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20182025
most citedBlind Face Restoration via Deep Multi-scale Component Dictionaries

14 citations · 23 across the 8 of their papers we have counts for

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13 papers · 1 filter

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…

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.CV20233 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.CV20231 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…