1 |
J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, 2015, pp. 3431-3440.
|
2 |
D. Seichter, M. Kohler, B. Lewandowski, T. Wengefeld, and H. M. Gross, "Efficient RGB-D semantic segmentation for indoor scene analysis," in Proceedings of 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi'an, China, 2021, pp. 13525-13531.
|
3 |
J. Fu, J. Liu, Y. Wang, J. Zhou, C. Wang, and H. Lu, "Stacked deconvolutional network for semantic segmentation," IEEE Transactions on Image Processing, 2019. https://doi.org/10.1109/TIP.2019.2895460
DOI
|
4 |
Y. Lee and J. Park, "CenterMask: real-time anchor-free instance segmentation," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, 2020, pp. 13903-13912.
|
5 |
O. Ronneberger, P. Fischer, and T. Brox, "U-Net: convolutional networks for biomedical image segmentation," in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015. Cham, Switzerland: Springer, 2015, pp. 234-241.
|
6 |
L. Gao, Y. Zhang, F. Zou, J. Shao, and J. Lai, "Unsupervised urban scene segmentation via domain adaptation," Neurocomputing, vol. 406, pp. 295-301, 2020.
DOI
|
7 |
J. Fu, J. Liu, H. Tian, Y. Li, Y. Bao, Z. Fang, and H. Lu, "Dual attention network for scene segmentation," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, 2019, pp. 3146-3154.
|
8 |
J. He, Z. Deng, L. Zhou, Y. Wang, and Y. Qiao, "Adaptive pyramid context network for semantic segmentation," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, 2019, pp. 7519-7528.
|
9 |
N. Tajbakhsh, L. Jeyaseelan, Q. Li, J. N. Chiang, Z. Wu, and X. Ding, "Embracing imperfect datasets: a review of deep learning solutions for medical image segmentation," Medical Image Analysis, vol. 63, article no. 101693, 2020. https://doi.org/10.1016/j.media.2020.101693
DOI
|
10 |
A. Hatamizadeh, A. Hoogi, D. Sengupta, W. Lu, B. Wilcox, D. Rubin, and D. Terzopoulos, "Deep active lesion segmentation," in Machine Learning in Medical Imaging. Cham, Switzerland: Springer, 2019, pp. 98- 105
|
11 |
K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, 2016, pp. 770-778.
|
12 |
A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," Advances in Neural Information Processing Systems, vol. 25, 1106-1114, 2012.
|
13 |
H. C. Li, S. S. Li, W. S. Hu, J. H. Feng, W. W. Sun, and Q. Du, "Recurrent feedback convolutional neural network for hyperspectral image classification," IEEE Geoscience and Remote Sensing Letters, vol. 19, article no. 5504405, 2021. https://doi.org/10.1109/LGRS.2021.3064349
DOI
|
14 |
S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: towards real-time object detection with region proposal networks," Advances in Neural Information Processing Systems, vol. 28, pp. 91-99, 2015.
|
15 |
JSpin, "Object detection," 2019 [online]. Available: https://nuggy875.tistory.com/20.
|
16 |
H. Noh, S. Hong, and B. Han, "Learning deconvolution network for semantic segmentation," in Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile, 2015, pp. 1520-1528.
|
17 |
K. He, X. Zhang, S. Ren, and J. Sun, "Identity mappings in deep residual networks," in Computer Vision - ECCV 2016. Cham, Switzerland: Springer, 2016, pp. 630-645.
|
18 |
D. Bolya, C. Zhou, F. Xiao, and Y. J. Lee, "YOLACT: real-time instance segmentation," in Proceedings of the IEEE International Conference on Computer Vision, Seoul, South Korea, 2019, pp. 9156-9165.
|
19 |
M. Majurski, P. Manescu, S. Padi, N. Schaub, N. Hotaling, C. Simon, and P. Bajcsy, "Cell image segmentation using generative adversarial networks, transfer learning, and augmentations," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, 2019, pp. 1114-1122.
|