Li, W.[Wen],
Xu, Z.[Zheng],
Xu, D.[Dong],
Dai, D.X.[Deng-Xin],
Van Gool, L.J.[Luc J.],
Domain Generalization and Adaptation Using Low Rank Exemplar SVMs,
PAMI(40), No. 5, May 2018, pp. 1114-1127.
IEEE DOI
1804
Linear programming, Logistics, Support vector machines, Testing,
Training, Videos, Visualization, Latent domains, domain adaptation,
exemplar SVMs
BibRef
Wang, Y.,
Li, W.,
Dai, D.X.[Deng-Xin],
Van Gool, L.J.[Luc J.],
Deep Domain Adaptation by Geodesic Distance Minimization,
TASKCV17(2651-2657)
IEEE DOI
1802
Adaptation models, Covariance matrices, Euclidean distance,
Feature extraction, Manifolds, Training data, Visualization
BibRef
Lee, W.[Woojin],
Kim, H.[Hoki],
Lee, J.W.[Jae-Wook],
Compact class-conditional domain invariant learning for multi-class
domain adaptation,
PR(112), 2021, pp. 107763.
Elsevier DOI
2102
Domain adaptation, Generalization bound,
Class-conditional domain invariant learning, Transfer Learning
BibRef
Li, H.L.[Hao-Liang],
Wang, S.Q.[Shi-Qi],
Wan, R.J.[Ren-Jie],
Kot, A.C.[Alex C.],
GMFAD: Towards Generalized Visual Recognition via Multilayer Feature
Alignment and Disentanglement,
PAMI(44), No. 3, March 2022, pp. 1289-1303.
IEEE DOI
2202
Adaptation models, Machine learning, Training, Task analysis,
Data models, Correlation, Training data, Generalization capability,
visual recognition
BibRef
Wang, H.[Hao],
Bi, X.J.[Xiao-Jun],
Domain generalization and adaptation based on second-order style
information,
PR(127), 2022, pp. 108595.
Elsevier DOI
2205
Domain generalization, Unsupervised domain adaptation,
Two-level style normalization and restitution,
Dynamic affine parameter
BibRef
Yuan, M.L.[Ming-Lei],
Cai, C.H.[Chun-Hao],
Lu, T.[Tong],
Wu, Y.R.[Yi-Rui],
Xu, Q.[Qian],
Zhou, S.J.[Shi-Jie],
A novel forget-update module for few-shot domain generalization,
PR(129), 2022, pp. 108704.
Elsevier DOI
2206
Few-shot classification, Domain adaptation, Few-shot domain generalization
BibRef
Duboudin, T.[Thomas],
Dellandréa, E.[Emmanuel],
Abgrall, C.[Corentin],
Hénaff, G.[Gilles],
Chen, L.M.[Li-Ming],
Encouraging Intra-Class Diversity Through a Reverse Contrastive Loss
for Single-Source Domain Generalization,
AROW21(51-60)
IEEE DOI
2112
Training, Deep learning, Heuristic algorithms, Neural networks,
Training data, Benchmark testing, Prediction algorithms
BibRef
Guillory, D.[Devin],
Shankar, V.[Vaishaal],
Ebrahimi, S.[Sayna],
Darrell, T.J.[Trevor J.],
Schmidt, L.[Ludwig],
Predicting with Confidence on Unseen Distributions,
ICCV21(1114-1124)
IEEE DOI
2203
Adaptation models, Uncertainty, Filtering, Veins, Training data,
Focusing, Machine learning, Recognition and classification,
Datasets and evaluation
BibRef
Wu, G.[Guile],
Gong, S.G.[Shao-Gang],
Collaborative Optimization and Aggregation for Decentralized Domain
Generalization and Adaptation,
ICCV21(6464-6473)
IEEE DOI
2203
Training, Adaptation models, Data privacy, Collaboration,
Benchmark testing, Predictive models, Data collection,
Recognition and classification
BibRef
Mansilla, L.[Lucas],
Echeveste, R.[Rodrigo],
Milone, D.H.[Diego H.],
Ferrante, E.[Enzo],
Domain Generalization via Gradient Surgery,
ICCV21(6610-6618)
IEEE DOI
2203
Training, Surgery, Interference, Computer architecture,
Picture archiving and communication systems, Data models,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Gong, Y.[Yunye],
Lin, X.[Xiao],
Yao, Y.[Yi],
Dietterich, T.G.[Thomas G.],
Divakaran, A.[Ajay],
Gervasio, M.[Melinda],
Confidence Calibration for Domain Generalization under Covariate
Shift,
ICCV21(8938-8947)
IEEE DOI
2203
Training, Adaptation models, Upper bound, Temperature,
Linear regression, Clustering algorithms, and ethics in vision
BibRef
Kim, D.[Daehee],
Yoo, Y.[Youngjun],
Park, S.H.[Seung-Hyun],
Kim, J.[Jinkyu],
Lee, J.[Jaekoo],
SelfReg: Self-supervised Contrastive Regularization for Domain
Generalization,
ICCV21(9599-9608)
IEEE DOI
2203
Training, Deep learning, Computational modeling,
Perturbation methods, Benchmark testing, Feature extraction,
Efficient training and inference methods
BibRef
Sariyildiz, M.B.[Mert Bulent],
Kalantidis, Y.[Yannis],
Larlus, D.[Diane],
Alahari, K.[Karteek],
Concept Generalization in Visual Representation Learning,
ICCV21(9609-9619)
IEEE DOI
2203
Training, Visualization, Adaptation models, Current transformers,
Search methods, Semantics, Supervised learning,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Shankar, V.[Vaishaal],
Dave, A.[Achal],
Roelofs, R.[Rebecca],
Ramanan, D.[Deva],
Recht, B.[Benjamin],
Schmidt, L.[Ludwig],
Do Image Classifiers Generalize Across Time?,
ICCV21(9641-9649)
IEEE DOI
2203
Analytical models, Perturbation methods, Speech recognition,
Predictive models, Benchmark testing, Robustness, Adversarial learning
BibRef
Paul, S.[Soumava],
Dutta, T.[Titir],
Biswas, S.[Soma],
Universal Cross-Domain Retrieval:
Generalizing Across Classes and Domains,
ICCV21(12036-12044)
IEEE DOI
2203
Training, Bridges, Protocols, Semantics, Task analysis, Testing,
Image and video retrieval, Recognition and classification
BibRef
Wang, Z.J.[Zi-Jian],
Luo, Y.[Yadan],
Qiu, R.[Ruihong],
Huang, Z.[Zi],
Baktashmotlagh, M.[Mahsa],
Learning to Diversify for Single Domain Generalization,
ICCV21(814-823)
IEEE DOI
2203
Training, Upper bound, Codes, Semantics, Benchmark testing,
Optimization, Recognition and classification,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Narayanan, M.[Maruthi],
Rajendran, V.[Vickram],
Kimia, B.[Benjamin],
Shape-Biased Domain Generalization via Shock Graph Embeddings,
ICCV21(1295-1305)
IEEE DOI
2203
Training, Sensitivity, Shape, Electric shock, Computational modeling,
Feature extraction, Recognition and classification,
grouping and shape
BibRef
Li, P.[Pan],
Li, D.[Da],
Li, W.[Wei],
Gong, S.G.[Shao-Gang],
Fu, Y.W.[Yan-Wei],
Hospedales, T.M.[Timothy M.],
A Simple Feature Augmentation for Domain Generalization,
ICCV21(8866-8875)
IEEE DOI
2203
Training, Codes, Computational modeling, Gaussian noise,
Stochastic processes, Representation learning
BibRef
Tang, Z.Q.[Zhi-Qiang],
Gao, Y.H.[Yun-He],
Zhu, Y.[Yi],
Zhang, Z.[Zhi],
Li, M.[Mu],
Metaxas, D.N.[Dimitris N.],
CrossNorm and SelfNorm for Generalization under Distribution Shifts,
ICCV21(52-61)
IEEE DOI
2203
Training, Bridges, Codes, Robustness, Task analysis,
Recognition and classification, Vision applications and systems
BibRef
Azimi, F.[Fatemeh],
Palacio, S.[Sebastian],
Raue, F.[Federico],
Hees, J.[Jörn],
Bertinetto, L.[Luca],
Dengel, A.[Andreas],
Self-Supervised Test-Time Adaptation on Video Data,
WACV22(2603-2612)
IEEE DOI
2202
Adapt due to changes in video.
Training, Adaptation models, Target tracking,
Computational modeling, Video sequences, Training data,
Vision Systems and Applications
BibRef
Mangla, P.[Puneet],
Chandhok, S.[Shivam],
Balasubramanian, V.N.[Vineeth N.],
Khan, F.S.[Fahad Shahbaz],
COCOA: Context-Conditional Adaptation for Recognizing Unseen Classes
in Unseen Domains,
WACV22(1618-1627)
IEEE DOI
2202
Visualization, Adaptation models, Fuses, Semantics,
Buildings, Benchmark testing, Transfer, Few-shot,
Semi- and Un- supervised Learning Deep Learning
BibRef
Kim, J.[Jin],
Lee, J.Y.[Ji-Young],
Park, J.[Jungin],
Min, D.B.[Dong-Bo],
Sohn, K.H.[Kwang-Hoon],
Self-Balanced Learning for Domain Generalization,
ICIP21(779-783)
IEEE DOI
2201
Training, Degradation, Adaptive systems, Image processing,
Training data, Predictive models, Domain generalization,
meta-learning
BibRef
Le, H.S.[Hoang Son],
Akmeliawati, R.[Rini],
Carneiro, G.[Gustavo],
Combining Data Augmentation and Domain Distance Minimisation to
Reduce Domain Generalisation Error,
DICTA21(01-08)
IEEE DOI
2201
Training, Adaptation models, Upper bound, Digital images,
Benchmark testing, Minimization, Picture archiving and communication systems
BibRef
Pandey, P.[Prashant],
Raman, M.[Mrigank],
Varambally, S.[Sumanth],
Ap, P.[Prathosh],
Generalization on Unseen Domains via Inference-time Label-Preserving
Target Projections,
CVPR21(12919-12928)
IEEE DOI
2111
Manifolds, Training, Machine learning,
Extraterrestrial measurements, Data models, Pattern recognition
BibRef
Li, G.R.[Guang-Rui],
Kang, G.L.[Guo-Liang],
Zhu, Y.[Yi],
Wei, Y.C.[Yun-Chao],
Yang, Y.[Yi],
Domain Consensus Clustering for Universal Domain Adaptation,
CVPR21(9752-9761)
IEEE DOI
2111
Benchmark testing, Pattern recognition
BibRef
Dubey, A.[Abhimanyu],
Ramanathan, V.[Vignesh],
Pentland, A.[Alex],
Mahajan, D.[Dhruv],
Adaptive Methods for Real-World Domain Generalization,
CVPR21(14335-14344)
IEEE DOI
2111
Training, Heart, Adaptation models,
Machine learning, Benchmark testing, Predictive models
BibRef
Choi, S.[Sungha],
Jung, S.[Sanghun],
Yun, H.[Huiwon],
Kim, J.T.[Joanne T.],
Kim, S.[Seungryong],
Choo, J.[Jaegul],
RobustNet: Improving Domain Generalization in Urban-Scene
Segmentation via Instance Selective Whitening,
CVPR21(11575-11585)
IEEE DOI
2111
Training, Deep learning, Image segmentation, Codes,
Robustness, Pattern recognition
BibRef
Fan, X.J.[Xin-Jie],
Wang, Q.F.[Qi-Fei],
Ke, J.J.[Jun-Jie],
Yang, F.[Feng],
Gong, B.Q.[Bo-Qing],
Zhou, M.Y.[Ming-Yuan],
Adversarially Adaptive Normalization for Single Domain Generalization,
CVPR21(8204-8213)
IEEE DOI
2111
Training, Adaptation models, Adaptive systems, Neural networks,
Benchmark testing, Tools, Data models
BibRef
Huang, J.X.[Jia-Xing],
Guan, D.[Dayan],
Xiao, A.[Aoran],
Lu, S.J.[Shi-Jian],
FSDR: Frequency Space Domain Randomization for Domain Generalization,
CVPR21(6887-6898)
IEEE DOI
2111
Training, Image segmentation, Frequency-domain analysis, Semantics,
Transform coding, Aerospace electronics, Frequency conversion
BibRef
Mitsuzumi, Y.[Yu],
Irie, G.[Go],
Ikami, D.[Daiki],
Shibata, T.[Takashi],
Generalized Domain Adaptation,
CVPR21(1084-1093)
IEEE DOI
2111
Benchmark testing, Pattern recognition
BibRef
Li, L.[Lei],
Gao, K.[Ke],
Cao, J.[Juan],
Huang, Z.[Ziyao],
Weng, Y.[Yepeng],
Mi, X.Y.[Xiao-Yue],
Yu, Z.Z.[Zheng-Ze],
Li, X.Y.[Xiao-Ya],
Xia, B.[Boyang],
Progressive Domain Expansion Network for Single Domain Generalization,
CVPR21(224-233)
IEEE DOI
2111
Training, Handheld computers, Computational modeling, Semantics,
Transforms, Performance gain, Generators
BibRef
Eguchi, S.[Shu],
Nakamura, R.[Ryo],
Tanaka, M.[Masaru],
Output augmentation works well without any domain knowledge,
MVA21(1-5)
DOI Link
2109
To improve generalization performance, without requiring data augmentation.
Training data, Task analysis, Image classification
BibRef
Borlino, F.C.[Francesco Cappio],
d'Innocente, A.[Antonio],
Tommasi, T.[Tatiana],
Rethinking Domain Generalization Baselines,
ICPR21(9227-9233)
IEEE DOI
2105
Deep learning, Writing, Tools, Robustness, Data models,
Pattern recognition, Standards
BibRef
Wang, Z.[Ziqi],
Loog, M.[Marco],
van Gemert, J.C.[Jan C.],
Respecting Domain Relations: Hypothesis Invariance for Domain
Generalization,
ICPR21(9756-9763)
IEEE DOI
2105
Training, Estimation, Distributed databases, Probabilistic logic,
Pattern recognition, Reliability, Domain generalization,
invariant representation
BibRef
Seo, S.[Seonguk],
Suh, Y.[Yumin],
Kim, D.W.[Dong-Wan],
Kim, G.[Geeho],
Han, J.W.[Jong-Woo],
Han, B.H.[Bo-Hyung],
Learning to Optimize Domain Specific Normalization for Domain
Generalization,
ECCV20(XXII:68-83).
Springer DOI
2011
BibRef
Du, Y.J.[Ying-Jun],
Xu, J.[Jun],
Xiong, H.[Huan],
Qiu, Q.A.[Qi-Ang],
Zhen, X.T.[Xian-Tong],
Snoek, C.G.M.[Cees G. M.],
Shao, L.[Ling],
Learning to Learn with Variational Information Bottleneck for Domain
Generalization,
ECCV20(X:200-216).
Springer DOI
2011
BibRef
Chattopadhyay, P.[Prithvijit],
Balaji, Y.[Yogesh],
Hoffman, J.[Judy],
Learning to Balance Specificity and Invariance for In and Out of Domain
Generalization,
ECCV20(IX:301-318).
Springer DOI
2011
BibRef
Wang, S.J.[Shu-Jun],
Yu, L.[Lequan],
Li, C.[Caizi],
Fu, C.W.[Chi-Wing],
Heng, P.A.[Pheng-Ann],
Learning from Extrinsic and Intrinsic Supervisions for Domain
Generalization,
ECCV20(IX:159-176).
Springer DOI
2011
BibRef
Qiao, F.,
Zhao, L.,
Peng, X.,
Learning to Learn Single Domain Generalization,
CVPR20(12553-12562)
IEEE DOI
2008
Training, Task analysis, Transportation, Adaptation models,
Robustness, Perturbation methods, Measurement
BibRef
Truong, D.T.[Dat T.],
Duong, C.N.[Chi Nhan],
Luu, K.[Khoa],
Tran, M.T.[Minh-Triet],
Le, N.[Ngan],
Domain Generalization via Universal Non-volume Preserving Approach,
CRV20(93-100)
IEEE DOI
2006
Digits, faces, pedestrians.
BibRef
Yue, X.,
Zhang, Y.,
Zhao, S.,
Sangiovanni-Vincentelli, A.,
Keutzer, K.,
Gong, B.,
Domain Randomization and Pyramid Consistency: Simulation-to-Real
Generalization Without Accessing Target Domain Data,
ICCV19(2100-2110)
IEEE DOI
2004
feature extraction, image representation,
image segmentation, learning (artificial intelligence),
Adaptation models
BibRef
Li, D.,
Zhang, J.,
Yang, Y.,
Liu, C.,
Song, Y.,
Hospedales, T.M.,
Episodic Training for Domain Generalization,
ICCV19(1446-1455)
IEEE DOI
2004
convolutional neural nets, feature extraction,
generalisation (artificial intelligence),
Data models
BibRef
d'Innocente, A.[Antonio],
Caputo, B.[Barbara],
Domain Generalization with Domain-Specific Aggregation Modules,
GCPR18(187-198).
Springer DOI
1905
BibRef
Motiian, S.,
Piccirilli, M.,
Adjeroh, D.A.,
Doretto, G.,
Unified Deep Supervised Domain Adaptation and Generalization,
ICCV17(5716-5726)
IEEE DOI
1802
feature extraction, image representation,
learning (artificial intelligence), statistical distributions,
Visualization
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Multi-Task Learning, Multiple Tasks, Transfer Learning, Domain Adaption .