论文标题
精炼动作边界以进行一阶段检测
Refining Action Boundaries for One-stage Detection
论文作者
论文摘要
当前的一阶段动作检测方法同时预测动作边界和相应的类别，不会估计或使用对其边界预测的信心度量，这可能导致边界不准确。我们通过额外的预测头将边界置信度的估计估计到一个无锚的检测中，该预测头可以以更高的置信度预测精制边界。我们在具有挑战性的Epic-Kitchens-100动作检测以及标准的Thumos14动作检测基准上获得了最先进的性能，并在ActivityNet-1.3基准上获得了改进。
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.
