This paper addresses the challenge of point-supervised temporal action detection, in which only one frame per action instance is annotated in the training set. Self-training aims to provide supplementary supervision for the training process by generating pseudo-labels (action proposals) from a base model. However, most current methods generate action proposals by applying manually designed thresholds to action classification probabilities and treating adjacent snippets as independent entities. As a result, these methods struggle to generate complete action proposals, exhibit sensitivity to fluctuations in action classification scores, and generate redundant and overlapping action proposals. This paper proposes a novel framework termed ADM-Loc, which stands for Actionness Distribution Modeling for point-supervised action Localization. ADM-Loc generates action proposals by fitting a composite distribution, comprising both Gaussian and uniform distributions, to the action classification signals. This fitting process is tailored to each action class present in the video and is applied separately for each action instance, ensuring the distinctiveness of their distributions. ADM-Loc significantly enhances the alignment between the generated action proposals and ground-truth action instances and offers high-quality pseudo-labels for self-training. Moreover, to model action boundary snippets, it enforces consistency in action classification scores during training by employing Gaussian kernels, supervised with the proposed loss functions. ADM-Loc outperforms the state-of-the-art point-supervised methods on THUMOS14 and ActivityNet-v1.2 datasets.

翻译：本文针对点监督时序动作检测的挑战，其中训练集中每个动作实例仅标注一帧。自训练旨在通过从基础模型生成伪标签（动作提议）为训练过程提供额外监督。然而，当前大多数方法通过手动设定阈值应用于动作分类概率，并将相邻片段视为独立实体来生成动作提议。因此，这些方法难以生成完整动作提议，对动作分类分数的波动敏感，并产生冗余和重叠的动作提议。本文提出一种名为ADM-Loc的新框架，即面向点监督动作定位的动作性分布建模。ADM-Loc通过将包含高斯分布和均匀分布的复合分布拟合至动作分类信号来生成动作提议。该拟合过程针对视频中每个动作类别进行定制，并分别应用于每个动作实例，确保其分布的区分性。ADM-Loc显著增强了生成动作提议与真实动作实例之间的对齐，并为自训练提供高质量的伪标签。此外，为建模动作边界片段，它通过采用高斯核并在所提出损失函数监督下强制训练过程中动作分类分数的一致性。在THUMOS14和ActivityNet-v1.2数据集上，ADM-Loc优于最先进的点监督方法。