53 citations · 147 across the 18 of their papers we have counts for
19 papers
MSTF-Net: A UAV-Oriented Multi-Spectral Video Segmentation Method via Modality-Robust, Scale-Adaptive, and Consistent Fusion
Chenwei Wang, Zhida Wang, Zelin Li +2
Multi-spectral video segmentation is essential for robust scene understanding in unmanned aerial vehicle (UAV) applications such as city planning, land use monitoring, traffic moni…
Recover Cell Tensor: Diffusion-Equivalent Tensor Completion for Fluorescence Microscopy Imaging
Chenwei Wang, Zhaoke Huang, Zelin Li +1
Fluorescence microscopy (FM) imaging is a fundamental technique for observing live cell division, one of the most essential processes in the cycle of life and death. Observing 3D l…
CTransformer: Deep-transformer-based 3D cell membrane tracking with subcellular-resolved molecular quantification
Zelin Li, Guoye Guan, Xiu Xian +13
Deep learning segmentation and fluorescence imaging techniques allow the cellular morphology of living embryos to be constructed spatiotemporally. These development processes invol…
An Interpretable Two-Stage Feature Decomposition Method for Deep Learning-based SAR ATR
Chenwei Wang, Renjie Xu, Congwen Wu +5
Synthetic aperture radar automatic target recognition (SAR ATR) has seen significant performance improvements with deep learning. However, the black-box nature of deep SAR ATR intr…
CiUAV: A Multi-Task 3D Indoor Localization System for UAVs based on Channel State Information
Cunyi Yin, Chenwei Wang, Jing Chen +4
Accurate indoor positioning for unmanned aerial vehicles (UAVs) is critical for logistics, surveillance, and emergency response applications, particularly in GPS-denied environment…
Volume Tells: Dual Cycle-Consistent Diffusion for 3D Fluorescence Microscopy De-noising and Super-Resolution
Zelin Li, Chenwei Wang, Zhaoke Huang +4
3D fluorescence microscopy is essential for understanding fundamental life processes through long-term live-cell imaging. However, due to inherent issues in imaging principles, it…
SAR ATR Method with Limited Training Data via an Embedded Feature Augmenter and Dynamic Hierarchical-Feature Refiner
Chenwei Wang, Siyi Luo, Yulin Huang +3
Without sufficient data, the quantity of information available for supervised training is constrained, as obtaining sufficient synthetic aperture radar (SAR) training data in pract…
Semi-Supervised SAR ATR Framework with Transductive Auxiliary Segmentation
Chenwei Wang, Xiaoyu Liu, Yulin Huang +4
Convolutional neural networks (CNNs) have achieved high performance in synthetic aperture radar (SAR) automatic target recognition (ATR). However, the performance of CNNs depends h…
Crucial Feature Capture and Discrimination for Limited Training Data SAR ATR
Chenwei Wang, Siyi Luo, Jifang Pei +3
Although deep learning-based methods have achieved excellent performance on SAR ATR, the fact that it is difficult to acquire and label a lot of SAR images makes these methods, whi…
An Entropy-Awareness Meta-Learning Method for SAR Open-Set ATR
Chenwei Wang, Siyi Luo, Jifang Pei +4
Existing synthetic aperture radar automatic target recognition (SAR ATR) methods have been effective for the classification of seen target classes. However, it is more meaningful a…
SAR Ship Target Recognition Via Multi-Scale Feature Attention and Adaptive-Weighed Classifier
Chenwei Wang, Jifang Pei, Siyi Luo +4
Maritime surveillance is indispensable for civilian fields, including national maritime safeguarding, channel monitoring, and so on, in which synthetic aperture radar (SAR) ship ta…
Unveiling Causalities in SAR ATR: A Causal Interventional Approach for Limited Data
Chenwei Wang, Xin Chen, You Qin +4
Synthetic aperture radar automatic target recognition (SAR ATR) methods fall short with limited training data. In this letter, we propose a causal interventional ATR method (CIATR)…
A deep deformable residual learning network for SAR images segmentation
Chenwei Wang, Jifang Pei, Yulin Huang +1
Reliable automatic target segmentation in Synthetic Aperture Radar (SAR) imagery has played an important role in the SAR fields. Different from the traditional methods, Spectral Re…
When Deep Learning Meets Multi-Task Learning in SAR ATR: Simultaneous Target Recognition and Segmentation
Chenwei Wang, Jifang Pei, Zhiyong Wang +4
With the recent advances of deep learning, automatic target recognition (ATR) of synthetic aperture radar (SAR) has achieved superior performance. By not being limited to the targe…
SAR Target Image Generation Method Using Azimuth-Controllable Generative Adversarial Network
Chenwei Wang, Jifang Pei, Xiaoyu Liu +4
Sufficient synthetic aperture radar (SAR) target images are very important for the development of researches. However, available SAR target images are often limited in practice, wh…
Global in Local: A Convolutional Transformer for SAR ATR FSL
Chenwei Wang, Yulin Huang, Xiaoyu Liu +3
Convolutional neural networks (CNNs) have dominated the synthetic aperture radar (SAR) automatic target recognition (ATR) for years. However, under the limited SAR images, the widt…
SAR Ship Target Recognition via Selective Feature Discrimination and Multifeature Center Classifier
Chenwei Wang, Siyi Luo, Jifang Pei +3
Maritime surveillance is not only necessary for every country, such as in maritime safeguarding and fishing controls, but also plays an essential role in international fields, such…
Causal SAR ATR with Limited Data via Dual Invariance
Chenwei Wang, You Qin, Li Li +5
Synthetic aperture radar automatic target recognition (SAR ATR) with limited data has recently been a hot research topic to enhance weak generalization. Despite many excellent meth…
SAR ATR under Limited Training Data Via MobileNetV3
Chenwei Wang, Siyi Luo, Lin Liu +4
In recent years, deep learning has been widely used to solve the bottleneck problem of synthetic aperture radar (SAR) automatic target recognition (ATR). However, most current meth…