  The vocabulary size in temporal action localization (TAL) is constrained by
the scarcity of large-scale annotated datasets. To address this, recent works
incorporate powerful pre-trained vision-language models (VLMs), such as CLIP,
to perform open-vocabulary TAL (OV-TAL). However, unlike VLMs trained on
extensive image/video-text pairs, existing OV-TAL methods still rely on small,
fully labeled TAL datasets for training an action localizer. In this paper, we
explore the scalability of self-training with unlabeled YouTube videos for
OV-TAL. Our self-training approach consists of two stages. First, a
class-agnostic action localizer is trained on a human-labeled TAL dataset and
used to generate pseudo-labels for unlabeled videos. Second, the large-scale
pseudo-labeled dataset is combined with the human-labeled dataset to train the
localizer. Extensive experiments demonstrate that leveraging web-scale videos
in self-training significantly enhances the generalizability of an action
localizer. Additionally, we highlighted issues with existing OV-TAL evaluation
schemes and proposed a new evaluation protocol. Code is released at
https://github.com/HYUNJS/STOV-TAL
