOI
2210
Training, Degradation, Object detection, Detectors,
Benchmark testing, Pattern recognition, Proposals,
retrieval
BibRef
Zhou, W.Z.[Wen-Zhang],
Du, D.W.[Da-Wei],
Zhang, L.[Libo],
Luo, T.J.[Tie-Jian],
Wu, Y.J.[Yan-Jun],
Multi-Granularity Alignment Domain Adaptation for Object Detection,
CVPR22(9571-9580)
IEEE DOI
2210
Representation learning, Aggregates, Object detection, Detectors,
Logic gates, Feature extraction, Recognition: detection,
Self- semi- meta- unsupervised learning
BibRef
Zhao, Y.Z.[Yi-Zhou],
Guo, X.[Xun],
Lu, Y.[Yan],
Semantic-aligned Fusion Transformer for One-shot Object Detection,
CVPR22(7591-7601)
IEEE DOI
2210
Correlation, Semantics, Object detection, Computer architecture,
Benchmark testing, Transformers, Recognition: detection,
Transfer/low-shot/long-tail learning
BibRef
Li, S.F.[Shuai-Feng],
Ye, M.[Mao],
Zhu, X.T.[Xia-Tian],
Zhou, L.H.[Li-Hua],
Xiong, L.[Lin],
Source-Free Object Detection by Learning to Overlook Domain Style,
CVPR22(8004-8013)
IEEE DOI
2210
Adaptation models, Semantics, Training data, Object detection,
Detectors, Self-supervised learning, Predictive models,
Self- semi- meta- unsupervised learning
BibRef
Lang, C.[Chunbo],
Cheng, G.[Gong],
Tu, B.[Binfei],
Han, J.W.[Jun-Wei],
Learning What Not to Segment:
A New Perspective on Few-Shot Segmentation,
CVPR22(8047-8057)
IEEE DOI
2210
Image segmentation, Adaptation models, Sensitivity, Semantics,
Predictive models, Performance gain, grouping and shape analysis
BibRef
Li, Y.J.[Yu-Jhe],
Dai, X.L.[Xiao-Liang],
Ma, C.Y.[Chih-Yao],
Liu, Y.C.[Yen-Cheng],
Chen, K.[Kan],
Wu, B.[Bichen],
He, Z.J.[Zi-Jian],
Kitani, K.[Kris],
Vajda, P.[Peter],
Cross-Domain Adaptive Teacher for Object Detection,
CVPR22(7571-7580)
IEEE DOI
2210
Training, Adaptation models, Annotations, Target recognition,
Computational modeling, Object detection, Data models,
Self- semi- meta- unsupervised learning
BibRef
Wu, J.X.[Jia-Xi],
Chen, J.X.[Jia-Xin],
He, M.Z.[Meng-Zhe],
Wang, Y.[Yiru],
Li, B.[Bo],
Ma, B.Q.[Bing-Qi],
Gan, W.H.[Wei-Hao],
Wu, W.[Wei],
Wang, Y.[Yali],
Huang, D.[Di],
Target-Relevant Knowledge Preservation for Multi-Source Domain
Adaptive Object Detection,
CVPR22(5291-5300)
IEEE DOI
2210
Knowledge engineering, Degradation, Object detection, Detectors,
Benchmark testing, Pattern recognition, Recognition: detection, retrieval
BibRef
Liu, Y.[Yahao],
Deng, J.H.[Jin-Hong],
Tao, J.[Jiale],
Chu, T.[Tong],
Duan, L.X.[Li-Xin],
Li, W.[Wen],
Undoing the Damage of Label Shift for Cross-domain Semantic
Segmentation,
CVPR22(7032-7042)
IEEE DOI
2210
Codes, Shape, Semantics, Benchmark testing, Data models,
Pattern recognition, Transfer/low-shot/long-tail learning,
grouping and shape analysis
BibRef
Prabhu, V.[Viraj],
Selvaraju, R.R.[Ramprasaath R.],
Hoffman, J.[Judy],
Naik, N.[Nikhil],
Can domain adaptation make object recognition work for everyone?,
L3D-IVU22(3980-3987)
IEEE DOI
2210
Geography, Adaptation models, Visualization, Image recognition,
Computational modeling
BibRef
Peng, D.[Duo],
Lei, Y.J.[Yin-Jie],
Hayat, M.[Munawar],
Guo, Y.L.[Yu-Lan],
Li, W.[Wen],
Semantic-Aware Domain Generalized Segmentation,
CVPR22(2584-2595)
IEEE DOI
2210
Deep learning, Adaptation models, Image segmentation,
Image analysis, Shape, Surface acoustic waves, Semantics,
Transfer/low-shot/long-tail learning
BibRef
Xu, J.R.[Jia-Rui],
de Mello, S.[Shalini],
Liu, S.[Sifei],
Byeon, W.[Wonmin],
Breuel, T.[Thomas],
Kautz, J.[Jan],
Wang, X.L.[Xiao-Long],
GroupViT: Semantic Segmentation Emerges from Text Supervision,
CVPR22(18113-18123)
IEEE DOI
2210
Representation learning, Image segmentation, Visualization, Shape,
Semantics, Object detection, Transformers, Vision + language,
Self- semi- meta- unsupervised learning
BibRef
Lee, S.H.[Seung-Hun],
Choi, W.[Wonhyeok],
Kim, C.J.[Chang-Jae],
Choi, M.W.[Min-Woo],
Im, S.H.[Sung-Hoon],
ADAS: A Direct Adaptation Strategy for Multi-Target Domain Adaptive
Semantic Segmentation,
CVPR22(19174-19184)
IEEE DOI
2210
Training, Representation learning, Adaptation models,
Image segmentation, Visualization, Semantics,
Transfer/low-shot/long-tail learning
BibRef
Zhang, J.M.[Jia-Ming],
Yang, K.L.[Kai-Lun],
Ma, C.X.[Chao-Xiang],
Reiß, S.[Simon],
Peng, K.Y.[Kun-Yu],
Stiefelhagen, R.[Rainer],
Bending Reality: Distortion-aware Transformers for Adapting to
Panoramic Semantic Segmentation,
CVPR22(16896-16906)
IEEE DOI
2210
Training, Image segmentation, Adaptation models, Annotations, Shape,
Semantics, Transformers, Scene analysis and understanding,
Self- semi- meta- unsupervised learning
BibRef
Phan, M.H.[Minh Hieu],
Ta, T.A.[The-Anh],
Phung, S.L.[Son Lam],
Tran-Thanh, L.[Long],
Bouzerdoum, A.[Abdesselam],
Class Similarity Weighted Knowledge Distillation for Continual
Semantic Segmentation,
CVPR22(16845-16854)
IEEE DOI
2210
Deep learning, Representation learning, Shape,
Computational modeling, Machine vision, Semantics, Segmentation
BibRef
Ji, D.Y.[De-Yi],
Wang, H.R.[Hao-Ran],
Tao, M.Y.[Ming-Yuan],
Huang, J.Q.[Jian-Qiang],
Hua, X.S.[Xian-Sheng],
Lu, H.T.[Hong-Tao],
Structural and Statistical Texture Knowledge Distillation for
Semantic Segmentation,
CVPR22(16855-16864)
IEEE DOI
2210
Knowledge engineering, Quantization (signal), Laplace equations,
Image analysis, Shape, Semantics, Force,
grouping and shape analysis
BibRef
Zhou, B.[Brady],
Krähenbühl, P.[Philipp],
Cross-view Transformers for real-time Map-view Semantic Segmentation,
CVPR22(13750-13759)
IEEE DOI
2210
Convolutional codes, Image segmentation, Navigation, Semantics,
Computer architecture, Transformers, Navigation and autonomous driving
BibRef
Li, R.H.[Rui-Huang],
Li, S.[Shuai],
He, C.H.[Chen-Hang],
Zhang, Y.[Yabin],
Jia, X.[Xu],
Zhang, L.[Lei],
Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic
Segmentation,
CVPR22(11583-11593)
IEEE DOI
2210
Training, Image segmentation, Statistical analysis, Shape, Semantics,
Pipelines, Predictive models, Segmentation,
Self- semi- meta- Transfer/low-shot/long-tail learning
BibRef
Zhang, W.Q.[Wen-Qiang],
Huang, Z.L.[Zi-Long],
Luo, G.Z.[Guo-Zhong],
Chen, T.[Tao],
Wang, X.G.[Xing-Gang],
Liu, W.Y.[Wen-Yu],
Yu, G.[Gang],
Shen, C.H.[Chun-Hua],
TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation,
CVPR22(12073-12083)
IEEE DOI
2210
Deep learning, Shape, Semantics, Computer architecture, Transformers,
Mobile handsets, Deep learning architectures and techniques,
grouping and shape analysis
BibRef
Liu, Y.[Yuanwei],
Liu, N.[Nian],
Cao, Q.L.[Qing-Long],
Yao, X.[Xiwen],
Han, J.W.[Jun-Wei],
Shao, L.[Ling],
Learning Non-target Knowledge for Few-shot Semantic Segmentation,
CVPR22(11563-11572)
IEEE DOI
2210
Codes, Shape, Semantics, Prototypes, Object segmentation,
Filtering algorithms, Segmentation, grouping and shape analysis,
Transfer/low-shot/long-tail learning
BibRef
Tian, Z.[Zhuotao],
Lai, X.[Xin],
Jiang, L.[Li],
Liu, S.[Shu],
Shu, M.[Michelle],
Zhao, H.S.[Heng-Shuang],
Jia, J.Y.[Jia-Ya],
Generalized Few-shot Semantic Segmentation,
CVPR22(11553-11562)
IEEE DOI
2210
Training, Image segmentation, Codes, Shape, Semantics, Prototypes,
grouping and shape analysis, Representation learning, Segmentation
BibRef
Guan, D.[Dayan],
Huang, J.X.[Jia-Xing],
Xiao, A.[Aoran],
Lu, S.J.[Shi-Jian],
Unbiased Subclass Regularization for Semi-Supervised Semantic
Segmentation,
CVPR22(9958-9968)
IEEE DOI
2210
Deep learning, Image segmentation, Semantics, Neural networks,
Object detection, Logic gates, Semisupervised learning,
grouping and shape analysis
BibRef
Gao, H.[Huan],
Guo, J.C.[Ji-Chang],
Wang, G.L.[Guo-Li],
Zhang, Q.[Qian],
Cross-Domain Correlation Distillation for Unsupervised Domain
Adaptation in Nighttime Semantic Segmentation,
CVPR22(9903-9913)
IEEE DOI
2210
Image segmentation, Correlation, Shape, Semantics, Lighting,
Feature extraction, grouping and shape analysis,
Self- semi- meta- Segmentation
BibRef
Hoyer, L.[Lukas],
Dai, D.X.[Deng-Xin],
Van Gool, L.J.[Luc J.],
DAFormer: Improving Network Architectures and Training Strategies for
Domain-Adaptive Semantic Segmentation,
CVPR22(9914-9925)
IEEE DOI
2210
Training, Image segmentation, Annotations, Semantics,
Network architecture, Transformers,
Transfer/low-shot/long-tail learning
BibRef
Lee, S.[Suhyeon],
Seong, H.[Hongje],
Lee, S.W.[Seong-Won],
Kim, E.T.[Eun-Tai],
WildNet: Learning Domain Generalized Semantic Segmentation from the
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CVPR22(9926-9936)
IEEE DOI
2210
Image segmentation, Codes, Semantics, Pattern recognition,
Self- semi- meta- Scene analysis and understanding
BibRef
Fan, J.S.[Jia-Shuo],
Gao, B.[Bin],
Jin, H.[Huan],
Jiang, L.H.[Li-Hui],
UCC: Uncertainty guided Cross-head Cotraining for Semi-Supervised
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CVPR22(9937-9946)
IEEE DOI
2210
Training, Deep learning, Uncertainty, Protocols, Semantics,
Neural networks, Self- semi- meta- Machine learning
BibRef
Huo, X.Y.[Xin-Yue],
Xie, L.X.[Ling-Xi],
Hu, H.[Hengtong],
Zhou, W.G.[Wen-Gang],
Li, H.Q.[Hou-Qiang],
Tian, Q.[Qi],
Domain-Agnostic Prior for Transfer Semantic Segmentation,
CVPR22(7065-7075)
IEEE DOI
2210
Representation learning, Protocols, Shape, Navigation, Semantics,
Training data, Transfer/low-shot/long-tail learning,
grouping and shape analysis
BibRef
Ren, H.X.[Han-Xiang],
Yang, Y.C.[Yan-Chao],
Wang, H.[He],
Shen, B.[Bokui],
Fan, Q.[Qingnan],
Zheng, Y.Y.[You-Yi],
Liu, C.K.[C. Karen],
Guibas, L.J.[Leonidas J.],
ADeLA: Automatic Dense Labeling with Attention for Viewpoint Shift in
Semantic Segmentation,
CVPR22(8069-8079)
IEEE DOI
2210
Image segmentation, Uncertainty, Shape, Semantics,
Robot vision systems, Object segmentation, Color,
Vision applications and systems
BibRef
Xie, B.H.[Bin-Hui],
Yuan, L.[Longhui],
Li, S.[Shuang],
Liu, C.H.[Chi Harold],
Cheng, X.[Xinjing],
Towards Fewer Annotations: Active Learning via Region Impurity and
Prediction Uncertainty for Domain Adaptive Semantic Segmentation,
CVPR22(8058-8068)
IEEE DOI
2210
Image segmentation, Adaptation models, Uncertainty, Annotations,
Shape, Impurities, Semantics, Transfer/low-shot/long-tail learning,
grouping and shape analysis
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
Chen, S.Y.[Si-Yuan],
Conditional Context-Aware Feature Alignment for Domain Adaptive
Detection Transformer,
MMMod22(I:272-283).
Springer DOI
2203
BibRef
Wang, X.[Xin],
Huang, T.E.[Thomas E.],
Liu, B.[Benlin],
Yu, F.[Fisher],
Wang, X.L.[Xiao-Long],
Gonzalez, J.E.[Joseph E.],
Darrell, T.J.[Trevor J.],
Robust Object Detection via Instance-Level Temporal Cycle Confusion,
ICCV21(9123-9132)
IEEE DOI
2203
Training, Adaptation models, Lightning, Detectors, Object detection,
Benchmark testing, Feature extraction,
Detection and localization in 2D and 3D
BibRef
Ramamonjison, R.[Rindra],
Banitalebi-Dehkordi, A.[Amin],
Kang, X.Y.[Xin-Yu],
Bai, X.L.[Xiao-Long],
Zhang, Y.[Yong],
SimROD: A Simple Adaptation Method for Robust Object Detection,
ICCV21(3550-3559)
IEEE DOI
2203
Adaptation models, Computational modeling, Object detection,
Computer architecture, Benchmark testing, Data models,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Rezaeianaran, F.[Farzaneh],
Shetty, R.[Rakshith],
Aljundi, R.[Rahaf],
Reino, D.O.[Daniel Olmeda],
Zhang, S.S.[Shan-Shan],
Schiele, B.[Bernt],
Seeking Similarities over Differences: Similarity-based Domain
Alignment for Adaptive Object Detection,
ICCV21(9184-9193)
IEEE DOI
2203
Training, Visualization, Computational modeling, Prototypes,
Detectors, Object detection,
Detection and localization in 2D and 3D
BibRef
Jing, T.T.[Tao-Tao],
Liu, H.F.[Hong-Fu],
Ding, Z.M.[Zheng-Ming],
Towards Novel Target Discovery Through Open-Set Domain Adaptation,
ICCV21(9302-9311)
IEEE DOI
2203
Representation learning, Visualization, Adaptation models,
Computational modeling, Semantics, Benchmark testing,
Vision + other modalities
BibRef
Lin, C.[Chuang],
Yuan, Z.H.[Ze-Huan],
Zhao, S.C.[Si-Cheng],
Sun, P.[Peize],
Wang, C.H.[Chang-Hu],
Cai, J.F.[Jian-Fei],
Domain-Invariant Disentangled Network for Generalizable Object
Detection,
ICCV21(8751-8760)
IEEE DOI
2203
Laser radar, Object detection, Detectors, Benchmark testing,
Image reconstruction, Image classification,
Representation learning
BibRef
Kundu, J.N.[Jogendra Nath],
Kulkarni, A.[Akshay],
Singh, A.[Amit],
Jampani, V.[Varun],
Babu, R.V.[R. Venkatesh],
Generalize then Adapt: Source-Free Domain Adaptive Semantic
Segmentation,
ICCV21(7026-7036)
IEEE DOI
2203
Training, Art, Target recognition, Semantics, Reliability theory,
Benchmark testing, Segmentation, grouping and shape,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Wang, Y.X.[Yu-Xi],
Peng, J.R.[Jun-Ran],
Zhang, Z.X.[Zhao-Xiang],
Uncertainty-aware Pseudo Label Refinery for Domain Adaptive Semantic
Segmentation,
ICCV21(9072-9081)
IEEE DOI
2203
Training, Adaptation models, Uncertainty, Semantics,
Predictive models, Noise measurement,
grouping and shape
BibRef
Li, S.[Shuang],
Xie, M.[Mixue],
Lv, F.R.[Fang-Rui],
Liu, C.H.[Chi Harold],
Liang, J.[Jian],
Qin, C.[Chen],
Li, W.[Wei],
Semantic Concentration for Domain Adaptation,
ICCV21(9082-9091)
IEEE DOI
2203
Adaptation models, Annotations, Semantics, Predictive models,
Benchmark testing, Feature extraction,
Adversarial learning
BibRef
Ning, M.[Munan],
Lu, D.[Donghuan],
Wei, D.[Dong],
Bian, C.[Cheng],
Yuan, C.[Chenglang],
Yu, S.[Shuang],
Ma, K.[Kai],
Zheng, Y.F.[Ye-Feng],
Multi-Anchor Active Domain Adaptation for Semantic Segmentation,
ICCV21(9092-9102)
IEEE DOI
2203
Training, Costs, Codes, Annotations, Semantics, Manuals,
Transfer/Low-shot/Semi/Unsupervised Learning,
grouping and shape
BibRef
Tian, K.[Kun],
Zhang, C.H.[Cheng-Hao],
Wang, Y.[Ying],
Xiang, S.M.[Shi-Ming],
Pan, C.H.[Chun-Hong],
Knowledge Mining and Transferring for Domain Adaptive Object
Detection,
ICCV21(9113-9122)
IEEE DOI
2203
Training, Degradation, Adaptation models, Correlation, Focusing,
Detectors, Object detection,
Representation learning
BibRef
Liu, Y.[Yahao],
Deng, J.H.[Jin-Hong],
Gao, X.C.[Xin-Chen],
Li, W.[Wen],
Duan, L.X.[Li-Xin],
BAPA-Net: Boundary Adaptation and Prototype Alignment for
Cross-domain Semantic Segmentation,
ICCV21(8781-8791)
IEEE DOI
2203
Image segmentation, Adaptation models, Semantics, Prototypes,
Benchmark testing, Convolutional neural networks,
grouping and shape
BibRef
Truong, T.D.[Thanh-Dat],
Duong, C.N.[Chi Nhan],
Le, N.[Ngan],
Phung, S.L.[Son Lam],
Rainwater, C.[Chase],
Luu, K.[Khoa],
BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in
Semantic Scene Segmentation,
ICCV21(8528-8537)
IEEE DOI
2203
Measurement, Adaptation models, Computational modeling, Semantics,
Benchmark testing, Minimization,
grouping and shape
BibRef
Shin, I.[Inkyu],
Kim, D.J.[Dong-Jin],
Cho, J.W.[Jae Won],
Woo, S.[Sanghyun],
Park, K.Y.[Kwan-Yong],
Kweon, I.S.[In So],
LabOR: Labeling Only if Required for Domain Adaptive Semantic
Segmentation,
ICCV21(8568-8578)
IEEE DOI
2203
Degradation, Adaptation models, Costs, Semantics, Labeling,
Transfer/Low-shot/Semi/Unsupervised Learning, Scene analysis and understanding
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
Peng, D.[Duo],
Lei, Y.J.[Yin-Jie],
Li, W.[Wen],
Zhang, P.P.[Ping-Ping],
Guo, Y.L.[Yu-Lan],
Sparse-to-dense Feature Matching: Intra and Inter domain Cross-modal
Learning in Domain Adaptation for 3D Semantic Segmentation,
ICCV21(7088-7097)
IEEE DOI
2203
Point cloud compression, Training, Image segmentation, Semantics,
Adversarial machine learning, Probability distribution,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Chen, C.Q.[Chao-Qi],
Li, J.C.[Jiong-Cheng],
Zheng, Z.B.[Ze-Biao],
Huang, Y.[Yue],
Ding, X.[Xinghao],
Yu, Y.Z.[Yi-Zhou],
Dual Bipartite Graph Learning:
A General Approach for Domain Adaptive Object Detection,
ICCV21(2683-2692)
IEEE DOI
2203
Training, Adaptation models, Semantics, Pipelines, Detectors,
Object detection, Benchmark testing,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Yao, X.X.[Xing-Xu],
Zhao, S.C.[Si-Cheng],
Xu, P.F.[Peng-Fei],
Yang, J.F.[Ju-Feng],
Multi-Source Domain Adaptation for Object Detection,
ICCV21(3253-3262)
IEEE DOI
2203
Adaptation models, Annotations, Supervised learning,
Object detection, Feature extraction, Approximation algorithms,
BibRef
Zhang, H.[Hui],
Ding, H.H.[Heng-Hui],
Prototypical Matching and Open Set Rejection for Zero-Shot Semantic
Segmentation,
ICCV21(6954-6963)
IEEE DOI
2203
Training, Visualization, Semantics, Prototypes, Segmentation,
grouping and shape,
BibRef
Baek, D.[Donghyeon],
Oh, Y.[Youngmin],
Ham, B.[Bumsub],
Exploiting a Joint Embedding Space for Generalized Zero-Shot Semantic
Segmentation,
ICCV21(9516-9525)
IEEE DOI
2203
Visualization, Semantics, Prototypes, Transforms, Benchmark testing,
Calibration, Transfer/Low-shot/Semi/Unsupervised Learning,
grouping and shape
BibRef
Cheng, J.X.[Jia-Xin],
Nandi, S.[Soumyaroop],
Natarajan, P.[Prem],
Abd-Almageed, W.[Wael],
SIGN: Spatial-information Incorporated Generative Network for
Generalized Zero-shot Semantic Segmentation,
ICCV21(9536-9546)
IEEE DOI
2203
Training, Image segmentation, Image coding, Annealing, Semantics,
Government, Benchmark testing,
grouping and shape
BibRef
Lu, Z.[Zhihe],
He, S.[Sen],
Zhu, X.T.[Xia-Tian],
Zhang, L.[Li],
Song, Y.Z.[Yi-Zhe],
Xiang, T.[Tao],
Simpler is Better:
Few-shot Semantic Segmentation with Classifier Weight Transformer,
ICCV21(8721-8730)
IEEE DOI
2203
Training, Image segmentation, Adaptation models,
Continuous wavelet transforms, Semantics, Transformers,
grouping and shape
BibRef
Tjio, G.[Gabriel],
Liu, P.[Ping],
Zhou, J.T.Y.[Joey Tian-Yi],
Goh, R.S.M.[Rick Siow Mong],
Adversarial Semantic Hallucination for Domain Generalized Semantic
Segmentation,
WACV22(3849-3858)
IEEE DOI
2202
Training, Image segmentation, Data privacy, Codes,
Semantics, Ash, Grouping and Shape
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
Zhang, Y.Z.[Yi-Zhe],
Borse, S.[Shubhankar],
Cai, H.[Hong],
Porikli, F.M.[Fatih M.],
AuxAdapt: Stable and Efficient Test-Time Adaptation for Temporally
Consistent Video Semantic Segmentation,
WACV22(2633-2642)
IEEE DOI
2202
Adaptation models, Uncertainty, Semantics, Neural networks,
Estimation, Streaming media, Stability analysis, Segmentation,
Semi- and Un- supervised Learning
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
Garg, P.[Prachi],
Saluja, R.[Rohit],
Balasubramanian, V.N.[Vineeth N],
Arora, C.[Chetan],
Subramanian, A.[Anbumani],
Jawahar, C.V.,
Multi-Domain Incremental Learning for Semantic Segmentation,
WACV22(2080-2090)
IEEE DOI
2202
Visualization, Roads, Semantics,
Computer architecture, Interference, Stability analysis,
Semi- and Un- supervised Learning
BibRef
Bevandic, P.[Petra],
Oršic, M.[Marin],
Grubišic, I.[Ivan],
Šaric, J.[Josip],
Šegvic, S.[Siniša],
Multi-domain semantic segmentation with overlapping labels *,
WACV22(2422-2431)
IEEE DOI
2202
Training, Visualization, Roads, Taxonomy, Semantics, Training data,
Benchmark testing, Semi- and Un- supervised Learning
BibRef
Razani, R.[Ryan],
Cheng, R.[Ran],
Li, E.[Enxu],
Taghavi, E.[Ehsan],
Ren, Y.[Yuan],
Liu, B.B.[Bing-Bing],
GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network,
ICCV21(16056-16065)
IEEE DOI
2203
Laser radar, Convolution, Semantics, Benchmark testing,
Computational efficiency, Scene analysis and understanding,
Vision for robotics and autonomous vehicles
BibRef
Cheng, R.[Ran],
Razani, R.[Ryan],
Taghavi, E.[Ehsan],
Li, E.[Enxu],
Liu, B.B.[Bing-Bing],
(AF)2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection
for Sparse Semantic Segmentation Network,
CVPR21(12542-12551)
IEEE DOI
2111
Learning systems, Laser radar, Fuses, Roads, Semantics, Feature extraction
BibRef
Zhang, P.[Pan],
Zhang, B.[Bo],
Zhang, T.[Ting],
Chen, D.[Dong],
Wang, Y.[Yong],
Wen, F.[Fang],
Prototypical Pseudo Label Denoising and Target Structure Learning for
Domain Adaptive Semantic Segmentation,
CVPR21(12409-12419)
IEEE DOI
2111
Training, Adaptation models, Codes, Supervised learning, Semantics,
Noise reduction, Prototypes
BibRef
Zhang, Y.X.[Yi-Xin],
Wang, Z.[Zilei],
Mao, Y.[Yushi],
RPN Prototype Alignment For Domain Adaptive Object Detector,
CVPR21(12420-12429)
IEEE DOI
2111
Degradation, Prototypes, Detectors,
Object detection, Feature extraction, Pattern recognition
BibRef
Chen, S.J.[Shuai-Jun],
Jia, X.[Xu],
He, J.Z.[Jian-Zhong],
Shi, Y.J.[Yong-Jie],
Liu, J.Z.[Jian-Zhuang],
Semi-supervised Domain Adaptation based on Dual-level Domain Mixing
for Semantic Segmentation,
CVPR21(11013-11022)
IEEE DOI
2111
Training, Adaptation models, Image segmentation,
Costs, Semantics, Benchmark testing
BibRef
Zhang, Q.[Qiang],
Zhao, S.[Shenlu],
Luo, Y.J.[Yong-Jiang],
Zhang, D.W.[Ding-Wen],
Huang, N.Z.[Nian-Zchang],
Han, J.G.[Jun-Gong],
ABMDRNet: Adaptive-weighted Bi-directional Modality Difference
Reduction Network for RGB-T Semantic Segmentation,
CVPR21(2633-2642)
IEEE DOI
2111
Image segmentation, Technological innovation,
Fuses, Semantics, Lighting, Imaging
BibRef
Liu, Y.[Yuang],
Zhang, W.[Wei],
Wang, J.[Jun],
Source-Free Domain Adaptation for Semantic Segmentation,
CVPR21(1215-1224)
IEEE DOI
2111
Adaptation models, Image segmentation, Semantics,
Benchmark testing, Pattern recognition, Convolutional neural networks
BibRef
Yang, J.Y.[Jin-Yu],
An, W.Z.[Wei-Zhi],
Yan, C.C.[Chao-Chao],
Zhao, P.L.[Pei-Lin],
Huang, J.Z.[Jun-Zhou],
Context-Aware Domain Adaptation in Semantic Segmentation,
WACV21(514-524)
IEEE DOI
2106
Adaptation models, Aggregates, Semantics
BibRef
Huang, J.X.[Jia-Xing],
Lu, S.J.[Shi-Jian],
Guan, D.[Dayan],
Zhang, X.B.[Xiao-Bing],
Contextual-relation Consistent Domain Adaptation for Semantic
Segmentation,
ECCV20(XV:705-722).
Springer DOI
2011
BibRef
Subhani, M.N.[M. Naseer],
Ali, M.[Mohsen],
Learning from Scale-invariant Examples for Domain Adaptation in
Semantic Segmentation,
ECCV20(XXII:290-306).
Springer DOI
2011
BibRef
Yang, J.Y.[Jin-Yu],
An, W.Z.[Wei-Zhi],
Wang, S.[Sheng],
Zhu, X.L.[Xin-Liang],
Yan, C.C.[Chao-Chao],
Huang, J.Z.[Jun-Zhou],
Label-driven Reconstruction for Domain Adaptation in Semantic
Segmentation,
ECCV20(XXVII:480-498).
Springer DOI
2011
BibRef
Paul, S.[Sujoy],
Tsai, Y.H.[Yi-Hsuan],
Schulter, S.[Samuel],
Roy-Chowdhury, A.K.[Amit K.],
Chandraker, M.[Manmohan],
Domain Adaptive Semantic Segmentation Using Weak Labels,
ECCV20(IX:571-587).
Springer DOI
2011
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
Yang, Y.,
Soatto, S.,
FDA: Fourier Domain Adaptation for Semantic Segmentation,
CVPR20(4084-4094)
IEEE DOI
2008
Semantics, Image segmentation, Training, Entropy, Adaptation models,
Task analysis, Frequency-domain analysis
BibRef
Wang, Z.,
Wei, Y.,
Feris, R.,
Xiong, J.,
Hwu, W.,
Huang, T.S.,
Shi, H.,
Alleviating Semantic-level Shift: A Semi-supervised Domain Adaptation
Method for Semantic Segmentation,
VL3W20(4043-4047)
IEEE DOI
2008
Semantics, Task analysis, Adaptation models, Image segmentation,
Training, Feature extraction, Urban areas
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
Lin, Y.X.[Yong-Xiang],
Tan, D.S.[Daniel Stanley],
Cheng, W.H.[Wen-Huang],
Chen, Y.Y.[Yung-Yao],
Hua, K.L.[Kai-Lung],
Spatially-Aware Domain Adaptation for Semantic Segmentation of Urban
Scenes,
ICIP19(1870-1874)
IEEE DOI
1910
Semantic segmentation, Domain adaptation, Spatial Structure
BibRef
Lv, F.M.[Feng-Mao],
Lian, Q.[Qing],
Yang, G.[Guowu],
Lin, G.S.[Guo-Sheng],
Pan, S.J.[Sinno Jialin],
Duan, L.X.[Li-Xin],
Domain Adaptive Semantic Segmentation Through Structure Enhancement,
TASKCV18(II:172-179).
Springer DOI
1905
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
Remote Sensing Semantic Segmentation .