Ziteng Cui (崔子藤)

I'm a Ph.D. student at The University of Tokyo, where I am supervised by Prof. Tatsuya Harada, before that I got my master's degree from Shanghai Jiao Tong University.

I mainly work on Computational Photography, Vision Robustness, and Vision Fairness.

Email  /  Google Scholar  /  Github

profile photo
Selected Publications

I'm now interested in the physics modeling of low-level tasks, and also interested in the neural radiance field. "*" means authors contribute equally.

Aleth-NeRF: Low-light Condition View Synthesis with Concealing Fields
Ziteng Cui, Lin Gu, Xiao Sun, Yu Qiao, Tatsuya Harada.
Arxiv, 2023  
website / arxiv / code / bibtex

Blight NeRF's volume rendering function with concealing fields, to handle novel view synthesis under low-light conditions.

Improving Fairness in Image Classification via Sketching
Ruichen Yao*, Ziteng Cui*, Xiaoxiao Li, Lin Gu.
NeurIPS Workshop TSRML, 2022  
arxiv / code / bibtex

Image-to-sketching may be an effective solution against image classification's unfairness, including both general scene and medical scene.

You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction
Ziteng Cui, Kunchang Li, Lin Gu, Shenghan Su, Peng Gao, Zhengkai Jiang, Yu Qiao, Tatsuya Harada.
BMVC, 2022  
website / arxiv / code / bibtex / demo / poster

A super light-weight (only 90k+ parameters) transformer-based network Illumination Adaptive Transformer, for real time image enhancement and exposure correction.

ECCV_Restoredet Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection
Ziteng Cui, Yingying Zhu, Lin Gu, Guo-Jun Qi, Xiaoxiao Li, Renrui Zhang, Zenghui Zhang, Tatsuya Harada.
ECCV, 2022  
arxiv / code / bibtex / poster

Combination detection with self-supervised super-resolution, for robust detection under various degradation conditions (noise, blurry, low-resolution).

ICCV_MAET Multitask AET with Orthogonal Tangent Regularity for Dark Object Detection
Ziteng Cui, Guo-Jun Qi, Lin Gu, Shaodi You, Zenghui Zhang, Tatsuya Harada.
ICCV, 2021  
arxiv / code / bibtex / poster

Using Camera-ISP pipeline for low-light image synthetic, then using self-supervised learning to improving the performance of low-light condition object detection.

SJTU Shanghai Jiao Tong University
1. National Scholarship
2. Excellent Graduate Student
Reviewer: ICCV 2023, BMVC 2023

This website is borrow from Jon Barron.
Also, consider using Leonid Keselman's Jekyll fork of this page.