Fetching the paper…
Reading the bibliography…
Foundation models pre-trained on web-scale vision-language data, such as CLIP, are widely used as cornerstones of powerful machine learning systems.
Papineni, K., Roukos, S., Ward, T., Zhu, W.J.: Bleu: a method for automatic evaluation of machine translation. In: Proceedings of the 40th annual meeting of the Association for Computational Linguistics. pp. 311–318 (2002)
2002
Earlier work this paper cites.
Lin, C.Y., Och, F.J.: Automatic evaluation of machine translation quality using longest common subsequence and skip-bigram statistics. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04). pp. 605–612 (2004)
2004
Earlier work this paper cites.
2013
Earlier work this paper cites.
Denkowski, M., Lavie, A.: Meteor universal: Language specific translation evaluation for any target language. In: Proceedings of the ninth workshop on statistical machine translation. pp. 376–380 (2014)
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13. pp. 740–755. Springer (2014)
2014
Earlier work this paper cites.
Mottaghi, R., Chen, X., Liu, X., Cho, N.G., Lee, S.W., Fidler, S., Urtasun, R., Yuille, A.: The role of context for object detection and semantic segmentation in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 891–898 (2014)
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
Ren, M., Kiros, R., Zemel, R.: Advances in neural information processing systems 28
2015
Earlier work this paper cites.
Vedantam, R., Lawrence Zitnick, C., Parikh, D.: Cider: Consensus-based image description evaluation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4566–4575 (2015)
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
Jabri, A., Joulin, A., Van Der Maaten, L.: Revisiting visual question answering baselines. In: European conference on computer vision. pp. 727–739. Springer (2016)
2016
Earlier work this paper cites.
Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II 14. pp. 694–711. Springer (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., Parikh, D.: Making the v in vqa matter: Elevating the role of image understanding in visual question answering. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 6904–6913 (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Arnab, A., Miksik, O., Torr, P.H.: On the robustness of semantic segmentation models to adversarial attacks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 888–897 (2018)
2018
Earlier work this paper cites.
Caesar, H., Uijlings, J., Ferrari, V.: Coco-stuff: Thing and stuff classes in context. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1209–1218 (2018)
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Poursaeed, O., Katsman, I., Gao, B., Belongie, S.: Generative adversarial perturbations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4422–4431 (2018)
2018
Earlier work this paper cites.
Song, Y., Shu, R., Kushman, N., Ermon, S.: Constructing unrestricted adversarial examples with generative models. Advances in Neural Information Processing Systems 31
2018
Earlier work this paper cites.
Tao, G., Ma, S., Liu, Y., Zhang, X.: Attacks meet interpretability: Attribute-steered detection of adversarial samples. Advances in Neural Information Processing Systems 31
2018
Earlier work this paper cites.
Wang, B., Yao, Y., Viswanath, B., Zheng, H., Zhao, B.Y.: With great training comes great vulnerability: Practical attacks against transfer learning. In: 27th USENIX security symposium (USENIX Security 18). pp. 1281–1297 (2018)
2018
Earlier work this paper cites.
Dong, Y., Pang, T., Su, H., Zhu, J.: Evading defenses to transferable adversarial examples by translation-invariant attacks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4312–4321 (2019)
2019
Earlier work this paper cites.
Ganeshan, A., BS, V., Babu, R.V.: Fda: Feature disruptive attack. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 8069–8079 (2019)
2019
Earlier work this paper cites.
Huang, Q., Katsman, I., He, H., Gu, Z., Belongie, S., Lim, S.N.: Enhancing adversarial example transferability with an intermediate level attack. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 4733–4742 (2019)
2019
Earlier work this paper cites.
Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., Madry, A.: Adversarial examples are not bugs, they are features. Advances in neural information processing systems 32
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
Lu, J., Batra, D., Parikh, D., Lee, S.: Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. Advances in neural information processing systems 32
2019
Earlier work this paper cites.
Naseer, M.M., Khan, S.H., Khan, M.H., Shahbaz Khan, F., Porikli, F.: Cross-domain transferability of adversarial perturbations. Advances in Neural Information Processing Systems 32
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
Xie, C., Zhang, Z., Zhou, Y., Bai, S., Wang, J., Ren, Z., Yuille, A.L.: Improving transferability of adversarial examples with input diversity. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2730–2739 (2019)
2019
Earlier work this paper cites.
Chen, L., Yan, X., Xiao, J., Zhang, H., Pu, S., Zhuang, Y.: Counterfactual samples synthesizing for robust visual question answering. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10800–10809 (2020)
2020
Earlier work this paper cites.
Chen, Y.C., Li, L., Yu, L., El Kholy, A., Ahmed, F., Gan, Z., Cheng, Y., Liu, J.: Uniter: Universal image-text representation learning. In: European conference on computer vision. pp. 104–120. Springer (2020)
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Inkawhich, N., Liang, K., Wang, B., Inkawhich, M., Carin, L., Chen, Y.: Perturbing across the feature hierarchy to improve standard and strict blackbox attack transferability. Advances in Neural Information Processing Systems 33
2020
Earlier work this paper cites.
Lu, Y., Jia, Y., Wang, J., Li, B., Chai, W., Carin, L., Velipasalar, S.: Enhancing cross-task black-box transferability of adversarial examples with dispersion reduction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 940–949 (2020)
2020
Cited alongside, same era.
Zou, J., Pan, Z., Qiu, J., Liu, X., Rui, T., Li, W.: Improving the transferability of adversarial examples with resized-diverse-inputs, diversity-ensemble and region fitting. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXII. pp. 563–579. Springer (2020)
2020
Cited alongside, same era.
Dancette, C., Cadene, R., Teney, D., Cord, M.: Beyond question-based biases: Assessing multimodal shortcut learning in visual question answering. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1574–1583 (2021)
2021
Cited alongside, same era.
Cha, J., Mun, J., Roh, B.: Learning to generate text-grounded mask for open-world semantic segmentation from only image-text pairs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
2023
Later among the works it cites.
Chen, B., Yin, J., Chen, S., Chen, B., Liu, X.: An adaptive model ensemble adversarial attack for boosting adversarial transferability. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 4489–4498 (October 2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Cho, S., Shin, H., Hong, S., An, S., Lee, S., Arnab, A., Seo, P.H., Kim, S.: Cat-seg: Cost aggregation for open-vocabulary semantic segmentation (2023)
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jia, C., Yang, Y., Xia, Y., Chen, Y.T., Parekh, Z., Pham, H., Le, Q., Sung, Y.H., Li, Z., Duerig, T.: Scaling up visual and vision-language representation learning with noisy text supervision. In: International conference on machine learning. pp. 4904–4916. PMLR (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Naseer, M., Khan, S., Hayat, M., Khan, F.S., Porikli, F.: On generating transferable targeted perturbations. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 7708–7717 (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Niu, Y., Tang, K., Zhang, H., Lu, Z., Hua, X.S., Wen, J.R.: Counterfactual vqa: A cause-effect look at language bias. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12700–12710 (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision. In: ICML (2021)
2021
Cited alongside, same era.
Salzmann, M., et al.: Learning transferable adversarial perturbations. Advances in Neural Information Processing Systems 34
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2023
Later among the works it cites.
Fei, J., Wang, T., Zhang, J., He, Z., Wang, C., Zheng, F.: Transferable decoding with visual entities for zero-shot image captioning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 3136–3146 (October 2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Huang, H., Chen, Z., Chen, H., Wang, Y., Zhang, K.: T-sea: Transfer-based self-ensemble attack on object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 20514–20523 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Li, Y., Wang, H., Duan, Y., Li, X.: Clip surgery for better explainability with enhancement in open-vocabulary tasks (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Lu, D., Wang, Z., Wang, T., Guan, W., Gao, H., Zheng, F.: Set-level guidance attack: Boosting adversarial transferability of vision-language pre-training models (2023)
2023
Later among the works it cites.
Luo, H., Gu, J., Liu, F., Torr, P.: An image is worth 1000 lies: Transferability of adversarial images across prompts on vision-language models. In: The Twelfth International Conference on Learning Representations (2023)
2023
Later among the works it cites.
Luo, H., Bao, J., Wu, Y., He, X., Li, T.: SegCLIP: Patch aggregation with learnable centers for open-vocabulary semantic segmentation. ICML (2023)
2023
Later among the works it cites.
Ma, C., Jiang, Y., Wen, X., Yuan, Z., Qi, X.: Codet: Co-occurrence guided region-word alignment for open-vocabulary object detection. In: Advances in Neural Information Processing Systems (2023)
2023
Later among the works it cites.
Mukhoti, J., Lin, T.Y., Poursaeed, O., Wang, R., Shah, A., Torr, P.H., Lim, S.N.: Open vocabulary semantic segmentation with patch aligned contrastive learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 19413–19423 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Ramos, R., Martins, B., Elliott, D., Kementchedjhieva, Y.: Smallcap: lightweight image captioning prompted with retrieval augmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2840–2849 (2023)
2023
Later among the works it cites.
Shayegani, E., Dong, Y., Abu-Ghazaleh, N.: Jailbreak in pieces: Compositional adversarial attacks on multi-modal language models (2023)
2023
Later among the works it cites.
Sun*, S., Li*, R., Torr, P., Gu, X., Li, S.: Clip as rnn: Segment countless visual concepts without training endeavor (Dec 2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Waseda, F., Nishikawa, S., Le, T.N., Nguyen, H.H., Echizen, I.: Closer look at the transferability of adversarial examples: How they fool different models differently. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 1360–1368 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Xie, B., Cao, J., Xie, J., Khan, F.S., Pang, Y.: Sed: A simple encoder-decoder for open-vocabulary semantic segmentation (2023)
2023
Later among the works it cites.
Xu, M., Zhang, Z., Wei, F., Hu, H., Bai, X.: San: Side adapter network for open-vocabulary semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)
2023
Later among the works it cites.
Xu, X., Xiong, T., Ding, Z., Tu, Z.: Masqclip for open-vocabulary universal image segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 887–898 (2023)
2023
Later among the works it cites.
Yi, M., Cui, Q., Wu, H., Yang, C., Yoshie, O., Lu, H.: A simple framework for text-supervised semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 7071–7080 (2023)
2023
Later among the works it cites.
Yu, Q., He, J., Deng, X., Shen, X., Chen, L.C.: Convolutions die hard: Open-vocabulary segmentation with single frozen convolutional clip. In: NeurIPS (2023)
2023
Later among the works it cites.
Zhang, J., Huang, Y., Wu, W., Lyu, M.R.: Transferable adversarial attacks on vision transformers with token gradient regularization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16415–16424 (2023)
2023
Later among the works it cites.
Zhao, A., Chu, T., Liu, Y., Li, W., Li, J., Duan, L.: Minimizing maximum model discrepancy for transferable black-box targeted attacks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8153–8162 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Zhou, Z., Hu, S., Li, M., Zhang, H., Zhang, Y., Jin, H.: Advclip: Downstream-agnostic adversarial examples in multimodal contrastive learning. In: Proceedings of the 31st ACM International Conference on Multimedia. pp. 6311–6320 (2023)
2023
Later among the works it cites.
Zou, A., Wang, Z., Carlini, N., Nasr, M., Kolter, J.Z., Fredrikson, M.: Universal and transferable adversarial attacks on aligned language models (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2024
Closest in time.
Nern, L.F., Raj, H., Georgi, M.A., Sharma, Y.: On transfer of adversarial robustness from pretraining to downstream tasks. Advances in Neural Information Processing Systems 36
2024
Closest in time.
2024
Closest in time.
Zhao, Y., Pang, T., Du, C., Yang, X., Li, C., Cheung, N.M.M., Lin, M.: On evaluating adversarial robustness of large vision-language models. Advances in Neural Information Processing Systems 36
2024
Closest in time.