Published nearly 20 SCI/EI indexed papers in international journals and domestic and foreign academic conferences, one of which was selected as an ESI highly cited paper (top 1%). Representative papers include:
- Aerial video classification with Window Semantic Enhanced Video Transformers (SCI 1st zone)
- Enhanced facial image essence transfer via semantic guidance (SCI 1st zone)
- Dual-Branch Residual Network for Cross-Domain Few-Shot Hyperspectral Image Classification with Refined Prototype (SCI 3rd zone)
- Towards Student Actions in Classroom Scenes: New Dataset and Baseline (SCI 1st zone)
- Local Descriptors-based Rectification Network for Few-Shot Remote Sensing Scene Classification (SCI 2nd zone)
- HDSA-Net: Haze Density and Semantic Awareness Network for Hyperspectral Image Dehazing (SCI 2nd zone)
- Few-Shot Remote Sensing Scene Classification via Subspace based on Multiscale Feature Learning (SCI 2nd zone)
- Few-Shot Learning with Prototype Rectification for Cross-Domain Hyperspectral Image Classification (SCI 1st zone)
- Deep Updated Subspace Networks for Few-Shot Remote Sensing Scene Classification (SCI 1st zone)
- Distribution Preserving-based Deep Semi-NMF for Data Representation (SCI 2nd zone)
Research Experience
Currently leading over ten projects including talent projects of Chongqing University of Posts and Telecommunications, Chongqing Municipal Education Commission projects, Chongqing Science and Technology Commission projects, China Postdoctoral Science Foundation general funding projects, National Natural Science Foundation of China projects, Chongqing educational reform projects, and special funding projects of the Chongqing Postdoctoral Science Foundation.
Education
Graduated from Chongqing University, obtained a Ph.D. in Engineering.
Background
Qing Bai Ren, Wenfeng Associate Professor, Master's Supervisor. Long-term research in machine learning, pattern recognition, signal processing, image processing, and remote sensing images, recently focusing on graph neural networks, hypergraph learning, meta-learning, noisy label learning, and imbalanced data classification.