Multimodal sensor fusion in the latent representation space
Fuente:
arXiv
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2022
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866908382133223424 |
|---|---|
| author | Piechocki, Robert J. Wang, Xiaoyang Bocus, Mohammud J. |
| author_facet | Piechocki, Robert J. Wang, Xiaoyang Bocus, Mohammud J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2208_02183 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Multimodal sensor fusion in the latent representation space Piechocki, Robert J. Wang, Xiaoyang Bocus, Mohammud J. Artificial Intelligence Human-Computer Interaction Machine Learning Signal Processing 68Txx I.2.4; H.1.2; I.2.6 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. |
| title | Multimodal sensor fusion in the latent representation space |
| topic | Artificial Intelligence Human-Computer Interaction Machine Learning Signal Processing 68Txx I.2.4; H.1.2; I.2.6 |
| url | https://arxiv.org/abs/2208.02183 |