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Jiacheng Li - IEEE Xplore Author Profile
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Jiacheng Li

Publications
9
Citations
37
Publications by Year
20222024
Co-Authors:
Show All Co-Authors (23)
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Jiacheng Li

Affiliation

University of Science and Technology of China (USTC), Hefei, China

Publication Topics

Biography

Jiacheng Li (Graduate Student Member, IEEE) received the BS degree in applied mathematics from Tongji University, in 2019. He is currently working toward the PhD degree with the University of Science and Technology of China. His research interests include computational photography, image processing, and visual content synthesis.(Based on document published on 14 May 2024).
Publications
9
Citations
37
Publications by Year
20222024
Co-Authors:
Show All Co-Authors (23)
Author's Published Works

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In-Loop Filtering via Trained Look-Up Tables

Zhuoyuan Li;Jiacheng Li;Yao Li;Li Li;Dong Liu;Feng Wu

2024 IEEE International Conference on Visual Communications and Image Processing (VCIP)
Year: 2024 | Conference Paper |
In-loop filtering (ILF) is a key technology in image/video coding for reducing the artifacts. Recently, neural network-based in-loop filtering methods achieve remarkable coding gains beyond the capability of advanced video coding standards, establishing themselves a promising candidate tool for future standards. However, the utilization of deep neural networks (DNN) brings high computational compl...Show More

In-Loop Filtering via Trained Look-Up Tables

Zhuoyuan Li;Jiacheng Li;Yao Li;Li Li;Dong Liu;Feng Wu

Year: 2024 | Conference Paper |

Look-Up Table Compression for Efficient Image Restoration

Yinglong Li;Jiacheng Li;Zhiwei Xiong

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Look-Up Table (LUT) has recently gained increasing at-tention for restoring High-Quality (HQ) images from Low-Quality (LQ) observations, thanks to its high computational efficiency achieved through a “space for time” strategy of caching learned LQ-HQ pairs. However, incorporating multiple LUTs for improved performance comes at the cost of a rapidly growing storage size, which is ultimately re-stri...Show More

Look-Up Table Compression for Efficient Image Restoration

Yinglong Li;Jiacheng Li;Zhiwei Xiong

Year: 2024 | Conference Paper |

Toward DNN of LUTs: Learning Efficient Image Restoration With Multiple Look-Up Tables

Jiacheng Li;Chang Chen;Zhen Cheng;Zhiwei Xiong

IEEE Transactions on Pattern Analysis and Machine Intelligence
Year: 2024 | Volume: 46, Issue: 12 | Journal Article |
Cited by: Papers (3)
The widespread usage of high-definition screens on edge devices stimulates a strong demand for efficient image restoration algorithms. The way of caching deep learning models in a look-up table (LUT) is recently introduced to respond to this demand. However, the size of a single LUT grows exponentially with the increase of its indexing capacity, which restricts its receptive field and thus the per...Show More

Toward DNN of LUTs: Learning Efficient Image Restoration With Multiple Look-Up Tables

Jiacheng Li;Chang Chen;Zhen Cheng;Zhiwei Xiong

Year: 2024 | Volume: 46, Issue: 12 | Journal Article |

Region-Aware Portrait Retouching With Sparse Interactive Guidance

Huimin Zeng;Jie Huang;Jiacheng Li;Zhiwei Xiong

IEEE Transactions on Multimedia
Year: 2024 | Volume: 26 | Journal Article |
Cited by: Papers (1)
Portrait retouching aims to improve the aesthetic quality of input portrait photos and especially requires human-region priority. The deep learning-based methods largely elevate the retouching efficiency and provide promising retouched results. However, existing portrait retouching methods focus on automatic retouching, which treats all human-regions equally and ignores users' preferences for spec...Show More

Region-Aware Portrait Retouching With Sparse Interactive Guidance

Huimin Zeng;Jie Huang;Jiacheng Li;Zhiwei Xiong

Year: 2024 | Volume: 26 | Journal Article |

An End-to-End Infrared and Visual Fusion Detection Framework for Near-Surface Defect in Moving Mode

Jiacheng Li;Bin Gao;Jieyi Xu;Lei Liu;Yu Zeng;Gaige Ru;Gui Yun Tian

2023 IEEE Far East NDT New Technology & Application Forum (FENDT)
Year: 2023 | Conference Paper |
Traditional infrared thermal testing (IRT) is susceptible to surface conditions such as variation of surface emissivity, which may lead to false detection and has high requirements for the flatness and cleanliness of the sample. Traditional visual testing (VT) could capture surface texture information with high resolution but lacks the ability to detect sub-surface defects. This paper proposes a m...Show More

An End-to-End Infrared and Visual Fusion Detection Framework for Near-Surface Defect in Moving Mode

Jiacheng Li;Bin Gao;Jieyi Xu;Lei Liu;Yu Zeng;Gaige Ru;Gui Yun Tian

Year: 2023 | Conference Paper |

Style Projected Clustering for Domain Generalized Semantic Segmentation

Wei Huang;Chang Chen;Yong Li;Jiacheng Li;Cheng Li;Fenglong Song;Youliang Yan;Zhiwei Xiong

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (16)
Existing semantic segmentation methods improve generalization capability, by regularizing various images to a canonical feature space. While this process contributes to generalization, it weakens the representation inevitably. In contrast to existing methods, we instead utilize the difference between images to build a better representation space, where the distinct style features are extracted and...Show More

Style Projected Clustering for Domain Generalized Semantic Segmentation

Wei Huang;Chang Chen;Yong Li;Jiacheng Li;Cheng Li;Fenglong Song;Youliang Yan;Zhiwei Xiong

Year: 2023 | Conference Paper |

Learning Steerable Function for Efficient Image Resampling

Jiacheng Li;Chang Chen;Wei Huang;Zhiqiang Lang;Fenglong Song;Youliang Yan;Zhiwei Xiong

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (5)
Image resampling is a basic technique that is widely employed in daily applications. Existing deep neural networks (DNNs) have made impressive progress in resampling performance. Yet these methods are still not the perfect substitute for interpolation, due to the issues of efficiency and continuous resampling. In this work, we propose a novel method of Learning Resampling Function (termed LeRF), w...Show More

Learning Steerable Function for Efficient Image Resampling

Jiacheng Li;Chang Chen;Wei Huang;Zhiqiang Lang;Fenglong Song;Youliang Yan;Zhiwei Xiong

Year: 2023 | Conference Paper |

Contextual Outpainting with Object-Level Contrastive Learning

Jiacheng Li;Chang Chen;Zhiwei Xiong

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2022 | Conference Paper |
Cited by: Papers (7)
We study the problem of contextual outpainting, which aims to hallucinate the missing background contents based on the remaining foreground contents. Existing image outpainting methods focus on completing object shapes or extending existing scenery textures, neglecting the semantically meaningful relationship between the missing and remaining contents. To explore the semantic cues provided by the ...Show More

Contextual Outpainting with Object-Level Contrastive Learning

Jiacheng Li;Chang Chen;Zhiwei Xiong

Year: 2022 | Conference Paper |

Reference-Guided Landmark Image Inpainting With Deep Feature Matching

Jiacheng Li;Zhiwei Xiong;Dong Liu

IEEE Transactions on Circuits and Systems for Video Technology
Year: 2022 | Volume: 32, Issue: 12 | Journal Article |
Cited by: Papers (5)
Despite impressive progress made by recent image inpainting methods, they often fail to predict the original content when the corrupted region contains unique structures, especially for landmark images. Applying similar images as a reference is helpful but introduces a style gap of textures, resulting in color misalignment. To this end, we propose a style-robust approach for reference-guided landm...Show More

Reference-Guided Landmark Image Inpainting With Deep Feature Matching

Jiacheng Li;Zhiwei Xiong;Dong Liu

Year: 2022 | Volume: 32, Issue: 12 | Journal Article |

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