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Graph Convolutional Network

Graph Convolutional Network with Multi-View Topology for Lightweight Skeleton-Based Action Recognition


Skeleton-based action recognition is an important subject in deep learning. Graph Convolutional Networks (GCNs) have demonstrated strong performance by modeling the human skeleton as a natural topological graph, representing the connections between joints. However, most existing methods rely on non-adaptive topologies or insufficiently expressive representations. To address these limitations, we propose a Multi-view Topology Refinement Graph Convolutional Network (MTR-GCN), which is efficient, lightweight, and delivers high performance.

Specifically:


We propose a new spatial topology modeling approach that incorporates two views. A dynamic view fuses joint information from dual streams in a pairwise manner, while a static view encodes the shortest static paths between joints, preserving the original connectivity relationships.

We propose a new MultiScale Temporal Convolutional Network (MSTC), which is efficient and lightweight. Furthermore, we introduce a new temporal topology strategy by modeling temporal frames as a graph, which strengthens the extraction of temporal features. By modeling the human skeleton as both a spatial and a temporal graph, we reveal a topological symmetry between space and time within the unified spatio-temporal framework.

The proposed model achieves state-of-the-art performance on several benchmark datasets, including NTU RGB + D (XSub: 92.8%, XView: 96.8%), NTU RGB + D 120 (XSub: 89.6%, XSet: 90.8%), and NW-UCLA (95.7%), demonstrating the effectiveness of our GCN module, TCN module, and overall architecture.

This work addresses the limitations in spatial and temporal modeling for skeleton-based action recognition. For spatial modeling, we propose the Multi-view Topology Refinement Graph Convolution (MTRGC), which integrates both dynamic and static perspectives to overcome the issues of catastrophic forgetting of skeletal topology and insufficient relational modeling capacity in conventional GCNs. Experimental results demonstrate that MTRGC achieves a synergistic effect-greater than the sum of its individual views-rather than a simple additive gain. For temporal modeling, we introduce the MultiScale Temporal Convolution (MSTC), which enables lightweight design without compromising accuracy; building on this, we propose Gated Channel-wise Temporal Topology (GCTT) to perform topological modeling along the temporal dimension, effectively enhancing temporal feature extraction.

Our model achieves state-of-the-art performance across multiple benchmarks. However, there still exists the issue of incomplete feature extraction. It remains a challenge whether better skeleton features can be extracted using methods other than topology modeling, or if improvements can be made in data preprocessing. These are the challenges we face. Future work may focus on further improving training efficiency and exploring more advanced multi-relational modeling techniques.

Graph theory, network topology, adjacency matrix, graph traversal, spanning tree, shortest path, minimum spanning tree, directed graph, undirected graph, weighted graph, bipartite graph, Eulerian path, Hamiltonian cycle, graph coloring, planar graph, tree decomposition, connectivity, centrality, clique, and graph isomorphism are essential concepts in modern computational and data science research

#GraphTheory, #NetworkAnalysis, #GraphTraversal, #ShortestPath, #SpanningTree, #DirectedGraph, #UndirectedGraph, #WeightedGraph, #BipartiteGraph, #EulerianPath, #HamiltonianCycle, #GraphColoring, #PlanarGraph, #TreeDecomposition, #GraphConnectivity, #GraphCentrality, #GraphClique, #GraphIsomorphism, #GraphScience, #ComplexGraphs

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