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Audio-visual speaker diarization aims at detecting "who spoke when" usingboth auditory and visual signals. Existing audio-visual diarization datasetsare mainly focused on indoor environments like meeting rooms or news studios,which are quite different from in-the-wild videos in many scenarios such asmovies, documentaries, and audience sitcoms. To develop diarization methods forthese challenging videos, we create the AVA Audio-Visual Diarization (AVA-AVD)dataset. Our experiments demonstrate that adding AVA-AVD into training set canproduce significantly better diarization models for in-the-wild videos despitethat the data is relatively small. Moreover, this benchmark is challenging dueto the diverse scenes, complicated acoustic conditions, and completelyoff-screen speakers. As a first step towards addressing the challenges, wedesign the Audio-Visual Relation Network (AVR-Net) which introduces a simpleyet effective modality mask to capture discriminative information based on facevisibility. Experiments show that our method not only can outperformstate-of-the-art methods but is more robust as varying the ratio of off-screenspeakers. Our data and code has been made publicly available athttps://github.com/showlab/AVA-AVD.

AVA-AVD: Audio-Visual Speaker Diarization in the Wild