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Domain generalization (DG) aims to enhance the model robustness against domain shifts without accessing target domains.
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Q. Xu, R. Zhang, Y. Zhang, Y. Wang, and Q. Tian, “A fourier-based framework for domain generalization,” in CVPR , 2021
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Y. Li, Q. Yu, M. Tan, J. Mei, P. Tang, W. Shen, A. Yuille, and C. Xie, “Shape-texture debiased neural network training,” in ICLR , 2021
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2021
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H. Nam, H. Lee, J. Park, W. Yoon, and D. Yoo, “Reducing domain gap by reducing style bias,” in CVPR , 2021
2021
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D. Kim, K. Wang, S. Sclaroff, and K. Saenko, “A broad study of pre-training for domain generalization and adaptation,” in ECCV , 2022
2022
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A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo et al. , “Segment anything,” in ICCV , 2023
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Y. Shi, J. Seely, P. Torr, N. Siddharth, A. Hannun, N. Usunier, and G. Synnaeve, “Gradient matching for domain generalization,” in ICLR , 2021
2021
Cited alongside, same era.
J. Cha, S. Chun, K. Lee, H.-C. Cho, S. Park, Y. Lee, and S. Park, “Swad: Domain generalization by seeking flat minima,” in NeurIPS , 2021
2021
Cited alongside, same era.
H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. Jégou, “Training data-efficient image transformers & distillation through attention,” in ICML , 2021
2021
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S. Choi, S. Jung, H. Yun, J. T. Kim, S. Kim, and J. Choo, “Robustnet: Improving domain generalization in urban-scene segmentation via instance selective whitening,” in CVPR , 2021
2021
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K. Zhou, Z. Liu, Y. Qiao, T. Xiang, and C. C. Loy, “Domain generalization: A survey,” TPAMI , 2022
2022
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X. Li, Y. Dai, Y. Ge, J. Liu, Y. Shan, and L. Duan, “Uncertainty modeling for out-of-distribution generalization,” in ICLR , 2022
2022
Cited alongside, same era.
X. Wang, J. Zhang, L. Qi, and Y. Shi, “Generalizable decision boundaries: Dualistic meta-learning for open set domain generalization,” in ICCV , 2023
2023
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L. Zhang, Z. Liu, W. Zhang, and D. Zhang, “Style uncertainty based self-paced meta learning for generalizable person re-identification,” TIP , 2023
2023
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C. Li, D. Zhang, W. Huang, and J. Zhang, “Cross contrasting feature perturbation for domain generalization,” in ICCV , 2023
2023
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J. Guo, N. Wang, L. Qi, and Y. Shi, “Aloft: A lightweight mlp-like architecture with dynamic low-frequency transform for domain generalization,” in CVPR , 2023
2023
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K. Zhou, Y. Yang, Y. Qiao, and T. Xiang, “Mixstyle neural networks for domain generalization and adaptation,” IJCV , 2023
2023
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B. Ren, Y. Liu, Y. Song, W. Bi, R. Cucchiara, N. Sebe, and W. Wang, “Masked jigsaw puzzle: A versatile position embedding for vision transformers,” in CVPR , 2023
2023
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A. Tripathi, R. Singh, A. Chakraborty, and P. Shenoy, “Edges to shapes to concepts: Adversarial augmentation for robust vision,” in CVPR , 2023
2023
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P. Wang, Z. Zhang, Z. Lei, and L. Zhang, “Sharpness-aware gradient matching for domain generalization,” in CVPR , 2023
2023
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S. Hemati, G. Zhang, A. Estiri, and X. Chen, “Understanding hessian alignment for domain generalization,” in ICCV , 2023
2023
Later among the works it cites.
X. Zhang, R. Xu, H. Yu, Y. Dong, P. Tian, and P. Cui, “Flatness-aware minimization for domain generalization,” in ICCV , 2023
2023
Later among the works it cites.
J. Guo, L. Qi, Y. Shi, and Y. Gao, “Place dropout: A progressive layer-wise and channel-wise dropout for domain generalization,” ACM TOMM , 2023
2023
Later among the works it cites.
Y. Fu, Y. Xie, Y. Fu, and Y.-G. Jiang, “Styleadv: Meta style adversarial training for cross-domain few-shot learning,” in CVPR , 2023
2023
Later among the works it cites.
J. Zhu, H. Bai, and L. Wang, “Patch-mix transformer for unsupervised domain adaptation: A game perspective,” in CVPR , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
S. Lee, J. Bae, and H. Y. Kim, “Decompose, adjust, compose: Effective normalization by playing with frequency for domain generalization,” in CVPR , 2023
2023
Later among the works it cites.
C.-H. Liao, W.-C. Chen, H.-T. Liu, Y.-R. Yeh, M.-C. Hu, and C.-S. Chen, “Domain invariant vision transformer learning for face anti-spoofing,” in WACV , 2023
2023
Later among the works it cites.
C. Zhao, Y. Wang, X. Jiang, Y. Shen, K. Song, D. Li, and D. Miao, “Learning domain invariant prompt for vision-language models,” TIP , 2024
2024
Closest in time.
M. Wang, Y. Liu, J. Yuan, S. Wang, Z. Wang, and W. Wang, “Inter-class and inter-domain semantic augmentation for domain generalization,” TIP , 2024
2024
Closest in time.
L. Qi, H. Yang, Y. Shi, and X. Geng, “Normaug: Normalization-guided augmentation for domain generalization,” TIP , 2024
2024
Closest in time.
J. Zhang, L. Qi, Y. Shi, and Y. Gao, “Exploring flat minima for domain generalization with large learning rates,” TKDE , 2024
2024
Closest in time.