Fetching the paper…
Reading the bibliography…
Test-time adaptation with pre-trained vision-language models has attracted increasing attention for tackling distribution shifts during the test time.
Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2004
Earlier work this paper cites.
Semi-supervised learning by entropy minimization
Yves Grandvalet and Yoshua Bengio · 2004
Earlier work this paper cites.
Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Sun database: Large-scale scene recognition from abbey to zoo
J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba · 2010
Earlier work this paper cites.
Cats and dogs
Omkar M. Parkhi, Andrea Vedaldi, Andrew Zisserman, and C. V. Jawahar · 2012
Earlier work this paper cites.
UCF101: A dataset of 101 human actions classes from videos in the wild
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
Earlier work this paper cites.
3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
Earlier work this paper cites.
Fine-grained visual classification of aircraft
S. Maji, J. Kannala, E. Rahtu, M. Blaschko, and A. Vedaldi · 2013
Earlier work this paper cites.
Food-101 – mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
Earlier work this paper cites.
Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, , and A. Vedaldi · 2014
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher · 2016
Earlier work this paper cites.
Unbounded cache model for online language modeling with open vocabulary
Edouard Grave, Moustapha M Cisse, and Armand Joulin · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
A simple cache model for image recognition
Emin Orhan · 2018
Cited alongside, same era.
Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2019
Cited alongside, same era.
Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2019
Cited alongside, same era.
Nlnl: Negative learning for noisy labels
Youngdong Kim, Junho Yim, Juseung Yun, and Junmo Kim · 2019
Cited alongside, same era.
Do imagenet classifiers generalize to imagenet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
Cited alongside, same era.
Learning robust global representations by penalizing local predictive power
Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing · 2019
Cited alongside, same era.
Joint negative and positive learning for noisy labels
Youngdong Kim, Juseung Yun, Hyounguk Shon, and Junmo Kim · 2021
Later among the works it 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, Gretchen Krueger, and Ilya Sutskever · 2021
Later among the works it cites.
Mamshad Nayeem Rizve, Kevin Duarte, Yogesh S Rawat, and Mubarak Shah · 2021
Later among the works it cites.
Tent: Fully test-time adaptation by entropy minimization
Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno A. Olshausen, and Trevor Darrell · 2021
Later among the works it cites.
Adaptive risk minimization: Learning to adapt to domain shift
Marvin Zhang, Henrik Marklund, Nikita Dhawan, Abhishek Gupta, Sergey Levine, and Chelsea Finn · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
Generalization through memorization: Nearest neighbor language models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis · 2020
Cited alongside, same era.
Improving robustness against common corruptions by covariate shift adaptation
Steffen Schneider, Evgenia Rusak, Luisa Eck, Oliver Bringmann, Wieland Brendel, and Matthias Bethge · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Test-time unsupervised domain adaptation
Thomas Varsavsky, Mauricio Orbes-Arteaga, Carole H Sudre, Mark S Graham, Parashkev Nachev, and M Jorge Cardoso · 2020
Cited alongside, same era.
VirTex: Learning Visual Representations from Textual Annotations
Karan Desai and Justin Johnson · 2021
Cited alongside, same era.
Parameter-free online test-time adaptation
Malik Boudiaf, Romain Mueller, Ismail Ben Ayed, and Luca Bertinetto · 2022
Later among the works it cites.
Contrastive test-time adaptation
Dian Chen, Dequan Wang, Trevor Darrell, and Sayna Ebrahimi · 2022
Later among the works it cites.
Scaling up vision-language pre-training for image captioning
Xiaowei Hu, Zhe Gan, Jianfeng Wang, Zhengyuan Yang, Zicheng Liu, Yumao Lu, and Lijuan Wang · 2022
Later among the works it cites.
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
Later among the works it cites.
Vision-language pre-training with triple contrastive learning
Jinyu Yang, Jiali Duan, Son Tran, Yi Xu, Sampath Chanda, Liqun Chen, Belinda Zeng, Trishul Chilimbi, and Junzhou Huang · 2022
Later among the works it cites.
Diverse data augmentation with diffusions for effective test-time prompt tuning
Chun-Mei Feng, Kai Yu, Yong Liu, Salman Khan, and Wangmeng Zuo · 2023
Later among the works it cites.
Temporal coherent test time optimization for robust video classification
Chenyu Yi, SIYUAN YANG, Yufei Wang, Haoliang Li, Yap peng Tan, and Alex Kot · 2023
Later among the works it cites.
Task residual for tuning vision-language models
Tao Yu, Zhihe Lu, Xin Jin, Zhibo Chen, and Xinchao Wang · 2023
Later among the works it cites.
Prompt, generate, then cache: Cascade of foundation models makes strong few-shot learners
Renrui Zhang, Xiangfei Hu, Bohao Li, Siyuan Huang, Hanqiu Deng, Yu Qiao, Peng Gao, and Hongsheng Li · 2023
Later among the works it cites.
Graphadapter: Tuning vision-language models with dual knowledge graph
Xin Li, Dongze Lian, Zhihe Lu, Jiawang Bai, Zhibo Chen, and Xinchao Wang · 2024
Closest in time.