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) Enterprise-Grade Vertical Industry Models and AI Agents, including Government Service AI Agent (e.g., Intelligent Official Document Processing), Enterprise Service AI Agent (e.g., Intelligent Inspection), Smart Education Industry AI Agent (e.g., Intelligent Learning Assistant and Teaching Resource Generation), Medical Imaging Industry AI Agent (e.g., Medical Imaging Diagnosis and Medical Report Generation), and Auto-Research. 

(2) Multimodal Computing and Robust Vision Perception: 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) Controllable AIGC and Deepfake Detection: New Deep Architectures and Models for Image/Video Generation and Editing (e.g., text2image, text2video and image2video), 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