Robust Multi-Modal Sensor Fusion: An Adversarial Approach

نویسندگان

چکیده

In recent years, multi-modal fusion has attracted a lot of research interest, both in academia, and industry. Multimodal entails the combination information from set different types sensors. Exploiting complementary sensors, we show that target detection classification problems can greatly benefit this approach result performance increase. To achieve gain, various sensors is shown to require some principled strategy ensure additional constructively used, positive impact on performance. We subsequently demonstrate viability proposed by weakening strong dependence functionality all hence introducing flexibility our solution lifting severe limitation unconstrained surveillance settings with potential environmental impact. Our data driven multimodal fusion, exploits selected optimal features an estimated latent space across modalities. This hidden learned via generative network conditioned individual sensor The space, as intrinsic structure, then exploited detecting damaged safeguarding fused system. Experimental results such automatic system robustness against noisy/damaged

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ژورنال

عنوان ژورنال: IEEE Sensors Journal

سال: 2021

ISSN: ['1558-1748', '1530-437X']

DOI: https://doi.org/10.1109/jsen.2020.3018698