Built on

I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” arXiv preprint arXiv:1412.6572 , 2014
Original
2014
Earlier work this paper cites.
F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 2016 fourth international conference on 3D vision (3DV) . Ieee, 2016, pp. 565–571
2016
Earlier work this paper cites.
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” arXiv preprint arXiv:1706.06083 , 2017
Original
2017
Earlier work this paper cites.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2980–2988
2017
Earlier work this paper cites.
D. Lin, Y. Ji, D. Lischinski, D. Cohen-Or, and H. Huang, “Multi-scale context intertwining for semantic segmentation,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 603–619
2018
Earlier work this paper cites.
H. Alhaija, S. Mustikovela, L. Mescheder, A. Geiger, and C. Rother, “Augmented reality meets computer vision: Efficient data generation for urban driving scenes,” International Journal of Computer Vision (IJCV) , 2018
2018
Earlier work this paper cites.
A. Kurakin, I. J. Goodfellow, and S. Bengio, “Adversarial examples in the physical world,” in Artificial intelligence safety and security . Chapman and Hall/CRC, 2018, pp. 99–112
2018
Earlier work this paper cites.
D. Lin, D. Shen, S. Shen, Y. Ji, D. Lischinski, D. Cohen-Or, and H. Huang, “Zigzagnet: Fusing top-down and bottom-up context for object segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 7490–7499
2019
Earlier work this paper cites.
D. Hendrycks and T. Dietterich, “Benchmarking neural network robustness to common corruptions and perturbations,” in International Conference on Learning Representations , 2019. [Online]. Available: https://openreview.net/forum?id=HJz6tiCqYm
2019
Earlier work this paper cites.
D. Shen, Y. Ji, P. Li, Y. Wang, and D. Lin, “Ranet: Region attention network for semantic segmentation,” Advances in Neural Information Processing Systems , vol. 33, pp. 13 927–13 938, 2020
2020
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Earlier work this paper cites.
Q. Guo, F. Juefei-Xu, X. Xie, L. Ma, J. Wang, B. Yu, W. Feng, and Y. Liu, “Watch out! motion is blurring the vision of your deep neural networks,” Advances in Neural Information Processing Systems , vol. 33, pp. 975–985, 2020
2020
Earlier work this paper cites.

Similar

A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Cited alongside, same era.
Q. Guo, F. Juefei-Xu, C. Zhou, W. Feng, Y. Liu, and S. Wang, “Sparta: Spatially attentive and adversarially robust activation,” arXiv preprint arXiv:2105.08269 , 2021
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2021
Cited alongside, same era.
B. Tian, F. Juefei-Xu, Q. Guo, X. Xie, X. Li, and Y. Liu, “Ava: Adversarial vignetting attack against visual recognition,” arXiv preprint arXiv:2105.05558 , 2021
Original
2021
Cited alongside, same era.
D. Lin, D. Shen, Y. Ji, S. Shen, M. Xie, W. Feng, and H. Huang, “Tagnet: Learning configurable context pathways for semantic segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022
2022
Cited alongside, same era.

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2023

On the Robustness of Segment Anything

Huang, Yihao, Cao, Yue, Li, Tianlin et al.

Understand

Segment anything model (SAM) has presented impressive objectness identification capability with the idea of prompt learning and a new collected large-scale dataset.

Given a prompt (e.g., points, bounding boxes, or masks) and an input image, SAM is able to generate valid segment masks for all objects indicated by the prompts, presenting high generalization across diverse scenarios and being a general method for zero-shot transfer to downstream vision tasks.
Nevertheless, it remains unclear whether SAM may introduce errors in certain threatening scenarios.
Clarifying this is of significant importance for applications that require robustness, such as autonomous vehicles.

Built on

I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” arXiv preprint arXiv:1412.6572 , 2014
Original
2014
Earlier work this paper cites.
F. Milletari, N. Navab, and S.-A. Ahmadi, “V-net: Fully convolutional neural networks for volumetric medical image segmentation,” in 2016 fourth international conference on 3D vision (3DV) . Ieee, 2016, pp. 565–571
2016
Earlier work this paper cites.
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” arXiv preprint arXiv:1706.06083 , 2017
Original
2017
Earlier work this paper cites.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2980–2988
2017
Earlier work this paper cites.
D. Lin, Y. Ji, D. Lischinski, D. Cohen-Or, and H. Huang, “Multi-scale context intertwining for semantic segmentation,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 603–619
2018
Earlier work this paper cites.
H. Alhaija, S. Mustikovela, L. Mescheder, A. Geiger, and C. Rother, “Augmented reality meets computer vision: Efficient data generation for urban driving scenes,” International Journal of Computer Vision (IJCV) , 2018
2018
Earlier work this paper cites.
A. Kurakin, I. J. Goodfellow, and S. Bengio, “Adversarial examples in the physical world,” in Artificial intelligence safety and security . Chapman and Hall/CRC, 2018, pp. 99–112
2018
Earlier work this paper cites.
D. Lin, D. Shen, S. Shen, Y. Ji, D. Lischinski, D. Cohen-Or, and H. Huang, “Zigzagnet: Fusing top-down and bottom-up context for object segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 7490–7499
2019
Earlier work this paper cites.
D. Hendrycks and T. Dietterich, “Benchmarking neural network robustness to common corruptions and perturbations,” in International Conference on Learning Representations , 2019. [Online]. Available: https://openreview.net/forum?id=HJz6tiCqYm
2019
Earlier work this paper cites.
D. Shen, Y. Ji, P. Li, Y. Wang, and D. Lin, “Ranet: Region attention network for semantic segmentation,” Advances in Neural Information Processing Systems , vol. 33, pp. 13 927–13 938, 2020
2020
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Earlier work this paper cites.
Q. Guo, F. Juefei-Xu, X. Xie, L. Ma, J. Wang, B. Yu, W. Feng, and Y. Liu, “Watch out! motion is blurring the vision of your deep neural networks,” Advances in Neural Information Processing Systems , vol. 33, pp. 975–985, 2020
2020
Earlier work this paper cites.

Similar

A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Cited alongside, same era.
Q. Guo, F. Juefei-Xu, C. Zhou, W. Feng, Y. Liu, and S. Wang, “Sparta: Spatially attentive and adversarially robust activation,” arXiv preprint arXiv:2105.08269 , 2021
Original
2021
Cited alongside, same era.
B. Tian, F. Juefei-Xu, Q. Guo, X. Xie, X. Li, and Y. Liu, “Ava: Adversarial vignetting attack against visual recognition,” arXiv preprint arXiv:2105.05558 , 2021
Original
2021
Cited alongside, same era.
D. Lin, D. Shen, Y. Ji, S. Shen, M. Xie, W. Feng, and H. Huang, “Tagnet: Learning configurable context pathways for semantic segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022
2022
Cited alongside, same era.

Then

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

W. F. Wiggins and A. S. Tejani, “On the opportunities and risks of foundation models for natural language processing in radiology,” Radiology: Artificial Intelligence , vol. 4, no. 4, p. e220119, 2022

2022

Cited alongside, same era.

J. Gu, H. Zhao, V. Tresp, and P. H. Torr, “Segpgd: An effective and efficient adversarial attack for evaluating and boosting segmentation robustness,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXIX . Springer, 2022, pp. 308–325

2022

Cited alongside, same era.

R. Gao, Q. Guo, F. Juefei-Xu, H. Yu, H. Fu, W. Feng, Y. Liu, and S. Wang, “Can you spot the chameleon? adversarially camouflaging images from co-salient object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 2150–2159

2022

Cited alongside, same era.

L. Sun, F. Juefei-Xu, Y. Huang, Q. Guo, J. Zhu, J. Feng, Y. Liu, and G. Pu, “Ala: Adversarial lightness attack via naturalness-aware regularizations,” arXiv preprint arXiv:2201.06070 , 2022

Original

2022

Cited alongside, same era.

OpenAI, “Gpt-4 technical report,” 2023

2023

Cited alongside, same era.

A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, P. Dollár, and R. Girshick, “Segment anything,” arXiv:2304.02643 , 2023

Original

2023