南京理工大学计算机科学与工程学院，副教授

毕业于上海交通大学-电子信息与电气工程学院-计算机科学与工程系，获工学博士学位

担任IEEE TPAMI / IEEE TKDE / IEEE TNNLS / IEEE T Cybernetics / IJCV / PR / Image and Vision Computing / Neurocomputing等期刊审稿人。

主持国家自然科学基金青年基金项目、面上项目、中国博士后基金面上项目和特别资助项目、南京理工大学科研启动基金等项目。

近期发表论文：

1. X. Cheng, Z. Fu, J. Yang. Deep moire network for image steganography, submitted to TMM.

2. X. Cheng, Z. Fu, J. Yang. Improved multi-scale dynamic feature encoding network for image demoireing, Pattern Recognition 2021 (CCF-B).

3. W. Zheng, S. Chen, Z. Fu, F. Zhu, H. Yan, J. Yang. Feature Selection Boosted by Unselected Features. IEEE TNNLS 2021 (CCF-B).

4. Z. Han, Z. Fu, S. Chen, J. Yang. Contrastive embedding for generalized zero-shot learning, CVPR 2021 (CCF-A).

5. X. Cheng, Z. Fu, J. Yang. Zero-Shot Image Super-Resolution with Depth Guided Internal Degradation Learning, ECCV 2020 (CCF-B).

6. Z. Han, Z. Fu, J. Yang. Learning the redundancy-free features for generalized zero-shot object recognition, CVPR 2020 (CCF-A).

7. Z. Han, Z. Fu, G. Li, J. Yang. Inference Guided Feature Generation for Generalized Zero-Shot Learning, Neurocomputing 2020.

8. X. Cheng, Z. Fu, J. Yang. Multi-scale dynamic feature encoding network for image demoireing, ICCV workshop 2019.

9. Z. Fu, T. Xiang, E. Kodirov, S. Gong. Zero-shot learning on semantic class prototype graph, IEEE TPAMI 2018 (CCF-A).

10. Z. Lu, Z. Fu, T. Xiang, P. Han, L. Wang, X. Gao. Learning from weak and noisy labels for semantic segmentation. IEEE TPAMI 2017 (CCF-A).

研究生课程：《智能科学技术前沿》