This technical report presents our solution to Ball Action Spotting in videos. Our method reached second place in the CVPR'23 SoccerNet Challenge. Details of this challenge can be found at https://www.soccer-net.org/tasks/ball-action-spotting. Our approach is developed based on a baseline model termed E2E-Spot, which was provided by the organizer of this competition. We first generated several variants of the E2E-Spot model, resulting in a candidate model set. We then proposed a strategy for selecting appropriate model members from this set and assigning an appropriate weight to each model. The aim of this strategy is to boost the performance of the resulting model ensemble. Therefore, we call our approach Boosted Model Ensembling (BME). Our code is available at https://github.com/ZJLAB-AMMI/E2E-Spot-MBS.

翻译：本技术报告介绍了我们在视频中球类动作定位问题上提出的解决方案。该方法在CVPR'23 SoccerNet挑战赛中获得了第二名。该挑战赛的详细信息可在https://www.soccer-net.org/tasks/ball-action-spotting 查阅。我们的方法基于比赛主办方提供的基线模型E2E-Spot进行开发。首先，我们生成了E2E-Spot模型的多个变体，构建了一个候选模型集合。随后，我们提出了一种策略，用于从该集合中筛选合适的模型成员，并为每个模型分配适当的权重。该策略旨在提升最终模型集成的性能。因此，我们将该方法命名为"增强型模型集成"（Boosted Model Ensembling, BME）。我们的代码已开源，地址为https://github.com/ZJLAB-AMMI/E2E-Spot-MBS。