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Vision-Language Models (VLMs) rely heavily on pretrained vision encoders to support downstream tasks such as image captioning, visual question answering, and zero-shot classification.
C. C. Aggarwal, A. Hinneburg, and D. A. Keim, “On the surprising behavior of distance metrics in high dimensional space,” in Database theory—ICDT 2001: 8th international conference London, UK, January 4–6, 2001 proceedings 8
2001
Earlier work this paper cites.
M.-E. Nilsback and A. Zisserman, “Automated flower classification over a large number of classes,” in 2008 Sixth Indian conference on computer vision, graphics & image processing
2008
Earlier work this paper cites.
A. Krizhevsky, G. Hinton, et al
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition
2009
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
B. A. Plummer, L. Wang, C. M. Cervantes, J. C. Caicedo, J. Hockenmaier, and S. Lazebnik, “Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models,” in Proceedings of the IEEE international conference on computer vision
2015
Earlier work this paper cites.
2017
Earlier work this paper cites.
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2018
Earlier work this paper cites.
D. Gurari, Q. Li, A. J. Stangl, A. Guo, C. Lin, K. Grauman, J. Luo, and J. P. Bigham, “Vizwiz grand challenge: Answering visual questions from blind people,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2018
Earlier work this paper cites.
B. S. Veeling, J. Linmans, J. Winkens, T. Cohen, and M. Welling, “Rotation equivariant cnns for digital pathology,” in Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II 11
2018
Earlier work this paper cites.
K. Marino, M. Rastegari, A. Farhadi, and R. Mottaghi, “Ok-vqa: A visual question answering benchmark requiring external knowledge,” in Proceedings of the IEEE/cvf conference on computer vision and pattern recognition
2019
Earlier work this paper cites.
P. Helber, B. Bischke, A. Dengel, and D. Borth, “Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
2019
Earlier work this paper cites.
Z. Jiang, T. Chen, T. Chen, and Z. Wang, “Robust pre-training by adversarial contrastive learning,” Advances in neural information processing systems
2020
Earlier work this paper cites.
S. Gowal, P.-S. Huang, A. van den Oord, T. Mann, and P. Kohli, “Self-supervised adversarial robustness for the low-label, high-data regime,” in International conference on learning representations
2020
Cited alongside, same era.
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar, et al
2020
Cited alongside, same era.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in International conference on machine learning
2020
Cited alongside, same era.
2020
Cited alongside, same era.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al
C. Schlarmann and M. Hein, “On the adversarial robustness of multi-modal foundation models,” in Proceedings of the IEEE/CVF International Conference on Computer Vision
2023
Later among the works it cites.
H. Bansal, N. Singhi, Y. Yang, F. Yin, A. Grover, and K.-W. Chang, “Cleanclip: Mitigating data poisoning attacks in multimodal contrastive learning,” in Proceedings of the IEEE/CVF International Conference on Computer Vision
2023
Later among the works it cites.
Z. Zhou, S. Hu, M. Li, H. Zhang, Y. Zhang, and H. Jin, “Advclip: Downstream-agnostic adversarial examples in multimodal contrastive learning,” in 31st ACM International Conference on Multimedia
2023
Later among the works it cites.
M. Team et al
2023
Later among the works it cites.
W.-L. Chiang, Z. Li, Z. Lin, Y. Sheng, Z. Wu, H. Zhang, L. Zheng, S. Zhuang, Y. Zhuang, J. E. Gonzalez, et al
2023
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2021
Cited alongside, same era.
2021
Cited alongside, same era.
M. Caron, H. Touvron, I. Misra, H. Jégou, J. Mairal, P. Bojanowski, and A. Joulin, “Emerging properties in self-supervised vision transformers,” in Proceedings of the IEEE/CVF international conference on computer vision
2021
Cited alongside, same era.
C. Guo, A. Sablayrolles, H. Jégou, and D. Kiela, “Gradient-based adversarial attacks against text transformers,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
2021
Cited alongside, same era.
L. Fan, S. Liu, P.-Y. Chen, G. Zhang, and C. Gan, “When does contrastive learning preserve adversarial robustness from pretraining to finetuning?,” Advances in neural information processing systems
2021
Cited alongside, same era.
2022
Cited alongside, same era.
L. Pan, C.-W. Hang, A. Sil, and S. Potdar, “Improved text classification via contrastive adversarial training,” in Proceedings of the AAAI Conference on Artificial Intelligence
2022
Cited alongside, same era.
R. OpenAI, “Gpt-4 technical report,” ArXiv
2023
Cited alongside, same era.
Later among the works it cites.
H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” Advances in neural information processing systems
2024
Closest in time.
Y. Zhao, T. Pang, C. Du, X. Yang, C. Li, N.-M. M. Cheung, and M. Lin, “On evaluating adversarial robustness of large vision-language models,” Advances in Neural Information Processing Systems
2024
Closest in time.
A. Wei, N. Haghtalab, and J. Steinhardt, “Jailbroken: How does llm safety training fail?,” Advances in Neural Information Processing Systems
2024
Closest in time.
N. Carlini, M. Nasr, C. A. Choquette-Choo, M. Jagielski, I. Gao, P. W. W. Koh, D. Ippolito, F. Tramer, and L. Schmidt, “Are aligned neural networks adversarially aligned?,” Advances in Neural Information Processing Systems
2024
Closest in time.
2024
Closest in time.
H. Zheng, X. Deng, W. Jiang, and W. Li, “A unified understanding of adversarial vulnerability regarding unimodal models and vision-language pre-training models,” in Proceedings of the 32nd ACM International Conference on Multimedia
2024
Closest in time.
H. Wang, K. Dong, Z. Zhu, H. Qin, A. Liu, X. Fang, J. Wang, and X. Liu, “Transferable multimodal attack on vision-language pre-training models,” in 2024 IEEE Symposium on Security and Privacy (SP)
2024
Closest in time.
Z. Zhou, P. Wang, Z. Liang, R. Zhang, and H. Bai, “Pair: Pre-denosing augmented image retrieval model for defending adversarial patches,” in Proceedings of the 32nd ACM International Conference on Multimedia
2024
Closest in time.
X. Li, W. Zhang, Y. Liu, Z. Hu, B. Zhang, and X. Hu, “Language-driven anchors for zero-shot adversarial robustness,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2024
Closest in time.