  Audio-visual event localization requires one to identify theevent which is
both visible and audible in a video (eitherat a frame or video level). To
address this task, we pro-pose a deep neural network named Audio-Visual
sequence-to-sequence dual network (AVSDN). By jointly taking bothaudio and
visual features at each time segment as inputs, ourproposed model learns global
and local event information ina sequence to sequence manner, which can be
realized in ei-ther fully supervised or weakly supervised settings.
Empiricalresults confirm that our proposed method performs favorablyagainst
recent deep learning approaches in both settings.
