Scholar
Zhi Chen
Google Scholar ID: 9ZypKEYAAAAJ
University of Southern Queensland
Citations & Impact
Citations
755
H-index
16
i10-index
20
Publications
20
Co-authors
11
list available
Publications
Resume (English only)
Academic Achievements
- First-author paper “Distribution Zero-Shot Learning for Visual Recognition” accepted by IEEE Transactions on Multimedia (JCR Q1, IF:9.7)
- First-author paper on Few-Shot Vision-Language Model Adaptation accepted by Pattern Recognition (JCR Q1, IF:7.6)
- First-author paper on Single-Image Editing accepted by Pattern Recognition (Special Issue on Beneficial Noise Learning)
- Co-developed the Global Wheat Full Semantic Organ Segmentation (GWFSS) Dataset, published in Plant Phenomics (JCR Q1, IF:6.4)
- Benchmark paper on video generative models for video retrieval accepted to ACM MM 2025 Dataset Track
- Paper on Test-Time Adaptation accepted to ACM MM 2025
- First-author paper on Zero-Shot Learning accepted to ICCV 2025
- Serving as Guest Editor for Complex & Intelligent Systems (JCR Q1) Special Issue on Efficient AI for Resource-Constrained and Complex Applications
- Paper on Continual Learning accepted to IJCAI 2025
- Paper on Continual Text-to-Video Retrieval accepted to SIGIR 2025
- Paper on Source-free Open-Set Domain Adaptation accepted to AAAI 2025
- Paper on Class-Agnostic Object Detection accepted to NeurIPS 2024
- Team solution “Coarse-to-Fine Prototype Refining Network” won 1st place in Point Cloud Completion and Reconstruction challenge at CVPPA@ECCV 2025
- Paper on In-the-wild Multimodal Disease Recognition accepted to ACM MM 2024
Background
- Currently Lecturer/Assistant Professor at the School of Mathematics, Physics and Computing, University of Southern Queensland (UniSQ), Australia
- Research focuses on developing generalizable and applicable machine learning and AI approaches
- Fundamental research primarily in computer vision, especially zero-shot learning
- Applied research mainly in AI for Agriculture and AI for Healthcare
- Actively recruiting highly motivated PhD students, Master’s students, research assistants, and visiting scholars in computer vision and related areas
