Towards Precise Action Spotting: Addressing Temporal Misalignment in Labels with Dynamic Label Assignment

Precise action spotting has attracted considerable attention due to its promising applications. While existing methods achieve substantial performance by employing well-designed model architecture, they overlook a significant challenge: the temporal misalignment inherent in ground-truth labels. This misalignment arises when frames labeled as containing events do not align accurately with the actual event times, often as a result of human annotation errors or the inherent difficulties in precisely identifying event boundaries across neighboring frames. To tackle this issue, we propose a novel dynamic label assignment strategy that allows predictions to have temporal offsets from ground-truth action times during training, ensuring consistent event spotting. Our method extends the concept of minimum-cost matching, which is utilized in the spatial domain for object detection, to the temporal domain. By calculating matching costs based on predicted action class scores and temporal offsets, our method dynamically assigns labels to the most likely predictions, even when the predicted times of these predictions deviate from ground-truth times, alleviating the negative effects of temporal misalignment in labels. We conduct extensive experiments and demonstrate that our method achieves state-of-the-art performance, particularly in conditions where events are visually distinct and temporal misalignment in labels is common.