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 |
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Virtual Presentation |
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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. |