In this paper, we propose a study on multi-modal (audio and video) action
spotting and classification in soccer videos. Action spotting and
classification are the tasks that consist in finding the temporal anchors of
events in a video and determine which event they are. This is an important
application of general activity understanding. Here, we propose an experimental
study on combining audio and video information at different stages of deep
neural network architectures. We used the SoccerNet benchmark dataset, which
contains annotated events for 500 soccer game videos from the Big Five European
leagues. Through this work, we evaluated several ways to integrate audio stream
into video-only-based architectures. We observed an average absolute
improvement of the mean Average Precision (mAP) metric of $7.43\%$ for the
action classification task and of $4.19\%$ for the action spotting task.

本文提出了一个研究，探讨在足球视频中进行多模态（音频和视频）的动作定位和分类。我们使用了 SoccerNet 基准数据集，并评估了在不同的深度神经网络结构中整合音频流的几种方法，并观察到在动作分类任务和动作定位任务中平均平均精度 (mAP) 指标分别提高了 7.43％和 4.19％。