Signal-SGN++: Enhanced Action Recognition with Spiking Graph Networks

This research explores a novel approach to action recognition using spiking graph networks, a bio-inspired architecture. The focus on topology and time-frequency analysis suggests an attempt to improve robustness and efficiency in understanding human actions from skeletal data.

• •Leverages spiking graph networks for skeleton-based action recognition.
• •Employs topology-enhanced techniques to improve performance.
• •Focuses on time-frequency analysis within the network architecture.