Search Results for author: Pedro P. B. de Gusmão

Found 2 papers, 0 papers with code

Protea: Client Profiling within Federated Systems using Flower

no code implementations3 Jul 2022 Wanru Zhao, Xinchi Qiu, Javier Fernandez-Marques, Pedro P. B. de Gusmão, Nicholas D. Lane

Federated Learning (FL) has emerged as a prospective solution that facilitates the training of a high-performing centralised model without compromising the privacy of users.

Federated Learning

SelfVIO: Self-Supervised Deep Monocular Visual-Inertial Odometry and Depth Estimation

no code implementations22 Nov 2019 Yasin Almalioglu, Mehmet Turan, Alp Eren Sari, Muhamad Risqi U. Saputra, Pedro P. B. de Gusmão, Andrew Markham, Niki Trigoni

In the last decade, numerous supervised deep learning approaches requiring large amounts of labeled data have been proposed for visual-inertial odometry (VIO) and depth map estimation.

Depth Estimation Pose Estimation +3

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