Signal-SGN: A Spiking Graph Convolutional Network for Skeletal Action Recognition via Learning Temporal-Frequency Dynamics

• URL: http://arxiv.org/abs/2408.01701v2
• Date: Fri, 18 Oct 2024 05:12:00 GMT
• Title: Signal-SGN: A Spiking Graph Convolutional Network for Skeletal Action Recognition via Learning Temporal-Frequency Dynamics
• Authors: Naichuan Zheng, Hailun Xia, Dapeng Liu,
• Abstract summary: In skeletal-based action recognition, Graph Convolutional Networks (GCNs) face limitations due to their complexity and high energy consumption. We propose a Signal-SGN(Spiking Graph Convolutional Network), which leverages the temporal dimension of skeletal sequences as the spiking timestep. Our experiments show that the proposed models not only surpass existing SNN-based methods in accuracy but also reduce computational storage costs during training.
• Score: 2.9578022754506605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In skeletal-based action recognition, Graph Convolutional Networks (GCNs) based methods face limitations due to their complexity and high energy consumption. Spiking Neural Networks (SNNs) have gained attention in recent years for their low energy consumption, but existing methods combining GCNs and SNNs fail to fully utilize the temporal characteristics of skeletal sequences, leading to increased storage and computational costs. To address this issue, we propose a Signal-SGN(Spiking Graph Convolutional Network), which leverages the temporal dimension of skeletal sequences as the spiking timestep and treats features as discrete stochastic signals. The core of the network consists of a 1D Spiking Graph Convolutional Network (1D-SGN) and a Frequency Spiking Convolutional Network (FSN). The SGN performs graph convolution on single frames and incorporates spiking network characteristics to capture inter-frame temporal relationships, while the FSN uses Fast Fourier Transform (FFT) and complex convolution to extract temporal-frequency features. We also introduce a multi-scale wavelet transform feature fusion module(MWTF) to capture spectral features of temporal signals, enhancing the model's classification capability. We propose a pluggable temporal-frequency spatial semantic feature extraction module(TFSM) to enhance the model's ability to distinguish features without increasing inference-phase consumption. Our numerous experiments on the NTU RGB+D, NTU RGB+D 120, and NW-UCLA datasets demonstrate that the proposed models not only surpass existing SNN-based methods in accuracy but also reduce computational and storage costs during training. Furthermore, they achieve competitive accuracy compared to corresponding GCN-based methods, which is quite remarkable.

Related papers

• Point Cloud Denoising With Fine-Granularity Dynamic Graph Convolutional Networks [58.050130177241186]
  Noise perturbations often corrupt 3-D point clouds, hindering downstream tasks such as surface reconstruction, rendering, and further processing. This paper introduces finegranularity dynamic graph convolutional networks called GDGCN, a novel approach to denoising in 3-D point clouds.
  arXiv  Detail & Related papers  (2024-11-21T14:19:32Z)
• BLIS-Net: Classifying and Analyzing Signals on Graphs [20.345611294709244]
  Graph neural networks (GNNs) have emerged as a powerful tool for tasks such as node classification and graph classification. We introduce the BLIS-Net (Bi-Lipschitz Scattering Net), a novel GNN that builds on the previously introduced geometric scattering transform. We show that BLIS-Net achieves superior performance on both synthetic and real-world data sets based on traffic flow and fMRI data.
  arXiv  Detail & Related papers  (2023-10-26T17:03:14Z)
• Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective [53.999128831324576]
  Graph neural networks (GNNs) have pioneered advancements in graph representation learning. This study investigates the role of graph convolution within the context of feature learning theory.
  arXiv  Detail & Related papers  (2023-06-24T10:21:11Z)
• STSC-SNN: Spatio-Temporal Synaptic Connection with Temporal Convolution and Attention for Spiking Neural Networks [7.422913384086416]
  Spiking Neural Networks (SNNs), as one of the algorithmic models in neuromorphic computing, have gained a great deal of research attention owing to temporal processing capability. Existing synaptic structures in SNNs are almost full-connections or spatial 2D convolution, neither which can extract temporal dependencies adequately. We take inspiration from biological synapses and propose a synaptic connection SNN model, to enhance the synapse-temporal receptive fields of synaptic connections. We show that endowing synaptic models with temporal dependencies can improve the performance of SNNs on classification tasks.
  arXiv  Detail & Related papers  (2022-10-11T08:13:22Z)
• Continual Spatio-Temporal Graph Convolutional Networks [87.86552250152872]
  We reformulating the Spatio-Temporal Graph Convolutional Neural Network as a Continual Inference Network. We observe up to 109x reduction in time complexity, on- hardware accelerations of 26x, and reductions in maximum allocated memory of 52% during online inference.
  arXiv  Detail & Related papers  (2022-03-21T14:23:18Z)
• Ultra-low Latency Spiking Neural Networks with Spatio-Temporal Compression and Synaptic Convolutional Block [4.081968050250324]
  Spiking neural networks (SNNs) have neuro-temporal information capability, low processing feature, and high biological plausibility. Neuro-MNIST, CIFAR10-S, DVS128 gesture datasets need to aggregate individual events into frames with a higher temporal resolution for event stream classification. We propose a processing-temporal compression method to aggregate individual events into a few time steps of NIST current to reduce the training and inference latency.
  arXiv  Detail & Related papers  (2022-03-18T15:14:13Z)
• Space-Time Graph Neural Networks [104.55175325870195]
  We introduce space-time graph neural network (ST-GNN) to jointly process the underlying space-time topology of time-varying network data. Our analysis shows that small variations in the network topology and time evolution of a system does not significantly affect the performance of ST-GNNs.
  arXiv  Detail & Related papers  (2021-10-06T16:08:44Z)
• Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning [9.88508686848173]
  Biological spiking neurons with intrinsic dynamics underlie the powerful representation and learning capabilities of the brain. Despite recent tremendous progress in spiking neural networks (SNNs) for handling Euclidean-space tasks, it still remains challenging to exploit SNNs in processing non-Euclidean-space data. Here we present a general spike-based modeling framework that enables the direct training of SNNs for graph learning.
  arXiv  Detail & Related papers  (2021-06-30T11:20:16Z)
• 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)
• Data-Driven Learning of Geometric Scattering Networks [74.3283600072357]
  We propose a new graph neural network (GNN) module based on relaxations of recently proposed geometric scattering transforms. Our learnable geometric scattering (LEGS) module enables adaptive tuning of the wavelets to encourage band-pass features to emerge in learned representations.
  arXiv  Detail & Related papers  (2020-10-06T01:20:27Z)

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.