SVIP: Sequence VerIfication for Procedures in Videos

In this paper, we propose a novel sequence verificationtask that aims to distinguish positive video pairs performingthe same action sequence from negative ones with step-leveltransformations but still conducting the same task. Such achallenging task resides in an open-set setting without prioraction detection or segmentation that requires event-levelor even frame-level annotations. To that end, we carefullyreorganize two publicly available action-related datasetswith step-procedure-task structure. To fully investigate theeffectiveness of any method, we collect a scripted videodataset enumerating all kinds of step-level transformationsin chemical experiments. Besides, a novel evaluation met-ric Weighted Distance Ratio is introduced to ensure equiva-lence for different step-level transformations during evalua-tion. In the end, a simple but effective baseline based on thetransformer with a novel sequence alignment loss is intro-duced to better characterize long-term dependency betweensteps, which outperforms other action recognition methods.Codes and data will be released.