论文标题
使用密集检测的锚点进行操作，重新审视：提交Soccernet Challenge 2022
Action Spotting using Dense Detection Anchors Revisited: Submission to the SoccerNet Challenge 2022
论文作者
论文摘要
这份简短的技术报告描述了我们对2022年Soccernet Challenge的行动发现的提交。挑战是CVPR 2022 ActivityNet研讨会的一部分。我们的提交是基于最近提出的方法，该方法着重于通过一组密集的检测锚来提高时间精度。由于其对时间精度的重视，这种方法显示出严格的平均地图度量的显着改善。紧张的平均图被用作挑战的评估标准，并使用较小的时间评估公差来定义，因此对小的时间错误更敏感。为了进一步改善结果，我们在这里引入了预处理和后处理步骤的小变化，并通过晚期融合结合了不同的输入特征类型。这些变化带来了进步，帮助我们在挑战中获得了第一名，并在使用数据集的标准实验协议时，还导致了Soccernet测试集的新最新。该报告简要审查了基于密集检测锚的动作发现方法，然后重点介绍了为挑战引入的修改。我们还描述了我们使用的实验方案和培训程序，并最终提出了我们的结果。
This brief technical report describes our submission to the Action Spotting SoccerNet Challenge 2022. The challenge was part of the CVPR 2022 ActivityNet Workshop. Our submission was based on a recently proposed method which focuses on increasing temporal precision via a densely sampled set of detection anchors. Due to its emphasis on temporal precision, this approach had shown significant improvements in the tight average-mAP metric. Tight average-mAP was used as the evaluation criterion for the challenge, and is defined using small temporal evaluation tolerances, thus being more sensitive to small temporal errors. In order to further improve results, here we introduce small changes in the pre- and post-processing steps, and also combine different input feature types via late fusion. These changes brought improvements that helped us achieve the first place in the challenge and also led to a new state-of-the-art on SoccerNet's test set when using the dataset's standard experimental protocol. This report briefly reviews the action spotting method based on dense detection anchors, then focuses on the modifications introduced for the challenge. We also describe the experimental protocols and training procedures we used, and finally present our results.
