Papers with Code 아카이브

Pose Estimation

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하위 태스크: 17
평가표: 31
보존된 평가 기록: 235

Pose Estimation is a computer vision task where the goal is to detect the position and orientation of a person or an object. Usually, this is done by predicting the location of specific keypoints like hands, head, elbows, etc. in case of Human Pose Estimation.

A common benchmark for this task is MPII Human Pose

<span style="color:grey; opacity: 0.6">( Image credit: Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose )</span>

코드

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

https://github.com/mindspore-ai/models/tree/master/research/cv/fairmot

Simple Baselines for Human Pose Estimation and Tracking

https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/attention/PolarizedSelfAttention.py

Polarized Self-Attention: Towards High-quality Pixel-wise Regression

https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/attention/TripletAttention.py

Rotate to Attend: Convolutional Triplet Attention Module

https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/conv/DynamicConv.py

Dynamic Convolution: Attention over Convolution Kernels

https://github.com/mindspore-lab/mindone

Simple Baselines for Human Pose Estimation and Tracking

https://github.com/mirthai/csa-net

Non-local Neural Networks

https://github.com/mirzaevinom/data_science_bowl_2018

https://github.com/mitmul/deeppose

DeepPose: Human Pose Estimation via Deep Neural Networks

https://github.com/mjdietzx/SimGAN

Learning from Simulated and Unsupervised Images through Adversarial Training

https://github.com/mks0601/TF-SimpleHumanPose

Simple Baselines for Human Pose Estimation and Tracking

https://github.com/modelscope/modelscope

Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks

https://github.com/modestyachts/imagenet-testbed

Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization

https://github.com/mohaEs/Train-Predict-Landmarks-by-flat-net

Automatic vocal tract landmark localization from midsagittal MRI data

https://github.com/mohammadreyaz/openposered

Convolutional Pose Machines

https://github.com/mohammadreyaz/pose

Convolutional Pose Machines

https://github.com/mohanliu/HRnet-semantic-segmentation-exp

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/mohsenzand/reschunk

Multiscale Residual Learning of Graph Convolutional Sequence Chunks for Human Motion Prediction

https://github.com/motlabs/dont-be-turtle

Stacked Hourglass Networks for Human Pose Estimation

https://github.com/mseg-dataset/mseg-semantic

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/mshn2000/PointFusion

PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation

https://github.com/mstfakts/building-detection-maskrcnn

https://github.com/msurtsukov/neural-ode

Neural Ordinary Differential Equations

https://github.com/mtorabirad/PricePrediction

XGBoost: A Scalable Tree Boosting System

https://github.com/muelea/tuch

On Self-Contact and Human Pose

https://github.com/mukul1093/Image-Super-Resolution

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/multimodallearning/pytorch-mask-rcnn

https://github.com/murdockhou/Single-Stage-Multi-person-Pose-Machines

Single-Stage Multi-Person Pose Machines

https://github.com/murdockhou/lightweight_openpose

Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose

https://github.com/murrman95/INF573Project2018

Accurate Image Super-Resolution Using Very Deep Convolutional Networks

https://github.com/muyistarsky/MaskRCNN

https://github.com/mv-lab/swin2sr

SwinIR: Image Restoration Using Swin Transformer

https://github.com/nabarunbaruaAIML/ATCC_Yolov5

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/nagadomi/waifu2x

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/nageshpindi/human-motion-prediction-master

On human motion prediction using recurrent neural networks

https://github.com/nail-hnu/esvio_aa

Event-based Stereo Visual Odometry

https://github.com/nail-hnu/esvo2

Event-based Stereo Visual Odometry

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