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.

通过增加时间精度、结合不同输入特征类型以及改进前后处理步骤，使用基于密集检测锚点的行动识别方法，该论文在评估标准 Tight average-mAP 上取得第一名并成为 SoccerNet 测试集上的最新技术。

使用密集检测锚点的动作定位再次审视：参加 SoccerNet Challenge 2022

Action Spotting using Dense Detection Anchors Revisited: Submission to  the SoccerNet Challenge 2022

To reveal the importance of temporal precision in ground truth audio event
labels, we collected precise (~0.1 sec resolution) "strong" labels for a
portion of the AudioSet dataset. We devised a temporally strong evaluation set
(including explicit negatives of varying difficulty) and a small strong-labeled
training subset of 67k clips (compared to the original dataset's 1.8M clips
labeled at 10 sec resolution). We show that fine-tuning with a mix of weak and
strongly labeled data can substantially improve classifier performance, even
when evaluated using only the original weak labels. For a ResNet50
architecture, d' on the strong evaluation data including explicit negatives
improves from 1.13 to 1.41. The new labels are available as an update to
AudioSet.

通过收集精确的强标签，证明时间精度对于地面真实音频事件标签的重要性。通过使用混合弱和强标记的数据进行微调，可以显著提高分类器的性能，其中 ResNet50 结构的 d' 从 1.13 提高到 1.41。