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of 181
pro vyhledávání: '"Ruiz Hidalgo, Javier"'
Modern machine learning systems are increasingly trained on large amounts of data embedded in high-dimensional spaces. Often this is done without analyzing the structure of the dataset. In this work, we propose a framework to study the geometric stru
Externí odkaz:
http://arxiv.org/abs/2210.17475
Processing 3D pointclouds with Deep Learning methods is not an easy task. A common choice is to do so with Graph Neural Networks, but this framework involves the creation of edges between points, which are explicitly not related between them. Histori
Externí odkaz:
http://arxiv.org/abs/2209.00949
This work presents SkinningNet, an end-to-end Two-Stream Graph Neural Network architecture that computes skinning weights from an input mesh and its associated skeleton, without making any assumptions on shape class and structure of the provided mesh
Externí odkaz:
http://arxiv.org/abs/2203.04746
Feature spaces in the deep layers of convolutional neural networks (CNNs) are often very high-dimensional and difficult to interpret. However, convolutional layers consist of multiple channels that are activated by different types of inputs, which su
Externí odkaz:
http://arxiv.org/abs/2110.11400
State-of-the-art neural network architectures continue to scale in size and deliver impressive generalization results, although this comes at the expense of limited interpretability. In particular, a key challenge is to determine when to stop trainin
Externí odkaz:
http://arxiv.org/abs/2107.12972
Multi-modal fusion has been proved to help enhance the performance of scene classification tasks. This paper presents a 2D-3D Fusion stage that combines 3D Geometric Features with 2D Texture Features obtained by 2D Convolutional Neural Networks. To g
Externí odkaz:
http://arxiv.org/abs/2009.11154
It is widely known that very small datasets produce overfitting in Deep Neural Networks (DNNs), i.e., the network becomes highly biased to the data it has been trained on. This issue is often alleviated using transfer learning, regularization techniq
Externí odkaz:
http://arxiv.org/abs/2005.08235
Autor:
Ferrer-Ferrer, Mar, Ruiz-Hidalgo, Javier, Gregorio, Eduard, Vilaplana, Verónica, Morros, Josep-Ramon, Gené-Mola, Jordi
Publikováno v:
In Biosystems Engineering September 2023 233:63-75
Geometric 3D scene classification is a very challenging task. Current methodologies extract the geometric information using only a depth channel provided by an RGB-D sensor. These kinds of methodologies introduce possible errors due to missing local
Externí odkaz:
http://arxiv.org/abs/1909.13470
Convolutional neural networks (CNNs) have demonstrated their capability to solve different kind of problems in a very huge number of applications. However, CNNs are limited for their computational and storage requirements. These limitations make diff
Externí odkaz:
http://arxiv.org/abs/1904.01987