activity
20192022
most citedVertex Feature Encoding and Hierarchical Temporal Modeling in a Spatial-Temporal Graph Convolutional Network for Action Recognition

21 citations · 35 across the 4 of their papers we have counts for

collaborators

5 papers

cs.LG20222 cited

A New Perspective for Understanding Generalization Gap of Deep Neural Networks Trained with Large Batch Sizes

Oyebade K. Oyedotun, Konstantinos Papadopoulos, Djamila Aouada

Deep neural networks (DNNs) are typically optimized using various forms of mini-batch gradient descent algorithm. A major motivation for mini-batch gradient descent is that with a…

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.CV20205 cited

SHARP 2020: The 1st Shape Recovery from Partial Textured 3D Scans Challenge Results

Alexandre Saint, Anis Kacem, Kseniya Cherenkova +7

The SHApe Recovery from Partial textured 3D scans challenge, SHARP 2020, is the first edition of a challenge fostering and benchmarking methods for recovering complete textured 3D…

cs.CV201921 cited

Vertex Feature Encoding and Hierarchical Temporal Modeling in a Spatial-Temporal Graph Convolutional Network for Action Recognition

Konstantinos Papadopoulos, Enjie Ghorbel, Djamila Aouada +1

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 Featur…

cs.CV2019

Localized Trajectories for 2D and 3D Action Recognition

Konstantinos Papadopoulos, Girum Demisse, Enjie Ghorbel +3

The Dense Trajectories concept is one of the most successful approaches in action recognition, suitable for scenarios involving a significant amount of motion. However, due to nois…