Výsledky vyhledávání - "Xuan, Wenjie"

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RFL-CDNet: Towards Accurate Change Detection via Richer Feature Learning

Autor: Gan, Yuhang, Xuan, Wenjie, Chen, Hang, Liu, Juhua, Du, Bo

Change Detection is a crucial but extremely challenging task of remote sensing image analysis, and much progress has been made with the rapid development of deep learning. However, most existing deep learning-based change detection methods mainly foc

Externí odkaz: http://arxiv.org/abs/2404.17765

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When ControlNet Meets Inexplicit Masks: A Case Study of ControlNet on its Contour-following Ability

Autor: Xuan, Wenjie, Xu, Yufei, Zhao, Shanshan, Wang, Chaoyue, Liu, Juhua, Du, Bo, Tao, Dacheng

ControlNet excels at creating content that closely matches precise contours in user-provided masks. However, when these masks contain noise, as a frequent occurrence with non-expert users, the output would include unwanted artifacts. This paper first

Externí odkaz: http://arxiv.org/abs/2403.00467

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PNT-Edge: Towards Robust Edge Detection with Noisy Labels by Learning Pixel-level Noise Transitions

Autor: Xuan, Wenjie, Zhao, Shanshan, Yao, Yu, Liu, Juhua, Liu, Tongliang, Chen, Yixin, Du, Bo, Tao, Dacheng

Relying on large-scale training data with pixel-level labels, previous edge detection methods have achieved high performance. However, it is hard to manually label edges accurately, especially for large datasets, and thus the datasets inevitably cont

Externí odkaz: http://arxiv.org/abs/2307.14070

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An End-to-end Supervised Domain Adaptation Framework for Cross-Domain Change Detection

Autor: Liu, Jia, Xuan, Wenjie, Gan, Yuhang, Liu, Juhua, Du, Bo

Existing deep learning-based change detection methods try to elaborately design complicated neural networks with powerful feature representations, but ignore the universal domain shift induced by time-varying land cover changes, including luminance f

Externí odkaz: http://arxiv.org/abs/2204.00154

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