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Aiwen Jiang - IEEE Xplore Author Profile
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Aiwen Jiang

Publications
10
Citations
211
Publications by Year
20182024
Co-Authors:
Show All Co-Authors (84)
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Aiwen Jiang

Affiliation

School of Digital Industry, Jiangxi Normal University, China

Publication Topics

Biography

Aiwen Jiang (Member, IEEE) was born in Jingdezhen, Jiangxi, China, in 1984. He received the B.S. degree in electronic engineering from the Nanjing University of Posts and Telecommunications, China, in 2005, and the Ph.D. degree in pattern recognition and intelligent system from the Institute of Automation, Chinese Academy of Sciences, China, in 2010.,Since 2010, he has been an Associate Professor with the School of Computer and Information Engineering, Jiangxi Normal University, China. From 2015 to 2016, he was a Visiting Researcher with NICTA, Australia National University. His research interests include computer vision and machine learning.(Based on document published on 17 March 2020).
Publications
10
Citations
211
Publications by Year
20182024
Co-Authors:
Show All Co-Authors (84)
Author's Published Works

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Dual-Path Coupled Image Deraining Network Via Spatial-Frequency Interaction

Yuhong He;Aiwen Jiang;Lingfang Jiang;Long Peng;Zhifeng Wang;Lu Wang

2024 IEEE International Conference on Image Processing (ICIP)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Transformers have recently emerged as a significant force in the field of image deraining. Existing image deraining methods utilize extensive research on self-attention. Though showcasing impressive results, they tend to neglect critical frequency information, as self-attention is generally less adept at capturing high-frequency details. To overcome this shortcoming, we have developed an innovativ...Show More

Dual-Path Coupled Image Deraining Network Via Spatial-Frequency Interaction

Yuhong He;Aiwen Jiang;Lingfang Jiang;Long Peng;Zhifeng Wang;Lu Wang

Year: 2024 | Conference Paper |

Music-driven Character Dance Video Generation based on Pre-trained Diffusion Model

Jiayu Xu;Changhong Liu;Juan Cai;Ji Ye;Zhenchun Lei;Aiwen Jiang

2024 International Joint Conference on Neural Networks (IJCNN)
Year: 2024 | Conference Paper |
Large-scale pre-trained models have shown significant progress in cross-modal generation tasks, especially in the text-to-image generation task. However, the pre-trained models for audio-guided video are rare. ControlNet [1] provides a new architecture to enhance the pre-trained diffusion models with task-specific conditions. Following the ControlNet [1] architecture, we propose a music-driven cha...Show More

Music-driven Character Dance Video Generation based on Pre-trained Diffusion Model

Jiayu Xu;Changhong Liu;Juan Cai;Ji Ye;Zhenchun Lei;Aiwen Jiang

Year: 2024 | Conference Paper |

Multi-Scale Cascaded Generator for Music-driven Dance Synthesis

Hao Hu;Changhong Liu;Yong Chen;Aiwen Jiang;Zhenchun Lei;Mingwen Wang

2022 International Joint Conference on Neural Networks (IJCNN)
Year: 2022 | Conference Paper |
Cited by: Papers (2)
Dance is a creative performance art and must keep coherent with the rhythm and style of music. To address these issues, most of the existing music-driven dance synthesis methods utilize deep generative models and capture the dynamic characteristics of dance motions. However, we observe that dance motions contain big-scale body part movements and small-scale joint movements that are mutually coordi...Show More

Multi-Scale Cascaded Generator for Music-driven Dance Synthesis

Hao Hu;Changhong Liu;Yong Chen;Aiwen Jiang;Zhenchun Lei;Mingwen Wang

Year: 2022 | Conference Paper |

Efficient and Accurate Multi-Scale Topological Network for Single Image Dehazing

Qiaosi Yi;Juncheng Li;Faming Fang;Aiwen Jiang;Guixu Zhang

IEEE Transactions on Multimedia
Year: 2022 | Volume: 24 | Journal Article |
Cited by: Papers (28)
Single image dehazing is a challenging ill-posed problem that has drawn significant attention in the last few years. Recently, convolutional neural networks have achieved great success in image dehazing. However, it is still difficult for these increasingly complex models to recover accurate details from the hazy image. In this paper, we pay attention to the feature extraction and utilization of t...Show More

Efficient and Accurate Multi-Scale Topological Network for Single Image Dehazing

Qiaosi Yi;Juncheng Li;Faming Fang;Aiwen Jiang;Guixu Zhang

Year: 2022 | Volume: 24 | Journal Article |

Making of Night Vision: Object Detection Under Low-Illumination

Yuxuan Xiao;Aiwen Jiang;Jihua Ye;Ming-Wen Wang

IEEE Access
Year: 2020 | Volume: 8 | Journal Article |
Cited by: Papers (55)
Object detection has so far achieved great success. However, almost all of current state-of-the-art methods focus on images with normal illumination, while object detection under low-illumination is often ignored. In this paper, we have extensively investigated several important issues related to the challenge low-illumination detection task, such as the importance of illumination on detection, th...Show More

Making of Night Vision: Object Detection Under Low-Illumination

Yuxuan Xiao;Aiwen Jiang;Jihua Ye;Ming-Wen Wang

Year: 2020 | Volume: 8 | Journal Article |

Progressive Back-Traced Dehazing Network Based on Multi-Resolution Recurrent Reconstruction

Qiaosi Yi;Aiwen Jiang;Juncheng Li;Jianyi Wan;Mingwen Wang

IEEE Access
Year: 2020 | Volume: 8 | Journal Article |
Cited by: Papers (3)
In order to alleviate adverse impacts of haze on high-level vision tasks, image dehazing attracts great attention from computer vision research field in recent years. Most of existing methods are grouped into physical prior based and non-physical data-driven based categories. However, image dehazing is a challenging ill-conditioned and inherently ambiguous problem. Due to random distribution and c...Show More

Progressive Back-Traced Dehazing Network Based on Multi-Resolution Recurrent Reconstruction

Qiaosi Yi;Aiwen Jiang;Juncheng Li;Jianyi Wan;Mingwen Wang

Year: 2020 | Volume: 8 | Journal Article |

Cumulative Rain Density Sensing Network for Single Image Derain

Long Peng;Aiwen Jiang;Qiaosi Yi;Mingwen Wang

IEEE Signal Processing Letters
Year: 2020 | Volume: 27 | Journal Article |
Cited by: Papers (20)
This paper focuses on single image derain, which aims to restore clear image from single rain image. Through full consideration of different frequency information preservation and the complicated interactions between rain-streaks and background, a novel end-to-end cumulative rain-density sensing network (CRDNet) is proposed for adaptive rain-streaks removal. An effective W-Net with powerful learni...Show More

Cumulative Rain Density Sensing Network for Single Image Derain

Long Peng;Aiwen Jiang;Qiaosi Yi;Mingwen Wang

Year: 2020 | Volume: 27 | Journal Article |

Static Crowd Scene Analysis via Deep Network with Multi-branch Dilated Convolution Blocks

Haoran Liu;Aiwen Jiang;Qiaosi Yi;Xiaolin Deng;Jianyi Wan;Mingwen Wang

2019 International Joint Conference on Neural Networks (IJCNN)
Year: 2019 | Conference Paper |
In this paper, we have proposed a static crowd scene analysis network via multi-branch dilated convolution block, called MDBNet. It focuses on a joint task of estimating crowd count and high-quality density map from static single image. The proposed MDBNet follows one-stage object detection framework, and consists of two parts: pre-trained convolutional layers as the front end for high-level featu...Show More

Static Crowd Scene Analysis via Deep Network with Multi-branch Dilated Convolution Blocks

Haoran Liu;Aiwen Jiang;Qiaosi Yi;Xiaolin Deng;Jianyi Wan;Mingwen Wang

Year: 2019 | Conference Paper |

Single Image Colorization Via Modified Cyclegan

Yuxuan Xiao;Aiwen Jiang;Changhong Liu;Mingwen Wang

2019 IEEE International Conference on Image Processing (ICIP)
Year: 2019 | Conference Paper |
Cited by: Papers (15)
In this paper, we focus on automatically colorizing single grayscale image without manual interventions. Most of existing methods tried to accurately restore unknown ground-truth colors and require paired training data for model optimization. However, the ideal restoration objective and strict training constraints limited their performance. Inspired by CycleGAN, we formulate the process of coloriz...Show More

Single Image Colorization Via Modified Cyclegan

Yuxuan Xiao;Aiwen Jiang;Changhong Liu;Mingwen Wang

Year: 2019 | Conference Paper |

NTIRE 2018 Challenge on Image Dehazing: Methods and Results

Cosmin Ancuti;Codruta O. Ancuti;Radu Timofte;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Vishal M. Patel;He Zhang;Vishwanath A. Sindagi;Ruhao Zhao;Xiaoping Ma;Yong Qin;Limin Jia;Klaus Friedel;Sehwan Ki;Hyeonjun Sim;Jae-Seok Choi;Sooye Kim;Soomin Seo;Saehun Kim;Munchurl Kim;Ranjan Mondal;Sanchayan Santra;Bhabatosh Chanda;Jinlin Liu;Kangfu Mei;Juncheng Li;Luyao;Faming Fang;Aiwen Jiang;Xiaochao Qu;Ting Liu;Pengfei Wang;Biao Sun;Jiangfan Deng;Yuhang Zhao;Ming Hong;Jingying Huang;Yizhi Chen;Erin Chen;Xiaoli Yu;Tingting Wu;Anil Genc;Deniz Engin;Hazim Kemal Ekenel;Wenzhe Liu;Tong Tong;Gen Li;Qinquan Gao;Zhan Li;Daofa Tang;Yuling Chen;Ziying Huo;Aitor Alvarez-Gila;Adrian Galdran;Alessandro Bria;Javier Vazquez-Corral;Marcelo Bertalmo;H. Seckin Demir;Omer Faruk Adil;Huynh Xuan Phung;Xin Jin;Jiale Chen;Chaowei Shan;Zhibo Chen

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2018 | Conference Paper |
Cited by: Papers (87)
This paper reviews the first challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The challenge had 2 tracks. Track 1 employed the indoor images (using I-HAZE dataset), while Track 2 outdoor images (using O-HAZE dataset). The hazy images have been captured in presence of real haze, generated by professional haze machines. I-HAZE data...Show More

NTIRE 2018 Challenge on Image Dehazing: Methods and Results

Cosmin Ancuti;Codruta O. Ancuti;Radu Timofte;Luc Van Gool;Lei Zhang;Ming-Hsuan Yang;Vishal M. Patel;He Zhang;Vishwanath A. Sindagi;Ruhao Zhao;Xiaoping Ma;Yong Qin;Limin Jia;Klaus Friedel;Sehwan Ki;Hyeonjun Sim;Jae-Seok Choi;Sooye Kim;Soomin Seo;Saehun Kim;Munchurl Kim;Ranjan Mondal;Sanchayan Santra;Bhabatosh Chanda;Jinlin Liu;Kangfu Mei;Juncheng Li;Luyao;Faming Fang;Aiwen Jiang;Xiaochao Qu;Ting Liu;Pengfei Wang;Biao Sun;Jiangfan Deng;Yuhang Zhao;Ming Hong;Jingying Huang;Yizhi Chen;Erin Chen;Xiaoli Yu;Tingting Wu;Anil Genc;Deniz Engin;Hazim Kemal Ekenel;Wenzhe Liu;Tong Tong;Gen Li;Qinquan Gao;Zhan Li;Daofa Tang;Yuling Chen;Ziying Huo;Aitor Alvarez-Gila;Adrian Galdran;Alessandro Bria;Javier Vazquez-Corral;Marcelo Bertalmo;H. Seckin Demir;Omer Faruk Adil;Huynh Xuan Phung;Xin Jin;Jiale Chen;Chaowei Shan;Zhibo Chen

Year: 2018 | Conference Paper |

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