  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.
