Papers › MAAS: Multi-modal Assignation for Active Speaker Detection
MAAS: Multi-modal Assignation for Active Speaker Detection
Juan León-Alcázar, Fabian Caba Heilbron, Ali Thabet, Bernard Ghanem
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\%.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Audio-Visual Active Speaker Detection | AVA-ActiveSpeaker | MAAS-TAN | validation mean average precision | 88.8% | #16 of 20 | Archive leaderboard | report |
| Audio-Visual Active Speaker Detection | AVA-ActiveSpeaker | MAAS-LAN | validation mean average precision | 85.1% | #19 of 20 | Archive leaderboard | report |
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