March 14, 2024, 4:45 a.m. | Morteza Bodaghi, Majid Hosseini, Raju Gottumukkala

cs.CV updates on

arXiv:2403.08077v1 Announce Type: new
Abstract: Multimodal deep learning methods capture synergistic features from multiple modalities and have the potential to improve accuracy for stress detection compared to unimodal methods. However, this accuracy gain typically comes from high computational cost due to the high-dimensional feature spaces, especially for intermediate fusion. Dimensionality reduction is one way to optimize multimodal learning by simplifying data and making the features more amenable to processing and analysis, thereby reducing computational complexity. This paper introduces an intermediate …

abstract accuracy arxiv computational cost deep learning detection feature features fusion however intermediate manifold multimodal multimodal deep learning multiple network spaces stress type

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