Cross-Modal Background Suppression for Audio-Visual Event Localization

Audiovisual Event (AVE) localization requires the model to jointly localize an event by observing audio and visual information. However, in unconstrained videos, both information types may be inconsistent or suffer from severe background noise. Hence this paper proposes a novel cross-modal background suppression network for AVE task, operating at the time- and event-level, aiming to improve localization performance through suppressing asynchronous audiovisual background frames from the examined events and reducing redundant noise. Specifically, the time-level background suppression scheme forces the audio and visual modality to focus on the related information in the temporal dimension that the opposite modality considers essential, and reduces attention to the segments that the other modal considers as background. The event-level background suppression scheme uses the class activation sequences predicted by audio and visual modalities to control the final event category prediction, which can effectively suppress noise events occurring accidentally in a single modality. Furthermore, we introduce a cross-modal gated attention scheme to extract relevant visual regions from complex scenes exploiting both global visual and audio signals. Extensive experiments show our method outperforms the state-of-the-art methods by a large margin in both supervised and weakly supervised AVE settings.