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
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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.
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Aleth-NeRF: Low-light Condition View Synthesis with Concealing Fields
Ziteng Cui,
Lin Gu, Xiao Sun, Yu Qiao, Tatsuya Harada.
Arxiv, 2023  
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Blight NeRF's volume rendering function with concealing fields, to handle novel view synthesis under low-light conditions.
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Improving Fairness in Image Classification via Sketching
Ruichen Yao*,
Ziteng Cui*,
Xiaoxiao Li,
Lin Gu.
NeurIPS Workshop TSRML, 2022  
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Image-to-sketching may be an effective solution against image classification's unfairness, including both general scene and medical scene.
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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  
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A super light-weight (only 90k+ parameters) transformer-based network Illumination Adaptive Transformer, for real time image enhancement and exposure correction.
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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  
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Combination detection with self-supervised super-resolution, for robust detection under various degradation conditions (noise, blurry, low-resolution).
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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  
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Using Camera-ISP pipeline for low-light image synthetic, then using self-supervised learning to improving the performance of low-light condition object detection.
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