Skeleton-based Action Recognition

insight - Skeleton-based Action Recognition

SkateFormer: Skeletal-Temporal Transformer for Human Action Recognition

Efficiently capturing skeletal-temporal relations for improved action recognition.

Leveraging Large Language Models for Accurate Skeleton-based Action Recognition

Large language models can be effectively leveraged as powerful action recognizers by projecting skeleton sequences into "action sentences" that are compatible with the models' pre-trained knowledge.

Spiking Graph Convolutional Network with Multimodal Fusion and Knowledge Distillation for Efficient Skeleton-based Action Recognition

A novel Spiking Graph Convolutional Network (SGN) with multimodal fusion and knowledge distillation is proposed to achieve efficient and accurate skeleton-based action recognition.

A Hybrid Dual-Branch Network for Robust Skeleton-Based Action Recognition

The proposed Hybrid Dual-Branch Network (HDBN) effectively combines Graph Convolutional Networks (GCNs) and Transformers to achieve robust and accurate skeleton-based action recognition.

Improved Graph Pooling Network for Efficient Skeleton-Based Action Recognition

The proposed Improved Graph Pooling Network (IGPN) incorporates a region-aware pooling strategy, cross fusion block, and information supplement module to enhance the representation ability of skeleton features while reducing computational overhead.

Enhancing Action Recognition from Low-Quality Skeleton Data via Part-Level Knowledge Distillation

The proposed knowledge distillation framework distills discriminative part-level knowledge from heterogeneous high-quality skeletons to enhance representations of low-quality skeletons, enabling accurate action recognition even in the presence of intensive noise.

About

Products | Resources

Insights