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Large pre-trained Vision-Language Models (VLMs) like CLIP, despite having remarkable generalization ability, are highly vulnerable to adversarial examples.
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
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Sun database: Large-scale scene recognition from abbey to zoo
Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba · 2010
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
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Ucf101: A dataset of 101 human actions classes from videos in the wild
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
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Food-101–mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson · 2014
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Explaining and Harnessing Adversarial Examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Convolutional neural networks for medical image analysis: Full training or fine tuning?
Nima Tajbakhsh, Jae Y. Shin, Suryakanth R. Gurudu, R. Todd Hurst, Christopher B. Kendall, Michael B. Gotway, and Jianming Liang · 2016
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Towards Evaluating the Robustness of Neural Networks
Nicholas Carlini and David Wagner · 2017
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Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
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Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Josh Tenenbaum, and Boris Katz · 2019
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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
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Using Pre-Training Can Improve Model Robustness and Uncertainty
Dan Hendrycks, Kimin Lee, and Mantas Mazeika · 2019
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Parameter-efficient transfer learning for nlp
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 2019
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Do ImageNet Classifiers Generalize to ImageNet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
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Adversarial Training for Free!
Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, and Tom Goldstein · 2019
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Robustness May Be at Odds with Accuracy
Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry · 2019
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Theoretically Principled Trade-off between Robustness and Accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric Xing, Laurent El Ghaoui, and Michael Jordan · 2019
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Understanding and Improving Fast Adversarial Training
Maksym Andriushchenko and Nicolas Flammarion · 2020
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Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning
Tianlong Chen, Sijia Liu, Shiyu Chang, Yu Cheng, Lisa Amini, and Zhangyang Wang · 2020
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Reliable Evaluation of Adversarial Robustness with an Ensemble of Diverse Parameter-free Attacks
BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi · 2022
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Prompt generation networks for efficient adaptation of frozen vision transformers
Jochem Loedeman, Maarten C Stol, Tengda Han, and Yuki M Asano · 2022
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Yuanhan Zhang, Kaiyang Zhou, and Ziwei Liu · 2022
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a-la-carte prompt tuning (apt): Combining distinct data via composable prompting
Benjamin Bowman, Alessandro Achille, Luca Zancato, Matthew Trager, Pramuditha Perera, Giovanni Paolini, and Stefano Soatto · 2023
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Visual Prompting for Adversarial Robustness
Aochuan Chen, Peter Lorenz, Yuguang Yao, Pin-Yu Chen, and Sijia Liu · 2023
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Francesco Croce and Matthias Hein · 2020
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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.
Large-Scale Adversarial Training for Vision-and-Language Representation Learning
Zhe Gan, Yen-Chun Chen, Linjie Li, Chen Zhu, Yu Cheng, and Jingjing Liu · 2020
Cited alongside, same era.
Robust Pre-Training by Adversarial Contrastive Learning
Ziyu Jiang, Tianlong Chen, Ting Chen, and Zhangyang Wang · 2020
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Adversarial Self-Supervised Contrastive Learning
Minseon Kim, Jihoon Tack, and Sung Ju Hwang · 2020
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Exploring versatile generative language model via parameter-efficient transfer learning
Zhaojiang Lin, Andrea Madotto, and Pascale Fung · 2020
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Overfitting in adversarially robust deep learning
Leslie Rice, Eric Wong, and J Zico Kolter · 2020
Cited alongside, same era.
Parameter-efficient fine-tuning of large-scale pre-trained language models
Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, Jing Yi, Weilin Zhao, Xiaozhi Wang, Zhiyuan Liu, Hai-Tao Zheng, Jianfei Chen, Yang Liu, Jie Tang, Juanzi Li, and Maosong Sun · 2023
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Improving Adversarial Robustness of Masked Autoencoders via Test-time Frequency-domain Prompting
Qidong Huang, Xiaoyi Dong, Dongdong Chen, Yinpeng Chen, Lu Yuan, Gang Hua, Weiming Zhang, and Nenghai Yu · 2023
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Adversarial Attacks on Foundational Vision Models, 2023
Nathan Inkawhich, Gwendolyn McDonald, and Ryan Luley · 2023
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Oodrobustbench: benchmarking and analyzing adversarial robustness under distribution shift
Lin Li, Yifei Wang, Chawin Sitawarin, and Michael Spratling · 2023
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Being comes from not-being: Open-vocabulary text-to-motion generation with wordless training
Junfan Lin, Jianlong Chang, Lingbo Liu, Guanbin Li, Liang Lin, Qi Tian, and Chang-wen Chen · 2023
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Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning
Rundong Luo, Yifei Wang, and Yisen Wang · 2023
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Understanding and mitigating overfitting in prompt tuning for vision-language models
Chengcheng Ma, Yang Liu, Jiankang Deng, Lingxi Xie, Weiming Dong, and Changsheng Xu · 2023
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Understanding Zero-shot Adversarial Robustness for Large-Scale Models
Chengzhi Mao, Scott Geng, Junfeng Yang, Xin Wang, and Carl Vondrick · 2023
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Lanit: Language-driven image-to-image translation for unlabeled data
Jihye Park, Sunwoo Kim, Soohyun Kim, Seokju Cho, Jaejun Yoo, Youngjung Uh, and Seungryong Kim · 2023
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Sgva-clip: Semantic-guided visual adapting of vision-language models for few-shot image classification
Fang Peng, Xiaoshan Yang, Linhui Xiao, Yaowei Wang, and Changsheng Xu · 2023
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Large ai models in health informatics: Applications, challenges, and the future
Jianing Qiu, Lin Li, Jiankai Sun, Jiachuan Peng, Peilun Shi, Ruiyang Zhang, Yinzhao Dong, Kyle Lam, Frank P-W Lo, Bo Xiao, et al · 2023
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On the Adversarial Robustness of Multi-Modal Foundation Models, 2023
Christian Schlarmann and Matthias Hein · 2023
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Galip: Generative adversarial clips for text-to-image synthesis
Ming Tao, Bing-Kun Bao, Hao Tang, and Changsheng Xu · 2023
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Better Diffusion Models Further Improve Adversarial Training, 2023
Zekai Wang, Tianyu Pang, Chao Du, Min Lin, Weiwei Liu, and Shuicheng Yan · 2023
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Prompting and tuning: A two-stage unsupervised domain adaptive person re-identification method on vision transformer backbone
Shengming Yu, Zhaopeng Dou, and Shengjin Wang · 2023
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On Evaluating Adversarial Robustness of Large Vision-Language Models, 2023
Yunqing Zhao, Tianyu Pang, Chao Du, Xiao Yang, Chongxuan Li, Ngai-Man Cheung, and Min Lin · 2023
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Zegclip: Towards adapting clip for zero-shot semantic segmentation
Ziqin Zhou, Yinjie Lei, Bowen Zhang, Lingqiao Liu, and Yifan Liu · 2023
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