  Audio-visual event localization aims to localize an event that is both
audible and visible in the wild, which is a widespread audio-visual scene
analysis task for unconstrained videos. To address this task, we propose a
Multimodal Parallel Network (MPN), which can perceive global semantics and
unmixed local information parallelly. Specifically, our MPN framework consists
of a classification subnetwork to predict event categories and a localization
subnetwork to predict event boundaries. The classification subnetwork is
constructed by the Multimodal Co-attention Module (MCM) and obtains global
contexts. The localization subnetwork consists of Multimodal Bottleneck
Attention Module (MBAM), which is designed to extract fine-grained
segment-level contents. Extensive experiments demonstrate that our framework
achieves the state-of-the-art performance both in fully supervised and weakly
supervised settings on the Audio-Visual Event (AVE) dataset.
