Action Triplet Recognition

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

7 leaderboard tables shown for this task, 7 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

4 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

9 shown of 9 papers with code (12 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Benchmarking Deep Reinforcement Learning for Continuous Control
  22 Apr 2016 15 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  Recently, researchers have made significant progress combining the advances in deep learning for learning feature representations with reinforcement learning.
• Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet Datasets
  11 Apr 2022 8 repositories listed
  We also develop a metrics library, ivtmetrics, for model evaluation on surgical triplets.
• Rendezvous: Attention Mechanisms for the Recognition of Surgical Action Triplets in Endoscopic Videos
  7 Sep 2021 8 repositories listed
  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.
• CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
  10 Apr 2022 6 repositories listed
  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.
• Recognition of Instrument-Tissue Interactions in Endoscopic Videos via Action Triplets
  10 Jul 2020 4 repositories listed
  Recognition of surgical activity is an essential component to develop context-aware decision support for the operating room.
• CholecTriplet2022: Show me a tool and tell me the triplet -- an endoscopic vision challenge for surgical action triplet detection
  13 Feb 2023 2 repositories listed
  This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection.
• EndoViT: pretraining vision transformers on a large collection of endoscopic images
  3 Apr 2024 1 repository listed
  These results demonstrate the effectiveness of our domain-specific pretraining approach in addressing the challenges of automated endoscopy video analysis.
• Rendezvous in Time: An Attention-based Temporal Fusion approach for Surgical Triplet Recognition
  30 Nov 2022 1 repository listed
  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.
• Dissecting Self-Supervised Learning Methods for Surgical Computer Vision
  1 Jul 2022 1 repository listed
  Correct transfer of these methods to surgery, as described and conducted in this work, leads to substantial performance gains over generic uses of SSL - up to 7.

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.