Multi-Scale Adaptive Graph Convolutional Network for Skeleton-Based Action Recognition

Fan, Yiqi; Wang, Xiaojuan; Lv, Tianqi; Wu, Lingrui

doi:10.1109/iccse49874.2020.9201897

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…For example, Li et al [20] used multi-scale multistreaming GCN to obtain more discriminating temporal features. Fan et al [21] selectively fused different scale features. Li et al [22] generated the next scale by removing some joints in the middle position.…”

Section: Multi-scale Convolutional Networkmentioning

confidence: 99%

Human Motion Prediction Based on a Multi-Scale Hypergraph for Intangible Cultural Heritage Dance Videos

Cai,

Cheng,

Liu

et al. 2023

Electronics

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Compared to traditional dance, intangible cultural heritage dance often involves the isotropic extension of choreographic actions, utilizing both upper and lower limbs. This characteristic choreography style makes the remote joints lack interaction, consequently reducing accuracy in existing human motion prediction methods. Therefore, we propose a human motion prediction method based on the multi-scale hypergraph convolutional network of the intangible cultural heritage dance video. Firstly, this method inputs the 3D human posture sequence from intangible cultural heritage dance videos. The hypergraph is designed according to the synergistic relationship of the human joints in the intangible cultural heritage dance video, which is used to represent the spatial correlation of the 3D human posture. Then, a multi-scale hypergraph convolutional network is constructed, utilizing multi-scale transformation operators to segment the human skeleton into different scales. This network adopts a graph structure to represent the 3D human posture at different scales, which is then used by the single-scalar fusion operator to spatial features in the 3D human posture sequence are extracted by fusing the feature information of the hypergraph and the multi-scale graph. Finally, the Temporal Graph Transformer network is introduced to capture the temporal dependence among adjacent frames within the time domain. This facilitates the extraction of temporal features from the 3D human posture sequence, ultimately enabling the prediction of future 3D human posture sequences. Experiments show that we achieve the best performance in both short-term and long-term human motion prediction when compared to Motion-Mixer and Motion-Attention algorithms on Human3.6M and 3DPW datasets. In addition, ablation experiments show that our method can predict more precise 3D human pose sequences, even in the presence of isotropic extensions of upper and lower limbs in intangible cultural heritage dance videos. This approach effectively addresses the issue of missing segments in intangible cultural heritage dance videos.

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Section: Multi-scale Convolutional Networkmentioning

confidence: 99%

Human Motion Prediction Based on a Multi-Scale Hypergraph for Intangible Cultural Heritage Dance Videos

Cai,

Cheng,

Liu

et al. 2023

Electronics

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“…For example, Y. Fan et al [ 65 ] conducted two more downsampling operations to extract additional graphs of different scales from the original graph.…”

Section: The Structure Of Skeleton Graphsmentioning

confidence: 99%

Skeleton Graph-Neural-Network-Based Human Action Recognition: A Survey

Feng

Meunier

2022

Sensors

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Human action recognition has been applied in many fields, such as video surveillance and human computer interaction, where it helps to improve performance. Numerous reviews of the literature have been done, but rarely have these reviews concentrated on skeleton-graph-based approaches. Connecting the skeleton joints as in the physical appearance can naturally generate a graph. This paper provides an up-to-date review for readers on skeleton graph-neural-network-based human action recognition. After analyzing previous related studies, a new taxonomy for skeleton-GNN-based methods is proposed according to their designs, and their merits and demerits are analyzed. In addition, the datasets and codes are discussed. Finally, future research directions are suggested.

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Attention Residual Connection Based Graph Convolution Networks for Skeleton-based Action Recognition

Chen

Jiang

Zhou

et al. 2021

2021 6th International Conference on Communication, Image and Signal Processing (CCISP)

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Multi-Scale Adaptive Graph Convolutional Network for Skeleton-Based Action Recognition

Cited by 4 publications

References 18 publications

Human Motion Prediction Based on a Multi-Scale Hypergraph for Intangible Cultural Heritage Dance Videos

Human Motion Prediction Based on a Multi-Scale Hypergraph for Intangible Cultural Heritage Dance Videos

Skeleton Graph-Neural-Network-Based Human Action Recognition: A Survey

Attention Residual Connection Based Graph Convolution Networks for Skeleton-based Action Recognition

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