https://github.com/thoughtmachines/Human-Pose-Estimation-using-HRNets
Deep High-Resolution Representation Learning for Human Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Deep High-Resolution Representation Learning for Human Pose Estimation
XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model
Unsupervised Meta-learning of Figure-Ground Segmentation via Imitating Visual Effects
Unsupervised Meta-learning of Figure-Ground Segmentation via Imitating Visual Effects
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Unsupervised Speech Decomposition via Triple Information Bottleneck
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Towards Robust Learning-Based Pose Estimation of Noncooperative Spacecraft
Towards Robust Learning-Based Pose Estimation of Noncooperative Spacecraft
End-to-end lyrics Recognition with Voice to Singing Style Transfer
Mini-GPTs: Efficient Large Language Models through Contextual Pruning
Avatar-Net: Multi-scale Zero-shot Style Transfer by Feature Decoration
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.