9.2k citations · 12.1k across the 87 of their papers we have counts for
15 papers · 1 filter
TSGCNet: Discriminative Geometric Feature Learning with Two-Stream GraphConvolutional Network for 3D Dental Model Segmentation
Lingming Zhang, Yue Zhao, Deyu Meng +5
The ability to segment teeth precisely from digitized 3D dental models is an essential task in computer-aided orthodontic surgical planning. To date, deep learning based methods ha…
Unsupervised Learning of Local Discriminative Representation for Medical Images
Huai Chen, Jieyu Li, Renzhen Wang +5
Local discriminative representation is needed in many medical image analysis tasks such as identifying sub-types of lesion or segmenting detailed components of anatomical structure…
Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction
Ziyi Yang, Jun Shu, Yong Liang +2
Current machine learning has made great progress on computer vision and many other fields attributed to the large amount of high-quality training samples, while it does not work ve…
Meta Feature Modulator for Long-tailed Recognition
Renzhen Wang, Kaiqin Hu, Yanwen Zhu +3
Deep neural networks often degrade significantly when training data suffer from class imbalance problems. Existing approaches, e.g., re-sampling and re-weighting, commonly address…
Learning to Purify Noisy Labels via Meta Soft Label Corrector
Yichen Wu, Jun Shu, Qi Xie +2
Recent deep neural networks (DNNs) can easily overfit to biased training data with noisy labels. Label correction strategy is commonly used to alleviate this issue by designing a m…
Deep Variational Network Toward Blind Image Restoration
Zongsheng Yue, Hongwei Yong, Qian Zhao +3
Blind image restoration (IR) is a common yet challenging problem in computer vision. Classical model-based methods and recent deep learning (DL)-based methods represent two differe…
From Rain Generation to Rain Removal
Hong Wang, Zongsheng Yue, Qi Xie +3
For the single image rain removal (SIRR) task, the performance of deep learning (DL)-based methods is mainly affected by the designed deraining models and training datasets. Most o…
Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution
Jing Yao, Danfeng Hong, Jocelyn Chanussot +3
The recent advancement of deep learning techniques has made great progress on hyperspectral image super-resolution (HSI-SR). Yet the development of unsupervised deep networks remai…
Dual Adversarial Network: Toward Real-world Noise Removal and Noise Generation
Zongsheng Yue, Qian Zhao, Lei Zhang +1
Real-world image noise removal is a long-standing yet very challenging task in computer vision. The success of deep neural network in denoising stimulates the research of noise gen…
MLR-SNet: Transferable LR Schedules for Heterogeneous Tasks
Jun Shu, Yanwen Zhu, Qian Zhao +2
The learning rate (LR) is one of the most important hyper-parameters in stochastic gradient descent (SGD) algorithm for training deep neural networks (DNN). However, current hand-d…
Meta Transition Adaptation for Robust Deep Learning with Noisy Labels
Jun Shu, Qian Zhao, Zongben Xu +1
To discover intrinsic inter-class transition probabilities underlying data, learning with noise transition has become an important approach for robust deep learning on corrupted la…
Structural Residual Learning for Single Image Rain Removal
Hong Wang, Yichen Wu, Qi Xie +3
To alleviate the adverse effect of rain streaks in image processing tasks, CNN-based single image rain removal methods have been recently proposed. However, the performance of thes…
A Model-driven Deep Neural Network for Single Image Rain Removal
Hong Wang, Qi Xie, Qian Zhao +1
Deep learning (DL) methods have achieved state-of-the-art performance in the task of single image rain removal. Most of current DL architectures, however, are still lack of suffici…
LT-Net: Label Transfer by Learning Reversible Voxel-wise Correspondence for One-shot Medical Image Segmentation
Shuxin Wang, Shilei Cao, Dong Wei +5
We introduce a one-shot segmentation method to alleviate the burden of manual annotation for medical images. The main idea is to treat one-shot segmentation as a classical atlas-ba…
Learning Adaptive Loss for Robust Learning with Noisy Labels
Jun Shu, Qian Zhao, Keyu Chen +2
Robust loss minimization is an important strategy for handling robust learning issue on noisy labels. Current robust loss functions, however, inevitably involve hyperparameter(s) t…