Audio-visual representation learning is an important task from the
perspective of designing machines with the ability to understand complex
events. To this end, we propose a novel multimodal framework that instantiates
multiple instance learning. We show that the learnt representations are useful
for classifying events and localizing their characteristic audio-visual
elements. The system is trained using only video-level event labels without any
timing information. An important feature of our method is its capacity to learn
from unsynchronized audio-visual events. We achieve state-of-the-art results on
a large-scale dataset of weakly-labeled audio event videos. Visualizations of
localized visual regions and audio segments substantiate our system's efficacy,
especially when dealing with noisy situations where modality-specific cues
appear asynchronously.

本文提出了一种基于多模态学习的新型框架，可以从非同步的音频和视觉事件中学习，用于事件分类和定位。使用该方法可以取得弱标签音频事件视频大规模数据集的最先进结果。