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Machine learning algorithms have achieved remarkable success across various disciplines, use cases and applications, under the prevailing assumption that training and test samples are drawn from the same distribution.
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D. Chen, D. Wang, T. Darrell, and S. Ebrahimi, “Contrastive test-time adaptation,” in IEEE Conference on Computer Vision and Pattern Recognition , 2022, pp. 295–305
2022
Cited alongside, same era.
L. Chen, Y. Zhang, Y. Song, J. Wang, and L. Liu, “Ost: Improving generalization of deepfake detection via one-shot test-time training,” in Advances in Neural Information Processing Systems , 2022
2022
Cited alongside, same era.
S. Choi, S. Yang, S. Choi, and S. Yun, “Improving test-time adaptation via shift-agnostic weight regularization and nearest source prototypes,” in European Conference on Computer Vision . Springer, 2022, pp. 440–458
2022
Cited alongside, same era.
M. Z. Darestani, J. Liu, and R. Heckel, “Test-time training can close the natural distribution shift performance gap in deep learning based compressed sensing,” in International Conference on Machine Learning , 2022
2022
Cited alongside, same era.
N. Ding, Y. Xu, Y. Tang, C. Xu, Y. Wang, and D. Tao, “Source-free domain adaptation via distribution estimation,” in IEEE Conference on Computer Vision and Pattern Recognition , 2022, pp. 7212–7222
2022
Cited alongside, same era.
——, “Hierarchical variational memory for few-shot learning across domains,” in International Conference on Learning Representations , 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
J. M. J. Valanarasu, P. Guo, V. Vibashan, and V. M. Patel, “On-the-fly test-time adaptation for medical image segmentation,” in Medical Imaging with Deep Learning , 2023
2023
Later among the works it cites.
H. Varma, A. Awasthi, and S. Sarawagi, “Conditional tree matching for inference-time adaptation of tree prediction models,” in International Conference on Machine Learning , 2023
2023
Later among the works it cites.
O. Veksler, “Test time adaptation with regularized loss for weakly supervised salient object detection,” in IEEE Conference on Computer Vision and Pattern Recognition , 2023, pp. 7360–7369
2023
Later among the works it cites.
Q. Wang, Y. Lv, Z. Xie, J. Huang et al. , “A simple yet effective strategy to robustify the meta learning paradigm,” Advances in Neural Information Processing Systems , 2023
2023
Later among the works it cites.
S. Wang, D. Zhang, Z. Yan, J. Zhang, and R. Li, “Feature alignment and uniformity for test time adaptation,” in IEEE Conference on Computer Vision and Pattern Recognition , 2023, pp. 20 050–20 060
2023
Later among the works it cites.
W. Wang, Z. Zhong, W. Wang, X. Chen, C. Ling, B. Wang, and N. Sebe, “Dynamically instance-guided adaptation: A backward-free approach for test-time domain adaptive semantic segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition , 2023, pp. 24 090–24 099
2023
Later among the works it cites.
Z. Wang, Y. Zhang, Z. Fang, L. Lan, W. Yang, and B. Han, “Soda: robust training of test-time data adaptors,” Advances in Neural Information Processing Systems , vol. 36, pp. 44 017–44 038, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Wen and S. Chaudhuri, “Batched low-rank adaptation of foundation models,” in International Conference on Learning Representations , 2023
2023
Later among the works it cites.
Z. Wen, S. Niu, G. Li, Q. Wu, M. Tan, and Q. Wu, “Test-time model adaptation for visual question answering with debiased self-supervisions,” IEEE Transactions on Multimedia , 2023
2023
Later among the works it cites.
T. Wu, F. Jia, X. Qi, J. T. Wang, V. Sehwag, S. Mahloujifar, and P. Mittal, “Uncovering adversarial risks of test-time adaptation,” in International Conference on Machine Learning , 2023
2023
Later among the works it cites.
Z. Xiao, X. Zhen, S. Liao, and C. G. M. Snoek, “Energy-based test sample adaptation for domain generalization,” in International Conference on Learning Representations , 2023
2023
Later among the works it cites.
S. Yang, Y. Ze, and H. Xu, “Movie: Visual model-based policy adaptation for view generalization,” Advances in Neural Information Processing Systems , vol. 36, 2023
2023
Later among the works it cites.
M. Yao, J. Huang, X. Jin, R. Xu, S. Zhou, M. Zhou, and Z. Xiong, “Generalized lightness adaptation with channel selective normalization,” in IEEE International Conference on Computer Vision , 2023
2023
Later among the works it cites.
H. Ye, Y. Ding, J. Li, and H. T. Ng, “Robust question answering against distribution shifts with test-time adaption: An empirical study,” in Findings of the Association for Computational Linguistics: EMNLP 2022 , 2023
2023
Later among the works it cites.
T. Yeo, O. F. Kar, Z. Sodagar, and A. Zamir, “Rapid network adaptation: Learning to adapt neural networks using test-time feedback,” in IEEE International Conference on Computer Vision , 2023
2023
Later among the works it cites.
C. Yi, S. YANG, Y. Wang, H. Li, Y.-p. Tan, and A. Kot, “Temporal coherent test time optimization for robust video classification,” in International Conference on Learning Representations , 2023
2023
Later among the works it cites.
L. Yi, G. Xu, P. Xu, J. Li, R. Pu, C. Ling, I. McLeod, and B. Wang, “When source-free domain adaptation meets learning with noisy labels,” in International Conference on Learning Representations , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
L. Yuan, B. Xie, and S. Li, “Robust test-time adaptation in dynamic scenarios,” in IEEE Conference on Computer Vision and Pattern Recognition , 2023, pp. 15 922–15 932
2023
Later among the works it cites.
L. Zancato, A. Achille, T. Y. Liu, M. Trager, P. Perera, and S. Soatto, “Train/test-time adaptation with retrieval,” in IEEE Conference on Computer Vision and Pattern Recognition , 2023, pp. 15 911–15 921
2023
Later among the works it cites.
J. Zhang, L. Qi, Y. Shi, and Y. Gao, “Domainadaptor: A novel approach to test-time adaptation,” in IEEE International Conference on Computer Vision , 2023
2023
Later among the works it cites.
T. Zhang, X. Wang, D. Zhou, D. Schuurmans, and J. E. Gonzalez, “Tempera: Test-time prompt editing via reinforcement learning,” in International Conference on Learning Representations , 2023
2023
Later among the works it cites.
Y. Zhang, X. Wang, K. Jin, K. Yuan, Z. Zhang, L. Wang, R. Jin, and T. Tan, “Adanpc: Exploring non-parametric classifier for test-time adaptation,” in International Conference on Machine Learning , 2023
2023
Later among the works it cites.
B. Zhao, C. Chen, and S.-T. Xia, “Delta: Degradation-free fully test-time adaptation,” in International Conference on Learning Representations , 2023
2023
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H. Zhao, Y. Liu, A. Alahi, and T. Lin, “On pitfalls of test-time adaptation,” in International Conference on Machine Learning , 2023
2023
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Y. Zhou, J. Ren, F. Li, R. Zabih, and S. N. Lim, “Test-time distribution normalization for contrastively learned visual-language models,” Advances in Neural Information Processing Systems , vol. 36, 2023
2023
Later among the works it cites.
Z. Zhou, L.-Z. Guo, L.-H. Jia, D. Zhang, and Y.-F. Li, “Ods: test-time adaptation in the presence of open-world data shift,” in International Conference on Machine Learning , 2023
2023
Later among the works it cites.
M. Alfarra, H. Itani, A. Pardo, M. Ramazanova, J. C. Perez, M. Müller, B. Ghanem et al. , “Evaluation of test-time adaptation under computational time constraints,” in International Conference on Machine Learning , 2024
2024
Closest in time.
S. Ambekar, Z. Xiao, J. Shen, X. Zhen, and C. G. M. Snoek, “Probabilistic test-time generalization by variational neighbor-labeling,” in Conference on Lifelong Learning Agents , 2024
2024
Closest in time.
S. An, S. Park, G. Kim, J. Baek, B. Lee, and S. Kim, “Context enhanced transformer for single image object detection in video data,” in AAAI Conference on Artificial Intelligence , 2024
2024
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Y. Bar, S. Shaer, and Y. Romano, “Protected test-time adaptation via online entropy matching: A betting approach,” in Advances in Neural Information Processing Systems , 2024
2024
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H. Cao, Y. Xu, J. Yang, P. Yin, X. Ji, S. Yuan, and L. Xie, “Reliable spatial-temporal voxels for multi-modal test-time adaptation,” in European Conference on Computer Vision , 2024
2024
Closest in time.
C. Chen, Y. Liu, L. Chen, and C. Zhang, “Test-time training for spatial-temporal forecasting,” in Proceedings of the 2024 SIAM International Conference on Data Mining (SDM) . SIAM, 2024, pp. 463–471
2024
Closest in time.
Y. Chen, S. Niu, Y. Wang, S. Xu, H. Song, and M. Tan, “Towards robust and efficient cloud-edge elastic model adaptation via selective entropy distillation,” in International Conference on Learning Representations , 2024
2024
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Z. Chen, Z. He, Z. Lu, X. Sun, and Z.-M. Lu, “Prompt-based test-time real image dehazing: a novel pipeline,” in European Conference on Computer Vision , 2024
2024
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H. Cho, T. Kim, Y. Jeong, and K.-J. Yoon, “Tta-evf: Test-time adaptation for event-based video frame interpolation via reliable pixel and sample estimation,” in IEEE Conference on Computer Vision and Pattern Recognition , 2024, pp. 25 701–25 711
2024
Closest in time.
Q. Cui, H. Sun, B. Li, J. Lu, and W. Li, “Human motion forecasting in dynamic domain shifts: A homeostatic continual test-time adaptation framework,” in European Conference on Computer Vision , 2024
2024
Closest in time.
K. Fan, T. Liu, X. Qiu, Y. Wang, L. Huai, Z. Shangguan, S. Gou, F. Liu, Y. Fu, Y. Fu et al. , “Test-time linear out-of-distribution detection,” in IEEE Conference on Computer Vision and Pattern Recognition , 2024
2024
Closest in time.
M. Farina, G. Franchi, G. Iacca, M. Mancini, and E. Ricci, “Frustratingly easy test-time adaptation of vision-language models,” in Advances in Neural Information Processing Systems , 2024
2024
Closest in time.
J. Gao, X. Yao, and C. Xu, “Fast-slow test-time adaptation for online vision-and-language navigation,” in International Conference on Machine Learning , 2024
2024
Closest in time.
P. Gao, S. Geng, R. Zhang, T. Ma, R. Fang, Y. Zhang, H. Li, and Y. Qiao, “Clip-adapter: Better vision-language models with feature adapters,” International Journal of Computer Vision , 2024
2024
Closest in time.
Z. Gao, X.-Y. Zhang, and C.-L. Liu, “Unified entropy optimization for open-set test-time adaptation,” in IEEE Conference on Computer Vision and Pattern Recognition , 2024
2024
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Y. Gou, H. Zhao, B. Li, X. Xiao, and X. Peng, “Test-time degradation adaptation for open-set image restoration,” in International Conference on Machine Learning , 2024
2024
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J. Guan, J. Liang, and R. He, “Backdoor defense via test-time detecting and repairing,” in IEEE Conference on Computer Vision and Pattern Recognition , 2024
2024
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S. Gui, X. Li, and S. Ji, “Active test-time adaptation: Theoretical analyses and an algorithm,” in International Conference on Learning Representations , 2024
2024
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M. Hardt and Y. Sun, “Test-time training on nearest neighbors for large language models,” in International Conference on Learning Representations , 2024
2024
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J.-T. He, F.-J. Tsai, J.-H. Wu, Y.-T. Peng, C.-C. Tsai, C.-W. Lin, and Y.-Y. Lin, “Domain-adaptive video deblurring via test-time blurring,” in European Conference on Computer Vision , 2024
2024
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T.-H. Hoang, D. M. Vo, and M. N. Do, “Persistent test-time adaptation in recurring testing scenarios,” in Advances in Neural Information Processing Systems , 2024
2024
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2024
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J. Hu, J. Lin, S. Gong, and W. Cai, “Relax image-specific prompt requirement in sam: A single generic prompt for segmenting camouflaged objects,” in AAAI Conference on Artificial Intelligence , 2024
2024
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H.-K. Jang, J. Kim, H. Kweon, and K.-J. Yoon, “Talos: Enhancing semantic scene completion via test-time adaptation on the line of sight,” in Advances in Neural Information Processing Systems , 2024
2024
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J. Jiang, Q. Zhou, Y. Li, X. Lu, X. Zhao, M. Wang, L. Ma, J. Chang, and J. Zhang, “Pcotta: Continual test-time adaptation for multi-task point cloud understanding,” in Advances in Neural Information Processing Systems , 2024
2024
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J. Kang, N. Kim, J. Ok, and S. Kwak, “Membn: Robust test-time adaptation via batch norm with statistics memory,” in European Conference on Computer Vision , 2024
2024
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A. Karmanov, D. Guan, S. Lu, A. El Saddik, and E. Xing, “Efficient test-time adaptation of vision-language models,” in IEEE Conference on Computer Vision and Pattern Recognition , 2024
2024
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D. Kim, S. Park, and J. Choo, “When model meets new normals: Test-time adaptation for unsupervised time-series anomaly detection,” in AAAI Conference on Artificial Intelligence , 2024
2024
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T. Klug, K. Wang, S. Ruschke, and R. Heckel, “Motionttt: 2d test-time-training motion estimation for 3d motion corrected mri,” in Advances in Neural Information Processing Systems , 2024
2024
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D. Lee, J. Yoon, and S. J. Hwang, “Becotta: Input-dependent online blending of experts for continual test-time adaptation,” in International Conference on Machine Learning , 2024
2024
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J.-H. Lee and J.-H. Chang, “Continual momentum filtering on parameter space for online test-time adaptation,” in International Conference on Learning Representations , 2024
2024
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——, “Stationary latent weight inference for unreliable observations from online test-time adaptation,” in International Conference on Machine Learning , 2024
2024
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J. Lee, D. Jung, S. Lee, J. Park, J. Shin, U. Hwang, and S. Yoon, “Entropy is not enough for test-time adaptation: From the perspective of disentangled factors,” in International Conference on Learning Representations , 2024
2024
Closest in time.
T. Lee, S. Chottananurak, T. Gong, and S.-J. Lee, “Aetta: Label-free accuracy estimation for test-time adaptation,” in IEEE Conference on Computer Vision and Pattern Recognition , 2024
2024
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H. Li, H. Lu, and Y.-C. Chen, “Bi-tta: Bidirectional test-time adapter for remote physiological measurement,” in European Conference on Computer Vision , 2024
2024
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B. Liberatori, A. Conti, P. Rota, Y. Wang, and E. Ricci, “Test-time zero-shot temporal action localization,” in IEEE Conference on Computer Vision and Pattern Recognition , 2024, pp. 18 720–18 729
2024
Closest in time.
H. Lin, Y. Zhang, S. Niu, S. Cui, and Z. Li, “Monotta: Fully test-time adaptation for monocular 3d object detection,” in European Conference on Computer Vision , 2024
2024
Closest in time.
C. Liu, Z. Wan, C. Ouyang, A. Shah, W. Bai, and R. Arcucci, “Zero-shot ecg classification with multimodal learning and test-time clinical knowledge enhancement,” in International Conference on Machine Learning , 2024
2024
Closest in time.
H. Liu, J. Qi, Z. Li, M. Hassanpour, Y. Wang, K. N. Plataniotis, and Y. Yu, “Test-time personalization with meta prompt for gaze estimation,” in AAAI Conference on Artificial Intelligence , 2024, pp. 3621–3629
2024
Closest in time.
J. Liu, R. Xu, S. Yang, R. Zhang, Q. Zhang, Z. Chen, Y. Guo, and S. Zhang, “Continual-mae: Adaptive distribution masked autoencoders for continual test-time adaptation,” in IEEE Conference on Computer Vision and Pattern Recognition , 2024
2024
Closest in time.
J. Liu, S. Yang, P. Jia, M. Lu, Y. Guo, W. Xue, and S. Zhang, “Vida: Homeostatic visual domain adapter for continual test time adaptation,” in International Conference on Learning Representations , 2024
2024
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J. Liu, J. Xie, F. Zhou, and S. He, “Question type-aware debiasing for test-time visual question answering model adaptation,” IEEE Transactions on Circuits and Systems for Video Technology , 2024
2024
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W. Liu, X. Shen, H. Li, X. Bi, B. Liu, C.-M. Pun, and X. Cun, “Depth-aware test-time training for zero-shot video object segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition , 2024, pp. 19 218–19 227
2024
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Z. Liu, H. Sun, Y. Peng, and J. Zhou, “Dart: Dual-modal adaptive online prompting and knowledge retention for test-time adaptation,” in AAAI Conference on Artificial Intelligence , 2024
2024
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J. Ma, “Improved self-training for test-time adaptation,” in IEEE Conference on Computer Vision and Pattern Recognition , 2024
2024
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2024
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Y. Mansour, X. Zhong, S. Caglar, and R. Heckel, “Ttt-mim: Test-time training with masked image modeling for denoising distribution shifts,” in European Conference on Computer Vision , 2024
2024
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R. A. Marsden, M. Döbler, and B. Yang, “Universal test-time adaptation through weight ensembling, diversity weighting, and prior correction,” in Winter Conference on Applications of Computer Vision , 2024, pp. 2555–2565
2024
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2024
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R. Modi and Y. S. Rawat, “Asynchronous perception machine for efficient test-time-training,” in Advances in Neural Information Processing Systems , 2024
2024
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Y. Nie, M. Fan, C. Long, Q. Zhang, J. Zhu, and X. Xu, “Incorporating test-time optimization into training with dual networks for human mesh recovery,” in Advances in Neural Information Processing Systems , 2024
2024
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S. Niu, C. Miao, G. Chen, P. Wu, and P. Zhao, “Test-time model adaptation with only forward passes,” in International Conference on Machine Learning , 2024
2024
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Y. Oh, J. Lee, J. Choi, D. Jung, U. Hwang, and S. Yoon, “Efficient diffusion-driven corruption editor for test-time adaptation,” in European Conference on Computer Vision , 2024
2024
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D. Osowiechi, G. A. V. Hakim, M. Noori, M. Cheraghalikhani, A. Bahri, M. Yazdanpanah, I. Ben Ayed, and C. Desrosiers, “Nc-ttt: A noise constrastive approach for test-time training,” in IEEE Conference on Computer Vision and Pattern Recognition , 2024
2024
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D. Osowiechi, M. Noori, G. A. V. Hakim, M. Yazdanpanah, A. Bahri, M. Cheraghalikhani, S. Dastani, F. Beizaee, I. B. Ayed, and C. Desrosiers, “Watt: Weight average test-time adaption of clip,” in Advances in Neural Information Processing Systems , 2024
2024
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D. Park, J. Jeong, S.-H. Yoon, J. Jeong, and K.-J. Yoon, “T4p: Test-time training of trajectory prediction via masked autoencoder and actor-specific token memory,” in IEEE Conference on Computer Vision and Pattern Recognition , 2024, pp. 15 065–15 076
2024
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H. Park, J. Hwang, S. Mun, S. Park, and J. Ok, “Medbn: Robust test-time adaptation against malicious test samples,” in IEEE Conference on Computer Vision and Pattern Recognition , 2024
2024
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H. Park, A. Gupta, and A. Wong, “Test-time adaptation for depth completion,” in IEEE Conference on Computer Vision and Pattern Recognition , 2024, pp. 20 519–20 529
2024
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J. Park, S. Y. Chun, and M. Seok, “Ul-vio: Ultra-lightweight visual-inertial odometry with noise robust test-time adaptation,” in European Conference on Computer Vision , 2024
2024
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W. Ren, X. Li, H. Chen, V. Rakesh, Z. Wang, M. Das, and V. G. Honavar, “Tablog: Test-time adaptation for tabular data using logic rules,” in International Conference on Machine Learning , 2024
2024
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Z. Ren, Y. Su, and X. Liu, “Chatgpt-powered hierarchical comparisons for image classification,” in Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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H. Shim, C. Kim, and E. Yang, “Cloudfixer: Test-time adaptation for 3d point clouds via diffusion-guided geometric transformation,” in European Conference on Computer Vision , 2024
2024
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——, “Towards real-world test-time adaptation: Tri-net self-training with balanced normalization,” in AAAI Conference on Artificial Intelligence , 2024
2024
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Z. Su, J. Guo, K. Yao, X. Yang, Q. Wang, and K. Huang, “Unraveling batch normalization for realistic test-time adaptation,” in AAAI Conference on Artificial Intelligence , 2024
2024
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H. Sun, L. Xu, S. Jin, P. Luo, C. Qian, and W. Liu, “Program: Prototype graph model based pseudo-label learning for test-time adaptation,” in International Conference on Learning Representations , 2024
2024
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2024
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D. Tomar, G. Vray, J.-P. Thiran, and B. Bozorgtabar, “Un-mixing test-time normalization statistics: Combatting label temporal correlation,” in International Conference on Learning Representations , 2024
2024
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C.-C. Tsai, Y.-C. Chen, and C.-S. Lu, “Test-time stain adaptation with diffusion models for histopathology image classification,” in European Conference on Computer Vision , 2024
2024
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Y.-Y. Tsai, F.-C. Chen, A. Y. Chen, J. Yang, C.-C. Su, M. Sun, and C.-H. Kuo, “Gda: Generalized diffusion for robust test-time adaptation,” in IEEE Conference on Computer Vision and Pattern Recognition , 2024
2024
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2024
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Y. Wang, A. Cheraghian, Z. Hayder, J. Hong, S. Ramasinghe, S. Rahman, D. Ahmedt-Aristizabal, X. Li, L. Petersson, and M. Harandi, “Backpropagation-free network for 3d test-time adaptation,” in IEEE Conference on Computer Vision and Pattern Recognition , 2024, pp. 23 231–23 241
2024
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Z. Wang, H. Huang, A. Zheng, and R. He, “Heterogeneous test-time training for multi-modal person re-identification,” in AAAI Conference on Artificial Intelligence , 2024
2024
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Z. Wang, Z. Chi, Y. Wu, L. Gu, Z. Liu, K. Plataniotis, and Y. Wang, “Distribution alignment for fully test-time adaptation with dynamic online data streams,” in European Conference on Computer Vision , 2024
2024
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2024
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Y. Wu, Z. Chi, Y. Wang, K. N. Plataniotis, and S. Feng, “Test-time domain adaptation by learning domain-aware batch normalization,” in AAAI Conference on Artificial Intelligence , 2024
2024
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Z. Xiao, J. Shen, M. M. Derakhshani, S. Liao, and C. G. M. Snoek, “Any-shift prompting for generalization over distributions,” in CVPR , 2024
2024
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B. Xiong, X. Yang, Y. Song, Y. Wang, and C. Xu, “Modality-collaborative test-time adaptation for action recognition,” in IEEE Conference on Computer Vision and Pattern Recognition , 2024, pp. 26 732–26 741
2024
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M. Yang, Y. Li, C. Zhang, P. Hu, and X. Peng, “Test-time adaptation against multi-modal reliability bias,” in International Conference on Learning Representations , 2024
2024
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S. Yang, J. Wu, J. Liu, X. Li, Q. Zhang, M. Pan, Y. Gan, Z. Chen, and S. Zhang, “Exploring sparse visual prompt for domain adaptive dense prediction,” in AAAI Conference on Artificial Intelligence , 2024
2024
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X. Yang, X. Chen, M. Li, K. Wei, and C. Deng, “A versatile framework for continual test-time domain adaptation: Balancing discriminability and generalizability,” in IEEE Conference on Computer Vision and Pattern Recognition , 2024
2024
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Y. Yang, H. Wu, A. I. Aviles-Rivero, Y. Zhang, J. Qin, and L. Zhu, “Genuine knowledge from practice: Diffusion test-time adaptation for video adverse weather removal,” in IEEE Conference on Computer Vision and Pattern Recognition , 2024
2024
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H. S. Yoon, E. Yoon, J. T. J. Tee, M. Hasegawa-Johnson, Y. Li, and C. D. Yoo, “C-tpt: Calibrated test-time prompt tuning for vision-language models via text feature dispersion,” in International Conference on Learning Representations , 2024
2024
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Y. Yu, S. Shin, S. Back, M. Ko, S. Noh, and K. Lee, “Domain-specific block selection and paired-view pseudo-labeling for online test-time adaptation,” in IEEE Conference on Computer Vision and Pattern Recognition , 2024
2024
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——, “Stamp: Outlier-aware test-time adaptation with stable memory replay,” in European Conference on Computer Vision , 2024
2024
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J. Yuan, B. Zhang, K. Gong, X. Yue, B. Shi, Y. Qiao, and T. Chen, “Reg-tta3d: Better regression makes better test-time adaptive 3d object detection,” in European Conference on Computer Vision , 2024
2024
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Y. Yuan, B. Xu, L. Hou, F. Sun, H. Shen, and X. Cheng, “Tea: Test-time energy adaptation,” in IEEE Conference on Computer Vision and Pattern Recognition , 2024, pp. 23 901–23 911
2024
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M. Zanella and I. Ben Ayed, “On the test-time zero-shot generalization of vision-language models: Do we really need prompt learning?” in IEEE Conference on Computer Vision and Pattern Recognition , 2024
2024
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C. Zhang, S. Stepputtis, K. Sycara, and Y. Xie, “Dual prototype evolving for test-time generalization of vision-language models,” in Advances in Neural Information Processing Systems , 2024
2024
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D.-C. Zhang, Z. Zhou, and Y.-F. Li, “Robust test-time adaptation for zero-shot prompt tuning,” in AAAI Conference on Artificial Intelligence , 2024, pp. 16 714–16 722
2024
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J. Zhang, J. Huang, X. Zhang, L. Shao, and S. Lu, “Historical test-time prompt tuning for vision foundation models,” in Advances in Neural Information Processing Systems , 2024
2024
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T. Zhang, J. Wang, H. Guo, T. Dai, B. Chen, and S.-T. Xia, “Boostadapter: Improving test-time adaptation via regional bootstrapping,” in Advances in Neural Information Processing Systems , 2024
2024
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Y. Zhang, H.-a. Gao, Z. Jiang, and H. Zhao, “Dual-frame fluid motion estimation with test-time optimization and zero-divergence loss,” in Advances in Neural Information Processing Systems , 2024
2024
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S. Zhao, X. Wang, L. Zhu, and Y. Yang, “Test-time adaptation with clip reward for zero-shot generalization in vision-language models,” in International Conference on Learning Representations , 2024
2024
Closest in time.
Y. Zhao, H. Bian, K. Chen, P. Ji, L. Qu, S.-y. Lin, W. Yu, H. Li, H. Chen, J. Shen, B. Raj, and M. Xu, “Metric from human: Zero-shot monocular metric depth estimation via test-time adaptation,” in Advances in Neural Information Processing Systems , 2024
2024
Closest in time.
Y. Zhao, T. Zhang, and H. Ji, “Test-time model adaptation for image reconstruction using self-supervised adaptive layers,” in European Conference on Computer Vision , 2024
2024
Closest in time.
Q. Zhou, K.-Y. Zhang, T. Yao, X. Lu, S. Ding, and L. Ma, “Test-time domain generalization for face anti-spoofing,” in IEEE Conference on Computer Vision and Pattern Recognition , 2024, pp. 175–187
2024
Closest in time.
S. Zhou, Z. Xiong, and F. Wu, “Test-time adaptation via style and structure guidance for histological image registration,” in AAAI Conference on Artificial Intelligence , 2024
2024
Closest in time.
Z. Zhu, X. Hong, Z. Ma, W. Zhuang, Y. Ma, Y. Dai, and Y. Wang, “Reshaping the online data buffering and organizing mechanism for continual test-time adaptation,” in European Conference on Computer Vision , 2024
2024
Closest in time.
T. Zou, S. Qu, Z. Li, A. Knoll, L. He, G. Chen, and C. Jiang, “Hgl: Hierarchical geometry learning for test-time adaptation in 3d point cloud segmentation,” in European Conference on Computer Vision , 2024
2024
Closest in time.
F. F. Niloy, S. M. Ahmed, D. S. Raychaudhuri, S. Oymak, and A. K. Roy-Chowdhury, “Effective restoration of source knowledge in continual test time adaptation,” in Winter Conference on Applications of Computer Vision , 2024, pp. 2091–2100
2091
Closest in time.