79 citations · 218 across the 24 of their papers we have counts for
24 papers
Neural Gradient Regularizer
Shuang Xu, Yifan Wang, Zixiang Zhao +6
Owing to its significant success, the prior imposed on gradient maps has consistently been a subject of great interest in the field of image processing. Total variation (TV), one o…
CBA: Improving Online Continual Learning via Continual Bias Adaptor
Quanziang Wang, Renzhen Wang, Yichen Wu +2
Online continual learning (CL) aims to learn new knowledge and consolidate previously learned knowledge from non-stationary data streams. Due to the time-varying training setting,…
Masked Contrastive Graph Representation Learning for Age Estimation
Yuntao Shou, Xiangyong Cao, Deyu Meng
Age estimation of face images is a crucial task with various practical applications in areas such as video surveillance and Internet access control. While deep learning-based age e…
Restoring Images Captured in Arbitrary Hybrid Adverse Weather Conditions in One Go
Ye-Cong Wan, Ming-Wen Shao, Yuan-Shuo Cheng +2
Adverse conditions typically suffer from stochastic hybrid weather degradations (e.g., rainy and hazy night), while existing image restoration algorithms envisage that weather degr…
HIDFlowNet: A Flow-Based Deep Network for Hyperspectral Image Denoising
Qizhou Wang, Li Pang, Xiangyong Cao +2
Hyperspectral image (HSI) denoising is essentially ill-posed since a noisy HSI can be degraded from multiple clean HSIs. However, existing deep learning (DL)-based approaches only…
PanFlowNet: A Flow-Based Deep Network for Pan-sharpening
Gang Yang, Xiangyong Cao, Wenzhe Xiao +4
Pan-sharpening aims to generate a high-resolution multispectral (HRMS) image by integrating the spectral information of a low-resolution multispectral (LRMS) image with the texture…
Hierarchical Supervision and Shuffle Data Augmentation for 3D Semi-Supervised Object Detection
Chuandong Liu, Chenqiang Gao, Fangcen Liu +3
State-of-the-art 3D object detectors are usually trained on large-scale datasets with high-quality 3D annotations. However, such 3D annotations are often expensive and time-consumi…
Probability-based Global Cross-modal Upsampling for Pansharpening
Zeyu Zhu, Xiangyong Cao, Man Zhou +2
Pansharpening is an essential preprocessing step for remote sensing image processing. Although deep learning (DL) approaches performed well on this task, current upsampling methods…
DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion
Zixiang Zhao, Haowen Bai, Yuanzhi Zhu +7
Multi-modality image fusion aims to combine different modalities to produce fused images that retain the complementary features of each modality, such as functional highlights and…
Interactive Segmentation as Gaussian Process Classification
Minghao Zhou, Hong Wang, Qian Zhao +4
Click-based interactive segmentation (IS) aims to extract the target objects under user interaction. For this task, most of the current deep learning (DL)-based methods mainly foll…
Deep Diversity-Enhanced Feature Representation of Hyperspectral Images
Jinhui Hou, Zhiyu Zhu, Junhui Hou +3
In this paper, we study the problem of efficiently and effectively embedding the high-dimensional spatio-spectral information of hyperspectral (HS) images, guided by feature divers…
NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical Development Patterns of Preterm Infants
Chenyu Xue, Fan Wang, Yuanzhuo Zhu +4
Deploying reliable deep learning techniques in interdisciplinary applications needs learned models to output accurate and (even more importantly) explainable predictions. Existing…
Orientation-Shared Convolution Representation for CT Metal Artifact Learning
Hong Wang, Qi Xie, Yuexiang Li +3
During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts in the captured CT images and then impair the clinical trea…
Low-Rank Tensor Function Representation for Multi-Dimensional Data Recovery
Yisi Luo, Xile Zhao, Zhemin Li +2
Since higher-order tensors are naturally suitable for representing multi-dimensional data in real-world, e.g., color images and videos, low-rank tensor representation has become on…
Deep Fourier Up-Sampling
Man Zhou, Hu Yu, Jie Huang +5
Existing convolutional neural networks widely adopt spatial down-/up-sampling for multi-scale modeling. However, spatial up-sampling operators (\emph{e.g.}, interpolation, transpos…
Imbalanced Semi-supervised Learning with Bias Adaptive Classifier
Renzhen Wang, Xixi Jia, Quanziang Wang +2
Pseudo-labeling has proven to be a promising semi-supervised learning (SSL) paradigm. Existing pseudo-labeling methods commonly assume that the class distributions of training data…
Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction
Hong Wang, Yuexiang Li, Deyu Meng +1
Inspired by the great success of deep neural networks, learning-based methods have gained promising performances for metal artifact reduction (MAR) in computed tomography (CT) imag…
Relational Experience Replay: Continual Learning by Adaptively Tuning Task-wise Relationship
Quanziang Wang, Renzhen Wang, Yuexiang Li +4
Continual learning is a promising machine learning paradigm to learn new tasks while retaining previously learned knowledge over streaming training data. Till now, rehearsal-based…
InDuDoNet+: A Deep Unfolding Dual Domain Network for Metal Artifact Reduction in CT Images
Hong Wang, Yuexiang Li, Haimiao Zhang +2
During the computed tomography (CT) imaging process, metallic implants within patients often cause harmful artifacts, which adversely degrade the visual quality of reconstructed CT…
InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction
Hong Wang, Yuexiang Li, Haimiao Zhang +4
For the task of metal artifact reduction (MAR), although deep learning (DL)-based methods have achieved promising performances, most of them suffer from two problems: 1) the CT ima…
Unsupervised Local Discrimination for Medical Images
Huai Chen, Renzhen Wang, Xiuying Wang +7
Contrastive learning, which aims to capture general representation from unlabeled images to initialize the medical analysis models, has been proven effective in alleviating the hig…
Residual Moment Loss for Medical Image Segmentation
Quanziang Wang, Renzhen Wang, Yuexiang Li +3
Location information is proven to benefit the deep learning models on capturing the manifold structure of target objects, and accordingly boosts the accuracy of medical image segme…
Instance-based Vision Transformer for Subtyping of Papillary Renal Cell Carcinoma in Histopathological Image
Zeyu Gao, Bangyang Hong, Xianli Zhang +6
Histological subtype of papillary (p) renal cell carcinoma (RCC), type 1 vs. type 2, is an essential prognostic factor. The two subtypes of pRCC have a similar pattern, i.e., the p…
Nuclei Grading of Clear Cell Renal Cell Carcinoma in Histopathological Image by Composite High-Resolution Network
Zeyu Gao, Jiangbo Shi, Xianli Zhang +6
The grade of clear cell renal cell carcinoma (ccRCC) is a critical prognostic factor, making ccRCC nuclei grading a crucial task in RCC pathology analysis. Computer-aided nuclei gr…