Temporal Action Localization (TAL) aims to predict both action category and temporal boundary of action
instances in untrimmed videos, i.e., start and end time. Existing works usually adopt fully-supervised solutions,
however, one of the practical bottlenecks in these solutions is the large amount of labeled training data
required. To reduce expensive human label cost, this paper focuses on a rarely investigated yet practical task
named semi-supervised TAL and proposes an effective active learning method, named AL-STAL. We leverage
four steps for actively selecting video samples with high informativeness and training the localization model,
named Train, Query, Annotate, Append. Two scoring functions that consider the uncertainty of localization
model are equipped in AL-STAL, thus facilitating the video sample ranking and selection. One takes entropy
of predicted label distribution as measure of uncertainty, named Temporal Proposal Entropy (TPE). And
the other introduces a new metric based on mutual information between adjacent action proposals, named
Temporal Context Inconsistency (TCI). To validate the effectiveness of proposed method, we conduct extensive
experiments on three benchmark datasets THUMOS’14, ActivityNet 1.3 and ActivityNet 1.2. Experiment results
show that AL-STAL outperforms the existing competitors and achieves satisfying performance compared with
fully-supervised learning.