2D Human Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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- 보존된 평가 기록
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What is Human Pose Estimation? Human pose estimation is the process of estimating the configuration of the body (pose) from a single, typically monocular, image. Background. Human pose estimation is one of the key problems in computer vision that has been studied for well over 15 years. The reason for its importance is the abundance of applications that can benefit from such a technology. For example, human pose estimation allows for higher-level reasoning in the context of human-computer interaction and activity recognition; it is also one of the basic building blocks for marker-less motion capture (MoCap) technology. MoCap technology is useful for applications ranging from character animation to clinical analysis of gait pathologies.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/modelscope/modelscope
DCT-Net: Domain-Calibrated Translation for Portrait Stylization
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/monishap123/mlhc2020
Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data
https://github.com/monniert/unicorn
https://github.com/mori97/CycleGAN-chainer
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/mousecpn/Domain-Generalization-via-Image-Stylization
Frustratingly Simple Domain Generalization via Image Stylization
https://github.com/mousecpn/Joint-Bilateral-Learning
Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer
https://github.com/mpaepper/stablediffusion_magicmix
https://github.com/mpsilfve/finer-data
https://github.com/mravanelli/pySpeechRev
The DIRHA-English corpus and related tasks for distant-speech recognition in domestic environments
https://github.com/mronta/CycleGAN-in-Keras
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/mshaikh2/GANs_Comparison
https://github.com/mstrutov/desbordante
Numerical Association Rule Mining: A Systematic Literature Review
https://github.com/mszpc/StyTR-2-mindspore
https://github.com/mtorabirad/PricePrediction
https://github.com/muhdhuz/Audio_NeuralStyle
https://github.com/mukul1093/Image-Super-Resolution
https://github.com/mukulanandchas/ART-GENERATION-USING-NEURAL-STYLE-TRANSFER
https://github.com/muriloime/awesome-stars
https://github.com/murrman95/INF573Project2018
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
https://github.com/mustafa1728/TA3N-Lightning
https://github.com/mustafaaljadery/gemma-2b-10m
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
https://github.com/mv-lab/swin2sr
https://github.com/mvenouziou/Project_Style_Transfer
https://github.com/nabarunbaruaAIML/ATCC_Yolov5
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/nagadomi/waifu2x
https://github.com/nahliabdelwahed/Face-Image-Deblurring
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/namanphy/style-transfer-through-back-translate
https://github.com/namtuanly/MTL-TabNet
https://github.com/nannau/DoWnGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/naoto0804/pytorch-AdaIN
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
https://github.com/national-center-for-ai-saudi-arabia/lm-evaluation-harness
https://github.com/ncbi-nlp/BLUE_Benchmark
BioSentVec: creating sentence embeddings for biomedical texts
https://github.com/ncoop57/gpt-code-clippy
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.