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/orhir/EdgeCape

공식 코드

Edge Weight Prediction For Category-Agnostic Pose Estimation

https://github.com/orhir/PoseAnything

공식 코드

A Graph-Based Approach for Category-Agnostic Pose Estimation

https://github.com/ostadabbas/SLP-Dataset-and-Code

공식 코드

Simultaneously-Collected Multimodal Lying Pose Dataset: Towards In-Bed Human Pose Monitoring under Adverse Vision Conditions

https://github.com/pksvision/Deep-WaveNet-Underwater-Image-Restoration

공식 코드

Wavelength-based Attributed Deep Neural Network for Underwater Image Restoration

https://github.com/radarFudan/mamba

공식 코드

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/robert80203/HuPR-A-Benchmark-for-Human-Pose-Estimation-Using-Millimeter-Wave-Radar

공식 코드

HuPR: A Benchmark for Human Pose Estimation Using Millimeter Wave Radar

https://github.com/sankadivandya/ks-kss-dataset

공식 코드

2D Pose Estimation based Child Action Recognition

https://github.com/state-spaces/mamba

공식 코드

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/take2rohit/poise

공식 코드

POISE: Pose Guided Human Silhouette Extraction under Occlusions

https://github.com/tiny-smart/SCAPE

공식 코드

SCAPE: A Simple and Strong Category-Agnostic Pose Estimator

https://github.com/visionlearninggroup/uda_poseestimation

공식 코드

A Unified Framework for Domain Adaptive Pose Estimation

https://github.com/xingyizhou/pose-hg-3d

공식 코드

Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach

https://github.com/yihui-he/epipolar-transformers

공식 코드

Epipolar Transformers

https://github.com/2023-MindSpore-1/ms-code-223/tree/main/OpenPose

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

https://github.com/2023-MindSpore-1/ms-code-5/tree/main/OpenPose

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

https://github.com/2023-MindSpore-4/Code-5/tree/main/MAML

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

https://github.com/2023-MindSpore-4/Code10/tree/main/OpenPose

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

https://github.com/2023-MindSpore-4/Code9/tree/main/OpenPose

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

https://github.com/2024-MindSpore-1/Code4/tree/main/OpenPose

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

https://github.com/AkashGanesan/PedestrianAttention

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

https://github.com/AlexEMG/DeepLabCut

SuperAnimal pretrained pose estimation models for behavioral analysis

https://github.com/AmazingDay1/TAME

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/AntreasAntoniou/HowToTrainYourMAMLPytorch

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

https://github.com/ArnoutDevos/maml-cifar-fs

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

https://github.com/BBDrive/Meta-SGD-RL

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

https://github.com/CASIA-IVA-Lab/ISP-reID

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/CMU-Perceptual-Computing-Lab/openpose_caffe_train

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

https://github.com/CMU-Perceptual-Computing-Lab/openpose_train

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

https://github.com/CMU-Perceptual-Computing-Lab/openpose_unity_plugin

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

https://github.com/COAOX/Cifar-Prototypical

Prototypical Networks for Few-shot Learning

https://github.com/ChetanTayal138/HAR-Web

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

https://github.com/CocoJam/MAML

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

https://github.com/D1vyansh/BodyJointDetection

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

https://github.com/DavHoffmann/Multi-humanDataGeneration

Learning to Train with Synthetic Humans

https://github.com/DrMMZ/ProtoNet

Prototypical Networks for Few-shot Learning

https://github.com/ECE740F21T01/pytorch-pose-hg-3d

Towards 3D Human Pose Estimation in the Wild: a Weakly-supervised Approach

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