Results 1 to 10 of about 90,914 (289)
Design of an Agile Training System Based on Wireless Mesh Network
IEEE Access, 2022 Given the problems of using traditional training methods and insufficient funds in college sports agility training, the agility training system based on Wireless MESH Network is developed. The lower computer realizes the automatic networking between nodes based on the ESP-MESH network, and describes the networking process, intra-group communication and
Tan Baohua, Enpu Wang
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NeuralAnnot: Neural Annotator for 3D Human Mesh Training Sets
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022 Most 3D human mesh regressors are fully supervised with 3D pseudo-GT human model parameters and weakly supervised with GT 2D/3D joint coordinates as the 3D pseudo-GTs bring great performance gain. The 3D pseudo-GTs are obtained by annotators, systems that iteratively fit 3D human model parameters to GT 2D/3D joint coordinates of training sets in the ...
Hongsuk Choi
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Optimal Training Sets for Bayesian Prediction of MeSH(R) Assignment [PDF]
Journal of the American Medical Informatics Association, 2008 The aim of this study was to improve naïve Bayes prediction of Medical Subject Headings (MeSH) assignment to documents using optimal training sets found by an active learning inspired method.The authors selected 20 MeSH terms whose occurrences cover a range of frequencies.
Sunghwan, Sohn +3 more
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Surgical training and research in mesh complications
ContinenceObjective:: To review and summarize the peer-reviewed literature for Surgical Training and Research in Mesh Complications. Methods:: An international author team reviewed available published literature. Results:: This paper reviews the current training situation and recognizes there are no fellowships globally which specifically train in mesh removal ...
Siobhan M Hartigan, Hashim Hashim
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MPT: Mesh Pre-Training with Transformers for Human Pose and Mesh Reconstruction
2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)Traditional methods of reconstructing 3D human pose and mesh from single images rely on paired image-mesh datasets, which can be difficult and expensive to obtain. Due to this limitation, model scalability is constrained as well as reconstruction performance.
Kevin Lin
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SEMANTIC SEGMENTATION OF URBAN TEXTURED MESHES THROUGH POINT SAMPLING [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2022 Textured meshes are becoming an increasingly popular representation combining the 3D geometry and radiometry of real scenes. However, semantic segmentation algorithms for urban mesh have been little investigated and do not exploit all radiometric ...
G. Grzeczkowicz +2 more
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Planar Delaunay Mesh Smoothing Method Based on Angle and a Deep Q-Network
Applied Sciences, 2023 Mesh quality is critical to the accuracy and efficiency of finite element calculations, and mesh smoothing is an essential means of reducing the number of poor elements and improving mesh quality.
Fu Zhao, Guangjun Sun
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Frontiers in Psychology, 2023 IntroductionBecause of the problems of insufficient funds and traditional training methods in college sports agile training, an agile training system based on a wireless ad hoc network was developed to evaluate the effect of improving the sensitive ...
Baohua Tan +12 more
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Synthetic Training for Monocular Human Mesh Recovery
CoRR, 2020 Recovering 3D human mesh from monocular images is a popular topic in computer vision and has a wide range of applications. This paper aims to estimate 3D mesh of multiple body parts (e.g., body, hands) with large-scale differences from a single RGB image. Existing methods are mostly based on iterative optimization, which is very time-consuming.
Yu Sun 0030 +6 more
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Journal of Chemical Engineering of Japan, 2023 Fast prediction of fluid flow and thermal fields during the growth of bulk silicon single crystals by the ‘Vertical Magnetic Field Applied Czochralski (VMCz) Method’ was successfully achieved by the application of Physics Informed Neural Networks (PINNs)
Yuto Takehara +2 more
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