1 code implementation • 15 May 2024 • Jiamei Xiong, Xuefeng Yan, Yongzhen Wang, Wei Zhao, Xiao-Ping Zhang, Mingqiang Wei
Since remote sensing images contain extensive small-scale texture structures, it is important to effectively restore image details from hazy images.
1 code implementation • 7 Mar 2024 • Yihua Fan, Yongzhen Wang, Mingqiang Wei, Fu Lee Wang, Haoran Xie
In this paper, we raise an intriguing question: can the combination of image restoration and object detection enhance detection performance in adverse weather conditions?
no code implementations • 17 Jan 2024 • Qinghua Huang, Yongzhen Wang
Knowledge graph (KG) based reasoning has been regarded as an effective means for the analysis of semantic networks and is of great usefulness in areas of information retrieval, recommendation, decision-making, and man-machine interaction.
no code implementations • 19 Jul 2023 • Ming Tong, Xuefeng Yan, Yongzhen Wang
Therefore, we propose an Uncertainty-Driven Multi-Scale Feature Fusion Network (UMFFNet) that learns the probability mapping distribution between paired images to estimate uncertainty.
1 code implementation • 31 Mar 2023 • Yongzhen Wang, Xuefeng Yan, Yanbiao Niu, Lina Gong, Yanwen Guo, Mingqiang Wei
In this study, we propose an effective image deraining paradigm for Mixture of rain REmoval, called DEMore-Net, which takes full account of the MOR effect.
no code implementations • 23 Jan 2023 • Yiyang Shen, Mingqiang Wei, Yongzhen Wang, Xueyang Fu, Jing Qin
Recent diffusion models have exhibited great potential in generative modeling tasks.
no code implementations • 29 Oct 2022 • Zhiheng Hu, Yongzhen Wang, Peng Li, Jie Qin, Haoran Xie, Mingqiang Wei
First, to maintain small targets in deep layers, we develop a multi-scale nested interaction module to explore a wide range of context information.
no code implementations • 28 Oct 2022 • Ming Tong, Yongzhen Wang, Peng Cui, Xuefeng Yan, Mingqiang Wei
Semi-UFormer can well leverage both the real-world hazy images and their uncertainty guidance information.
1 code implementation • 3 Sep 2022 • Yongzhen Wang, Xuefeng Yan, Kaiwen Zhang, Lina Gong, Haoran Xie, Fu Lee Wang, Mingqiang Wei
Adverse weather conditions such as haze, rain, and snow often impair the quality of captured images, causing detection networks trained on normal images to generalize poorly in these scenarios.
1 code implementation • 2 Sep 2022 • Jie Wang, Yongzhen Wang, Yidan Feng, Lina Gong, Xuefeng Yan, Haoran Xie, Fu Lee Wang, Mingqiang Wei
Image smoothing is a fundamental low-level vision task that aims to preserve salient structures of an image while removing insignificant details.
1 code implementation • 4 May 2022 • Yongzhen Wang, Xuefeng Yan, Fu Lee Wang, Haoran Xie, Wenhan Yang, Mingqiang Wei, Jing Qin
From a different yet new perspective, this paper explores contrastive learning with an adversarial training effort to leverage unpaired real-world hazy and clean images, thus bridging the gap between synthetic and real-world haze is avoided.
1 code implementation • 28 Apr 2022 • Yiyang Shen, Yongzhen Wang, Mingqiang Wei, Honghua Chen, Haoran Xie, Gary Cheng, Fu Lee Wang
Rain is one of the most common weather which can completely degrade the image quality and interfere with the performance of many computer vision tasks, especially under heavy rain conditions.
1 code implementation • 6 Apr 2022 • Yiyang Shen, Mingqiang Wei, Sen Deng, Wenhan Yang, Yongzhen Wang, Xiao-Ping Zhang, Meng Wang, Jing Qin
To bridge the two domain gaps, we propose a semi-supervised detail-recovery image deraining network (Semi-DRDNet) with dual sample-augmented contrastive learning.
no code implementations • 29 Apr 2021 • Yongzhen Wang, Xiaozhong Liu, Katy Börner, Jun Lin, Yingnan Ju, Changlong Sun, Luo Si
Objective: Ubiquitous internet access is reshaping the way we live, but it is accompanied by unprecedented challenges in preventing chronic diseases that are usually planted by long exposure to unhealthy lifestyles.
no code implementations • COLING 2020 • Sheng Bi, Xiya Cheng, Yuan-Fang Li, Yongzhen Wang, Guilin Qi
Question generation over knowledge bases (KBQG) aims at generating natural-language questions about a subgraph, i. e. a set of (connected) triples.
no code implementations • 7 Oct 2020 • Xiya Cheng, Sheng Bi, Guilin Qi, Yongzhen Wang
In this paper, we propose a knowledge-attentive neural network model, which introduces legal schematic knowledge about charges and exploit the knowledge hierarchical representation as the discriminative features to differentiate confusing charges.
no code implementations • 24 Jun 2020 • Guoqing Zhu, Naga Anjaneyulu Kopalle, Yongzhen Wang, Xiaozhong Liu, Kemi Jona, Katy Börner
How does your education impact your professional career?
1 code implementation • EMNLP 2018 • Yongzhen Wang, Xiaozhong Liu, Zheng Gao
Conventional solutions to automatic related work summarization rely heavily on human-engineered features.