# Fusion of Deep Neural Networks for Activity Recognition: A Regular Vine Copula Based Approach

arXiv: https://arxiv.org/abs/1905.02703 · HTML: https://mlsanity.com/paper/1905.02703/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2019.

## Built on by (most cited first, 3 of 3)

- [Interpretable Data Fusion for Distributed Learning: A Representative Approach via Gradient Matching](https://mlsanity.com/paper/2405.03782/) (2024)
- [PFedDST: Personalized Federated Learning with Decentralized Selection Training](https://mlsanity.com/paper/2502.07750/) (2025)
- [Teacher-Student Knowledge Distillation for Radar Perception on Embedded Accelerators](https://mlsanity.com/paper/2303.07586/) (2023)

## Abstract (arXiv)

In this paper, we propose regular vine copula based fusion of multiple deep neural network classifiers for the problem of multi-sensor based human activity recognition. We take the cross-modal dependence into account by employing regular vine copulas that are extremely flexible and powerful graphical models to characterize complex dependence among multiple modalities. Multiple deep neural networks are used to extract high-level features from multi-sensing modalities, with each deep neural network processing the data collected from a single sensor. The extracted high-level features are then combined using a regular vine copula model. Numerical experiments are conducted to demonstrate the effectiveness of our approach.