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

We present a model for temporally precise action spotting in videos, which
uses a dense set of detection anchors, predicting a detection confidence and
corresponding fine-grained temporal displacement for each anchor. We experiment
with two trunk architectures, both of which are able to incorporate large
temporal contexts while preserving the smaller-scale features required for
precise localization: a one-dimensional version of a u-net, and a Transformer
encoder (TE). We also suggest best practices for training models of this kind,
by applying Sharpness-Aware Minimization (SAM) and mixup data augmentation. We
achieve a new state-of-the-art on SoccerNet-v2, the largest soccer video
dataset of its kind, with marked improvements in temporal localization.
Additionally, our ablations show: the importance of predicting the temporal
displacements; the trade-offs between the u-net and TE trunks; and the benefits
of training with SAM and mixup.

通过使用一组密集的检测锚点，预测每个锚点的检测置信度和相应的精细时间位移量，我们提出了一种视频中时间精准行动检测的模型，并尝试了两种主干架构，一维版的 u-net 和 Transformer 编码器 (TE), 并采用锐度感知最小化 (SAM) 和混合数据增强方法进行模型训练，取得了 SoccerNet-v2 数据集上的最新最佳性能，同时证明了预测时间位移量，不同主干架构的权衡，采用 SAM 和 mixup 进行训练的好处等相关实验结果。