papers

Publications (6)

cs.CV2024

UNIC-Adapter: Unified Image-instruction Adapter with Multi-modal Transformer for Image Generation

Lunhao Duan, Shanshan Zhao, Wenjun Yan +7

Recently, text-to-image generation models have achieved remarkable advancements, particularly with diffusion models facilitating high-quality image synthesis from textual descripti…

cs.IT2020

Convolutional Neural Networks for Space-Time Block Coding Recognition

Wenjun Yan, Qing Ling, Limin Zhang

We apply the latest advances in machine learning with deep neural networks to the tasks of radio modulation recognition, channel coding recognition, and spectrum monitoring. This p…

physics.med-ph2024

Insights into Polycrystalline Microstructure of Blood Films with 3D Mueller Matrix Imaging Approach

Volodimyr A. Ushenko, Anton Sdobnov, Liliya Trifonyuk +10

We introduce a 3D Mueller Matrix (MM) image reconstruction technique using digital holographic approach for the layer-by-layer profiling thin films with polycrystalline structures,…

cs.CV2018

Left Ventricle Segmentation via Optical-Flow-Net from Short-axis Cine MRI: Preserving the Temporal Coherence of Cardiac Motion

Wenjun Yan, Yuanyuan Wang, Zeju Li +2

Quantitative assessment of left ventricle (LV) function from cine MRI has significant diagnostic and prognostic value for cardiovascular disease patients. The temporal movement of…

eess.IV2019

The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GAN

Wenjun Yan, Yuanyuan Wang, Shengjia Gu +4

Convolutional neural network (CNN), in particular the Unet, is a powerful method for medical image segmentation. To date Unet has demonstrated state-of-art performance in many comp…

cs.CV2022

Super-Resolution Domain Adaptation Networks for Semantic Segmentation via Pixel and Output Level Aligning

Junfeng Wu, Zhenjie Tang, Congan Xu +3

Recently, Unsupervised Domain Adaptation (UDA) has attracted increasing attention to address the domain shift problem in the semantic segmentation task. Although previous UDA metho…