Commuting Conditional GANs for Robust Multi-Modal Fusion.
Year: 2019
Type: preprint
Abstract: This paper presents a data driven approach to multi-modal fusion, where optimal features for each sensor are selected from a common hidden space between the different modalities. The existence of such a hidden space is then used in order to detect damaged sensors and safeguard the performance of the system. Experimental results show that such an approach can make the system robust against noisy/damaged sensors, without requiring human intervention to inform the system about the damage.
Source: arXiv (Cornell University)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2948322480