In this paper, we propose a novel sequence verification task that aims to
distinguish positive video pairs performing the same action sequence from
negative ones with step-level transformations but still conducting the same
task. Such a challenging task resides in an open-set setting without prior
action detection or segmentation that requires event-level or even frame-level
annotations. To that end, we carefully reorganize two publicly available
action-related datasets with step-procedure-task structure. To fully
investigate the effectiveness of any method, we collect a scripted video
dataset enumerating all kinds of step-level transformations in chemical
experiments. Besides, a novel evaluation metric Weighted Distance Ratio is
introduced to ensure equivalence for different step-level transformations
during evaluation. In the end, a simple but effective baseline based on the
transformer encoder with a novel sequence alignment loss is introduced to
better characterize long-term dependency between steps, which outperforms other
action recognition methods. Codes and data will be released.

该研究提出了一项基于视频配对的序列验证任务，通过转换步骤以及在不使用事件级或帧级注释的情况下进行相同任务，以区分具有相同操作序列的正面视频配对和负面视频配对，并引入了一种新的评估指标来确保不同步骤级别的变换之间的等价性，并且利用基于 Transformers 的编码器以及新的序列对齐损失函数实现了对长期依赖性的更好的特征化，从而优于其他动作识别方法。