  The recently proposed action spotting task consists in finding the exact
timestamp in which an event occurs. This task fits particularly well for soccer
videos, where events correspond to salient actions strictly defined by soccer
rules (a goal occurs when the ball crosses the goal line). In this paper, we
devise a lightweight and modular network for action spotting, which can
simultaneously predict the event label and its temporal offset using the same
underlying features. We enrich our model with two training strategies: the
first one for data balancing and uniform sampling, the second for masking
ambiguous frames and keeping the most discriminative visual cues. When tested
on the SoccerNet dataset and using standard features, our full proposal exceeds
the current state of the art by 3 Average-mAP points. Additionally, it reaches
a gain of more than 10 Average-mAP points on the test set when fine-tuned in
combination with a strong 2D backbone.
