Zobrazeno 1 - 10
of 1 191
pro vyhledávání: '"Huang,Yulin"'
Autor:
Yin, Jiaheng, Shi, Zhengxin, Zhang, Jianshen, Lin, Xiaomin, Huang, Yulin, Qi, Yongzhi, Qi, Wei
In recent years, numerous Transformer-based models have been applied to long-term time-series forecasting (LTSF) tasks. However, recent studies with linear models have questioned their effectiveness, demonstrating that simple linear layers can outper
Externí odkaz:
http://arxiv.org/abs/2408.09723
Convolutional neural networks (CNNs) have achieved high performance in synthetic aperture radar (SAR) automatic target recognition (ATR). However, the performance of CNNs depends heavily on a large amount of training data. The insufficiency of labele
Externí odkaz:
http://arxiv.org/abs/2308.16633
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 as in rescue support and illegal immigration control. Most of the exi
Externí odkaz:
http://arxiv.org/abs/2308.10250
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 and challenging to distinguish the unseen target classes, i.e., open se
Externí odkaz:
http://arxiv.org/abs/2308.10251
Maritime surveillance is indispensable for civilian fields, including national maritime safeguarding, channel monitoring, and so on, in which synthetic aperture radar (SAR) ship target recognition is a crucial research field. The core problem to real
Externí odkaz:
http://arxiv.org/abs/2308.10247
Without sufficient data, the quantity of information available for supervised training is constrained, as obtaining sufficient synthetic aperture radar (SAR) training data in practice is frequently challenging. Therefore, current SAR automatic target
Externí odkaz:
http://arxiv.org/abs/2308.10243
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, which originally performed well, perform weakly. This may be because most
Externí odkaz:
http://arxiv.org/abs/2308.10911
Autor:
Wang, Chenwei, Qin, You, Li, Li, Luo, Siyi, Huang, Yulin, Pei, Jifang, Zhang, Yin, Yang, Jianyu
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 methods being proposed, a fundamental theory is lacked to explain what pro
Externí odkaz:
http://arxiv.org/abs/2308.09412
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) to formulate the problem of limited SAR data which helps us uncover t
Externí odkaz:
http://arxiv.org/abs/2308.09396
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 Residual (SR) and CFAR detector, with the recent adavance in machine lea
Externí odkaz:
http://arxiv.org/abs/2308.07627