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평가표

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보존된 평가 기록

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코드

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

https://github.com/D1vyansh/BodyJointDetection

Convolutional Pose Machines

https://github.com/DC1991/FS_Net

FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism

https://github.com/DELTA37/LatentKeypointGAN

LatentKeypointGAN: Controlling Images via Latent Keypoints

https://github.com/DagnyT/GLAMpoints-PyTorch

GLAMpoints: Greedily Learned Accurate Match points

https://github.com/DanailKoychev/neural-style

A Neural Algorithm of Artistic Style

https://github.com/DanielLongo/AdversarialTrain

Generative Adversarial Text to Image Synthesis

https://github.com/DanielLongo/GANs

Generative Adversarial Text to Image Synthesis

https://github.com/DanielLongo/cGANs

Generative Adversarial Text to Image Synthesis

https://github.com/Daniil-Osokin/gccpm-look-into-person-cvpr19.pytorch

Convolutional Pose Machines

https://github.com/Daniil-Osokin/lightweight-human-pose-estimation-3d-demo.pytorch

Single-Shot Multi-Person 3D Pose Estimation From Monocular RGB

https://github.com/Daniil-Osokin/lightweight-human-pose-estimation.pytorch

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

https://github.com/DaphneAntotsiou/task-oriented-hand-retargeting

Spatial Attention Deep Net with Partial PSO for Hierarchical Hybrid Hand Pose Estimation

https://github.com/Darius-Liesis/HRNet-works

HigherHRNet: Scale-Aware Representation Learning for Bottom-Up Human Pose Estimation

https://github.com/DarkGeekMS/Tensorflow-GANs-Architectures-Implementation

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/DarkGeekMS/artistic-style-transfer-using-texture-synthesis

Style-Transfer via Texture-Synthesis

https://github.com/DarshanDeshpande/jax-models

Visual Attention Network

https://github.com/DarshanDeshpande/research-paper-implementations/tree/main/tensorflow/stytr2

StyTr$^2$: Image Style Transfer with Transformers

https://github.com/Data-drone/cvnd_image_captioning

Show and Tell: A Neural Image Caption Generator

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

Learning to Train with Synthetic Humans

https://github.com/Davidnh8/artAI

A Neural Algorithm of Artistic Style

https://github.com/Dawars/SqueezeNet-tf

A Neural Algorithm of Artistic Style

https://github.com/DeNA/Chainer_Mask_R-CNN

Mask R-CNN

https://github.com/DeadAt0m/ActiveSparseShifts-PyTorch

Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions

https://github.com/DeepMotionEditing/deep-motion-editing

Unpaired Motion Style Transfer from Video to Animation

https://github.com/DeepikaKaranji/CycleGAN-Unpaired-Image-translation

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/DengpanFu/LUPerson

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

https://github.com/DenisTome/Lifting-from-the-Deep-release

Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image

https://github.com/Dictator890/ESRGAN-Android-Demo

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/DiegoArcelli/De-Stylization-Network

Recognizing Instagram Filtered Images with Feature De-stylization

https://github.com/DineshAnalyticsandAI/Pneumonia-Detection-Chest-X-Rays

CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

https://github.com/DivJAth/DeepLearning5922

Mask R-CNN

https://github.com/Divsigma/2020-cs213n

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

https://github.com/DixinFan/st-gcn

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/Djmcflush/Funky-Movement

HP-GAN: Probabilistic 3D human motion prediction via GAN

https://github.com/Djmcflush/Quantum-Hackathon

Show and Tell: A Neural Image Caption Generator

https://github.com/DmitryRyumin/ICCV-2023-Papers

Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement

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