MK-SGN: A Spiking Graph Convolutional Network with Multimodal Fusion and Knowledge Distillation for Skeleton-based Action Recognition

• URL: http://arxiv.org/abs/2404.10210v4
• Date: Fri, 13 Dec 2024 03:11:30 GMT
• Title: MK-SGN: A Spiking Graph Convolutional Network with Multimodal Fusion and Knowledge Distillation for Skeleton-based Action Recognition
• Authors: Naichuan Zheng, Hailun Xia, Zeyu Liang, Yuchen Du,
• Abstract summary: MK-SGN is proposed to leverage the energy efficiency of Spiking Neural Networks (SNNs) for skeleton-based action recognition.<n>By integrating the energy-saving properties of SNNs with the graph representation capabilities of GCNs, MK-SGN achieves significant reductions in energy consumption.<n>The proposed method achieves a remarkable reduction in energy consumption, exceeding 98% compared to conventional GCN-based approaches.
• Score: 0.8942525984879532
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, multimodal Graph Convolutional Networks (GCNs) have achieved remarkable performance in skeleton-based action recognition. The reliance on high-energy-consuming continuous floating-point operations inherent in GCN-based methods poses significant challenges for deployment in energy-constrained, battery-powered edge devices. To address these limitations, MK-SGN, a Spiking Graph Convolutional Network with Multimodal Fusion and Knowledge Distillation, is proposed to leverage the energy efficiency of Spiking Neural Networks (SNNs) for skeleton-based action recognition for the first time. By integrating the energy-saving properties of SNNs with the graph representation capabilities of GCNs, MK-SGN achieves significant reductions in energy consumption while maintaining competitive recognition accuracy. Firstly, we formulate a Spiking Multimodal Fusion (SMF) module to effectively fuse multimodal skeleton data represented as spike-form features. Secondly, we propose the Self-Attention Spiking Graph Convolution (SA-SGC) module and the Spiking Temporal Convolution (STC) module, to capture spatial relationships and temporal dynamics of spike-form features. Finally, we propose an integrated knowledge distillation strategy to transfer information from the multimodal GCN to the SGN, incorporating both intermediate-layer distillation and soft-label distillation to enhance the performance of the SGN. MK-SGN exhibits substantial advantages, surpassing state-of-the-art GCN frameworks in energy efficiency and outperforming state-of-the-art SNN frameworks in recognition accuracy. The proposed method achieves a remarkable reduction in energy consumption, exceeding 98\% compared to conventional GCN-based approaches. This research establishes a robust baseline for developing high-performance, energy-efficient SNN-based models for skeleton-based action recognition

Related papers

• Spiking Meets Attention: Efficient Remote Sensing Image Super-Resolution with Attention Spiking Neural Networks [57.17129753411926]
  Spiking neural networks (SNNs) are emerging as a promising alternative to traditional artificial neural networks (ANNs) We propose SpikeSR, which achieves state-of-the-art performance across various remote sensing benchmarks such as AID, DOTA, and DIOR.
  arXiv  Detail & Related papers  (2025-03-06T09:06:06Z)
• SNN-Driven Multimodal Human Action Recognition via Event Camera and Skeleton Data Fusion [0.7910116766220068]
  We propose a novel Spiking Neural Network (SNN)-driven framework for multimodal human action recognition. Our framework is centered on two key innovations: (1) a novel multimodal SNN architecture that employs distinct backbone networks for each modality, and (2) a pioneering SNN-based discretized information bottleneck mechanism.
  arXiv  Detail & Related papers  (2025-02-19T02:50:51Z)
• Signal-SGN: A Spiking Graph Convolutional Network for Skeletal Action Recognition via Learning Temporal-Frequency Dynamics [2.9578022754506605]
  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.
  arXiv  Detail & Related papers  (2024-08-03T07:47:16Z)
• Continuous Spiking Graph Neural Networks [43.28609498855841]
  Continuous graph neural networks (CGNNs) have garnered significant attention due to their ability to generalize existing discrete graph neural networks (GNNs) We introduce the high-order structure of COS-GNN, which utilizes the second-order ODE for spiking representation and continuous propagation. We provide the theoretical proof that COS-GNN effectively mitigates the issues of exploding and vanishing gradients, enabling us to capture long-range dependencies between nodes.
  arXiv  Detail & Related papers  (2024-04-02T12:36:40Z)
• Enhancing Energy Efficiency and Reliability in Autonomous Systems Estimation using Neuromorphic Approach [0.0]
  This study focuses on introducing an estimation framework based on spike coding theories and spiking neural networks (SNN) We propose an SNN-based Kalman filter (KF), a fundamental and widely adopted optimal strategy for well-defined linear systems. Based on the modified sliding innovation filter (MSIF) we present a robust strategy called SNN-MSIF.
  arXiv  Detail & Related papers  (2023-07-16T06:47:54Z)
• Spiking Variational Graph Auto-Encoders for Efficient Graph Representation Learning [10.65760757021534]
  We propose an SNN-based deep generative method, namely the Spiking Variational Graph Auto-Encoders (S-VGAE) for efficient graph representation learning. We conduct link prediction experiments on multiple benchmark graph datasets, and the results demonstrate that our model consumes significantly lower energy with the performances superior or comparable to other ANN- and SNN-based methods for graph representation learning.
  arXiv  Detail & Related papers  (2022-10-24T12:54:41Z)
• MGNNI: Multiscale Graph Neural Networks with Implicit Layers [53.75421430520501]
  implicit graph neural networks (GNNs) have been proposed to capture long-range dependencies in underlying graphs. We introduce and justify two weaknesses of implicit GNNs: the constrained expressiveness due to their limited effective range for capturing long-range dependencies, and their lack of ability to capture multiscale information on graphs at multiple resolutions. We propose a multiscale graph neural network with implicit layers (MGNNI) which is able to model multiscale structures on graphs and has an expanded effective range for capturing long-range dependencies.
  arXiv  Detail & Related papers  (2022-10-15T18:18:55Z)
• DG-STGCN: Dynamic Spatial-Temporal Modeling for Skeleton-based Action Recognition [77.87404524458809]
  We propose a new framework for skeleton-based action recognition, namely Dynamic Group Spatio-Temporal GCN (DG-STGCN) It consists of two modules, DG-GCN and DG-TCN, respectively, for spatial and temporal modeling. DG-STGCN consistently outperforms state-of-the-art methods, often by a notable margin.
  arXiv  Detail & Related papers  (2022-10-12T03:17:37Z)
• Spiking Graph Convolutional Networks [19.36064180392385]
  SpikingGCN is an end-to-end framework that aims to integrate the embedding of GCNs with the biofidelity characteristics of SNNs. We show that SpikingGCN on a neuromorphic chip can bring a clear advantage of energy efficiency into graph data analysis.
  arXiv  Detail & Related papers  (2022-05-05T16:44:36Z)
• SpatioTemporal Focus for Skeleton-based Action Recognition [66.8571926307011]
  Graph convolutional networks (GCNs) are widely adopted in skeleton-based action recognition. We argue that the performance of recent proposed skeleton-based action recognition methods is limited by the following factors. Inspired by the recent attention mechanism, we propose a multi-grain contextual focus module, termed MCF, to capture the action associated relation information.
  arXiv  Detail & Related papers  (2022-03-31T02:45:24Z)
• Multi-scale Graph Convolutional Networks with Self-Attention [2.66512000865131]
  Graph convolutional networks (GCNs) have achieved remarkable learning ability for dealing with various graph structural data. Over-smoothing phenomenon as a crucial issue of GCNs remains to be solved and investigated. We propose two novel multi-scale GCN frameworks by incorporating self-attention mechanism and multi-scale information into the design of GCNs.
  arXiv  Detail & Related papers  (2021-12-04T04:41:24Z)
• Multi-Scale Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition [140.18376685167857]
  A simple yet effective multi-scale semantics-guided neural network is proposed for skeleton-based action recognition. MS-SGN achieves the state-of-the-art performance on the NTU60, NTU120, and SYSU datasets.
  arXiv  Detail & Related papers  (2021-11-07T03:50:50Z)
• Fusion-GCN: Multimodal Action Recognition using Graph Convolutional Networks [0.5801044612920815]
  Fusion-GCN is an approach for multimodal action recognition using Graph Convolutional Networks (GCNs) We integrate various sensor data modalities into a graph that is trained using a GCN model for multi-modal action recognition.
  arXiv  Detail & Related papers  (2021-09-27T10:52:33Z)
• Towards Efficient Graph Convolutional Networks for Point Cloud Handling [181.59146413326056]
  We aim at improving the computational efficiency of graph convolutional networks (GCNs) for learning on point clouds. A series of experiments show that optimized networks have reduced computational complexity, decreased memory consumption, and accelerated inference speed.
  arXiv  Detail & Related papers  (2021-04-12T17:59:16Z)
• 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)
• On the spatial attention in Spatio-Temporal Graph Convolutional Networks for skeleton-based human action recognition [97.14064057840089]
  Graphal networks (GCNs) promising performance in skeleton-based human action recognition by modeling a sequence of skeletons as a graph. Most of the recently proposed G-temporal-based methods improve the performance by learning the graph structure at each layer of the network.
  arXiv  Detail & Related papers  (2020-11-07T19:03:04Z)
• 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)
• Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation [56.73834525802723]
  Lightweight Dynamic Graph Convolutional Networks (LDGCNs) are proposed. LDGCNs capture richer non-local interactions by synthesizing higher order information from the input graphs. We develop two novel parameter saving strategies based on the group graph convolutions and weight tied convolutions to reduce memory usage and model complexity.
  arXiv  Detail & Related papers  (2020-10-09T06:03:46Z)
• Binarized Graph Neural Network [65.20589262811677]
  We develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters. Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches. Experiments indicate that the proposed binarized graph neural network, namely BGN, is orders of magnitude more efficient in terms of both time and space.
  arXiv  Detail & Related papers  (2020-04-19T09:43:14Z)

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.