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pro vyhledávání: '"ZHAO, TIANYI"'
Graph Neural Networks (GNNs) excel in diverse tasks, yet their applications in high-stakes domains are often hampered by unreliable predictions. Although numerous uncertainty quantification methods have been proposed to address this limitation, they
Externí odkaz:
http://arxiv.org/abs/2406.18763
Semantic analysis on visible (RGB) and infrared (IR) images has gained attention for its ability to be more accurate and robust under low-illumination and complex weather conditions. Due to the lack of pre-trained foundation models on the large-scale
Externí odkaz:
http://arxiv.org/abs/2404.17360
In the rapidly evolving landscape of artificial intelligence, multimodal learning systems (MMLS) have gained traction for their ability to process and integrate information from diverse modality inputs. Their expanding use in vital sectors such as he
Externí odkaz:
http://arxiv.org/abs/2402.05355
In recent years, object detection utilizing both visible (RGB) and thermal infrared (IR) imagery has garnered extensive attention and has been widely implemented across a diverse array of fields. By leveraging the complementary properties between RGB
Externí odkaz:
http://arxiv.org/abs/2401.10731
Graph Neural Networks (GNNs) are powerful tools for learning representations on graphs, such as social networks. However, their vulnerability to privacy inference attacks restricts their practicality, especially in high-stake domains. To address this
Externí odkaz:
http://arxiv.org/abs/2308.13513
Pan-sharpening, as one of the most commonly used techniques in remote sensing systems, aims to inject spatial details from panchromatic images into multispectral images (MS) to obtain high-resolution multispectral images. Since deep learning has rece
Externí odkaz:
http://arxiv.org/abs/2306.16181
Radio frequency fingerprinting has been proposed for device identification. However, experimental studies also demonstrated its sensitivity to deployment changes. Recent works have addressed channel impacts by developing robust algorithms accounting
Externí odkaz:
http://arxiv.org/abs/2303.14312
Publikováno v:
In Energy 30 December 2024 313
Publikováno v:
In Image and Vision Computing December 2024 152
Publikováno v:
In Applied Energy 1 November 2024 373