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20112024
most citedEnsemble of Example-Dependent Cost-Sensitive Decision Trees

25 citations · 73 across the 12 of their papers we have counts for

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13 papers · 1 filter

cs.CV20222 cited

Leveraging Equivariant Features for Absolute Pose Regression

Mohamed Adel Musallam, Vincent Gaudilliere, Miguel Ortiz del Castillo +2

While end-to-end approaches have achieved state-of-the-art performance in many perception tasks, they are not yet able to compete with 3D geometry-based methods in pose estimation.…

cs.CV20212 cited

Disentangled Face Identity Representations for joint 3D Face Recognition and Expression Neutralisation

Anis Kacem, Kseniya Cherenkova, Djamila Aouada

In this paper, we propose a new deep learning-based approach for disentangling face identity representations from expressive 3D faces. Given a 3D face, our approach not only extrac…

cs.CV20217 cited

Face-GCN: A Graph Convolutional Network for 3D Dynamic Face Identification/Recognition

Konstantinos Papadopoulos, Anis Kacem, Abdelrahman Shabayek +1

Face identification/recognition has significantly advanced over the past years. However, most of the proposed approaches rely on static RGB frames and on neutral facial expressions…

cs.CV20211 cited

SPARK: SPAcecraft Recognition leveraging Knowledge of Space Environment

Mohamed Adel Musallam, Kassem Al Ismaeil, Oyebade Oyedotun +3

This paper proposes the SPARK dataset as a new unique space object multi-modal image dataset. Image-based object recognition is an important component of Space Situational Awarenes…

cs.CV2021

LSPnet: A 2D Localization-oriented Spacecraft Pose Estimation Neural Network

Albert Garcia, Mohamed Adel Musallam, Vincent Gaudilliere +4

Being capable of estimating the pose of uncooperative objects in space has been proposed as a key asset for enabling safe close-proximity operations such as space rendezvous, in-or…

cs.CV20218 cited

PvDeConv: Point-Voxel Deconvolution for Autoencoding CAD Construction in 3D

Kseniya Cherenkova, Djamila Aouada, Gleb Gusev

We propose a Point-Voxel DeConvolution (PVDeConv) module for 3D data autoencoder. To demonstrate its efficiency we learn to synthesize high-resolution point clouds of 10k points th…