# Space-Time Domain Tensor Neural Networks: An Application on Human Pose Classification

arXiv: https://arxiv.org/abs/2004.08153 · HTML: https://mlsanity.com/paper/2004.08153/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2020.

## Built on by (most cited first, 1 of 1)

- [AffRankNet+: Ranking Affect Using Privileged Information](https://mlsanity.com/paper/2108.05598/) (2021)

## Abstract (arXiv)

Recent advances in sensing technologies require the design and development of pattern recognition models capable of processing spatiotemporal data efficiently. In this study, we propose a spatially and temporally aware tensor-based neural network for human pose classification using three-dimensional skeleton data. Our model employs three novel components. First, an input layer capable of constructing highly discriminative spatiotemporal features. Second, a tensor fusion operation that produces compact yet rich representations of the data, and third, a tensor-based neural network that processes data representations in their original tensor form. Our model is end-to-end trainable and characterized by a small number of trainable parameters making it suitable for problems where the annotated data is limited. Experimental evaluation of the proposed model indicates that it can achieve state-of-the-art performance.