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Test-time prompt tuning enhances zero-shot generalization of vision-language models but tends to ignore the relatedness among test samples during inference.
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Test-time training with self-supervision for generalization under distribution shifts
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Test-time classifier adjustment module for model-agnostic domain generalization
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
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Ttt++: When does self-supervised test-time training fail or thrive?
Yuejiang Liu, Parth Kothari, Bastien van Delft, Baptiste Bellot-Gurlet, Taylor Mordan, and Alexandre Alahi · 2021
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Surgical fine-tuning improves adaptation to distribution shifts
Yoonho Lee, Annie S Chen, Fahim Tajwar, Ananya Kumar, Huaxiu Yao, Percy Liang, and Chelsea Finn · 2023
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A comprehensive survey on test-time adaptation under distribution shifts
Jian Liang, Ran He, and Tieniu Tan · 2023
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Ttn: A domain-shift aware batch normalization in test-time adaptation
Hyesu Lim, Byeonggeun Kim, Jaegul Choo, and Sungha Choi · 2023
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Swapprompt: Test-time prompt adaptation for vision-language models
Xiaosong Ma, Jie Zhang, Song Guo, and Wenchao Xu · 2023
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Towards stable test-time adaptation in dynamic wild world
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Learning transferable visual models from natural language supervision
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Tent: Fully test-time adaptation by entropy minimization
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Test time adaptation via conjugate pseudo-labels
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Test-time prompt tuning for zero-shot generalization in vision-language models
Manli Shu, Weili Nie, De-An Huang, Zhiding Yu, Tom Goldstein, Anima Anandkumar, and Chaowei Xiao · 2022
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Align your prompts: Test-time prompting with distribution alignment for zero-shot generalization
Jameel Hassan Abdul Samadh, Hanan Gani, Noor Hazim Hussein, Muhammad Uzair Khattak, Muzammal Naseer, Fahad Khan, and Salman Khan · 2023
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Visual-language prompt tuning with knowledge-guided context optimization
Hantao Yao, Rui Zhang, and Changsheng Xu · 2023
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Adanpc: Exploring non-parametric classifier for test-time adaptation
Yifan Zhang, Xue Wang, Kexin Jin, Kun Yuan, Zhang Zhang, Liang Wang, Rong Jin, and Tieniu Tan · 2023
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Prompt-aligned gradient for prompt tuning
Beier Zhu, Yulei Niu, Yucheng Han, Yue Wu, and Hanwang Zhang · 2023
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Test-time training on nearest neighbors for large language models
Moritz Hardt and Yu Sun · 2024
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Entropy is not enough for test-time adaptation: From the perspective of disentangled factors
Jonghyun Lee, Dahuin Jung, Saehyung Lee, Junsung Park, Juhyeon Shin, Uiwon Hwang, and Sungroh Yoon · 2024
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Consistency-guided prompt learning for vision-language models
Shuvendu Roy and Ali Etemad · 2024
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Beyond model adaptation at test time: A survey
Zehao Xiao and Cees GM Snoek · 2024
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Any-shift prompting for generalization over distributions
Zehao Xiao, Jiayi Shen, Mohammad Mahdi Derakhshani, Shengcai Liao, and Cees G. M. Snoek · 2024
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C-tpt: Calibrated test-time prompt tuning for vision-language models via text feature dispersion
Hee Suk Yoon, Eunseop Yoon, Joshua Tian Jin Tee, Mark Hasegawa-Johnson, Yingzhen Li, and Chang D Yoo · 2024
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Robust test-time adaptation for zero-shot prompt tuning
Ding-Chu Zhang, Zhi Zhou, and Yu-Feng Li · 2024
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Test-time adaptation with clip reward for zero-shot generalization in vision-language models
Shuai Zhao, Xiaohan Wang, Linchao Zhu, and Yi Yang · 2024
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