Current one-stage action detection methods, which simultaneously predict action boundaries and the corresponding class, do not estimate or use a measure of confidence in their boundary predictions, which can lead to inaccurate boundaries. We incorporate the estimation of boundary confidence into one-stage anchor-free detection, through an additional prediction head that predicts the refined boundaries with higher confidence. We obtain state-of-the-art performance on the challenging EPIC-KITCHENS-100 action detection as well as the standard THUMOS14 action detection benchmarks, and achieve improvement on the ActivityNet-1.3 benchmark.
翻译:目前单阶段行动探测方法同时预测行动边界和相应的类别,不估计或使用对其边界预测的信任度,从而导致边界的不准确;我们将边界信任度的估算纳入一个阶段的无锚探测,方法是增加一个预测头,以更高的信心预测完善的边界;我们在具有挑战性的EPIC-KITCHENS-100行动探测以及标准的THUMOOS14行动探测基准方面取得最新成绩,并改进活动网-1.3基准。