Recognizing and localizing events in videos is a fundamental task for video
understanding. Since events may occur in auditory and visual modalities,
multimodal detailed perception is essential for complete scene comprehension.
Most previous works attempted to analyze videos from a holistic perspective.
However, they do not consider semantic information at multiple scales, which
makes the model difficult to localize events in different lengths. In this
paper, we present a Multimodal Pyramid Attentional Network
(\textbf{MM-Pyramid}) for event localization. Specifically, we first propose
the attentive feature pyramid module. This module captures temporal pyramid
features via several stacking pyramid units, each of them is composed of a
fixed-size attention block and dilated convolution block. We also design an
adaptive semantic fusion module, which leverages a unit-level attention block
and a selective fusion block to integrate pyramid features interactively.
Extensive experiments on audio-visual event localization and weakly-supervised
audio-visual video parsing tasks verify the effectiveness of our approach.

提出了一种名为 MM-Pyramid 的多模式金字塔式注意力网络，该网络使用注意力特征金字塔模块和语义融合模块，以实现视频中事件的识别和定位。