Tri-axial Self-Attention for Concurrent Activity Recognition

Yanyi Zhang; Xinyu Li; Kaixiang Huang; Yehan Wang; Shuhong Chen; Ivan Marsic

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arXiv · 1812.02817

Tri-axial Self-Attention for Concurrent Activity Recognition

Yanyi Zhang·Xinyu Li·Kaixiang Huang·Yehan Wang·Shuhong Chen·Ivan Marsic

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Abstract

We present a system for concurrent activity recognition. To extract features associated with different activities, we propose a feature-to-activity attention that maps the extracted global features to sub-features associated with individual activities. To model the temporal associations of individual activities, we propose a transformer-network encoder that models independent temporal associations for each activity. To make the concurrent activity prediction aware of the potential associations between activities, we propose self-attention with an association mask. Our system achieved state-of-the-art or comparable performance on three commonly used concurrent activity detection datasets. Our visualizations demonstrate that our system is able to locate the important spatial-temporal features for final decision making. We also showed that our system can be applied to general multilabel classification problems.

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Yanyi Zhang, Xinyu Li, Kaixiang Huang, Yehan Wang, Shuhong Chen, Ivan Marsic. 2018-12-06. Tri-axial Self-Attention for Concurrent Activity Recognition. https://arxiv.org/abs/1812.02817

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