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    MAAS: Multi-modal Assignation for Active Speaker Detection

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    Type
    Preprint
    Authors
    León-Alcázar, Juan
    Heilbron, Fabian Caba
    Thabet, Ali Kassem cc
    Ghanem, Bernard cc
    KAUST Department
    King Abdullah University of Science and Technology.
    Visual Computing Center (VCC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Date
    2021-01-11
    Permanent link to this record
    http://hdl.handle.net/10754/666896
    
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    Abstract
    Active speaker detection requires a solid integration of multi-modal cues. While individual modalities can approximate a solution, accurate predictions can only be achieved by explicitly fusing the audio and visual features and modeling their temporal progression. Despite its inherent muti-modal nature, current methods still focus on modeling and fusing short-term audiovisual features for individual speakers, often at frame level. In this paper we present a novel approach to active speaker detection that directly addresses the multi-modal nature of the problem, and provides a straightforward strategy where independent visual features from potential speakers in the scene are assigned to a previously detected speech event. Our experiments show that, an small graph data structure built from a single frame, allows to approximate an instantaneous audio-visual assignment problem. Moreover, the temporal extension of this initial graph achieves a new state-of-the-art on the AVA-ActiveSpeaker dataset with a mAP of 88.8\%.
    Publisher
    arXiv
    arXiv
    arXiv:2101.03682
    Additional Links
    https://arxiv.org/pdf/2101.03682
    Collections
    Preprints; Electrical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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