39 citations · 50 across the 5 of their papers we have counts for
6 papers
Continuous Conditional Random Field Convolution for Point Cloud Segmentation
Fei Yang, Franck Davoine, Huan Wang +1
Point cloud segmentation is the foundation of 3D environmental perception for modern intelligent systems. To solve this problem and image segmentation, conditional random fields (C…
Efficient Möbius Transformations and their applications to Dempster-Shafer Theory: Clarification and implementation
Maxime Chaveroche, Franck Davoine, Véronique Cherfaoui
Dempster-Shafer Theory (DST) generalizes Bayesian probability theory, offering useful additional information, but suffers from a high computational burden. A lot of work has been d…
Efficient exact computation of the conjunctive and disjunctive decompositions of D-S Theory for information fusion: Translation and extension
Maxime Chaveroche, Franck Davoine, Véronique Cherfaoui
Dempster-Shafer Theory (DST) generalizes Bayesian probability theory, offering useful additional information, but suffers from a high computational burden. A lot of work has been d…
Fusion of neural networks, for LIDAR-based evidential road mapping
Edouard Capellier, Franck Davoine, Veronique Cherfaoui +1
LIDAR sensors are usually used to provide autonomous vehicles with 3D representations of their environment. In ideal conditions, geometrical models could detect the road in LIDAR s…
Focal points and their implications for Möbius Transforms and Dempster-Shafer Theory
Maxime Chaveroche, Franck Davoine, Véronique Cherfaoui
Dempster-Shafer Theory (DST) generalizes Bayesian probability theory, offering useful additional information, but suffers from a much higher computational burden. A lot of work has…
Explicit Inductive Bias for Transfer Learning with Convolutional Networks
Xuhong Li, Yves Grandvalet, Franck Davoine
In inductive transfer learning, fine-tuning pre-trained convolutional networks substantially outperforms training from scratch. When using fine-tuning, the underlying assumption is…