李晶晶
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  • 入职时间:2019-07-30
  • 所在单位:计算机科学与工程学院(网络空间安全学院)
  • 学历:博士研究生毕业
  • 办公地点:主楼B1-705
  • 性别:
  • 联系方式:jjl at uestc dot edu dot cn
  • 学位:工学博士学位
  • 职称:副教授
  • 在职信息:在岗
  • 毕业院校:电子科技大学
  • 硕士生导师
  • 曾获荣誉:2017年博士后创新人才支持计划
    2018年中国电子学会优秀博士论文奖
    2018年ACM成都地区优秀博士论文奖
    2019年电子科技大学学术新人奖
  • 个人简介
  • 研究方向
  • 社会兼职
  • 教育经历
  • 工作经历
  • 团队成员
  • 其他联系方式

李晶晶,现为电子科技大学计算机科学与工程学院副教授,澳大利亚昆士兰大学博士生指导教师,电子科技大学与澳大利亚昆士兰大学联合培养博士。“博新计划”博士后。博士学位论文获得2018年ACM成都优秀博士论文奖和2018年中国电子学会优秀博士论文奖。入选2019年电子科技大学“学术新人奖”,2020年电子科技大学“人才托举计划”青年项目。主要研究方向为机器学习,计算机视觉和多媒体,特别是欠标注场景下的机器学习。目前已在TPAMI,TIP,TKDE,MM和CVPR等JCR一区期刊及CCF A类会议上发表长文三十余篇,获得授权专利六项。担任CCF推荐期刊MTAP客座编委, TPAMI, TIP, TCYB, TNNLS, TKDE, AAAI, MM等期刊和会议审稿人。指导本科生发表中科院一区论文,指导硕士研究生均获得电子科技大学优秀毕业生称号,指导博士生获得国家奖学金。最新信息请访问 https://lijin118.github.io/        (👇向下滑动鼠标滚轮查看更多内容)

Jingjing Li received the M.Sc. and Ph.D. degrees in computer science from the University of Electronic Science and Technology of China in 2013 and 2017, respectively. He is currently an A/Professor with the School of Computer Science and Engineering, University of Electronic Science and Technology of China. His current research interests include computer vision, machine learning and multimedia analysis, especially transfer learning, domain adaptation and zero-shot learning. Dr. Jingjing Li has published over 30 peer-reviewed papers on top-ranking journals and conferences, including IEEE TPAMI, TIP, TKDE, TCYB, TNNLS, TMM, TCSVT, TIST, CVPR, ACM MM, AAAI, IJCAI and CIKM. He has long served as a reviewer for IEEE TPAMI, TIP, TCYB, TNNLS, TDS, PR, AAAI and ACM MM. He is a leading guest editor for MTAP on SI of transfer learning.  

Please visit https://lijin118.github.io/ for latest updates!  



What's New

  • [TOP] 2019.04.27 Our paper "Maximum Density Divergence for Domain Adaptation" is accepted to IEEE TPAMI. In this work, we propose a novel and generalized learning loss for domain adaptation. Code and data are released at https://github.com/lijin118/ATM .

  • [TOP] 2019.09.15 Our paper "Transfer Independently Together" published on IEEE TCYB has been recognized as an ESI hot paper and highly cited paper.

  • [TOP] 2019.10.20 One ACM'MM 2019 paper has been selected as the best paper candidate.

  • 2020.07.29 Three papers accepted to ACM Multimedia 2020. Two of them hanlde zero-shot learning and one addresses incomplete cross-modal retrieval.

Publications


2020

  • Jingjing Li, Erpeng Chen, Zhengming Ding, Lei Zhu, Ke Lu and Heng Tao Shen, Maximum Density Divergence for Domain Adaptation, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2020, CCF A, [pdf] [code]

  • Jingjing Li, Mengmeng Jing, Lei Zhu, Zhengming Ding, Ke Lu and Yang Yang, Learning Modality-Invariant Latent Representations for Generalized Zero-shot Learning, ACM Multimedia 2020, CCF A [pdf] [code]

  • Mengmeng Jing, Jingjing Li, Ke Lu, Lei Zhu, Yang Yang and Zi Huang., Incomplete Cross-modal Retrieval with Dual-Aligned Variational Autoencoders, ACM Multimedia 2020, CCF A [pdf] [code] (Corresponding author)

  • Mengmeng Jing, Jidong Zhao, Jingjing Li, Lei Zhu, Yang Yang and Heng Tao Shen, Adaptive Component Embedding for Domain Adaptation, IEEE Transactions on Cybernetics (TCYB), 2020, JCR I, [pdf] [code] (Corresponding author)

  • Mengmeng Jing, Jingjing Li, Lei Zhu, Ke Ku and Yang Yang, Learning Explicitly Transferable Representations for Domain Adaptation, Neural Networks (NN), 2020, JCR I, [pdf] [code] (Corresponding author)

  • Lin Zuo, Mengmeng Jing, Jingjing Li, Lei Zhu, Ke Lu and Yang Yang, Challenging Tough Samples in Unsupervised Domain Adaptation, Pattern Recognition (PR), 2020, JCR I, [pdf] [code] (Corresponding author)

  • Zhi Chen, Sen Wang, Jingjing Li and Zi Huang., Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patches, ACM Multimedia 2020, CCF A [pdf] [code]

  • Hui Cui, Lei Zhu, Jingjing Li, Yang Yang, Liqiang Nie, Scalable Deep Hashing for Large-Scale Social Image Retrieval, IEEE Transactions on Image Processing (TIP) 2020, CCF A, [pdf] [code]

  • Zhi Chen, Jingjing Li, Yadan Luo, Zi Huang and Yang Yang, CANZSL: Cycle-Consistent Adversarial Networks for Zero-Shot Learning from Natural Language, WACV 2020, [pdf] [code]

  • Ruihong Qiu, Zi Huang, Jingjing Li and Hongzhi Yin. Exploiting Cross-Session Information for Session-based Recommendation with Graph Neural Networks. ACM Transaction on Information Systems (TOIS) 2020, CCF A, [pdf] [code]

  • Lei Zhu, Xu Lu, Zhiyong Cheng, Jingjing Li, Huaxiang Zhang. Deep Collaborative Multi-view Hashing for Large-scale Image Search. IEEE Transactions on Image Processing (TIP), 2020, CCF A, JCR I [pdf] [code]

  • Xu Lu, Lei Zhu, Jingjing Li, Huaxiang Zhang, Heng Tao Shen. Efficient Supervised Discrete Multi-view Hashing for Large-scale Multimedia Search. IEEE Transactions on Multimedia (TMM), 2020, JCR I, [pdf] [code]

  • Yang Xu, Lei Zhu, Zhiyong Cheng, Jingjing Li, Jiande Sun. Multi-Feature Discrete Collaborative Filtering for Fast Cold-start Recommendation. AAAI Conference on Artificial Intelligence (AAAI), 2020, CCF A [pdf] [code]

  • Dan Shi, Lei Zhu, Yikun Li, Jingjing Li, Xiushan Nie. Robust Structured Graph Clustering. IEEE Transactions on Neural Network and Learning Systems (TNNLS), JCR I, 2020, [pdf] [code]

  • Lei Zhu, Hui Cui, Zhiyong Cheng, Jingjing Li, Zheng Zhang. Dual-level Semantic Transfer Deep Hashing for Efficient Social Image Retrieval. IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2020, [pdf] [code]

  • Lei Zhu, Xu Lu, Zhiyong Cheng, Jingjing Li, Huaxiang Zhang. Flexible Multi-modal Hashing for Scalable Multimedia Retrieval. ACM Transactions on Intelligent Systems and Technology (TIST), 2020, [pdf] [code]


  • 2019
  • Jingjing Li, Erpeng Chen, Ke Lu, Zhengming Ding, Lei Zhu and Zi Huang., Cycle-consistent Conditional Adversarial Transfer Networks, ACM Multimedia 2019, CCF A [pdf] [code]

  • Jingjing Li, Mengmeng Jing, Ke Lu, Lei Zhu, Yang Yang and Zi Huang., Alleviating Feature Confusion for Generative Zero-shot Learning, ACM Multimedia 2019, CCF A [pdf] [code]

  • Jingjing Li, Mengmeng Jing, Zhengming Ding, Lei Zhu and Zi Huang, Leveraging the Invariant Side of Generative Zero-Shot Learning, IEEE CVPR 2019, CCF A [pdf] [code]

  • Jingjing Li, Mengmeng Jing, Ke Lu, Lei Zhu, Yang Yang and Zi Huang, From Zero-Shot Learning to Cold-Start Recommendation, AAAI 2019, CCF A [pdf] [code]

  • Jingjing Li, Mengmeng Jing, Ke Lu, Lei Zhu and Heng Tao Shen, Locality Preserving Joint Transfer for Domain Adaptation, IEEE Transactions on Image Processing (TIP) 2019, CCF A [pdf] [code]

  • Jingjing Li, Ke Lu, Zi Huang and Heng Tao Shen, On both Cold-Start and Long-Tail Recommendation with Social Data, IEEE Transactions on Knowledge Discovering and Engineering (TKDE), 2019, CCF A [pdf] [code]

  • Jingjing Li, Mengmeng Jing, Yue Xie, Ke Lu and Zi Huang, Agile Domain Adaptation, IJCNN 2019, [pdf] [code]

  • Chaoqun Zheng, Lei Zhu, Xu Lu, Jingjing Li, Zhiyong Cheng, Hanwang Zhang, Fast Discrete Collaborative Multi-modal Hashing for Large-scale Multimedia Retrieval, IEEE Transactions on Knowledge Discovering and Engineering (TKDE), 2019, CCF A [pdf] [code]

  • Mengmeng Jing, Jingjing Li, Ke Lu, Jieyan Liu, Zi Huang, Adaptive Component Embedding for Unsupervised Domain Adaptation, IEEE International Conference on Multimedia and Expo (ICME), 2019, [pdf] [code]

  • Yi Bin, Chaofan Tao, Yang Yang, Zi Huang, Jingjing Li, Heng Tao Shen, MR-NET: Exploiting Mutual Relation for Visual Relationship Detection, AAAI, 2019, [pdf] [code]

  • Yudong Han, Lei Zhu, Zhiyong Cheng, Jingjing Li, Xiaobai Liu, Discrete Optimal Graph Clustering, IEEE Transcations on Cybernetics (TCYB), 2019, [pdf] [code]

  • Hui Cui, Lei Zhu, Jingjing Li, Yang Yang, Liqiang Nie, Scalable Deep Hashing for Large-scale Social Image Retrieval, IEEE Transcations on Image Processing (TIP), 2019, [pdf] [code]

  • Xu Lu, Lei Zhu, Zhiyong Cheng, Jingjing Li, Xiushan Nie and Huaxiang Zhang, Flexible Online Multi-modal Hashing for Large-scale Multimedia Retrieval, ACM Multimedia 2019, [pdf] [code]

  • Yadan Luo, Zi Huang, Zheng Zhang, Ziwei Wang, Jingjing Li, Yang Yang, Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation, ACM Multimedia 2019, [pdf] [code]

  • Ruihong Qiu, Jingjing Li, Zi Huang and Hongzhi Yin, Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks, CIKM 2019, [pdf] [code]

  • Jiwei Wei, Yang Yang, Jingjing Li, Lei Zhu, Lin Zuo and Heng Tao Shen, Residual Graph Convolutional Networks for Zero-Shot Learning, ACM Multimedia Asia 2019, [pdf] [code]


  • 2018
  • Jingjing Li, Ke Lu, Lei Zhu, Zi Huang and Jidong Zhao, I read, I saw, I tell: Texts Assisted Fine-grained Visual Classification, ACM Multimedia 2018, CCF A [pdf] [code]

  • Jingjing Li, Ke Lu, Zi Huang, Heng Tao Shen. Transfer Independently Together: A Generalized Framework for Domain Adaptation. IEEE Transactions on Cybernetics (TCYB), 2018, JCR I [pdf] [code]

  • Jingjing Li, Ke Lu, Lei Zhu, Zi Huang, Heng Tao Shen. Heterogeneous Domain Adaptation through Progressive Alignment, IEEE Transactions on Neural Network and Learning Systems (TNNLS), 2018, JCR I [pdf] [code]

  • Jieyan Liu, Jingjing Li, Ke Lu, Coupled local–global adaptation for multi-source transfer learning, Neurocomputing, 2018. [pdf] [code]


  • Previous
  • Jingjing Li, Yue Wu, Jidong Zhao, Ke Lu. Low-Rank Discriminant Embedding for Multiview Learning. IEEE Transactions on Cybernetics (TCYB), 2017, JCR I [pdf] [code]

  • Jingjing Li, Ke Lu, Zi Huang, Heng Tao Shen. Two Birds One Stone: On both Cold-Start and Long-Tail Recommendation. ACM Multimedia (ACM MM), Mountain View, 2017. CCF A [pdf] [code]

  • Jingjing Li, Ke Lu, Jidong Zhao. Joint Feature Selection and Structure Preservation for Domain Adaptation. IJCAI, 2016, CCF A [pdf] [code]

  • Jingjing Li, Yue Wu, Jidong Zhao, Ke Lu. Multi-manifold Sparse Graph Embedding for Multi‐modal Image Classification. Neurocomputing, 2016 [pdf] [code]

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