Papers with Code 아카이브

2D Pose Estimation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

2

평가표

9

보존된 평가 기록

21

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/ash3n/Prototypical-Network-TF

Prototypical Networks for Few-shot Learning

https://github.com/baodi23/hourglass-facekeypoints-detection

Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

https://github.com/baoshengyu/deep-high-resolution-net.pytorch

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/barnrang/Prototypical-network-keras-reimplementation

Prototypical Networks for Few-shot Learning

https://github.com/blake58/openpose

Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

https://github.com/catalpaaa/demansia

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/chengguixiong1010/rnn_STAF_2dpose

Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

https://github.com/chenyouxin113/sota-status-investigation

OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

https://github.com/chonyy/AI-basketball-analysis

OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

https://github.com/chuanqichen/deepcoaching

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/chuxiaoselena/SparsenessMeetsDeepness

Sparseness Meets Deepness: 3D Human Pose Estimation from Monocular Video

https://github.com/clrrrr/promp_plus

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/cnguyen10/few_shot_meta_learning

Prototypical Networks for Few-shot Learning

https://github.com/cnielly/prototypical-networks-omniglot

Prototypical Networks for Few-shot Learning

https://github.com/code-implementation1/Code5/tree/main/MAML

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/crashmoon/MAML-Pytorch-Multi-GPUs

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/csyanbin/MAML-Pytorch-Multi-GPUs

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/cyvius96/prototypical-network-pytorch

Prototypical Networks for Few-shot Learning

https://github.com/d-shivam/Pose-estimation-based-action-recognition-for-help-Situation-Identification

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/damedollaforthree/nrml

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/daniegr/EfficientPose

OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

https://github.com/debjani-bhowmick/harmonic-net-pytorch

Rotation Equivariant Siamese Networks for Tracking

https://github.com/dkalpakchi/ReproducingSCAPytorch

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/dragen1860/MAML-Pytorch

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/dragen1860/MAML-Pytorch-RL

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/dragen1860/Reptile-Pytorch

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/ducongju/HRNet

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/ebadrian/metadl

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/fabian57fabian/prototypical-networks-few-shot-learning

Prototypical Networks for Few-shot Learning

https://github.com/facebookresearch/sapiens

Sapiens: Foundation for Human Vision Models

https://github.com/fastforwardlabs/learning-to-learn

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/fcr3/OpenVINO_Tutorials

Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

https://github.com/fcr3/wine_detector

Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

https://github.com/fengyidong123/openpose-master

Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

https://github.com/flyinglynx/capeformer

https://github.com/fmu2/PyTorch-MAML

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.