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Pre-trained vision-language models (VLMs), exemplified by CLIP, demonstrate remarkable adaptability across zero-shot classification tasks without additional training.
Stochastic neighbor embedding
Geoffrey E Hinton and Sam Roweis · 2002
Earlier work this paper cites.
Evaluating prediction-time batch normalization for robustness under covariate shift
Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D’Amour, Balaji Lakshminarayanan, and Jasper Snoek · 2006
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Semi-supervised learning. 2006
Olivier Chapelle, Bernhard Scholkopf, and Alexander Zien · 2006
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A tutorial on conformal prediction
Glenn Shafer and Vladimir Vovk · 2008
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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Deeper, broader and artier domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2017
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Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
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Visda: A synthetic-to-real benchmark for visual domain adaptation
Xingchao Peng, Ben Usman, Neela Kaushik, Dequan Wang, Judy Hoffman, and Kate Saenko · 2018
Earlier work this paper cites.
Label propagation for deep semi-supervised learning
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondrej Chum · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
Jian Liang, Dapeng Hu, and Jiashi Feng · 2020
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Test-time training with self-supervision for generalization under distribution shifts
Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei A. Efros, and Moritz Hardt · 2020
Earlier work this paper cites.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Earlier work this paper cites.
Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
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Tent: Fully test-time adaptation by entropy minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell · 2021
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Tip-adapter: Training-free clip-adapter for better vision-language modeling
Renrui Zhang, Rongyao Fang, Peng Gao, Wei Zhang, Kunchang Li, Jifeng Dai, Yu Qiao, and Hongsheng Li · 2021
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Domain-agnostic test-time adaptation by prototypical training with auxiliary data
Qilong Wu, Xiangyu Yue, and Alberto Sangiovanni-Vincentelli · 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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Test-time training with masked autoencoders
Yossi Gandelsman, Yu Sun, Xinlei Chen, and Alexei A Efros · 2022
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Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023
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Clip-driven universal model for organ segmentation and tumor detection
Jie Liu, Yixiao Zhang, Jie-Neng Chen, Junfei Xiao, Yongyi Lu, Bennett A Landman, Yixuan Yuan, Alan Yuille, Yucheng Tang, and Zongwei Zhou · 2023
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Padclip: Pseudo-labeling with adaptive debiasing in clip for unsupervised domain adaptation
Zhengfeng Lai, Noranart Vesdapunt, Ning Zhou, Jun Wu, Cong Phuoc Huynh, Xuelu Li, Kah Kuen Fu, and Chen-Nee Chuah · 2023
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Improving entropy-based test-time adaptation from a clustering view
Guoliang Lin, Hanjiang Lai, Yan Pan, and Jian Yin · 2023
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A gentle introduction to conformal prediction and distribution-free uncertainty quantification
Anastasios N Angelopoulos and Stephen Bates · 2021
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Frozen clip models are efficient video learners
Ziyi Lin, Shijie Geng, Renrui Zhang, Peng Gao, Gerard de Melo, Xiaogang Wang, Jifeng Dai, Yu Qiao, and Hongsheng Li · 2022
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Audioclip: Extending clip to image, text and audio
Andrey Guzhov, Federico Raue, Jörn Hees, and Andreas Dengel · 2022
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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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Test-time adaptation via conjugate pseudo-labels
Sachin Goyal, Mingjie Sun, Aditi Raghunanthan, and Zico Kolter · 2022
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Memo: Test time robustness via adaptation and augmentation
Marvin Zhang, Sergey Levine, and Chelsea Finn · 2022
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Continual test-time domain adaptation
Qin Wang, Olga Fink, Luc Van Gool, and Dengxin Dai · 2022
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Towards open-set test-time adaptation utilizing the wisdom of crowds in entropy minimization
Jungsoo Lee, Debasmit Das, Jaegul Choo, and Sungha Choi · 2023
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Test-time distribution normalization for contrastively learned visual-language models
Yifei Zhou, Juntao Ren, Fengyu Li, Ramin Zabih, and Ser Nam Lim · 2023
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Calip: zero-shot enhancement of clip with parameter-free attention
Ziyu Guo, Renrui Zhang, Longtian Qiu, Xianzheng Ma, Xupeng Miao, Xuming He, and Bin Cui · 2023
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Tttflow: Unsupervised test-time training with normalizing flow
David Osowiechi, Gustavo A. Vargas Hakim, Mehrdad Noori, Milad Cheraghalikhani, Ismail Ayed, and Christian Desrosiers · 2023
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Clust3: Information invariant test-time training
Gustavo A Vargas Hakim, David Osowiechi, Mehrdad Noori, Milad Cheraghalikhani, Ali Bahri, Ismail Ben Ayed, and Christian Desrosiers · 2023
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On the potential of CLIP for compositional logical reasoning
Justin Brody · 2023
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Efficient test-time adaptation of vision-language models, 2024
Adilbek Karmanov, Dayan Guan, Shijian Lu, Abdulmotaleb El Saddik, and Eric Xing · 2024
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