  In recent years, audio-visual event localization has attracted much
attention. It's purpose is to detect the segment containing audio-visual events
and recognize the event category from untrimmed videos. Existing methods use
audio-guided visual attention to lead the model pay attention to the spatial
area of the ongoing event, devoting to the correlation between audio and visual
information but ignoring the correlation between audio and spatial motion. We
propose a past and future motion extraction (pf-ME) module to mine the visual
motion from videos ,embedded into the past and future motion guided network
(PFAGN), and motion guided audio attention (MGAA) module to achieve focusing on
the information related to interesting events in audio modality through the
past and future visual motion. We choose AVE as the experimental verification
dataset and the experiments show that our method outperforms the
state-of-the-arts in both supervised and weakly-supervised settings.
