https://github.com/3dperceptionlab/hierarchicalactionrecognition
공식 코드Enhancing Action Recognition by Leveraging the Hierarchical Structure of Actions and Textual Context
Papers with Code 아카이브
Fine-grained Action Recognition
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ParitoshParmar/HalluciNet
공식 코드HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN
https://github.com/ParitoshParmar/MTL-AQA
공식 코드What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
https://github.com/acewjh/fsfg
공식 코드Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-Learning
https://github.com/davidsvy/fractal_video
공식 코드Pre-training for Action Recognition with Automatically Generated Fractal Datasets
https://github.com/dingyn-reno/mmfs
공식 코드Fine-grained Action Analysis: A Multi-modality and Multi-task Dataset of Figure Skating
https://github.com/grassknoted/Modified-Two-Stream-Fusion-for-Real-time-ActionRecognition
공식 코드Real-time Action Recognition for Fine-Grained Actions and The Hand Wash Dataset
https://github.com/jayleicn/singularity
공식 코드https://github.com/jhong93/vpd
공식 코드Video Pose Distillation for Few-Shot, Fine-Grained Sports Action Recognition
https://github.com/kahnchana/mvu
공식 코드https://github.com/kylehuang9/sefar
공식 코드https://github.com/sofiabroome/painface-recognition
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/MindSpore-scientific/code-11/tree/main/pytorch_convgru
ConvGRU in Fine-grained Pitching Action Recognition for Action Outcome Prediction
https://github.com/MindSpore-scientific/code-13/tree/main/pytorch_convgru
ConvGRU in Fine-grained Pitching Action Recognition for Action Outcome Prediction
https://github.com/ParitoshParmar/HalluciNet--PyTorch
HalluciNet-ing Spatiotemporal Representations Using a 2D-CNN
https://github.com/ThomasDupiereux/ADLproject
Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding Manifold
https://github.com/ThomasDupiereux/EnGAN
Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding Manifold
https://github.com/aurooj/Hand-Segmentation-in-the-Wild
https://github.com/jayleicn/ClipBERT
https://github.com/jonmun/MM-SADA-code
Multi-Modal Domain Adaptation for Fine-Grained Action Recognition
https://github.com/luciferbobo/dae-aqa
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
https://github.com/maharshi95/Pose2vec
Unsupervised Feature Learning of Human Actions as Trajectories in Pose Embedding Manifold
https://github.com/nzl-thu/musdl
What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment
https://github.com/tobyperrett/few-shot-action-recognition
Few-shot Action Recognition with Prototype-centered Attentive Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.