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Titlebook: Advanced Intelligent Computing Technology and Applications; 20th International C De-Shuang Huang,Xiankun Zhang,Jiayang Guo Conference proce

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樓主: CURD
31#
發(fā)表于 2025-3-26 21:48:47 | 只看該作者
Depth-NeuS: Neural Implicit Surfaces Learning for Multi-view Reconstruction Based on Depth Information optimization. Additionally, we integrate photometric loss and geometric loss into loss function as geometric consistency loss to achieve geometric constraints. Empirical experiments have showcased the superior performance of Depth-NeuS over existing technologies across various scenarios. Moreover
32#
發(fā)表于 2025-3-27 03:16:00 | 只看該作者
33#
發(fā)表于 2025-3-27 06:31:41 | 只看該作者
Boosting Robustness of Silhouette-Based Gait Recognition Against Adversarial Attacksnhance edge information in images. The objective is to compel deep neural networks to focus more on semantic information in gait silhouette images and reduce feature deviations induced by adversarial perturbations. The method can significantly improve the adversarial robustness of silhouette-based g
34#
發(fā)表于 2025-3-27 11:56:53 | 只看該作者
35#
發(fā)表于 2025-3-27 15:16:49 | 只看該作者
36#
發(fā)表于 2025-3-27 18:49:44 | 只看該作者
Sparse Discriminant Graph Embedding for Feature Extractiongonal constraint and a sparse constraint simultaneously, ensuring the preservation of key information from the original data while enhancing robustness against noise. Extensive experiments on four real-world databases demonstrate the competitiveness of SDGE against state-of-the-art feature extractio
37#
發(fā)表于 2025-3-27 22:31:43 | 只看該作者
A Multi-Scale Additive Enhanced Network for Remote Sensing Scene Classificationting the problem of high inter class similarity. A Multi-Scale Additive Enhanced Network (MSAENet) is proposed based on MSAE Block and Bridging Residual Module (BRM) and is validated on two datasets, WHU-SIRI and AID. Based on experimental data, the classification accuracy of MSAENet is better than
38#
發(fā)表于 2025-3-28 02:12:54 | 只看該作者
SeWi: A Framework Enhancing CSI-Based Human Activity Recognitionifferent models as the basic models for SeWi. We also analyze the effective range of hyperparameters for this segmentation method. The results indicate that SeWi exhibits varying degrees of improvement for different models. Of particular note, using ResNet18 as the basic model for SeWi, the accuracy
39#
發(fā)表于 2025-3-28 09:55:54 | 只看該作者
40#
發(fā)表于 2025-3-28 12:42:51 | 只看該作者
Research on Hidden Mind-Wandering Detection Algorithm for Online Classroom Based on Temporal Analysiorithm can quickly and effectively detect students’ distraction phenomena, including daydreaming, distraction, and hidden activities like using mobile phones in blind spots of the camera’s visual capture. This research is significant for helping teachers evaluate students’ performance in online clas
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