https://github.com/CAMMA-public/cholect45
공식 코드Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet Datasets
Papers with Code 아카이브
Action Triplet Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
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Recognising action as a triplet of subject verb and object. Example HOI = Human Object Interaction, Surgical IVT = Instrument Verb Target, etc.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/CAMMA-public/cholect50
공식 코드https://github.com/CAMMA-public/cholectriplet2021
공식 코드CholecTriplet2021: A benchmark challenge for surgical action triplet recognition
https://github.com/camma-public/attention-tripnet
공식 코드https://github.com/camma-public/cholectriplet2022
공식 코드https://github.com/camma-public/ivtmetrics
공식 코드Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet Datasets
https://github.com/camma-public/rendezvous-in-time
공식 코드Rendezvous in Time: An Attention-based Temporal Fusion approach for Surgical Triplet Recognition
https://github.com/camma-public/selfsupsurg
공식 코드Dissecting Self-Supervised Learning Methods for Surgical Computer Vision
https://github.com/camma-public/tripnet
공식 코드Recognition of Instrument-Tissue Interactions in Endoscopic Videos via Action Triplets
https://github.com/rllab/rllab
공식 코드Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/Dam930/rllab
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/bstadie/third_person_im
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/camma-public/mcit-ig
Data Splits and Metrics for Method Benchmarking on Surgical Action Triplet Datasets
https://github.com/camma-public/rendezvous
Recognition of Instrument-Tissue Interactions in Endoscopic Videos via Action Triplets
https://github.com/camma-public/ssg-qa
https://github.com/camma-public/ssg-vqa
https://github.com/cathywu/rllab-multiagent
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/cbfinn/maml_rl
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/jachiam/cpo
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/openai/rllab
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/rejuvyesh/rllab
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/richardrl/cartpole-request-for-research
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/rll/rllab
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/rlworkgroup/garage
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/russellmendonca/maesn_suite
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/sisl/event-driven-rllab
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/sisl/gail-driver
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/wyndwarrior/imitation_from_observation
Benchmarking Deep Reinforcement Learning for Continuous Control
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.