MAAS: Multi-Modal Assignation for Active Speaker Detection

Juan Léon Alcázar, Fabian Caba, Ali K. Thabet, Bernard Ghanem; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 265-274


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 local information, allows to approximate an instantaneous audio-visual assignment problem. Moreover, the temporal extension of this initial graph achieves a new state-of-the-art performance on the AVA-ActiveSpeaker dataset with a mAP of 88.8%.

Related Material

@InProceedings{Alcazar_2021_ICCV, author = {Alc\'azar, Juan L\'eon and Caba, Fabian and Thabet, Ali K. and Ghanem, Bernard}, title = {MAAS: Multi-Modal Assignation for Active Speaker Detection}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {265-274} }