Flexible graph convolutional network for 3D human pose estimation

• URL: http://arxiv.org/abs/2407.19077v1
• Date: Fri, 26 Jul 2024 20:46:28 GMT
• Title: Flexible graph convolutional network for 3D human pose estimation
• Authors: Abu Taib Mohammed Shahjahan, A. Ben Hamza,
• Abstract summary: We introduce Flex-GCN, a flexible graph convolutional network designed to learn graph representations that capture broader global information and dependencies. At its core is the flexible graph convolution, which aggregates features from both immediate and second-order neighbors of each node. Our network architecture comprises residual blocks of flexible graph convolutional layers, as well as a global response normalization layer for global feature aggregation, normalization and calibration.
• Score: 4.696083734269233
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Although graph convolutional networks exhibit promising performance in 3D human pose estimation, their reliance on one-hop neighbors limits their ability to capture high-order dependencies among body joints, crucial for mitigating uncertainty arising from occlusion or depth ambiguity. To tackle this limitation, we introduce Flex-GCN, a flexible graph convolutional network designed to learn graph representations that capture broader global information and dependencies. At its core is the flexible graph convolution, which aggregates features from both immediate and second-order neighbors of each node, while maintaining the same time and memory complexity as the standard convolution. Our network architecture comprises residual blocks of flexible graph convolutional layers, as well as a global response normalization layer for global feature aggregation, normalization and calibration. Quantitative and qualitative results demonstrate the effectiveness of our model, achieving competitive performance on benchmark datasets.

Related papers

• Multi-hop graph transformer network for 3D human pose estimation [4.696083734269233]
  We introduce a multi-hop graph transformer network designed for 2D-to-3D human pose estimation in videos. The proposed network architecture consists of a graph attention block composed of stacked layers of multi-head self-attention and graph convolution with learnable adjacency matrix. The integration of dilated convolutional layers enhances the model's ability to handle spatial generalizations required for accurate localization of the human body joints.
  arXiv  Detail & Related papers  (2024-05-05T21:29:20Z)
• Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation [153.92387500677023]
  We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations. The proposed graph Transformer encoder combines graph convolutions and self-attentions in a Transformer to model both local and global interactions. We also propose a novel self-guided pre-training method for graph representation learning.
  arXiv  Detail & Related papers  (2024-01-15T14:36:38Z)
• Network Alignment with Transferable Graph Autoencoders [79.89704126746204]
  We propose a novel graph autoencoder architecture designed to extract powerful and robust node embeddings. We prove that the generated embeddings are associated with the eigenvalues and eigenvectors of the graphs. Our proposed framework also leverages transfer learning and data augmentation to achieve efficient network alignment at a very large scale without retraining.
  arXiv  Detail & Related papers  (2023-10-05T02:58:29Z)
• ResolvNet: A Graph Convolutional Network with multi-scale Consistency [47.98039061491647]
  We introduce the concept of multi-scale consistency. At the graph-level, multi-scale consistency refers to the fact that distinct graphs describing the same object at different resolutions should be assigned similar feature vectors. We introduce ResolvNet, a flexible graph neural network based on the mathematical concept of resolvents.
  arXiv  Detail & Related papers  (2023-09-30T16:46:45Z)
• Iterative Graph Filtering Network for 3D Human Pose Estimation [5.177947445379688]
  Graph convolutional networks (GCNs) have proven to be an effective approach for 3D human pose estimation. In this paper, we introduce an iterative graph filtering framework for 3D human pose estimation. Our approach builds upon the idea of iteratively solving graph filtering with Laplacian regularization.
  arXiv  Detail & Related papers  (2023-07-29T20:46:44Z)
• GrannGAN: Graph annotation generative adversarial networks [72.66289932625742]
  We consider the problem of modelling high-dimensional distributions and generating new examples of data with complex relational feature structure coherent with a graph skeleton. The model we propose tackles the problem of generating the data features constrained by the specific graph structure of each data point by splitting the task into two phases. In the first it models the distribution of features associated with the nodes of the given graph, in the second it complements the edge features conditionally on the node features.
  arXiv  Detail & Related papers  (2022-12-01T11:49:07Z)
• Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning Networks [11.421162988355146]
  Heterogeneous graph neural network has unleashed great potential on graph representation learning. We design a relation-centered Pooling and Convolution for Heterogeneous Graph learning Network, namely PC-HGN, to enable relation-specific sampling and cross-relation convolutions. We evaluate the performance of the proposed model by comparing with state-of-the-art graph learning models on three different real-world datasets.
  arXiv  Detail & Related papers  (2022-10-31T08:43:32Z)
• Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification [63.56018768401328]
  We propose a Multi-level Graph Convolutional Network (GCN) with Automatic Graph Learning method (MGCN-AGL) for HSI classification. By employing attention mechanism to characterize the importance among spatially neighboring regions, the most relevant information can be adaptively incorporated to make decisions. Our MGCN-AGL encodes the long range dependencies among image regions based on the expressive representations that have been produced at local level.
  arXiv  Detail & Related papers  (2020-09-19T09:26:20Z)
• Smoothness Sensor: Adaptive Smoothness-Transition Graph Convolutions for Attributed Graph Clustering [10.905770964670191]
  We propose a smoothness sensor for attributed graph clustering based on adaptive smoothness-transition graph convolutions. As an alternative to graph-level smoothness, a novel fine-gained node-wise level assessment of smoothness is proposed. Experiments show that the proposed methods significantly outperform 12 other state-of-the-art baselines in terms of three different metrics.
  arXiv  Detail & Related papers  (2020-09-12T08:12:27Z)
• Tensor Graph Convolutional Networks for Multi-relational and Robust Learning [74.05478502080658]
  This paper introduces a tensor-graph convolutional network (TGCN) for scalable semi-supervised learning (SSL) from data associated with a collection of graphs, that are represented by a tensor. The proposed architecture achieves markedly improved performance relative to standard GCNs, copes with state-of-the-art adversarial attacks, and leads to remarkable SSL performance over protein-to-protein interaction networks.
  arXiv  Detail & Related papers  (2020-03-15T02:33:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.