Hello! This is the GitHub space for the Vision Lab led by Professor Xinggang Wang. We are based at the Artificial Intelligence Institute, School of Electronic Information and Communications, Huazhong University of Science and Technology (HUST).
Our research focuses on computer vision and deep learning. We are particularly interested in:
- Multimodal Foundation Models
- Visual Representation Learning
- Object Detection, Segmentation, and Tracking
- End-to-end Autonomous Driving
- Novel Neural Architectures
Our group strives to push the boundaries of visual intelligence and has produced highly influential works in the field, including CCNet, Mask Scoring R-CNN, FairMOT, ByteTrack, EVA, MapTR, Vectorized Autonomous Driving (VAD), DiffusionDrive, Vision Mamba (Vim), 4D Gaussian Splatting (4DGS), YOLOS, YOLO-World, and LightningDiT & VA-VAE.
We actively contribute to the research community through publications and open-source projects.
- Research Collaboration: We are open to collaborations in our areas of interest. Please feel free to reach out to Prof. Xinggang Wang (xgwang # hust.edu.cn).
- Prospective Students: Our group has a strong track record of mentoring Ph.D. and Master's students who lead impactful publications. Interested students can find more information on Prof. Wang's faculty page.
- Using Our Code: You are welcome to explore and use the code in our repositories. Please ensure you cite the corresponding publications appropriately. Specific details can usually be found in the README files of individual repositories.
- Contributing to Projects: For guidelines on contributing to specific projects (e.g., bug reports, pull requests), please check the individual repositories.
- Prof. Wang's HUST Faculty Webpage: http://faculty.hust.edu.cn/xwang - Find Prof. Wang's official profile and contact information.
- Personal Homepage (also this GitHub Pages site): https://xwcv.github.io - Contains information on research, publications, and more.
- Google Scholar Profile: https://scholar.google.com/citations?hl=en&user=qNCTLV0AAAAJ&view_op=list_works - View a comprehensive list of publications and citations.
- Repositories: Explore the repositories within this GitHub organization for code and datasets related to our research publications.