我悲伤
发表于 2025-3-28 17:47:54
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SHOCK
发表于 2025-3-28 21:49:48
Gerd Balzer,Claus Neumann information, we construct a local spatial-temporal graph (LSTG) by extracting intermediate CNN features of local regions based on landmark-guided attention and defining their geometric relationships. We utilize topology-learnable ST-GCNs to exploit the local dynamics and implicit relations. Finally
companion
发表于 2025-3-29 00:51:54
Gerd Balzer,Claus Neumann previous layers to make full use of multi-scale features. Additionally, the attention modules in the skip connection are added to detect the position and edges of cloud accurately. After that, the second U-shape network is utilized to optimize the preliminary segmentations from the first network, t
玛瑙
发表于 2025-3-29 04:29:39
Gerd Balzer,Claus Neumann information is used to convert 3D point cloud data obtained from real and virtual perspectives into a common world coordinate system, making it possible to obtain full-surface 3D data of the object. Finally, benefited from the robust and high-performance calibration method, experimental results ver
愤世嫉俗者
发表于 2025-3-29 07:21:28
Gerd Balzer,Claus Neumann information is used to convert 3D point cloud data obtained from real and virtual perspectives into a common world coordinate system, making it possible to obtain full-surface 3D data of the object. Finally, benefited from the robust and high-performance calibration method, experimental results ver
flex336
发表于 2025-3-29 13:28:38
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虚弱的神经
发表于 2025-3-29 18:04:29
Gerd Balzer,Claus Neumannges of having fewer parameters and needing fewer samples. We have validated the proposed method on three datasets including MNIST, MNIST-12k, MNIST-scaled and MNIST-rand datasets. Experiment results show that RSN compares favourably to the state-of-the-art in classifying/recognising rotated and scal
血友病
发表于 2025-3-29 23:09:09
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