https://github.com/CognitiveAISystems/MIKASA-Robo
공식 코드Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with Reinforcement Learning
Papers with Code 아카이브
Skills Assessment
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MichiganCOG/Surgical_Hands_RELEASE
공식 코드Temporally Guided Articulated Hand Pose Tracking in Surgical Videos
https://github.com/ParitoshParmar/MTL-AQA
공식 코드What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
https://github.com/ParitoshParmar/Piano-Skills-Assessment
공식 코드https://github.com/smerdov/CS_GO_Sensors_Data_Analysis
공식 코드AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous Sensors
https://github.com/smerdov/eSports_Sensors_Dataset
공식 코드https://github.com/smerdov/smart_chair_client
공식 코드https://github.com/InfoX-SEU/DAE-AQA
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
https://github.com/InfoX-SEU/DAE_AQA
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
https://github.com/asmerdov/DataCollectionSystem
https://github.com/bcv-uniandes/grasp
https://github.com/bcv-uniandes/matis
https://github.com/hfawaz/miccai18
Evaluating surgical skills from kinematic data using convolutional neural networks
https://github.com/luciferbobo/dae-aqa
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
https://github.com/nzl-thu/musdl
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
https://github.com/smerdov/DataCollectionSystem
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.