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pro vyhledávání: '"Du, Qiangang"'
Autor:
Du, Qiangang, Peng, Jinlong, Chen, Xu, He, Qingdong, He, Liren, Nie, Qiang, Zhu, Wenbing, Chi, Mingmin, Wang, Yabiao, Wang, Chengjie
Change detection is widely applied in remote sensing image analysis. Existing methods require training models separately for each dataset, which leads to poor domain generalization. Moreover, these methods rely heavily on large amounts of high-qualit
Externí odkaz:
http://arxiv.org/abs/2404.11326
Autor:
Du, Qiangang, Peng, Jinlong, Wang, Changan, Chen, Xu, He, Qingdong, Zhu, Wenbing, Chi, Mingmin, Wang, Yabiao, Wang, Chengjie
Change detection aims to identify remote sense object changes by analyzing data between bitemporal image pairs. Due to the large temporal and spatial span of data collection in change detection image pairs, there are often a significant amount of tas
Externí odkaz:
http://arxiv.org/abs/2404.11318
Autor:
He, Liren, Jiang, Zhengkai, Peng, Jinlong, Liu, Liang, Du, Qiangang, Hu, Xiaobin, Zhu, Wenbing, Chi, Mingmin, Wang, Yabiao, Wang, Chengjie
In the field of multi-class anomaly detection, reconstruction-based methods derived from single-class anomaly detection face the well-known challenge of "learning shortcuts", wherein the model fails to learn the patterns of normal samples as it shoul
Externí odkaz:
http://arxiv.org/abs/2403.11561