Search Results for author: Yulin Huang

Found 14 papers, 4 papers with code

Semi-Supervised SAR ATR Framework with Transductive Auxiliary Segmentation

no code implementations31 Aug 2023 Chenwei Wang, Xiaoyu Liu, Yulin Huang, Siyi Luo, Jifang Pei, Jianyu Yang, Deqing Mao

The recognition performance of 94. 18\% can be achieved under 20 training samples in each class with simultaneous accurate segmentation results.

Few-Shot Learning Inductive Bias +1

SAR Ship Target Recognition via Selective Feature Discrimination and Multifeature Center Classifier

no code implementations20 Aug 2023 Chenwei Wang, Siyi Luo, Jifang Pei, Yulin Huang, Yin Zhang, Jianyu Yang

However, the characteristics of SAR ship images, large inner-class variance, and small interclass difference lead to the whole features containing useless partial features and a single feature center for each class in the classifier failing with large inner-class variance.

An Entropy-Awareness Meta-Learning Method for SAR Open-Set ATR

no code implementations20 Aug 2023 Chenwei Wang, Siyi Luo, Jifang Pei, Xiaoyu Liu, Yulin Huang, Yin Zhang, Jianyu Yang

In this letter, we propose an entropy-awareness meta-learning method that improves the exclusiveness of feature distribution of known classes which means our method is effective for not only classifying the seen classes but also encountering the unseen other classes.

Meta-Learning Open Set Learning

SAR Ship Target Recognition Via Multi-Scale Feature Attention and Adaptive-Weighed Classifier

no code implementations20 Aug 2023 Chenwei Wang, Jifang Pei, Siyi Luo, Weibo Huo, Yulin Huang, Yin Zhang, Jianyu Yang

Therefore, we proposed a SAR ship recognition method via multi-scale feature attention and adaptive-weighted classifier to enhance features in each scale, and adaptively choose the effective feature scale for accurate recognition.

SAR ATR Method with Limited Training Data via an Embedded Feature Augmenter and Dynamic Hierarchical-Feature Refiner

no code implementations20 Aug 2023 Chenwei Wang, Siyi Luo, Yulin Huang, Jifang Pei, Yin Zhang, Jianyu Yang

The designed augmenter increases the amount of information available for supervised training and improves the separability of the extracted features.

Crucial Feature Capture and Discrimination for Limited Training Data SAR ATR

1 code implementation20 Aug 2023 Chenwei Wang, Siyi Luo, Jifang Pei, Yulin Huang, Yin Zhang, Jianyu Yang

Based on the initial recognition results, the feature capture module automatically searches and locks the crucial image regions for correct recognition, which we named as the golden key of image.

Causal SAR ATR with Limited Data via Dual Invariance

1 code implementation18 Aug 2023 Chenwei Wang, You Qin, Li Li, Siyi Luo, Yulin Huang, Jifang Pei, Yin Zhang, Jianyu Yang

As a result, it has a detrimental causal effect damaging the efficacy of feature $X$ extracted from SAR images, leading to weak generalization of SAR ATR with limited data.

Unveiling Causalities in SAR ATR: A Causal Interventional Approach for Limited Data

no code implementations18 Aug 2023 Chenwei Wang, Xin Chen, You Qin, Siyi Luo, Yulin Huang, Jifang Pei, Jianyu Yang

Then, a feature discrimination approach with hybrid similarity measurement is introduced to measure and mitigate the structural and vector angle impacts of varying imaging conditions on the extracted features from SAR images.

Causal Inference Data Augmentation

A deep deformable residual learning network for SAR images segmentation

no code implementations15 Aug 2023 Chenwei Wang, Jifang Pei, Yulin Huang, Jianyu Yang

In this paper, we proposed a deep deformable residual learning network for target segmentation that attempts to preserve the precise contour of the target.

Learning Theory Segmentation

When Deep Learning Meets Multi-Task Learning in SAR ATR: Simultaneous Target Recognition and Segmentation

no code implementations14 Aug 2023 Chenwei Wang, Jifang Pei, Zhiyong Wang, Yulin Huang, Junjie Wu, Haiguang Yang, Jianyu Yang

In this paper, we propose a new multi-task learning approach for SAR ATR, which could obtain the accurate category and precise shape of the targets simultaneously.

Learning Theory Multi-Task Learning +1

SAR Target Image Generation Method Using Azimuth-Controllable Generative Adversarial Network

no code implementations10 Aug 2023 Chenwei Wang, Jifang Pei, Xiaoyu Liu, Yulin Huang, Deqing Mao, Yin Zhang, Jianyu Yang

The similarity discriminator can differentiate the generated SAR target images from the real SAR images to ensure the accuracy of the generated, while the azimuth predictor measures the difference of azimuth between the generated and the desired to ensure the azimuth controllability of the generated.

Generative Adversarial Network Image Generation

Global in Local: A Convolutional Transformer for SAR ATR FSL

no code implementations10 Aug 2023 Chenwei Wang, Yulin Huang, Xiaoyu Liu, Jifang Pei, Yin Zhang, Jianyu Yang

Convolutional neural networks (CNNs) have dominated the synthetic aperture radar (SAR) automatic target recognition (ATR) for years.

Few-Shot Learning

SAR ATR under Limited Training Data Via MobileNetV3

1 code implementation27 Jun 2023 Chenwei Wang, Siyi Luo, Lin Liu, Yin Zhang, Jifang Pei, Yulin Huang, Jianyu Yang

In recent years, deep learning has been widely used to solve the bottleneck problem of synthetic aperture radar (SAR) automatic target recognition (ATR).

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