  Temporal Action Localization (TAL) has experienced remarkable success under
the supervised learning paradigm. However, existing TAL methods are rooted in
the closed set assumption, which cannot handle the inevitable unknown actions
in open-world scenarios. In this paper, we, for the first time, step toward the
Open Set TAL (OSTAL) problem and propose a general framework OpenTAL based on
Evidential Deep Learning (EDL). Specifically, the OpenTAL consists of
uncertainty-aware action classification, actionness prediction, and temporal
location regression. With the proposed importance-balanced EDL method,
classification uncertainty is learned by collecting categorical evidence
majorly from important samples. To distinguish the unknown actions from
background video frames, the actionness is learned by the positive-unlabeled
learning. The classification uncertainty is further calibrated by leveraging
the guidance from the temporal localization quality. The OpenTAL is general to
enable existing TAL models for open set scenarios, and experimental results on
THUMOS14 and ActivityNet1.3 benchmarks show the effectiveness of our method.
The code and pre-trained models are released at
https://www.rit.edu/actionlab/opental.
