Rendezvous in Time: An Attention-based Temporal Fusion approach for Surgical Triplet Recognition
arXiv:2211.16963 · doi:10.1007/s11548-023-02914-1
Abstract
One of the recent advances in surgical AI is the recognition of surgical activities as triplets of (instrument, verb, target). Albeit providing detailed information for computer-assisted intervention, current triplet recognition approaches rely only on single frame features. Exploiting the temporal cues from earlier frames would improve the recognition of surgical action triplets from videos. In this paper, we propose Rendezvous in Time (RiT) - a deep learning model that extends the state-of-the-art model, Rendezvous, with temporal modeling. Focusing more on the verbs, our RiT explores the connectedness of current and past frames to learn temporal attention-based features for enhanced triplet recognition. We validate our proposal on the challenging surgical triplet dataset, CholecT45, demonstrating an improved recognition of the verb and triplet along with other interactions involving the verb such as (instrument, verb). Qualitative results show that the RiT produces smoother predictions for most triplet instances than the state-of-the-arts. We present a novel attention-based approach that leverages the temporal fusion of video frames to model the evolution of surgical actions and exploit their benefits for surgical triplet recognition.
arXiv commentAccepted at IPCAI, 2023. Project Page: https://github.com/CAMMA-public/rendezvous-in-time
References in corpus (5)
- Surgical Data Science: Enabling Next-Generation Surgery
- TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks
- Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos
- CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer Assisted Interventions
- CholecTriplet2021: A benchmark challenge for surgical action triplet recognition