Zihao Ye

Hi, I’m Zihao Ye (叶子皓), which means “Luminous Leaf” in the cultural context of China. I received my M.S. from Carnegie Mellon University, where I worked with Prof. Pradeep Ravikumar on tabular foundation models and structured representation learning.

Previously, I earned my B.S. through a joint program between QMUL and BUPT. During my undergraduate studies, I worked with Prof. Changjae Oh on computer vision and trustworthy AI.

My current research interests are representation learning theory and application, with a recent focus on structure-aware modeling for tabular and relational data. My previous experience can be found in my bio.

In addition to my research pursuits, I am also passionate about music. I play guitar 🎸 and sing.

news

Jun 7, 2024	A paper on reference-guided image filtering (de-raining) was accepted to ICIP 2024! 🎉✨

selected publications

2024

ICIP

Improving Image de-raining Models using Reference-guided Transformers

Zihao Ye, Jaehoon Cho, and Changjae Oh

IEEE International Conference on Image Processing, 2024

ABSTRACT arXiv PDF Blog Code Poster

Image de-raining is a critical task in computer vision to improve visibility and enhance the robustness of outdoor vision systems. While recent advances in de-raining meth- ods have achieved remarkable performance, the challenge remains to produce high-quality and visually pleasing de- rained results. In this paper, we present a reference-guided de-raining filter, a transformer network that enhances de- raining results using a reference clean image as guidance. We leverage the capabilities of the proposed module to further refine the images de-rained by existing methods. We vali- date our method on three datasets and show that our module can improve the performance of existing prior-based, CNN- based, and transformer-based approaches.