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Temporal action localization aims to identify the boundaries and categoriesof actions in videos, such as scoring a goal in a football match. Single-framesupervision has emerged as a labor-efficient way to train action localizers asit requires only one annotated frame per action. However, it often suffers frompoor performance due to the lack of precise boundary annotations. To addressthis issue, we propose a visual analysis method that aligns similar actions andthen propagates a few user-provided annotations (e.g. , boundaries, categorylabels) to similar actions via the generated alignments. Our method models thealignment between actions as a heaviest path problem and the annotationpropagation as a quadratic optimization problem. As the automatically generatedalignments may not accurately match the associated actions and could produceinaccurate localization results, we develop a storyline visualization toexplain the localization results of actions and their alignments. Thisvisualization facilitates users in correcting wrong localization results andmisalignments. The corrections are then used to improve the localizationresults of other actions. The effectiveness of our method in improvinglocalization performance is demonstrated through quantitative evaluation and acase study.

Enhancing Single-Frame Supervision for Better Temporal Action Localization