  Weakly-supervised Temporal Action Localization (WSTAL) aims to localize
actions in untrimmed videos using only video-level supervision. Latest WSTAL
methods introduce pseudo label learning framework to bridge the gap between
classification-based training and inferencing targets at localization, and
achieve cutting-edge results. In these frameworks, a classification-based model
is used to generate pseudo labels for a regression-based student model to learn
from. However, the quality of pseudo labels in the framework, which is a key
factor to the final result, is not carefully studied. In this paper, we propose
a set of simple yet efficient pseudo label quality enhancement mechanisms to
build our FuSTAL framework. FuSTAL enhances pseudo label quality at three
stages: cross-video contrastive learning at proposal Generation-Stage,
prior-based filtering at proposal Selection-Stage and EMA-based distillation at
Training-Stage. These designs enhance pseudo label quality at different stages
in the framework, and help produce more informative, less false and smoother
action proposals. With the help of these comprehensive designs at all stages,
FuSTAL achieves an average mAP of 50.8% on THUMOS'14, outperforming the
previous best method by 1.2%, and becomes the first method to reach the
milestone of 50%.
