Rendezvous in Time: An Attention-based Temporal Fusion approach for Surgical Triplet Recognition

TL;DR

This paper introduces RiT, a deep learning model that enhances surgical triplet recognition by incorporating temporal attention to better understand the evolution of actions in surgical videos.

Contribution

The paper presents a novel temporal attention-based fusion method extending existing models for improved surgical triplet recognition.

Findings

Improved recognition accuracy for verbs and triplets.

Smoother and more consistent triplet predictions.

Effective temporal modeling of surgical actions.

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.