Recently, temporal action localization (TAL), i.e., finding specific action
segments in untrimmed videos, has attracted increasing attentions of the
computer vision community. State-of-the-art solutions for TAL involves
evaluating the frame-level probabilities of three action-indicating phases,
i.e. starting, continuing, and ending; and then post-processing these
predictions for the final localization. This paper delves deep into this
mechanism, and argues that existing methods, by modeling these phases as
individual classification tasks, ignored the potential temporal constraints
between them. This can lead to incorrect and/or inconsistent predictions when
some frames of the video input lack sufficient discriminative information. To
alleviate this problem, we introduce two regularization terms to mutually
regularize the learning procedure: the Intra-phase Consistency (IntraC)
regularization is proposed to make the predictions verified inside each phase;
and the Inter-phase Consistency (InterC) regularization is proposed to keep
consistency between these phases. Jointly optimizing these two terms, the
entire framework is aware of these potential constraints during an end-to-end
optimization process. Experiments are performed on two popular TAL datasets,
THUMOS14 and ActivityNet1.3. Our approach clearly outperforms the baseline both
quantitatively and qualitatively. The proposed regularization also generalizes
to other TAL methods (e.g., TSA-Net and PGCN). code:
this https URL

本文针对时间行为定位（TAL）在不带剪辑的视频中查找特定动作片段方面，深入研究了现有方法在模拟这些阶段时，忽略了它们之间的潜在时间约束。作者提出了 IntraC 和 InterC 两个正则化项，使整个框架可以在端对端优化过程中适应这些潜在约束条件，并在 THUMOS14 和 ActivityNet1.3 两个流行的 TAL 数据集上进行了实验。结果表明，本方法在定量和定性上都优于基线，并且所提出的正则化方法还可以推广到其他 TAL 方法（例如，TSA-Net 和 PGCN）。