  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.
