5 papers
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…