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。