  Online Temporal Action Localization (On-TAL) is a critical task that aims to
instantaneously identify action instances in untrimmed streaming videos as soon
as an action concludes -- a major leap from frame-based Online Action Detection
(OAD). Yet, the challenge of detecting overlapping actions is often overlooked
even though it is a common scenario in streaming videos. Current methods that
can address concurrent actions depend heavily on class information, limiting
their flexibility. This paper introduces ActionSwitch, the first class-agnostic
On-TAL framework capable of detecting overlapping actions. By obviating the
reliance on class information, ActionSwitch provides wider applicability to
various situations, including overlapping actions of the same class or
scenarios where class information is unavailable. This approach is complemented
by the proposed "conservativeness loss", which directly embeds a conservative
decision-making principle into the loss function for On-TAL. Our ActionSwitch
achieves state-of-the-art performance in complex datasets, including
Epic-Kitchens 100 targeting the challenging egocentric view and FineAction
consisting of fine-grained actions.
