  It is now well established from a variety of studies that there is a
significant benefit from combining video and audio data in detecting active
speakers. However, either of the modalities can potentially mislead audiovisual
fusion by inducing unreliable or deceptive information. This paper outlines
active speaker detection as a multi-objective learning problem to leverage best
of each modalities using a novel self-attention, uncertainty-based multimodal
fusion scheme. Results obtained show that the proposed multi-objective learning
architecture outperforms traditional approaches in improving both mAP and AUC
scores. We further demonstrate that our fusion strategy surpasses, in active
speaker detection, other modality fusion methods reported in various
disciplines. We finally show that the proposed method significantly improves
the state-of-the-art on the AVA-ActiveSpeaker dataset.
