We tackle the problem of localizing temporal intervals of actions with only a
single frame label for each action instance for training. Owing to label
sparsity, existing work fails to learn action completeness, resulting in
fragmentary action predictions. In this paper, we propose a novel framework,
where dense pseudo-labels are generated to provide completeness guidance for
the model. Concretely, we first select pseudo background points to supplement
point-level action labels. Then, by taking the points as seeds, we search for
the optimal sequence that is likely to contain complete action instances while
agreeing with the seeds. To learn completeness from the obtained sequence, we
introduce two novel losses that contrast action instances with background ones
in terms of action score and feature similarity, respectively. Experimental
results demonstrate that our completeness guidance indeed helps the model to
locate complete action instances, leading to large performance gains especially
under high IoU thresholds. Moreover, we demonstrate the superiority of our
method over existing state-of-the-art methods on four benchmarks: THUMOS'14,
GTEA, BEOID, and ActivityNet. Notably, our method even performs comparably to
recent fully-supervised methods, at the 6 times cheaper annotation cost. Our
code is available at this https URL

本文提出了一种新框架，生成密集伪标签以提供完整性指导，通过优化序列来学习完整性，并引入两个新的损失以对比动作分数和特征相似性，实现了动作实例的准确定位，相较于现有最先进的方法，这种方式的标注成本还更低。