  When watching videos, the occurrence of a visual event is often accompanied
by an audio event, e.g., the voice of lip motion, the music of playing
instruments. There is an underlying correlation between audio and visual
events, which can be utilized as free supervised information to train a neural
network by solving the pretext task of audio-visual synchronization. In this
paper, we propose a novel self-supervised framework with co-attention mechanism
to learn generic cross-modal representations from unlabelled videos in the
wild, and further benefit downstream tasks. Specifically, we explore three
different co-attention modules to focus on discriminative visual regions
correlated to the sounds and introduce the interactions between them.
Experiments show that our model achieves state-of-the-art performance on the
pretext task while having fewer parameters compared with existing methods. To
further evaluate the generalizability and transferability of our approach, we
apply the pre-trained model on two downstream tasks, i.e., sound source
localization and action recognition. Extensive experiments demonstrate that our
model provides competitive results with other self-supervised methods, and also
indicate that our approach can tackle the challenging scenes which contain
multiple sound sources.
