activity
20182025
most citedShift-Net: Image Inpainting via Deep Feature Rearrangement

46 citations · 108 across the 15 of their papers we have counts for

collaborators

16 papers

cs.CV2025

2D Gaussian Splatting with Semantic Alignment for Image Inpainting

Hongyu Li, Chaofeng Chen, Xiaoming Li +1

Gaussian Splatting (GS), a recent technique for converting discrete points into continuous spatial representations, has shown promising results in 3D scene modeling and 2D image su…

eess.IV2024

REHRSeg: Unleashing the Power of Self-Supervised Super-Resolution for Resource-Efficient 3D MRI Segmentation

Zhiyun Song, Yinjie Zhao, Xiaomin Li +9

High-resolution (HR) 3D magnetic resonance imaging (MRI) can provide detailed anatomical structural information, enabling precise segmentation of regions of interest for various me…

cs.CV2023

MetaF2N: Blind Image Super-Resolution by Learning Efficient Model Adaptation from Faces

Zhicun Yin, Ming Liu, Xiaoming Li +3

Due to their highly structured characteristics, faces are easier to recover than natural scenes for blind image super-resolution. Therefore, we can extract the degradation represen…

cs.CV2023

Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models

Minheng Ni, Yabo Zhang, Kailai Feng +3

Zero-shot referring image segmentation is a challenging task because it aims to find an instance segmentation mask based on the given referring descriptions, without training on th…

cs.CV2023★ 3 cited

VQ-Font: Few-Shot Font Generation with Structure-Aware Enhancement and Quantization

Mingshuai Yao, Yabo Zhang, Xianhui Lin +2

Few-shot font generation is challenging, as it needs to capture the fine-grained stroke styles from a limited set of reference glyphs, and then transfer to other characters, which…

cs.CV2023★ 1 cited

Learning Generative Structure Prior for Blind Text Image Super-resolution

Xiaoming Li, Wangmeng Zuo, Chen Change Loy

Blind text image super-resolution (SR) is challenging as one needs to cope with diverse font styles and unknown degradation. To address the problem, existing methods perform charac…

cs.CV2022

Semantic-shape Adaptive Feature Modulation for Semantic Image Synthesis

Zhengyao Lv, Xiaoming Li, Zhenxing Niu +2

Recent years have witnessed substantial progress in semantic image synthesis, it is still challenging in synthesizing photo-realistic images with rich details. Most previous method…

cs.CV2022

Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors

Chaofeng Chen, Xinyu Shi, Yipeng Qin +4

A key challenge of real-world image super-resolution (SR) is to recover the missing details in low-resolution (LR) images with complex unknown degradations (e.g., downsampling, noi…

cs.CV2021★ 4 cited

Learning Semantic Person Image Generation by Region-Adaptive Normalization

Zhengyao Lv, Xiaoming Li, Xin Li +4

Human pose transfer has received great attention due to its wide applications, yet is still a challenging task that is not well solved. Recent works have achieved great success to…

cs.CV2020★ 14 cited

Progressive Semantic-Aware Style Transformation for Blind Face Restoration

Chaofeng Chen, Xiaoming Li, Lingbo Yang +3

Face restoration is important in face image processing, and has been widely studied in recent years. However, previous works often fail to generate plausible high quality (HQ) resu…

cs.CV2020★ 14 cited

Blind Face Restoration via Deep Multi-scale Component Dictionaries

Xiaoming Li, Chaofeng Chen, Shangchen Zhou +3

Recent reference-based face restoration methods have received considerable attention due to their great capability in recovering high-frequency details on real low-quality images.…

cs.CV2020

Face Super-Resolution Guided by 3D Facial Priors

Xiaobin Hu, Wenqi Ren, John LaMaster +5

State-of-the-art face super-resolution methods employ deep convolutional neural networks to learn a mapping between low- and high- resolution facial patterns by exploring local app…

cs.CV2018★ 2 cited

Learning Symmetry Consistent Deep CNNs for Face Completion

Xiaoming Li, Ming Liu, Jieru Zhu +4

Deep convolutional networks (CNNs) have achieved great success in face completion to generate plausible facial structures. These methods, however, are limited in maintaining global…

cs.CV2018★ 15 cited

Identity Preserving Face Completion for Large Ocular Region Occlusion

Yajie Zhao, Weikai Chen, Jun Xing +5

We present a novel deep learning approach to synthesize complete face images in the presence of large ocular region occlusions. This is motivated by recent surge of VR/AR displays…

cs.CV2018★ 9 cited

Learning Warped Guidance for Blind Face Restoration

Xiaoming Li, Ming Liu, Yuting Ye +3

This paper studies the problem of blind face restoration from an unconstrained blurry, noisy, low-resolution, or compressed image (i.e., degraded observation). For better recovery…

cs.CV2018★ 46 cited

Shift-Net: Image Inpainting via Deep Feature Rearrangement

Zhaoyi Yan, Xiaoming Li, Mu Li +2

Deep convolutional networks (CNNs) have exhibited their potential in image inpainting for producing plausible results. However, in most existing methods, e.g., context encoder, the…