In this paper, we present a one-stage framework TriDet for temporal action
detection. Existing methods often suffer from imprecise boundary predictions
due to the ambiguous action boundaries in videos. To alleviate this problem, we
propose a novel Trident-head to model the action boundary via an estimated
relative probability distribution around the boundary. In the feature pyramid
of TriDet, we propose an efficient Scalable-Granularity Perception (SGP) layer
to mitigate the rank loss problem of self-attention that takes place in the
video features and aggregate information across different temporal
granularities. Benefiting from the Trident-head and the SGP-based feature
pyramid, TriDet achieves state-of-the-art performance on three challenging
benchmarks: THUMOS14, HACS and EPIC-KITCHEN 100, with lower computational
costs, compared to previous methods. For example, TriDet hits an average mAP of
$69.3\%$ on THUMOS14, outperforming the previous best by $2.5\%$, but with only
$74.6\%$ of its latency. The code is released to
this https URL.

本文提出了一种一阶段框架 TriDet，用于时间动作检测，其结合了 Trident-head 和 SGP-based 特征金字塔的快速、高效特性，取得了在三个基准测试中的最优性能，表现出优异的时间动作检测结果。