An End-to-End Spatial-Temporal Transformer Model for Surgical Action Triplet Recognition

Surgical activity recognition plays an important role in computer assisted surgery. Recently, surgical action triplet has become the representative definition of fine-grained surgical activity, which is a combination of three components in the form of . In this work, we propose an end-to-end spatial-temporal transformer model trained with multi-task auxiliary supervisions, establishing a powerful baseline for surgical action triplet recognition. Rigorous experiments are conducted on a publicly available dataset CholecT45 for ablation studies and comparisons with state-of-the-arts. Experimental results show that our method outperforms state-of-the-arts by 6.8%, achieving 36.5% mAP for triplet recognition. Our method won the 2nd place in action triplet recognition racing track of CholecTriplet 2022 Challenge, which also demonstrates the superior capability of our method.