DD-GCN: Directed Diffusion Graph Convolutional Network for Skeleton-based Human Action Recognition [PDF]
, 2023 Graph Convolutional Networks (GCNs) have been widely used in skeleton-based human action recognition. In GCN-based methods, the spatio-temporal graph is fundamental for capturing motion patterns.
Mao, Yingchi, Huang, Qian, Li, Chang
core
IML-GCN: Improved Multi-Label Graph Convolutional Network for Efficient yet Precise Image Classification [PDF]
, 2022 peer reviewedIn this paper, we propose the Improved Multi-Label Graph Convolutional Network (IML-GCN) as a precise and efficient framework for multi-label image classification.
GHORBEL, Enjie +3 more
core +1 more source
A Modulation Classification Algorithm Based on Feature-Embedding Graph Convolutional Network
IEEE AccessDeep-learning is widely used in modulation classification to reduce labor and improve the efficiency. Graph convolutional network (GCN) is a type of feature extraction network for graph data.
Huali Zhu +4 more
doaj +1 more source
Multi-Semantic Alignment Graph Convolutional Network
Connection Science, 2022 Graph Convolutional Network (GCN) is a powerful emerging deep learning technique for learning graph data. However, there are still some challenges for GCN. For example, the model is shallow; the performance is poor when labelled nodes are severely scarce.
Jisheng Qin +3 more
doaj +1 more source
CR-GCN: Channel-Relationships-Based Graph Convolutional Network for EEG Emotion Recognition
, 2022 Electroencephalography (EEG) is recorded by electrodes from different areas of the brain and is commonly used to measure neuronal activity. EEG-based methods have been widely used for emotion recognition recently.
Xu, Zhikang +11 more
core +1 more source
Space-time-separable graph convolutional network for pose forecasting [PDF]
, 2021 Human pose forecasting is a complex structured-data sequence-modelling task, which has received increasing attention, also due to numerous potential applications.
Sofianos, Theodoros +3 more
core +1 more source
, 2020 This paper explores the value of weak-ties in classifying academic literature with the use of graph convolutional neural networks. Our experiments look at the results of treating weak-ties as if they were strong-ties to determine if that assumption ...
Gita Sukthankar +2 more
core +1 more source
Hyperspectral Image Classification With Context-Aware Dynamic Graph Convolutional Network
, 2021 In hyperspectral image (HSI) classification, spatial context has demonstrated its significance in achieving promising performance. However, conventional spatial context-based methods simply assume that spatially neighboring pixels should correspond to ...
Wan, Sheng +5 more
core +2 more sources
Gaze-Aware Graph Convolutional Network for Social Relation Recognition
IEEE Access, 2021 Social relation, as the basic relation in our daily life, is vital for social action analysis. However, how to learn the social feature between people is still not tackled.
Xingming Yang +4 more
doaj +1 more source
Dynamic Dense Graph Convolutional Network for Skeleton-based Human Motion Prediction [PDF]
, 2023 Graph Convolutional Networks (GCN) which typically follows a neural message passing framework to model dependencies among skeletal joints has achieved high success in skeleton-based human motion prediction task.
Gao, Yuan +4 more
core +1 more source