  Recently, Weakly-supervised Temporal Action Localization (WTAL) has been
densely studied but there is still a large gap between weakly-supervised models
and fully-supervised models. It is practical and intuitive to annotate temporal
boundaries of a few examples and utilize them to help WTAL models better detect
actions. However, the train-test discrepancy of action localization strategy
prevents WTAL models from leveraging semi-supervision for further improvement.
At training time, attention or multiple instance learning is used to aggregate
predictions of each snippet for video-level classification; at test time, they
first obtain action score sequences over time, then truncate segments of scores
higher than a fixed threshold, and post-process action segments. The
inconsistent strategy makes it hard to explicitly supervise the action
localization model with temporal boundary annotations at training time. In this
paper, we propose a Train-Test Consistent framework, TTC-Loc. In both training
and testing time, our TTC-Loc localizes actions by comparing scores of action
classes and predicted threshold, which enables it to be trained with
semi-supervision. By fixing the train-test discrepancy, our TTC-Loc
significantly outperforms the state-of-the-art performance on THUMOS'14,
ActivityNet 1.2 and 1.3 when only video-level labels are provided for training.
With full annotations of only one video per class and video-level labels for
the other videos, our TTC-Loc further boosts the performance and achieves
33.4\% mAP (IoU threshold 0.5) on THUMOS's 14.
