  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.
