Search Results for author:
Found 6 papers, 4 papers with code
Weakly Supervised Temporal Convolutional Networks for Fine-grained Surgical Activity Recognition
In this work, we propose to use coarser and easier-to-annotate activity labels, namely phases, as weak supervision to learn step recognition with fewer step annotated videos.
CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection
This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection.
Ranked #1 on
Action Triplet Detection
on CholecT50 (Challenge)
CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
In this paper, we present the challenge setup and assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge.
Ranked #1 on
Action Triplet Recognition
on CholecT50 (Challenge)
(using extra training data)
Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos
To achieve this task, we introduce our new model, the Rendezvous (RDV), which recognizes triplets directly from surgical videos by leveraging attention at two different levels.
Ranked #1 on
Action Triplet Recognition
on CholecT50
Multi-Task Temporal Convolutional Networks for Joint Recognition of Surgical Phases and Steps in Gastric Bypass Procedures
Conclusion: In this work, we present a multi-task multi-stage temporal convolutional network for surgical activity recognition, which shows improved results compared to single-task models on the Bypass40 gastric bypass dataset with multi-level annotations.
Recognition of Instrument-Tissue Interactions in Endoscopic Videos via Action Triplets
Recognition of surgical activity is an essential component to develop context-aware decision support for the operating room.
Ranked #1 on
Action Triplet Recognition
on CholecT40
