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 method.
Benchmark evidence is limited
Evidence graph: 2 refs, 1 links.
Utility signals: depth 60/100, 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.
- Track assumptions and missing details in an experiment log before coding.
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 · 40 stars
Hugging Face artifacts
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Research context
Tasks
None detected
Methods
Transformer
Domains
None detected
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