刘芳　二级教授

硕士/博士生导师

博士学科：(081203)计算机应用技术

硕士学科：(081203)计算机应用技术

工作单位：人工智能学院

通信地址：西安电子科技大学224信箱

电子邮箱：f63liu@163.com

办公电话：88204310

办公地点：主楼II区2层228

刘芳，女，二级教授，博士生导师，享受国务院特殊津贴的专家，国家自然科学奖获得者，建国七十周年奖章获得者，IEEE高级会员，现为西安电子科技大学华山特聘教授和学术带头人，教育部长江学者创新团队学术带头人，主要研究方向包括大数据感知与模式识别、机器学习与智能图像处理、多模态学习及可解释性等。目前，刘芳教授的谷歌学术引用量为28000+、H指数为77、i10指数为486，入选全球前2%顶尖科学家榜单。在领域内主流期刊和会议上（TNNLS、TIP、TCYB、TMM、TCSVT、TGRS、PR、AAAI、IJCAI、ACMM、ECCV等）发表论文100余篇，以第一完成人获国家发明专利100余项，获省部级一等奖励以上成果10余项，培养的博士和硕士多人入选省级、一级学会优秀论文，数十人获国家相关人才计划支持，所指导的学生在领域内主流竞赛（CVPR、ICCV、ECCV、IGRSS等）获得100余项冠亚季军。

刘芳教授长期致力于人工智能核心算法的研究，所开展的基于自然启发的智能优化算法获得国家自然科学二等奖，这是从人类进化的可解释性角度的典型代表性工作，其中特别是针对高维奇异点的检测和识别难题，这也是模型攻击与安全性研究的核心，从表征、学习、优化等角度提出了系列启发式的有效解决办法。刘芳教授参加了包括“973”、国家自然科学基金重点项目和总装预研等国家科研项目，同时承担和完成了包括“863”、国家自然科学基金和国防重大基础科研基金等二十余项国家科研任务，获2004年陕西省创新能手、获陕西省科学技术进步二等奖（1998年）、国家教育部科技进步二等奖（1999年）、2008年陕西省科学技术一等奖、2009年国家教育部自然科学奖一等奖、2009年陕西省高等学校科学技术奖一等奖、2010年陕西省科学技术奖一等奖、2011年教育部高等学校技术发明奖二等奖、2011年中国电子学会电子信息科学技术奖 二等奖、2012年陕西省科学技术奖一等奖、2017年陕西省科学技术奖一等奖和2013年国家自然科学奖二等奖等多项科技奖励。

刘芳教授合作出版《免疫优化计算、学习与识别》、《智能数据挖掘与知识发现》、《图像多尺度几何分析理论与应用——后小波分析理论与应用》、《自然计算、机器学习与图像理解前沿》、《智能SAR图像处理与解译》、《高分辨遥感影像学习与识别》、《稀疏学习、分类与识别》、《认知计算与多目标优化》、《量子计算、优化与学习》、《深度学习、优化与识别》、《遥感影像深度学习智能解译与识别》、《模式识别》、《简明人工智能》、《人工智能、类脑计算与图像解译前沿》、《遥感脑理论及应用》和《深度学习的理论基础与核心算法》等专著十余部。

学术主页：谷歌学术DBLP

近几年部分论文（更多论文）：

• Yang Liu,Fang Liu, Licheng Jiao, Qianyue Bao, Long Sun, Shuo Li, Lingling Li, Xu Liu. Multi-grained Gradual Inference Model for Multimedia Event Extraction [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2024.NEW!
• Yang Liu,Fang Liu, Licheng Jiao, Qianyue Bao, Lingling Li, Yuwei Guo, Puhua Chen. A Knowledge-based Hierarchical Causal Inference Network for Video Action Recognition[J].IEEE Transactions on Multimedia, 2024.NEW!
• Jiahao Wang,Fang Liu, Licheng Jiao, Yingjia Gao, Hao Wang, Lingling Li, Puhua Chen, Xu Liu and Shuo Li. Satellite Video Object Tracking based on Location Prompts[J].IEEE Transactions on Circuits and Systems for Video Technology, 2024.NEW!
• Hao Wang,Fang Liu, Licheng Jiao, Jiahao Wang, Zehua Hao, Shuo Li, Lingling Li, Puhua Chen and Xu Liu. ViLT-CLIP: Video and Language Tuning CLIP with Multimodal Prompt Learning and Scenario-guided Optimization [C].In Proceedings of the AAAI Conference on Artificial Intelligence, 2024.NEW!
• Fang Liu, Xiaoxue Qian, Licheng Jiao, Xixangrong Zhang, Lingling Li and Yuanhao Cui. Contrastive Learning-Based Dual Dynamic GCN for SAR Image Scene Classification[J].IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 1, pp. 390-404, Jan. 2024.NEW!
• Shuo Li,Fang Liu, Licheng Jiao, Xu Liu and Puhua Chen, Learning Salient Feature for Salient Object Detection Without Labels[J].IEEE Transactions on Cybernetics, vol.53, no.2, pp. 1012-1025, 2023.
• Shuo Li,Fang Liu, Zehua Hao, Licheng Jiao, Xu Liu and Yuwei Guo. MinEnt: Minimum entropy for self-supervised representation learning[J].Pattern Recognition, vol.138, 109364, 2023.
• Pengfang Li,Fang Liu, Licheng Jiao, Shuo Lia, Lingling Li, Xu Liu and Xinyan Huang. Knowledge Transduction for Cross-Domain Few-Shot Learning[J].Pattern Recognition, 2023.
• Xiaoxue Qian,Fang Liu, Licheng Jiao, Xiangrong Zhang, Xinyan Huang, Shuo Li, Puhua Chen and Xu Liu. Knowledge transfer evolutionary search for lightweight neural architecture with dynamic inference[J].Pattern Recognition, vol.143, 109790, 2023.
• 黄欣研,刘芳, 鲍骞月, 李任鹏, 刘旭, 李玲玲, 陈璞花, 刘洋. 基于多任务学习和身份约束的生成对抗网络人脸校正识别方法[J].电子学报, Vol.51, No.10, pp:2936-2949, 2023.
• Xinyan Huang,Fang Liu, Yuanhao Cui, Puhua Chen, Lingling Li, Pengfang Li. Faster and Better: A Lightweight Transformer Network for Remote Sensing Scene Classification[J]. Remote Sensing, 15(14):3645, 2023.
• Jiahao Wang,Fang Liu, Licheng Jiao, Hao Wang, Hua Yang, Xu Liu, Lingling Li and Puhua Chen. SSCFNet: A Spatial-spectral Cross Fusion Network for Remote Sensing Change Detection[J].Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023.
• Jiahao Wang,Fang Liu, Hao Wang, Xu Liu, Licheng Jiao, Hua Yang, Lingling Li and Puhua Chen. SDCDNet: A Semi-Dual Change Detection Network Framework with Super-Weak Lable for Remote Sensing Image[J].IEEE Transactions on Geoscience and Remote Sensing, VOL.61, 5612714, 2023.
• Yake Zhang,Fang Liu, Licheng Jiao, Shuyuan Yang, Lingling Li, Meijuan Yang, Jianlong Wang and Xu Liu. Curvelet Adversarial Augmented Neural Network for SAR Image Classification[J].IEEE Transactions on Geoscience and Remote Sensing, VOL.61, 4400717, 2023.
• Pengfang Li,Fang Liu, Licheng Jiao, Lingling Li, Puhua Chen and Shuo Li. Task context transformer and GCN for few-shot learning of cross-domain[J].Neurocomputing, vol.548, 2023, 126433.
• Shuo Li,Fang Liuand Licheng Jiao. Self-Training Multi-Sequence Learning with Transformer for Weakly Supervised Video Anomaly Detection[C].In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36(2), pp. 1395-1403, 2022.
• Qianyue Bao,Fang Liu, Yang Liu, Licheng Jiao, Xu Liu and Lingling Li. Hierarchical scene normality-binding modeling for anomaly detection in surveillance videos[C].In Proceedings of the 30th ACM International Conference on Multimedia, pp. 6103-6112, 2022.
• Shuo Li,Fang Liu, Licheng Jiao, Puhua Chen, Xu Liu and Liling Li. MFNet: A Novel GNN-Based Multi-Level Feature Network With Superpixel Priors[J].In IEEE Transactions on Image Processing, vol. 31, pp. 7306-7321, 2022.
• Yuanhao Cui,Fang Liu, Licheng Jiao, Yuwei Guo, Xuefeng Liang, Lingling Li, Shuyuan Yang and Xiaoxue Qian.Polarimetric Multipath Convolutional Neural Network for PolSAR Image Classification[J].IEEE Transactions on Geoscience and Remote Sensing, VOL. 60, 5207118, 2022.
• Xiaoxue Qian,Fang Liu, Licheng Jiao, Xiangrong Zhang, Puhua Chen, Lingling Li, Jing Gu and Yuanhao Cui. A Hybrid Network With Structural Constraints for SAR Image Scene Classification[J].IEEE Transactions on Geoscience and Remote Sensing, VOL. 60, 5202717, 2022.
• Shuo Li,Fang Liu, Zehua Hao, Kaibo Zhao and Licheng Jiao. Unsupervised Few-Shot Image Classification by Learning Features into Clustering Space[C].In European Conference on Computer Vision, vol. 13691, pp. 420-436, 2022.
• Yaoyang Du,Fang Liu, Licheng Jiao, Zehua Hao, Shuo Li, Xu Liu, Jing Liu. Augmentative contrastive learning for one-shot object detection[J].Neurocomputing, 513: 13-24, 2022.
• Yuanhao Cui,Fang Liu, Xu Liu, Lingling Li and Xiaoxue Qian. TCSPANet: Two-Staged Contrastive Learning and Sub-Patch Attention Based Network for PolSAR Image Classification[J].Remote Sensing, 2022, 14, 2451.
• 李鹏芳,刘芳, 李玲玲, 刘旭, 冯志玺, 焦李成, 熊怡梦. 嵌入标签语义的元特征再学习和重加权小样本目标检.计算机学报，Vol.45, No.12, pp:2561-2575, 2022.
• Xiaoxue Qian,Fang Liu, Licheng Jiao, Xiangrong Zhang, Yuwei Guo, Xu Liu and Yuanhao Cui. Ridgelet-Nets With Speckle Reduction Regularization for SAR Image Scene Classification[J].IEEE Transactions on Geoscience and Remote Sensing, Vol.59, No.11, pp:9290-9306, 2021.
• Yake Zhang,Fang Liu, Licheng Jiao, Shuyuan Yang, Lingling Li and Meijuan Yang. Discriminative Sketch Topic Model With Structural Constraint for SAR Image Classification[J].IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, Vol.13:5730-5745, 2020.
• Fang Liu, Puhua Chen, Yuanjie Li, Licheng Jiao, Dashen Cui, Yuanhao Cui and Jing Gu. Structural feature learning-based unsupervised semantic segmentation of synthetic aperture radar image[J].Journal of Applied Remote Sensing, 13(1): 014501-014501, 2019.
• Wan Li,Fang Liu, Licheng Jiao and Fei Hu. Video Reconstruction Based on Intrinsic Tensor Sparsity Model[J].Signal Processing: Image Communication, 72:113-125, 2019.
• Wan Li,Fang Liu, Licheng Jiao and Fei Hu. Multi-Scale Residual Reconstruction Neural Network With Non-Local Constraint[J].IEEE Access, 7:70910-70918, 2019.

[1] 大数据感知与模式识别

[2] 机器学习与智能图像处理

[3] 多模态学习及可解释性