Liruirui

Journal Articles

1 T. Chen, R. Li, J. Fu, and D. Jiang, “Tucker bilinear attention network for multi-scale remote sensing

object detection,” IEEE Geoscience and Remote Sensing Letters, 2023.

2 R. Li, T. Chen, Y. Liu, and H. Jiang, “Coupleunet: Swin transformer coupling cnns makes strong

contextual encoders for vhr image road extraction,” International Journal of Remote Sensing, vol. 44,

no. 18, pp. 5788–5813, 2023.

3 J. Li, R. Li, R. Han, and S. Wang, “Self-relabeling for noise-tolerant retina vessel segmentation through

label reliability estimation,” BMC Medical Imaging, vol. 22, no. 1, p. 8, 2022.

4 W. Hu, Y. Huang, F. Zhang, R. Li, and H. Li, “Seqface: Learning discriminative features by using face

sequences,” IET Image Processing, vol. 15, no. 11, pp. 2548–2558, 2021.

5 Z. Huang and R. Li, “Orientated silhouette matching for single-shot ship instance segmentation,” IEEE

Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 463–477, 2021.

6 J. Liu, R. Li, and C. Sun, “Co-correcting: Noise-tolerant medical image classification via mutual label

correction,” IEEE transactions on medical imaging, vol. 40, no. 12, pp. 3580–3592, 2021.

7 H. Zheng, J. Wang, J. Zhang, and R. Li, “Irts: An intelligent and reliable transmission scheme for screen

updates delivery in daas,” ACM Transactions on Multimedia Computing, Communications, and

Applications (TOMM), vol. 17, no. 3, pp. 1–24, 2021.

8 R. Li, B. Gao, and Q. Xu, “Gated auxiliary edge detection task for road extraction with weight-balanced

loss,” IEEE Geoscience and Remote Sensing Letters, vol. 18, no. 5, pp. 786–790, 2020.

9 S. Lu, M. Hu, R. Li, and Y. Xu, “A novel adaptive weighted loss design in adversarial learning for retinal

nerve fiber layer defect segmentation,” Ieee Access, vol. 8, pp. 132 348–132 359, 2020.

10 Y. Xu, S. Lu, H. Li, and R. Li, “Mixed maximum loss design for optic disc and optic cup segmentation

with deep learning from imbalanced samples,” Sensors, vol. 19, no. 20, p. 4401, 2019.

11 K. Yue, L. Yang, R. Li, W. Hu, F. Zhang, and W. Li, “Treeunet: Adaptive tree convolutional neural

networks for subdecimeter aerial image segmentation,” ISPRS Journal of Photogrammetry and Remote

Sensing, vol. 156, pp. 1–13, 2019.

12 R. Li, W. Liu, L. Yang, et al., “Deepunet: A deep fully convolutional network for pixel-level sea-land

segmentation,” IEEE journal of selected topics in applied earth observations and remote sensing, vol. 11,

no. 11, pp. 3954–3962, 2018.

Conference Proceedings

1 R. R. Li, M. H. Yue, X. q. Jin, and Y. Wang, “Multi-view feature fusion encoding and memory-driven

report generation for octa fundus image diagnosis.,” in Proceedings of the Second IEEE International

Conference on Medical Artificial Intelligence, Chongqing, China, 2024.

2 N. H. Wang, S. S. Hong, K. X. Meng, and R. R. Li, “Enhancing the quality of pseudo labels in 2d human

pose estimation via a debiasing-teacher approach,” in Proceedings of the 27th International Conference on

Pattern Recognition (ICPR), Kolkata, Inida, 2024.

3 N. H. Wang and R. R. Li, “A comprehensive framework for debiased sample selection across all noise

types,” in Proceedings of The Pacific Rim International Conference on Artificial Intelligence (PRICAI),

Kyoto, Japan, 2024.

4 N. H. Wang, Y. K. Yang, H. x. Yang, and R. R. Li, “Enhancing fairness and robustness in label-noise

learning through advanced sample selection and adversarial optimization,” in Proceedings of the 27th

International Conference on Pattern Recognition (ICPR), Kolkata, Inida, 2024.

5 Y. Liu, X. Liu, K. Meng, H. Yang, and R. Li, “Enhancing 3d human body reconstruction: A local

information-based approach with improved generalization and accuracy,” in 2023 International

Conference on Image Processing, Computer Vision and Machine Learning (ICICML), IEEE, 2023,

pp. 671–677.

6 T. Chen, D. Jiang, and R. Li, “Swin transformers make strong contextual encoders for vhr image road

extraction,” in IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium, IEEE,

2022, pp. 3019–3022.

7 W. Ding, D. Jiang, and R. Li, “Efficient global context graph convolution for hyperspectral image

classification,” in IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium, IEEE,

2022, pp. 1728–1731.

8 X. Jin, H. Li, and R. Li, “Dformer: Dual-path transformers for geometric and appearance features

reasoning in diabetic retinopathy grading,” in Chinese Conference on Pattern Recognition and Computer

Vision (PRCV), Springer, 2022, pp. 401–416.

9 J. Liu, D. Jiang, Y. Yang, and R. Li, “Agreement or disagreement in noise-tolerant mutual learning?” In

2022 26th International Conference on Pattern Recognition (ICPR), IEEE, 2022, pp. 4801–4807.

10 H. Zhang, D. Jiang, and R. Li, “Sgdanet: Gland instance segmentation based on spatial and geometric

dual-path attention modules,” in 2022 5th International Conference on Pattern Recognition and Artificial

Intelligence (PRAI), IEEE, 2022, pp. 1033–1039.

11 W. Hu, Y. Huang, F. Zhang, and R. Li, “Noise-tolerant paradigm for training face recognition cnns,” in

Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 11 887–11 896.