aerobic 发表于 2025-3-30 10:12:40
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MCNet: A Multi-scale and Cascade Network for Semantic Segmentation of Remote Sensing Images,apability of the network. Results show that the proposed MCNet outperformed the baseline networks, achieving an average overall accuracy of 86.91% and 87.82% on the two datasets, respectively. In conclusion, the multi-scale and cascade semantic segmentation network can improve the accuracy of land cGREEN 发表于 2025-3-30 20:32:34
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,WikiCPRL: A Weakly Supervised Approach for Wikipedia Concept Prerequisite Relation Learning,n. Secondly, a graph attention layer is defined to fuse the context information of each concept in the concept map so as to update their feature representations. Finally, we use the VGAE model to reconstruct the concept map, and then obtain the concept prerequisite graph. Extensive experiments on bo惊奇 发表于 2025-3-31 02:09:56
MCNet: A Multi-scale and Cascade Network for Semantic Segmentation of Remote Sensing Images,apability of the network. Results show that the proposed MCNet outperformed the baseline networks, achieving an average overall accuracy of 86.91% and 87.82% on the two datasets, respectively. In conclusion, the multi-scale and cascade semantic segmentation network can improve the accuracy of land c咯咯笑 发表于 2025-3-31 08:21:44
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,W-MRI: A Multi-output Residual Integration Model for Global Weather Forecasting,xperimental conditions. Moreover, experiments show that our model has a stable and significant advantage in short-to-medium-range forecasting, and the longer the forecasting time-step, the more obvious the performance advantage of W-MRI, showing that the residual network has great advantages in weatAllodynia 发表于 2025-3-31 13:41:55
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