教师简介

李继昌博士，中山大学计算机学院副教授、博士生导师，国家级青年人才，逸仙优秀学者。博士毕业于香港大学计算机科学系，曾任鹏城实验室多智能体与具身智能研究所助理研究员。长期深耕计算机视觉与机器学习前沿方向，现阶段重点融合视觉内容理解与基础大模型、世界模型理论，围绕空间/物理/自主智能体/具身智能（Spatial/Physical/Agentic/Embodied AI）等开展系统性研究。多项学术成果发表于 IEEE TIP、TNNLS 等权威期刊，以及 CVPR、ICCV、ECCV、AAAI 等计算机领域顶会；常年担任 CVPR、ICCV、ECCV、NeurIPS、AAAI 及 IEEE TPAMI、TIP、TNNLS、TMM 等国内外顶级期刊与会议审稿人、程序委员会委员。

研究领域

空间/物理/自主智能体/具身人工智能（Spatial/Physical/Agentic/Embodied AI）、基础模型与多模态大模型、世界模型、视觉内容理解、开放世界学习、弱监督与半监督学习等

工作经历

2026年 - 至今，中山大学 计算机学院，副教授、博士生导师

2024年 - 2026年，鹏城实验室 多智能体与具身智能研究所，助理研究员、博士生导师

教育背景

2020年 - 2024年，香港大学， 计算机科学，哲学博士（PhD）

2017年 - 2020年，华南理工大学，计算机技术，工学硕士

2013年 - 2017年，华南师范大学，通信工程，工学学士

代表性论著

（# 共同作者；* 通讯作者）

[1] Jichang Li, Guanbin Li, Hui Cheng, Zicheng Liao, and Yizhou Yu. FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels. In The Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2024.

[2] Jichang Li, Guanbin Li, and Yizhou Yu. Inter-Domain Mixup for Semi-Supervised Domain Adaptation. Pattern Recognition (PR), 2023.

[3] Jichang Li, Guanbin Li, and Yizhou Yu. Adaptive Betweenness Clustering for Semi-Supervised Domain Adaptation. IEEE Transactions on Image Processing (TIP), 2023.

[4] Jichang Li, Guanbin Li, Feng Li, and Yizhou Yu. Neighborhood Collective Estimation for Noisy Label Identification and Correction. In European Conference on Computer Vision (ECCV), 2022.

[5] Jichang Li, Guanbin Li, Yemin Shi, and Yizhou Yu. Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

[6] Jichang Li, Si Wu, Cheng Liu, Zhiwen Yu, and Hau-San Wong. Semi-Supervised Deep Coupled Ensemble Learning with Classification Landmark Exploration. IEEE Transactions on Image Processing (TIP), 2019.
[7] Yuxiang Zhou#, Jichang Li#, Yanhao Zhang, Haonan Lu, and Guanbin Li. Mobile-Agent-RAG: Driving Smart Multi-Agent Coordination with Contextual Knowledge Empowerment for Long-Horizon Mobile Automation. In The Fortieth AAAI Conference on Artificial Intelligence (AAAI), 2026.

[8] Yinqi Cai#, Jichang Li#, Zhaolun Li, Weikai Chen, Rushi Lan, Xi Xie, Xiaonan Luo, and Guanbin Li. DeepShield: Fortifying Deepfake Video Detection with Local and Global Forgery Analysis. In International Conference on Computer Vision (ICCV), 2025.

[9] Zhaolun Li, Jichang Li*, Yinqi Cai, Junye Chen, Xiaonan Luo, Guanbin Li, and Rushi Lan*. FakeRadar: Probing Forgery Outliers to Detect Unknown Deepfake Videos. In International Conference on Computer Vision (ICCV), 2025.

[10] Yiqi Zou, Kuo Wang, Jichang Li*, Chuan Wang, Shuangyin Liu*, Liang Lin, and Guanbin Li*. Benefiting from OOD Samples in Open-Set Semi-Supervised Object Detection. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2026.