activity
20232025
most citedUni-COAL: A Unified Framework for Cross-Modality Synthesis and Super-Resolution of MR Images

2 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2025

ReactDiff: Latent Diffusion for Facial Reaction Generation

Jiaming Li, Sheng Wang, Xin Wang +4

Given the audio-visual clip of the speaker, facial reaction generation aims to predict the listener's facial reactions. The challenge lies in capturing the relevance between video…

eess.IV2024

Inter-slice Super-resolution of Magnetic Resonance Images by Pre-training and Self-supervised Fine-tuning

Xin Wang, Zhiyun Song, Yitao Zhu +4

In clinical practice, 2D magnetic resonance (MR) sequences are widely adopted. While individual 2D slices can be stacked to form a 3D volume, the relatively large slice spacing can…

cs.CV2024

Two-stage Cytopathological Image Synthesis for Augmenting Cervical Abnormality Screening

Zhenrong Shen, Manman Fei, Xin Wang +4

Automatic thin-prep cytologic test (TCT) screening can assist pathologists in finding cervical abnormality towards accurate and efficient cervical cancer diagnosis. Current automat…

eess.IV20232 cited

Uni-COAL: A Unified Framework for Cross-Modality Synthesis and Super-Resolution of MR Images

Zhiyun Song, Zengxin Qi, Xin Wang +11

Cross-modality synthesis (CMS), super-resolution (SR), and their combination (CMSR) have been extensively studied for magnetic resonance imaging (MRI). Their primary goals are to e…

cs.CV2023

MeLo: Low-rank Adaptation is Better than Fine-tuning for Medical Image Diagnosis

Yitao Zhu, Zhenrong Shen, Zihao Zhao +5

The common practice in developing computer-aided diagnosis (CAD) models based on transformer architectures usually involves fine-tuning from ImageNet pre-trained weights. However,…