  Temporal action localization aims to identify the boundaries and categories
of actions in videos, such as scoring a goal in a football match. Single-frame
supervision has emerged as a labor-efficient way to train action localizers as
it requires only one annotated frame per action. However, it often suffers from
poor performance due to the lack of precise boundary annotations. To address
this issue, we propose a visual analysis method that aligns similar actions and
then propagates a few user-provided annotations (e.g. , boundaries, category
labels) to similar actions via the generated alignments. Our method models the
alignment between actions as a heaviest path problem and the annotation
propagation as a quadratic optimization problem. As the automatically generated
alignments may not accurately match the associated actions and could produce
inaccurate localization results, we develop a storyline visualization to
explain the localization results of actions and their alignments. This
visualization facilitates users in correcting wrong localization results and
misalignments. The corrections are then used to improve the localization
results of other actions. The effectiveness of our method in improving
localization performance is demonstrated through quantitative evaluation and a
case study.
