Papers with Code 아카이브

3D Absolute Human Pose Estimation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 4
평가표: 3
보존된 평가 기록: 6

This task aims to solve absolute (camera-centric not root-relative) 3D human pose estimation.

<span style="color:grey; opacity: 0.6">( Image credit: RootNet )</span>

코드

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

https://github.com/imatge-upc/3D-GAN-superresolution

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/isaaccorley/torchrs

Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks

https://github.com/itsuki8914/ESRGAN-TensorFlow

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/jaingaurav3/GAN-Hacks

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/jaivanti/Super-Resolution-using-ConvNet

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/jcao216/DAIN_Modified

Video Enhancement with Task-Oriented Flow

https://github.com/jiangzhongkai/ifly-algorithm_challenge

XGBoost: A Scalable Tree Boosting System

https://github.com/jimmycv07/DiffIR2VR-Zero

Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution

https://github.com/jixiaozhong/RealSR

https://github.com/jlanday/Language-Detection

XGBoost: A Scalable Tree Boosting System

https://github.com/jlanday/Model-Selection-for-Pion-Photoproduction

XGBoost: A Scalable Tree Boosting System

https://github.com/jlanday/Towards-the-Minimal-Spectrum-of-Excited-Baryons

XGBoost: A Scalable Tree Boosting System

https://github.com/jlanday/X-ray-Object-Classification

XGBoost: A Scalable Tree Boosting System

https://github.com/joezengcbs/mini_xgboost_from_scratch

XGBoost: A Scalable Tree Boosting System

https://github.com/jshermeyer/VDSR4Geo

Accurate Image Super-Resolution Using Very Deep Convolutional Networks

https://github.com/jupiterman/Super-Resolution-Images

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/kadirnar/bsrgan-pip

Designing a Practical Degradation Model for Deep Blind Image Super-Resolution

https://github.com/kai422/iart

Enhancing Video Super-Resolution via Implicit Resampling-based Alignment

https://github.com/kdha0727/cancer-instance-segmentation-from-tissue

RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation

https://github.com/khyeyoon/Video-Super-Resolution

EDVR: Video Restoration with Enhanced Deformable Convolutional Networks

https://github.com/kingcheng2000/GAN

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/kingcong/RefineNet

RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation

https://github.com/kingcong/gpu_ESRGAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/kingcong/gpu_RefineNet

RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation

https://github.com/koi953215/NaRCan

Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution

https://github.com/kornia/kornia

DeFMO: Deblurring and Shape Recovery of Fast Moving Objects

https://github.com/kunjan-mhaske/Image-super-resolution-model-using-4-layers-CNN-from-PyTorch

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/kwantommy/breast-cancer-diagnosis

XGBoost: A Scalable Tree Boosting System

https://github.com/l3th4l/SuperRes

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/laomao0/BIN

Video Enhancement with Task-Oriented Flow

https://github.com/laowng/GISR

Deep Back-Projection Networks For Super-Resolution

https://github.com/lee-aaron/Image-Super-Resolution

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/leftthomas/espcn

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/linxi159/GAN-training-tricks

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/linxi159/Tips-and-tricks-to-train-GANs

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/liuzhen03/ADNet

EDVR: Video Restoration with Enhanced Deformable Convolutional Networks

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