The goal of Temporal Action Localization (TAL) is to find the categories and
temporal boundaries of actions in an untrimmed video. Most TAL methods rely
heavily on action recognition models that are sensitive to action labels rather
than temporal boundaries. More importantly, few works consider the background
frames that are similar to action frames in pixels but dissimilar in semantics,
which also leads to inaccurate temporal boundaries. To address the challenge
above, we propose a Boundary-Aware Proposal Generation (BAPG) method with
contrastive learning. Specifically, we define the above background frames as
hard negative samples. Contrastive learning with hard negative mining is
introduced to improve the discrimination of BAPG. BAPG is independent of the
existing TAL network architecture, so it can be applied plug-and-play to
mainstream TAL models. Extensive experimental results on THUMOS14 and
ActivityNet-1.3 demonstrate that BAPG can significantly improve the performance
of TAL.

通过引入对比学习，并将背景帧作为难负样本，提出了一种独立于现有 Temporal Action Localization（TAL）网络架构的边界感知候选生成方法（BAPG），用于改善 TAL 的性能。在 THUMOS14 和 ActivityNet-1.3 数据集上的实验结果表明，BAPG 能够显著提升 TAL 的性能。