[1] |
Zhu J Y, Park T, Isola P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 2223-2232. |
[2] |
Fan L, Zhao H, Hu H, et al. Survey of target detection based on deep convolutional neural networks [J]. Optics and Precision Engineering, 2020, 28(5): 1152-1164. (in Chinese) |
[3] |
Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014: 580-587. |
[4] |
Girshick R. Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision, 2015: 1440-1448. |
[5] |
Ren S, He K, Girshick R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[C]// Advances in Neural Information Processing Systems, 2015, 28: 91-99. |
[6] |
Liu W, Anguelov D, Erhan D, et al. SSD: Single shot multibox detector[C]//European Conference on Computer Vision, 2016: 21-37. |
[7] |
Redmon J, Farhadi A. YOLOv3: An incremental improvement [J]. ArXiv Preprint, 2018, ArXiv: 1804.02767. |
[8] |
Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 2980-2988. |
[9] |
Wu T, Zhang Z, Liu Y, et al. A lightweight small object detection algorithm based on improved SSD [J]. Infrared and Laser Engineering, 2018, 47(7): 0703005. (in Chinese) doi: 10.3788/IRLA201847.0703005 |
[10] |
Di X, Lin Z, Chen S. Dim moving object detection based on projection into the 2D frequency domain [J]. Infrared and Laser Engineering, 2013, 42(12): 3447-3452. (in Chinese) |
[11] |
Wu Y, Wang Y, Sun H, et al. LSS-target detection in complex sky backgrounds [J]. Chinese Optics, 2019, 12(4): 853-865. (in Chinese) doi: 10.3788/co.20191204.0853 |
[12] |
Gong X, Ouyang H. Improvement of tiny YOLOV3 target detection [J]. Optics and Precision Engineering, 2020, 28(4): 988-995. (in Chinese) |
[13] |
Wang C, An J, Jiang X, et al. Region proposal optimization algorithm based on convolutional neural networks [J]. Chinese Optics, 2019, 12(6): 1348-1361. (in Chinese) doi: 10.3788/co.20191206.1348 |
[14] |
Ganin Y, Lempitsky V. Unsupervised domain adaptation by backpropagation[C]//International Conference on Machine Learning, PMLR, 2015: 1180-1189. |
[15] |
Xie R, Yu F, Wang J, et al. Multi-level domain adaptive learning for cross-domain detection[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops, 2019. |
[16] |
Hsu H K, Yao C H, Tsai Y H, et al. Progressive domain adaptation for object detection[C]//Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2020: 749-757. |
[17] |
Zheng Y, Huang D, Liu S, et al. Cross-domain object detection through coarse-to-fine feature adaptation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020: 13766-13775. |
[18] |
Li H, Wan R, Wang S, et al. Unsupervised domain adaptation in the wild via disentangling representation learning [J]. International Journal of Computer Vision, 2021, 129(2): 267-283. doi: 10.1007/s11263-020-01364-5 |
[19] |
Liu M Y, Breuel T, Kautz J. Unsupervised image-to-image translation networks[C]//Advances in Neural Information Processing Systems, 2017: 700-708. |
[20] |
Xu K, Qin M, Sun F, et al. Learning in the frequency domain[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020: 1740-1749. |
[21] |
Yang Y, Soatto S. FDA: Fourier domain adaptation for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2020: 4085-4095. |
[22] |
Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7132-7141. |
[23] |
He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778. |
[24] |
Paszke A, Gross S, Massa F, et al. Pytorch: An imperative style, high-performance deep learning library[C]//Advances in Neural Information Processing Systems, 2019, 32: 8026-8037. |