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• W3133570188 abstract "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." @default.
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• W3133570188 date "2021-03-05" @default.
• W3133570188 modified "2023-10-01" @default.
• W3133570188 title "OperA: Attention-Regularized Transformers for Surgical Phase Recognition" @default.
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