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/mohammadreyaz/openposered

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

https://github.com/mohammadreyaz/pose

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

https://github.com/msfuxian/DualAttentionNet

Prototypical Networks for Few-shot Learning

https://github.com/mzeromiko/vmamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/nish-97v/3D-human-pose-estimation

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

https://github.com/novicestone/meta-cetm

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

https://github.com/nus-hpc-ai-lab/dyvm

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/nyu-larx/attack_metarl

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

https://github.com/open-mmlab/mmdetection

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/orobix/Prototypical-Networks-for-Few-shot-Learning-PyTorch

Prototypical Networks for Few-shot Learning

https://github.com/oscarknagg/few-shot

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

https://github.com/osmr/imgclsmob

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/oublalkhalid/AI-OpenPose

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

https://github.com/potsawee/maml

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

https://github.com/prajjwal1/fluence

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

https://github.com/psh150204/MAML

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

https://github.com/qdhill/openpose

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

https://github.com/quic/aimet-model-zoo

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

https://github.com/radioactiverockets/OpenPoseBreackoutGame

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

https://github.com/ray-project/ray/tree/master/rllib

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

https://github.com/raymon-tian/hourglass-facekeypoints-detection

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

https://github.com/revanurambareesh/multiperson

Unsupervised Cross-Modal Alignment for Multi-Person 3D Pose Estimation

https://github.com/s-a-malik/multi-few

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

https://github.com/saadbutt32/Conversion-of-Pakistan-Sign-Languag-into-Text-and-Speech-using-OpenPose-and-Machine-Learning

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

https://github.com/saadbutt32/Pakistan-Sign-Language-Interpreter

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

https://github.com/sananand007/Xrelab

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

https://github.com/schatty/prototypical-networks-tf

Prototypical Networks for Few-shot Learning

https://github.com/sdll/hrnet-pose-estimation

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/seblee97/task_weighted_maml

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

https://github.com/shaohua0116/MultiDigitMNIST

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

https://github.com/shendb2022/htd-mamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/sheryl-ai/MetaPred

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

https://github.com/shihenw/convolutional-pose-machines-release

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

https://github.com/shunzh/pytorch-maml-rl

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

https://github.com/sicara/easy-few-shot-learning

Prototypical Networks for Few-shot Learning

https://github.com/sidney1505/arc_maml_transformer

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.