  In this paper we introduce OperA, a transformer-based model that accurately
predicts surgical phases from long video sequences. A novel attention
regularization loss encourages the model to focus on high-quality frames during
training. Moreover, the attention weights are utilized to identify
characteristic high attention frames for each surgical phase, which could
further be used for surgery summarization. OperA is thoroughly evaluated on two
datasets of laparoscopic cholecystectomy videos, outperforming various
state-of-the-art temporal refinement approaches.
