no code implementations • 21 Oct 2022 • Oyebade K. Oyedotun, Konstantinos Papadopoulos, Djamila Aouada
As such, our main exposition in this paper is to investigate and provide new perspectives for the source of generalization loss for DNNs trained with a large batch size.
no code implementations • 19 Apr 2021 • Konstantinos Papadopoulos, Anis Kacem, Abdelrahman Shabayek, Djamila Aouada
This has two disadvantages.
no code implementations • 26 Oct 2020 • Alexandre Saint, Anis Kacem, Kseniya Cherenkova, Konstantinos Papadopoulos, Julian Chibane, Gerard Pons-Moll, Gleb Gusev, David Fofi, Djamila Aouada, Bjorn Ottersten
Additionally, two unique datasets of 3D scans are proposed, to provide raw ground-truth data for the benchmarks.
no code implementations • 20 Dec 2019 • Konstantinos Papadopoulos, Enjie Ghorbel, Djamila Aouada, Björn Ottersten
This paper extends the Spatial-Temporal Graph Convolutional Network (ST-GCN) for skeleton-based action recognition by introducing two novel modules, namely, the Graph Vertex Feature Encoder (GVFE) and the Dilated Hierarchical Temporal Convolutional Network (DH-TCN).
Ranked #11 on Action Recognition on NTU RGB+D 120
no code implementations • 10 Apr 2019 • Konstantinos Papadopoulos, Girum Demisse, Enjie Ghorbel, Michel Antunes, Djamila Aouada, Björn Ottersten
The Dense Trajectories concept is one of the most successful approaches in action recognition, suitable for scenarios involving a significant amount of motion.