5 citations · 7 across the 6 of their papers we have counts for
6 papers
Mixture of Balanced Information Bottlenecks for Long-Tailed Visual Recognition
Yifan Lan, Xin Cai, Jun Cheng +1
Deep neural networks (DNNs) have achieved significant success in various applications with large-scale and balanced data. However, data in real-world visual recognition are usually…
Diffusion Priors for Variational Likelihood Estimation and Image Denoising
Jun Cheng, Shan Tan
Real-world noise removal is crucial in low-level computer vision. Due to the remarkable generation capabilities of diffusion models, recent attention has shifted towards leveraging…
Transfer CLIP for Generalizable Image Denoising
Jun Cheng, Dong Liang, Shan Tan
Image denoising is a fundamental task in computer vision. While prevailing deep learning-based supervised and self-supervised methods have excelled in eliminating in-distribution n…
Image Denoising via Style Disentanglement
Jingwei Niu, Jun Cheng, Shan Tan
Image denoising is a fundamental task in low-level computer vision. While recent deep learning-based image denoising methods have achieved impressive performance, they are black-bo…
Score Priors Guided Deep Variational Inference for Unsupervised Real-World Single Image Denoising
Jun Cheng, Tao Liu, Shan Tan
Real-world single image denoising is crucial and practical in computer vision. Bayesian inversions combined with score priors now have proven effective for single image denoising b…
Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising
Xuan Liu, Yaoqin Xie, Jun Cheng +3
Denoising low-dose computed tomography (CT) images is a critical task in medical image computing. Supervised deep learning-based approaches have made significant advancements in th…