| Paper ID | MLSP-17.4 |
| Paper Title |
PROGRESSIVE SPATIO-TEMPORAL GRAPH CONVOLUTIONAL NETWORK FOR SKELETON-BASED HUMAN ACTION RECOGNITION |
| Authors |
Negar Heidari, Alexandros Iosifidis, Aarhus University, Denmark |
| Session | MLSP-17: Graph Neural Networks |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-DEEP] Deep learning techniques |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Graph convolutional networks have been very successful in skeleton-based human action recognition where the sequence of skeletons is modeled as a graph. However, most of the graph convolutional network-based methods in this area train a deep feed-forward network with a fixed topology that leads to high computational complexity and restricts their application in low computation scenarios. In this paper, we propose a method to automatically find a compact and problem-specific topology for spatio-temporal graph convolutional networks in a progressive manner. Experimental results on two widely used datasets for skeleton-based human action recognition indicate that the proposed method has competitive or even better classification performance compared to the state-of-the-art methods while it has much lower computational complexity. |
