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In this paper we introduce OperA, a transformer-based model that accuratelypredicts surgical phases from long video sequences. A novel attentionregularization loss encourages the model to focus on high-quality frames duringtraining. Moreover, the attention weights are utilized to identifycharacteristic high attention frames for each surgical phase, which couldfurther be used for surgery summarization. OperA is thoroughly evaluated on twodatasets of laparoscopic cholecystectomy videos, outperforming variousstate-of-the-art temporal refinement approaches.

OperA: Attention-Regularized Transformers for Surgical Phase Recognition