Fetching the paper…
Reading the bibliography…
Monocular Depth Estimation (MDE) plays a vital role in applications such as autonomous driving.
2013
Earlier work this paper cites.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,”
2013
Earlier work this paper cites.
D.-H. Lee
2013
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,”
2014
Earlier work this paper cites.
R. Garg, V. K. Bg, G. Carneiro, and I. Reid, “Unsupervised cnn for single view depth estimation: Geometry to the rescue,” in
2016
Earlier work this paper cites.
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, “Deepfool: a simple and accurate method to fool deep neural networks,” in
2016
Earlier work this paper cites.
T. Zhou, M. Brown, N. Snavely, and D. G. Lowe, “Unsupervised learning of depth and ego-motion from video,” in
2017
Earlier work this paper cites.
C. Godard, O. Mac Aodha, and G. J. Brostow, “Unsupervised monocular depth estimation with left-right consistency,” in
2017
Earlier work this paper cites.
N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in
2017
Earlier work this paper cites.
T. B. Brown, D. Mané, A. Roy, M. Abadi, and J. Gilmer, “Adversarial patch,”
2017
Earlier work this paper cites.
K. Eykholt, I. Evtimov, E. Fernandes, B. Li, A. Rahmati, C. Xiao, A. Prakash, T. Kohno, and D. Song, “Robust physical-world attacks on deep learning visual classification,” in
2018
Earlier work this paper cites.
C. Wang, J. M. Buenaposada, R. Zhu, and S. Lucey, “Learning depth from monocular videos using direct methods,” in
2018
Earlier work this paper cites.
Z. Yin and J. Shi, “Geonet: Unsupervised learning of dense depth, optical flow and camera pose,” in
2018
Earlier work this paper cites.
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” in
2018
Earlier work this paper cites.
F. Tramèr, A. Kurakin, N. Papernot, I. Goodfellow, D. Boneh, and P. McDaniel, “Ensemble adversarial training: Attacks and defenses,” in
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. Arnab, O. Miksik, and P. H. Torr, “On the robustness of semantic segmentation models to adversarial attacks,” in
2018
Earlier work this paper cites.
A. Athalye, L. Engstrom, A. Ilyas, and K. Kwok, “Synthesizing robust adversarial examples,” in
2018
Earlier work this paper cites.
Z. Wu, Y. Xiong, S. X. Yu, and D. Lin, “Unsupervised feature learning via non-parametric instance discrimination,” in
2018
Earlier work this paper cites.
J. Bian, Z. Li, N. Wang, H. Zhan, C. Shen, M.-M. Cheng, and I. Reid, “Unsupervised scale-consistent depth and ego-motion learning from monocular video,” in
2019
Earlier work this paper cites.
R. Wang, S. M. Pizer, and J.-M. Frahm, “Recurrent neural network for (un-) supervised learning of monocular video visual odometry and depth,” in
2019
Earlier work this paper cites.
C. Godard, O. Mac Aodha, M. Firman, and G. J. Brostow, “Digging into self-supervised monocular depth prediction,” in
2019
Earlier work this paper cites.
J. Watson, M. Firman, G. J. Brostow, and D. Turmukhambetov, “Self-supervised monocular depth hints,” in
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
H. Zhang and J. Wang, “Towards adversarially robust object detection,” in
2019
Cited alongside, same era.
Y. Carmon, A. Raghunathan, L. Schmidt, J. C. Duchi, and P. S. Liang, “Unlabeled data improves adversarial robustness,” in
2019
Cited alongside, same era.
J.-B. Alayrac, J. Uesato, P.-S. Huang, A. Fawzi, R. Stanforth, and P. Kohli, “Are labels required for improving adversarial robustness?” in
2019
Cited alongside, same era.
Y. Cao, C. Xiao, B. Cyr, Y. Zhou, W. Park, S. Rampazzi, Q. A. Chen, K. Fu, and Z. M. Mao, “Adversarial sensor attack on lidar-based perception in autonomous driving,” in
2019
Cited alongside, same era.
M. Ye, X. Zhang, P. C. Yuen, and S.-F. Chang, “Unsupervised embedding learning via invariant and spreading instance feature,” in
2019
Cited alongside, same era.
J. Rauber, R. Zimmermann, M. Bethge, and W. Brendel, “Foolbox native: Fast adversarial attacks to benchmark the robustness of machine learning models in pytorch, tensorflow, and jax,”
2020
Later among the works it cites.
Y. Ming, X. Meng, C. Fan, and H. Yu, “Deep learning for monocular depth estimation: A review,”
2021
Later among the works it cites.
F. Wimbauer, N. Yang, L. von Stumberg, N. Zeller, and D. Cremers, “Monorec: Semi-supervised dense reconstruction in dynamic environments from a single moving camera,” in
2021
Later among the works it cites.
M. Ramamonjisoa, M. Firman, J. Watson, V. Lepetit, and D. Turmukhambetov, “Single image depth prediction with wavelet decomposition,” in
2021
Later among the works it cites.
J. Watson, O. Mac Aodha, V. Prisacariu, G. Brostow, and M. Firman, “The temporal opportunist: Self-supervised multi-frame monocular depth,” in
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Karpathy, “Tesla use per-pixel depth estimation with self-supervised learning,” 2020,
2020
Cited alongside, same era.
L. von Stumberg, P. Wenzel, N. Yang, and D. Cremers, “Lm-reloc: Levenberg-marquardt based direct visual relocalization,” in
2020
Cited alongside, same era.
Z. Zhang, X. Zhu, Y. Li, X. Chen, and Y. Guo, “Adversarial attacks on monocular depth estimation,”
2020
Cited alongside, same era.
A. Wong, S. Cicek, and S. Soatto, “Targeted adversarial perturbations for monocular depth prediction,” in
2020
Cited alongside, same era.
C.-H. Ho and N. Nvasconcelos, “Contrastive learning with adversarial examples,” in
2020
Cited alongside, same era.
M. Kim, J. Tack, and S. J. Hwang, “Adversarial self-supervised contrastive learning,” in
2020
Cited alongside, same era.
N. Yang, L. v. Stumberg, R. Wang, and D. Cremers, “D3vo: Deep depth, deep pose and deep uncertainty for monocular visual odometry,” in
2020
Cited alongside, same era.
2021
Later among the works it cites.
X. Chen, C. Xie, M. Tan, L. Zhang, C.-J. Hsieh, and B. Gong, “Robust and accurate object detection via adversarial learning,” in
2021
Later among the works it cites.
P.-C. Chen, B.-H. Kung, and J.-C. Chen, “Class-aware robust adversarial training for object detection,” in
2021
Later among the works it cites.
X. Xu, H. Zhao, and J. Jia, “Dynamic divide-and-conquer adversarial training for robust semantic segmentation,” in
2021
Later among the works it cites.
J. Tu, H. Li, X. Yan, M. Ren, Y. Chen, M. Liang, E. Bitar, E. Yumer, and R. Urtasun, “Exploring adversarial robustness of multi-sensor perception systems in self driving,” in
2021
Later among the works it cites.
Y. Cao, N. Wang, C. Xiao, D. Yang, J. Fang, R. Yang, Q. A. Chen, M. Liu, and B. Li, “Invisible for both camera and lidar: Security of multi-sensor fusion based perception in autonomous driving under physical-world attacks,” in
2021
Later among the works it cites.
X. Chen and K. He, “Exploring simple siamese representation learning,” in
2021
Later among the works it cites.
Z. Deng, L. Zhang, K. Vodrahalli, K. Kawaguchi, and J. Y. Zou, “Adversarial training helps transfer learning via better representations,”
2021
Later among the works it cites.
R. Duan, X. Mao, A. K. Qin, Y. Chen, S. Ye, Y. He, and Y. Yang, “Adversarial laser beam: Effective physical-world attack to dnns in a blink,” in
2021
Later among the works it cites.
R. Peng, R. Wang, Y. Lai, L. Tang, and Y. Cai, “Excavating the potential capacity of self-supervised monocular depth estimation,” in
2021
Later among the works it cites.
Z. Cheng, J. Liang, H. Choi, G. Tao, Z. Cao, D. Liu, and X. Zhang, “Physical attack on monocular depth estimation with optimal adversarial patches,” in
2022
Later among the works it cites.
G. Tao, G. Shen, Y. Liu, S. An, Q. Xu, S. Ma, P. Li, and X. Zhang, “Better trigger inversion optimization in backdoor scanning,” in
2022
Later among the works it cites.
A. Petrovai and S. Nedevschi, “Exploiting pseudo labels in a self-supervised learning framework for improved monocular depth estimation,” in
2022
Later among the works it cites.
B. Cheng, I. Misra, A. G. Schwing, A. Kirillov, and R. Girdhar, “Masked-attention mask transformer for universal image segmentation,” in
2022
Later among the works it cites.
K. Zhou, L. Hong, C. Chen, H. Xu, C. Ye, Q. Hu, and Z. Li, “Devnet: Self-supervised monocular depth learning via density volume construction,” in
2022
Later among the works it cites.
Z. Cheng, J. Liang, G. Tao, D. Liu, and X. Zhang, “Adversarial training of self-supervised monocular depth estimation against physical-world attacks,”
2023
Later among the works it cites.
Y. Lu, Q. Wang, S. Ma, T. Geng, Y. V. Chen, H. Chen, and D. Liu, “Transflow: Transformer as flow learner,” in
2023
Later among the works it cites.
Z. Cheng, H. Choi, S. Feng, J. C. Liang, G. Tao, D. Liu, M. Zuzak, and X. Zhang, “Fusion is not enough: Single modal attack on fusion models for 3d object detection,” in
2024
Closest in time.
Z. Cheng, Z. Liu, T. Guo, S. Feng, D. Liu, M. Tang, and X. Zhang, “Badpart: Unified black-box adversarial patch attacks against pixel-wise regression tasks,” in
2024
Closest in time.