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Object detection, as a fundamental computer vision task, has achieved a remarkable progress with the emergence of deep neural networks.
Lin, J.: Divergence measures based on the shannon entropy. IEEE Trans. Information Theory 37
1991
Earlier work this paper cites.
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: International Conference on Learning Representations (ICLR) (2014)
2014
Earlier work this paper cites.
Lin, T., Maire, M., Belongie, S.J., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft COCO: common objects in context. In: European Conference on Computer Vision (ECCV). pp. 740–755 (2014)
2014
Earlier work this paper cites.
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I.J., Fergus, R.: Intriguing properties of neural networks. In: International Conference on Learning Representations (ICLR) (2014)
2014
Earlier work this paper cites.
Everingham, M., Eslami, S.M.A., Gool, L.V., Williams, C.K.I., Winn, J.M., Zisserman, A.: The pascal visual object classes challenge: A retrospective. Int. J. Comput. Vis. 111
2015
Earlier work this paper cites.
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: International Conference on Learning Representations (ICLR) (2015)
2015
Earlier work this paper cites.
Ren, S., He, K., Girshick, R.B., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems (NeurIPS). pp. 91–99 (2015)
2015
Earlier work this paper cites.
Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: A unified embedding for face recognition and clustering. In: Computer Vision and Pattern Recognition (CVPR). pp. 815–823 (2015)
2015
Earlier work this paper cites.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (ICLR) (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.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S.E., Fu, C., Berg, A.C.: SSD: single shot multibox detector. In: European Conference on Computer Vision (ECCV). pp. 21–37 (2016)
2016
Earlier work this paper cites.
Carlini, N., Wagner, D.A.: Towards evaluating the robustness of neural networks. In: IEEE Symposium on Security and Privacy. pp. 39–57 (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Xie, C., Wang, J., Zhang, Z., Zhou, Y., Xie, L., Yuille, A.L.: Adversarial examples for semantic segmentation and object detection. In: International Conference on Computer Vision (ICCV). pp. 1378–1387 (2017)
2017
Cited alongside, same era.
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: Conference on Computer Vision and Pattern Recognition (CVPR). pp. 6230–6239 (2017)
2017
Cited alongside, same era.
Chen, L., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: European Conference on Computer Vision (ECCV). pp. 833–851 (2018)
2018
Cited alongside, same era.
Chen, S., Cornelius, C., Martin, J., Chau, D.H.P.: Shapeshifter: Robust physical adversarial attack on faster R-CNN object detector. In: Machine Learning and Knowledge Discovery in Databases - European Conference (ECML). pp. 52–68 (2018)
2018
Cited alongside, same era.
Wei, X., Liang, S., Chen, N., Cao, X.: Transferable adversarial attacks for image and video object detection. In: International Joint Conference on Artificial Intelligence (IJCAI). pp. 954–960 (2019)
2019
Later among the works it cites.
Zhang, H., Wang, J.: Towards adversarially robust object detection. In: International Conference on Computer Vision (ICCV). pp. 421–430 (2019)
2019
Later among the works it cites.
Zhang, H., Yu, Y., Jiao, J., Xing, E.P., Ghaoui, L.E., Jordan, M.I.: Theoretically principled trade-off between robustness and accuracy. In: International Conference on Machine Learning (ICML). pp. 7472–7482 (2019)
2019
Later among the works it cites.
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: European Conference on Computer Vision (ECCV). pp. 213–229 (2020)
2020
Later among the works it cites.
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Liao, F., Liang, M., Dong, Y., Pang, T., Hu, X., Zhu, J.: Defense against adversarial attacks using high-level representation guided denoiser. In: Computer Vision and Pattern Recognition (CVPR). pp. 1778–1787 (2018)
2018
Cited alongside, same era.
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., Vladu, A.: Towards deep learning models resistant to adversarial attacks. In: International Conference on Learning Representations (ICLR) (2018)
2018
Cited alongside, same era.
Tramèr, F., Kurakin, A., Papernot, N., Goodfellow, I.J., Boneh, D., McDaniel, P.D.: Ensemble adversarial training: Attacks and defenses. In: International Conference on Learning Representations (ICLR) (2018)
2018
Cited alongside, same era.
Xiao, C., Li, B., Zhu, J., He, W., Liu, M., Song, D.: Generating adversarial examples with adversarial networks. In: International Joint Conference on Artificial Intelligence (IJCAI). pp. 3905–3911 (2018)
2018
Cited alongside, same era.
Howard, A., Pang, R., Adam, H., Le, Q.V., Sandler, M., Chen, B., Wang, W., Chen, L., Tan, M., Chu, G., Vasudevan, V., Zhu, Y.: Searching for mobilenetv3. In: International Conference on Computer Vision (ICCV). pp. 1314–1324 (2019)
2019
Cited alongside, same era.
Liu, X., Yang, H., Liu, Z., Song, L., Chen, Y., Li, H.: DPATCH: an adversarial patch attack on object detectors. In: Workshop on Thirty-Third AAAI Conference on Artificial Intelligence (AAAI) (2019)
2019
Cited alongside, same era.
Qin, C., Martens, J., Gowal, S., Krishnan, D., Dvijotham, K., Fawzi, A., De, S., Stanforth, R., Kohli, P.: Adversarial robustness through local linearization. In: Advances in Neural Information Processing Systems (NeurIPS). pp. 13824–13833 (2019)
2019
Cited alongside, same era.
Su, J., Vargas, D.V., Sakurai, K.: One pixel attack for fooling deep neural networks. IEEE Trans. Evolutionary Computation 23
2019
Cited alongside, same era.
Chen, Y., Dai, X., Liu, M., Chen, D., Yuan, L., Liu, Z.: Dynamic convolution: Attention over convolution kernels. In: Computer Vision and Pattern Recognition (CVPR). pp. 11027–11036 (2020)
2020
Later among the works it cites.
Yang, J., Jiang, Y., Huang, X., Ni, B., Zhao, C.: Learning black-box attackers with transferable priors and query feedback. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)
2020
Later among the works it cites.
Zhang, J., Xu, X., Han, B., Niu, G., Cui, L., Sugiyama, M., Kankanhalli, M.S.: Attacks which do not kill training make adversarial learning stronger. In: International Conference on Machine Learning (ICML). pp. 11278–11287 (2020)
2020
Later among the works it cites.
Benz, P., Zhang, C., Kweon, I.S.: Batch normalization increases adversarial vulnerability and decreases adversarial transferability: A non-robust feature perspective. In: International Conference on Computer Vision (ICCV). pp. 7818–7827 (2021)
2021
Later among the works it cites.
Chen, P., Kung, B., Chen, J.: Class-aware robust adversarial training for object detection. In: Computer Vision and Pattern Recognition (CVPR). pp. 10420–10429 (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
Liang, S., Wu, B., Fan, Y., Wei, X., Cao, X.: Parallel rectangle flip attack: A query-based black-box attack against object detection. In: International Conference on Computer Vision (ICCV). pp. 7697–7707 (2021)
2021
Later among the works it cites.