9.2k citations · 12.1k across the 87 of their papers we have counts for
89 papers
A Low-rank Matching Attention based Cross-modal Feature Fusion Method for Conversational Emotion Recognition
Yuntao Shou, Huan Liu, Xiangyong Cao +2
Conversational emotion recognition (CER) is an important research topic in human-computer interactions. {Although recent advancements in transformer-based cross-modal fusion method…
T-former: An Efficient Transformer for Image Inpainting
Ye Deng, Siqi Hui, Sanping Zhou +2
Benefiting from powerful convolutional neural networks (CNNs), learning-based image inpainting methods have made significant breakthroughs over the years. However, some nature of C…
DAC-MR: Data Augmentation Consistency Based Meta-Regularization for Meta-Learning
Jun Shu, Xiang Yuan, Deyu Meng +1
Meta learning recently has been heavily researched and helped advance the contemporary machine learning. However, achieving well-performing meta-learning model requires a large amo…
Unsupervised Hyperspectral Pansharpening via Low-rank Diffusion Model
Xiangyu Rui, Xiangyong Cao, Li Pang +3
Hyperspectral pansharpening is a process of merging a high-resolution panchromatic (PAN) image and a low-resolution hyperspectral (LRHS) image to create a single high-resolution hy…
Random Weights Networks Work as Loss Prior Constraint for Image Restoration
Man Zhou, Naishan Zheng, Jie Huang +5
In this paper, orthogonal to the existing data and model studies, we instead resort our efforts to investigate the potential of loss function in a new perspective and present our b…
Regularize implicit neural representation by itself
Zhemin Li, Hongxia Wang, Deyu Meng
This paper proposes a regularizer called Implicit Neural Representation Regularizer (INRR) to improve the generalization ability of the Implicit Neural Representation (INR). The IN…
Guaranteed Tensor Recovery Fused Low-rankness and Smoothness
Hailin Wang, Jiangjun Peng, Wenjin Qin +2
The tensor data recovery task has thus attracted much research attention in recent years. Solving such an ill-posed problem generally requires to explore intrinsic prior structures…
Improve Noise Tolerance of Robust Loss via Noise-Awareness
Kehui Ding, Jun Shu, Deyu Meng +1
Robust loss minimization is an important strategy for handling robust learning issue on noisy labels. Current approaches for designing robust losses involve the introduction of noi…
A Data-driven Loss Weighting Scheme across Heterogeneous Tasks for Image Denoising
Xiangyu Rui, Xiangyong Cao, Xile Zhao +2
In a variational denoising model, weight in the data fidelity term plays the role of enhancing the noise-removal capability. It is profoundly correlated with noise information, whi…
S2S-WTV: Seismic Data Noise Attenuation Using Weighted Total Variation Regularized Self-Supervised Learning
Zitai Xu, Yisi Luo, Bangyu Wu +1
Seismic data often undergoes severe noise due to environmental factors, which seriously affects subsequent applications. Traditional hand-crafted denoisers such as filters and regu…
Fast Noise Removal in Hyperspectral Images via Representative Coefficient Total Variation
Jiangjun Peng, Hailin Wang, Xiangyong Cao +3
Mining structural priors in data is a widely recognized technique for hyperspectral image (HSI) denoising tasks, whose typical ways include model-based methods and data-based metho…
A Learnable Optimization and Regularization Approach to Massive MIMO CSI Feedback
Zhengyang Hu, Guanzhang Liu, Qi Xie +3
Channel state information (CSI) plays a critical role in achieving the potential benefits of massive multiple input multiple output (MIMO) systems. In frequency division duplex (FD…
KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution
Jiahong Fu, Hong Wang, Qi Xie +3
Although current deep learning-based methods have gained promising performance in the blind single image super-resolution (SISR) task, most of them mainly focus on heuristically co…
Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel Fusion
Danfeng Hong, Jing Yao, Deyu Meng +2
Enormous efforts have been recently made to super-resolve hyperspectral (HS) images with the aid of high spatial resolution multispectral (MS) images. Most prior works usually perf…
Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation
Yue Zhao, Lingming Zhang, Yang Liu +6
Precise segmentation of teeth from intra-oral scanner images is an essential task in computer-aided orthodontic surgical planning. The state-of-the-art deep learning-based methods…
Diagnosing Batch Normalization in Class Incremental Learning
Minghao Zhou, Quanziang Wang, Jun Shu +2
Extensive researches have applied deep neural networks (DNNs) in class incremental learning (Class-IL). As building blocks of DNNs, batch normalization (BN) standardizes intermedia…
Low-light Image Enhancement by Retinex Based Algorithm Unrolling and Adjustment
Xinyi Liu, Qi Xie, Qian Zhao +2
Motivated by their recent advances, deep learning techniques have been widely applied to low-light image enhancement (LIE) problem. Among which, Retinex theory based ones, mostly f…
CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning
Jun Shu, Xiang Yuan, Deyu Meng +1
Modern deep neural networks can easily overfit to biased training data containing corrupted labels or class imbalance. Sample re-weighting methods are popularly used to alleviate t…
Exact Decomposition of Joint Low Rankness and Local Smoothness Plus Sparse Matrices
Jiangjun Peng, Yao Wang, Hongying Zhang +2
It is known that the decomposition in low-rank and sparse matrices (\textbf{L+S} for short) can be achieved by several Robust PCA techniques. Besides the low rankness, the local sm…
Variational Inverting Network for Statistical Inverse Problems of Partial Differential Equations
Junxiong Jia, Yanni Wu, Peijun Li +1
To quantify uncertainties in inverse problems of partial differential equations (PDEs), we formulate them into statistical inference problems using Bayes' formula. Recently, well-j…
Local Patch Network with Global Attention for Infrared Small Target Detection
Fang Chen, Chenqiang Gao, Fangcen Liu +4
Infrared small target detection plays an important role in the infrared search and tracking applications. In recent years, deep learning techniques were introduced to this task and…
Infrared Small-Dim Target Detection with Transformer under Complex Backgrounds
Fangcen Liu, Chenqiang Gao, Fang Chen +3
The infrared small-dim target detection is one of the key techniques in the infrared search and tracking system. Since the local regions similar to infrared small-dim targets sprea…
Fourier Series Expansion Based Filter Parametrization for Equivariant Convolutions
Qi Xie, Qian Zhao, Zongben Xu +1
It has been shown that equivariant convolution is very helpful for many types of computer vision tasks. Recently, the 2D filter parametrization technique plays an important role wh…
RCDNet: An Interpretable Rain Convolutional Dictionary Network for Single Image Deraining
Hong Wang, Qi Xie, Qian Zhao +4
As a common weather, rain streaks adversely degrade the image quality. Hence, removing rains from an image has become an important issue in the field. To handle such an ill-posed s…