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Found 8 papers, 4 papers with code
Geometric Deep Learning for Computer-Aided Design: A Survey
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 engineers approach and enhance the design process.
Learning Diversified Feature Representations for Facial Expression Recognition in the Wild
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.
Facial Expression Recognition
Facial Expression Recognition (FER)
Continual Spatio-Temporal Graph Convolutional Networks
Graph-based reasoning over skeleton data has emerged as a promising approach for human action recognition.
Ranked #7 on
Skeleton Based Action Recognition
on Kinetics-Skeleton dataset
(GFLOPS per prediction metric)
Progressive Spatio-Temporal Bilinear Network with Monte Carlo Dropout for Landmark-based Facial Expression Recognition with Uncertainty Estimation
In this paper, we propose a method which learns an optimized compact network topology for real-time facial expression recognition utilizing localized facial landmark features.
Facial Expression Recognition
Facial Expression Recognition (FER)
Progressive Spatio-Temporal Graph Convolutional Network for Skeleton-Based Human Action Recognition
Graph convolutional networks (GCNs) have been very successful in skeleton-based human action recognition where the sequence of skeletons is modeled as a graph.
On the spatial attention in Spatio-Temporal Graph Convolutional Networks for skeleton-based human action recognition
Graph convolutional networks (GCNs) achieved promising performance in skeleton-based human action recognition by modeling a sequence of skeletons as a spatio-temporal graph.
Temporal Attention-Augmented Graph Convolutional Network for Efficient Skeleton-Based Human Action Recognition
In this paper, we propose a temporal attention module (TAM) for increasing the efficiency in skeleton-based action recognition by selecting the most informative skeletons of an action at the early layers of the network.
Progressive Graph Convolutional Networks for Semi-Supervised Node Classification
Graph convolutional networks have been successful in addressing graph-based tasks such as semi-supervised node classification.
