Dr. Wuzhen Shi (Assistant Professor, Master Supervisor, IEEE Senior Member) received the Degree of Doctor of Philosophy in Computer Applied Technology from Harbin Institute of Technology in April 2020, and joined the College of Electronics and Information Engineering of Shenzhen University in June 2020 as an assistant professor and distinguished associate researcher. He won the 23rd Outstanding Doctoral Thesis Award of Harbin Institute of Technology and the 2021 Outstanding Doctoral Thesis Award of Heilongjiang Artificial Intelligence Society. His research interests include image/video compression and enhancement, affective computing, AIGC, etc. He has published more than 40 papers, with representative work published in top international journals such as IEEE TIP, IEEE TCSVT, IEEE TMM, IEEE TII and IEEE TIM, as well as top international conferences such as CVPR and DCC. In particular, one of his representative papers was selected as an ESI Highly Cited Paper. His research has been funded by the National Natural Science Foundation of China, the Natural Science Foundation of Guangdong Province, and the Shenzhen Science and Technology Innovation Commission. He serves as a reviewer for more than 20 international journals and conferences, including IEEE TPAMI, IEEE TIP, IEEE TCSVT, IEEE TII, IEEE JSTSP, etc.

[1]Image/Video Compression and Enhancement

[2]Affective Computing

[3]AIGC

[1]W. Shi, J. Su, Y. Wen, Y. Liu. "Light field image super-resolution using a Content-Aware Spatial-Angular Interaction network." Displays (2024): 102782.

[2]W. Shi, F. Tao, Y. Wen. "Joint super-resolution-based fast face image coding for human and machine vision." The Visual Computer (2024): 1-14.

[3]B. Yao, W. Shi*. "Speaker-Centric Multimodal Fusion Networks for Emotion Recognition in Conversations." ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024.

[4]S. Meng, W. Shi*. "Fusing Structure and Appearance Features in Facial Expression Recognition Transformer." ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024.

[5]Y. Wen, Y. Wu, L. Bi, W. Shi*, et al. "A Transformer-Assisted Cascade Learning Network for Choroidal Vessel Segmentation." Journal of Computer Science and Technology 39.2 (2024): 286-304.

[6]Z. Yin, Z. Wu, W. Shi. "Scalable compressive sampling network with progressive hierarchical subspace learning." Pattern Recognition 156 (2024): 110769.

[7]W. Shi, D. Li, Y. Wen and W. Yang, "Occlusion-Aware Graph Neural Networks for Skeleton Action Recognition," in IEEE Transactions on Industrial Informatics, vol. 19, no. 10, pp. 10288-10298, Oct. 2023.

[8]W. Shi, F. Tao and Y. Wen, "Structure-Aware Deep Networks and Pixel-Level Generative Adversarial Training for Single Image Super-Resolution," in IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-14, 2023, Art no. 5007614.

[9]W. Shi, Z. Liu, Y. Li, Y. Wen. "Light-weight 3D mesh generation networks based on multi-stage and progressive knowledge distillation." Displays 80 (2023): 102527.

[10]W. Shi, and S. Liu. "Hiding message using a cycle generative adversarial network." ACM Transactions on Multimedia Computing, Communications and Applications 18.3s (2022): 1-15.

[11]Z. Yin, W. Shi, Z. Wu, J. Zhang, “Multilevel wavelet-based hierarchical networks for image compressed sensing”, Pattern Recognition, 2022, 129: 108758.

[12]W. Yang and W. Shi, "Detail Generation and Fusion Networks for Image Inpainting," ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, Singapore, 2022, pp. 2335-2339.

[13]D. Li, Dan, and W. Shi*. "Partially occluded skeleton action recognition based on multi-stream fusion graph convolutional networks." Advances in Computer Graphics: 38th Computer Graphics International Conference, CGI 2021, Virtual Event, September 6–10, 2021, Proceedings 38. Springer International Publishing, 2021.

[14]W. Shi, S. Liu, F. Jiang and D. Zhao, "Video Compressed Sensing Using a Convolutional Neural Network," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, no. 2, pp. 425-438, Feb. 2021.

[15]W. Shi, F. Jiang, S. Liu and D. Zhao, "Image Compressed Sensing Using Convolutional Neural Network," in IEEE Transactions on Image Processing, vol. 29, pp. 375-388, 2020.

[16]W. Shi, F. Jiang, S. Liu, D. Zhao. Scalable convolutional neural network for image compressed sensing[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019: 12290-12299.

[17]W. Shi, F. Jiang, S. Zhang, R. Wang, D. Zhao, H. Zhou. "Hierarchical residual learning for image denoising." Signal Processing: Image Communication 76 (2019): 243-251.

[18]W. Shi, F. Jiang, S. Liu and D. Zhao, "Multi-Scale Deep Networks for Image Compressed Sensing," 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece, 2018, pp. 46-50.

[19]W. Shi, F. Jiang and D. Zhao, "Single image super-resolution with dilated convolution based multi-scale information learning inception module," 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China, 2017, pp. 977-981.

[20]W. Shi, F. Jiang, S. Zhang and D. Zhao, "Deep networks for compressed image sensing," 2017 IEEE International Conference on  Multimedia and Expo (ICME), Hong Kong, China, 2017, pp. 877-882.