By default, we render 1D and 2D visualizations of the graph with odgi ... The manifold nature of typical variation graphs means that they are very likely to look linear locally. By running a ...

To address these challenges, we propose the Linear Graph Network (LGNet), which theoretically compresses multi-layer GCNs into a single layer, enabling the embedding of new nodes during inference.

This paper introduces a High-Resolution Graph Convolutional Network (HR-GCN) designed to address the challenges of 2D-3D whole-body pose estimation. The proposed HR-GCN leverages the structural ...