  While deep learning has been widely used for video analytics, such as video
classification and action detection, dense action detection with fast-moving
subjects from sports videos is still challenging. In this work, we release yet
another sports video benchmark \TheName{} for \emph{\underline{P}}ing
\emph{\underline{P}}ong-\emph{\underline{A}}ction detection, which consists of
2,721 video clips collected from the broadcasting videos of professional table
tennis matches in World Table Tennis Championships and Olympiads. We work with
a crew of table tennis professionals and referees on a specially designed
annotation toolbox to obtain fine-grained action labels (in 14 classes) for
every ping-pong action that appeared in the dataset, and formulate two sets of
action detection problems -- \emph{action localization} and \emph{action
recognition}. We evaluate a number of commonly-seen action recognition (e.g.,
TSM, TSN, Video SwinTransformer, and Slowfast) and action localization models
(e.g., BSN, BSN++, BMN, TCANet), using \TheName{} for both problems, under
various settings. These models can only achieve 48\% area under the AR-AN curve
for localization and 82\% top-one accuracy for recognition since the ping-pong
actions are dense with fast-moving subjects but broadcasting videos are with
only 25 FPS. The results confirm that \TheName{} is still a challenging task
and can be used as a special benchmark for dense action detection from videos.
