Rendezvous: Attention mechanisms for the recognition of surgical action triplets in endoscopic videos
Results and benchmarks
Rendezvous: Attention mechanisms for the recognition of surgical action triplets in endoscopic videos presents a transformer approach for computer science.
Benchmark evidence is limited
Evidence graph: 2 refs, 1 links.
Utility signals: depth 65/100, grounding 58/100, status medium.
Implementation
No direct implementation yet
Maintained implementation evidence is not confirmed for this paper yet.
Use the implementation status and reproduction sections for the current action plan.
No verified maintained repo yet
There is no verified maintained implementation yet. Use this baseline plan to decide whether to prototype now or defer.
- No direct maintained implementation was found. Use the paper PDF and citation graph to design a baseline reproduction.
- Start from related paper: CholecTriplet2021: A benchmark challenge for surgical action triplet recognition.
- Start from this likely method family: attention mechanism.
Time to first repro: a few days
Recommendation evidence is currently too limited for a maintained-repo choice. Use Implementation Status and Reproduction Path for a practical baseline plan.
- Estimate is based on paper-only reproduction flow
Reproduction readiness
No repo
No verified implementation available
- No maintained repository has been identified for this paper. Check adjacent implementations or HF artifacts below.
Hardware requirements
- Expect multi-day setup/compute for meaningful reproduction based on current guidance.
Validation caveat
Framework baselines
- Hugging Face Transformers training guide
Modern transformer training baseline.
- PyTorch nn.Transformer docs
Reference transformer building block implementation.
Repositories and ecosystem
No additional verified repositories beyond the primary recommendation.
These repositories had low-confidence matching signals and are hidden by default.
- CAMMA-public/rendezvous
Confidence: Low · 42 stars
Hugging Face artifacts
No trustworthy direct or curated related Hugging Face artifacts were found yet. Use targeted searches to quickly locate candidate models, datasets, and demos.
Tip: start with models, then check datasets and spaces if you need evaluation data or demos.
Research context
209
Citations
105
References
Tasks
Computer science, Task (project management), Workflow, Rendezvous, Verb, Human–computer interaction, endoscopic video analysis, surgical workflow analysis
Methods
Transformer, attention mechanism, spatial attention, semantic attention
Domains
Artificial intelligence, Action (physics), Machine learning, Natural language processing
Related papers
- CholecTriplet2021: A benchmark challenge for surgical action triplet recognitionSearch on Paper2Code
2023 · Semantic similarity
- Surgical Activity Triplet Recognition via Triplet DisentanglementSearch on Paper2Code
2023 · Semantic similarity
- Chain-of-Look Prompting for Verb-centric Surgical Triplet Recognition in Endoscopic VideosSearch on Paper2Code
2023 · Semantic similarity
- Forest Graph Convolutional Network for Surgical Action Triplet Recognition in Endoscopic VideosSearch on Paper2Code
2022 · Semantic similarity
- Lightweight Rendezvous Model Based on Pruning and Knowledge Distillation for Action Triplet Recognition in Laparoscopic Surgery VideosSearch on Paper2Code
2024 · Semantic similarity
- Recognition of Instrument-Tissue Interactions in Endoscopic Videos via Action TripletsSearch on Paper2Code
2020 · Semantic similarity
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