Progressive Spatio-Temporal Graph Convolutional Network for Skeleton-Based Human Action Recognition

• URL: http://arxiv.org/abs/2011.05668v2
• Date: Mon, 26 Apr 2021 20:43:10 GMT
• Title: Progressive Spatio-Temporal Graph Convolutional Network for Skeleton-Based Human Action Recognition
• Authors: Negar Heidari and Alexandros Iosifidis
• Abstract summary: We propose a method to automatically find a compact and problem-specific network for graph convolutional networks in a progressive manner. Experimental results on two datasets for skeleton-based human action recognition indicate that the proposed method has competitive or even better classification performance.
• Score: 97.14064057840089
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph convolutional networks (GCNs) have been very successful in skeleton-based human action recognition where the sequence of skeletons is modeled as a graph. However, most of the GCN-based methods in this area train a deep feed-forward network with a fixed topology that leads to high computational complexity and restricts their application in low computation scenarios. In this paper, we propose a method to automatically find a compact and problem-specific topology for spatio-temporal graph convolutional networks in a progressive manner. Experimental results on two widely used datasets for skeleton-based human action recognition indicate that the proposed method has competitive or even better classification performance compared to the state-of-the-art methods with much lower computational complexity.

Related papers

• Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling [83.77955213766896]
  Graph convolutional networks (GCNs) have recently achieved great empirical success in learning graphstructured data. To address its scalability issue, graph topology sampling has been proposed to reduce the memory and computational cost of training Gs. This paper provides first theoretical justification of graph topology sampling in training (up to) three-layer GCNs.
  arXiv  Detail & Related papers  (2022-07-07T21:25:55Z)
• Optimal Propagation for Graph Neural Networks [51.08426265813481]
  We propose a bi-level optimization approach for learning the optimal graph structure. We also explore a low-rank approximation model for further reducing the time complexity.
  arXiv  Detail & Related papers  (2022-05-06T03:37:00Z)
• CCasGNN: Collaborative Cascade Prediction Based on Graph Neural Networks [0.49269463638915806]
  Cascade prediction aims at modeling information diffusion in the network. Recent efforts devoted to combining network structure and sequence features by graph neural networks and recurrent neural networks. We propose a novel method CCasGNN considering the individual profile, structural features, and sequence information.
  arXiv  Detail & Related papers  (2021-12-07T11:37:36Z)
• Skeleton-based Hand-Gesture Recognition with Lightweight Graph Convolutional Networks [14.924672048447338]
  Graph convolutional networks (GCNs) aim at extending deep learning to arbitrary irregular domains, such as graphs. We introduce a novel method that learns the topology of input graphs as a part of GCN design. Experiments conducted on the challenging task of skeleton-based hand-gesture recognition show the high effectiveness of the learned GCNs.
  arXiv  Detail & Related papers  (2021-04-09T09:06:53Z)
• Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action Recognition [126.51241919472356]
  We design a simple and highly modularized graph convolutional network architecture for skeleton-based action recognition. Our network is constructed by repeating a building block that aggregates multi-granularity information from both the spatial and temporal paths.
  arXiv  Detail & Related papers  (2020-11-26T14:43:04Z)
• Temporal Attention-Augmented Graph Convolutional Network for Efficient Skeleton-Based Human Action Recognition [97.14064057840089]
  Graphal networks (GCNs) have been very successful in modeling non-Euclidean data structures. Most GCN-based action recognition methods use deep feed-forward networks with high computational complexity to process all skeletons in an action. We propose a temporal attention module (TAM) for increasing the efficiency in skeleton-based action recognition.
  arXiv  Detail & Related papers  (2020-10-23T08:01:55Z)
• Progressive Graph Convolutional Networks for Semi-Supervised Node Classification [97.14064057840089]
  Graph convolutional networks have been successful in addressing graph-based tasks such as semi-supervised node classification. We propose a method to automatically build compact and task-specific graph convolutional networks.
  arXiv  Detail & Related papers  (2020-03-27T08:32:16Z)
• Shift Aggregate Extract Networks [3.3263205689999453]
  We introduce an architecture based on deep hierarchical decompositions to learn effective representations of large graphs. Our framework extends classic R-decompositions used in kernel methods, enabling nested part-of-part relations. We show empirically that our approach is able to outperform current state-of-the-art graph classification methods on large social network datasets.
  arXiv  Detail & Related papers  (2017-03-16T09:52:48Z)

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.