RESEARCH INTERESTS: 

My research interests mainly include Multimedia Computing, Computer Vision and Pattern Recognition. I am particularly interested in the following topics: 

Current topics:  

(1) Autonomous and Controllable Large Models in Smart Education, including Smart Problem-Solving and Question Generation, Handwritten Answer Sheet Recognition, Digital Human Teaching and Topic Explanation, Automatic Data Annotation, and Automatic Grading of Subjective Questions (e.g., fill-in-the-blank and writing tasks) for various subjects. 

(2) Robust Vision Perception in Adverse Conditions: 1) Degraded Image Restoration and Enhancement: New Deep Architectures and Models for Degraded Image/Video Restoration (e.g., denoising, deraining, deblurring and dehazing), Low-Light Image Enhancement, and High-Level Task Driven Low-Level Vision; 2) Autonomous Driving Perception: BEV/OCC Prediction and Perception, High-Quality and Generalizable Autonomous Driving Data Generation, Lightweight Deployment and Real-Time Inference of Autonomous Driving Models. 

(3) Image/Video Generation, Digital Human and Deepfake Detection: New Deep Architectures and Models for Text-to-Image/Video Generation, Image-to-Video Generation, Automatic PPT Generation, Deepfake Detection, and Emotional Digital Human.  

(4) Model Compression and Deployment of DNNs: New Network Quantization Methods or Lightweight Architectures for High-level and Low-level Vision Tasks, with deployment on resource limited edge devices (e.g., mobile devices, robot vision and visual imaging system).  

Previous topics (Discontinued): 

(5) Deep Degraded Image Restoration and Enhancement: New Deep Architectures and Models for Degraded Image/Video Restoration (e.g., denoising, deraining, deblurring and dehazing), Low-Light Image Enhancement, and High-Level Task Driven Low-Level Vision, with emerging applications (e.g., autonomous driving, visual imaging systems, video surveillance and robot vision) in complex environments.  

(6) Low-Dimensional Modeling of High-Dimensional Data: Sparse Dictionary Learning, Low-Rank Coding, Concept Factorization, Manifold Learning, and Their Deep/Multilayer Extensions, for feature learning and extraction.   

(7) Semi-Supervised Classifier Modeling: Graph based Novel Label Propagation Algorithms, and Deep Semi-Supervised Learning, with application to image annotation and classification. 

PROJECT & FUNDING: 

AWARD & HONOR: 

COLLABORATORS: 

• Prof. Shuicheng Yan ( Sea AI Lab, Singapore; National University of Singapore; ACM/AAAI/IEEE/IAPR Fellow, Fellow of Singapore Academy of Engineering)

• Prof. Cheng-Lin Liu (Chinese Academy of Sciences; IEEE/IAPR Fellow)

• Prof. Tommy W.S. Chow (City University of Hong Kong; IEEE Fellow)

• Prof. Meng Wang (Hefei University of Technology, China; IEEE/IAPR Fellow)

• Prof. Richang Hong (Hefei University of Technology, China)

• Prof. Yi Yang (Zhejiang University, China; University of Technology Sydney, Australia)

• Prof. Mingliang Xu (Zhengzhou University, China)

• Prof. Guangcan Liu (School of Automation, Southeast University, China)

• Dr. Sheng Li (School of Data Science, University of Virginia, USA)

• Dr. Xiaojie Jin (Bytedance Research, USA)

• Prof. Haijun Zhang (Harbin Institute of Technology, Shenzhen, China)

• Prof. Mingbo Zhao (Donghua University, Shanghai, China)

• Dr. Jicong Fan (The Chinese University of Hong Kong, Shenzhen, China)

RESOURCE LINKS: 

• CCF Journal and Conference Ranking

• Machine Learning & AI Journal Rankings (by Guide2Research)

• Machine Learning & AI Conference Rankings (by Guide2Research)

• Google Scholar Metrics - Journal and Conference Ranking

• UCI Machine Learning (ML) Repository

• Papers With Code: The Latest Papers in Machine Learning and Computer Vision

• Reproducible Research in Computational Science:  Papers and Codes

• Digital Library, CS Journal & Conference List from LAMDA at Nanjing University