Graph convolutional networks (GCNs) emerge as the most successful learning models for graph-structured data. Despite their success, existing GCNs usually ignore the entangled latent factors typically ...

The excellent performance of graph convolutional networks (GCNs) on non-Euclidean data has drawn widespread attention from the hyperspectral image classification (HSIC) community, where the predefined ...

The relevance is e v = a T · R e l u W · h v + b, the gating embedding is g v = Sigmoid W · h v, ∀ v ∈ 1, 2, ⋯ N. The a T, W, and b are attention embeddings, weight parameters, and biases, ...

Official Pytorch implementation of "Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose", ECCV 2020. rgb-image single-view eccv 3d-mesh 2d-human-pose ...

Attention-Graph-Convolution-Network-for-Image-Segmentation-in-Big-SAR-Imagery-Data this code implements the method proposed in paper "Attention Graph Convolution Network for Image Segmentation in Big ...

Keywords: electroencephalogram, motor imagery, brain-computer interface, coherence-based graph convolutional network, spinal cord injury. Citation: Li H, Liu M, Yu X, Zhu J, Wang C, Chen X, Feng C, ...