English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Skeleton-based action recognition has achieved remarkable results in humanaction recognition with the development of graph convolutional networks (GCNs).However, the recent works tend to construct complex learning mechanisms withredundant training and exist a bottleneck for long time-series. To solve theseproblems, we propose the Temporal-Spatio Graph ConvNeXt (TSGCNeXt) to exploreefficient learning mechanism of long temporal skeleton sequences. Firstly, anew graph learning mechanism with simple structure, Dynamic-Static SeparateMulti-graph Convolution (DS-SMG) is proposed to aggregate features of multipleindependent topological graphs and avoid the node information being ignoredduring dynamic convolution. Next, we construct a graph convolution trainingacceleration mechanism to optimize the back-propagation computing of dynamicgraph learning with 55.08\% speed-up. Finally, the TSGCNeXt restructure theoverall structure of GCN with three Spatio-temporal learningmodules,efficiently modeling long temporal features. In comparison withexisting previous methods on large-scale datasets NTU RGB+D 60 and 120,TSGCNeXt outperforms on single-stream networks. In addition, with the ema modelintroduced into the multi-stream fusion, TSGCNeXt achieves SOTA levels. On thecross-subject and cross-set of the NTU 120, accuracies reach 90.22% and 91.74%.

TSGCNeXt: Dynamic-Static Multi-Graph Convolution for Efficient Skeleton-Based Action Recognition with Long-term Learning Potential