51 citations · 59 across the 8 of their papers we have counts for
8 papers
Geometric Deep Learning for Computer-Aided Design: A Survey
Negar Heidari, Alexandros Iosifidis
Geometric Deep Learning techniques have become a transformative force in the field of Computer-Aided Design (CAD), and have the potential to revolutionize how designers and enginee…
Learning Diversified Feature Representations for Facial Expression Recognition in the Wild
Negar Heidari, Alexandros Iosifidis
Diversity of the features extracted by deep neural networks is important for enhancing the model generalization ability and accordingly its performance in different learning tasks.…
Continual Spatio-Temporal Graph Convolutional Networks
Lukas Hedegaard, Negar Heidari, Alexandros Iosifidis
Graph-based reasoning over skeleton data has emerged as a promising approach for human action recognition. However, the application of prior graph-based methods, which predominantl…
Progressive Spatio-Temporal Bilinear Network with Monte Carlo Dropout for Landmark-based Facial Expression Recognition with Uncertainty Estimation
Negar Heidari, Alexandros Iosifidis
Deep neural networks have been widely used for feature learning in facial expression recognition systems. However, small datasets and large intra-class variability can lead to over…
Progressive Spatio-Temporal Graph Convolutional Network for Skeleton-Based Human Action Recognition
Negar Heidari, Alexandros Iosifidis
Graph convolutional networks (GCNs) have been very successful in skeleton-based human action recognition where the sequence of skeletons is modeled as a graph. However, most of the…
On the spatial attention in Spatio-Temporal Graph Convolutional Networks for skeleton-based human action recognition
Negar Heidari, Alexandros Iosifidis
Graph convolutional networks (GCNs) achieved promising performance in skeleton-based human action recognition by modeling a sequence of skeletons as a spatio-temporal graph. Most o…
Temporal Attention-Augmented Graph Convolutional Network for Efficient Skeleton-Based Human Action Recognition
Negar Heidari, Alexandros Iosifidis
Graph convolutional networks (GCNs) have been very successful in modeling non-Euclidean data structures, like sequences of body skeletons forming actions modeled as spatio-temporal…
Progressive Graph Convolutional Networks for Semi-Supervised Node Classification
Negar Heidari, Alexandros Iosifidis
Graph convolutional networks have been successful in addressing graph-based tasks such as semi-supervised node classification. Existing methods use a network structure defined by t…