Bocus, Mohammud J.

Piechocki, Robert J.

Wang, Xiaoyang

English

A new method for multimodal sensor fusion is introduced. The technique relies on a two-stage process. In the first stage, a multimodal generative model is constructed from unlabelled training data. In the second stage, the generative model serves as a reconstruction prior and the search manifold for the sensor fusion tasks. The method also handles cases where observations are accessed only via subsampling i.e. compressed sensing. We demonstrate the effectiveness and excellent performance on a range of multimodal fusion experiments such as multisensory classification, denoising, and recovery from subsampled observations

Explore Bristol Research

Multimodal sensor fusion in the latent representation space

https://research-information.bris.ac.uk/en/publications/aee7556d-295e-4883-9203-5c5f52b680ab

Multimodal sensor fusion in the latent representation space

Abstract

Similar works

Full text

Available Versions

Explore Bristol Research