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