https://github.com/oneTaken/pytorch_fast_style_transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
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.
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Deep High-Resolution Representation Learning for Human Pose Estimation
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
Customizing Sequence Generation with Multi-Task Dynamical Systems
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
Predicting Factuality of Reporting and Bias of News Media Sources
Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks
Inverting face embeddings with convolutional neural networks
Domain Adaptation for Object Detection via Style Consistency
StyPath: Style-Transfer Data Augmentation For Robust Histology Image Classification
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
PubLayNet: largest dataset ever for document layout analysis
PubTables-1M: Towards comprehensive table extraction from unstructured documents
Exploring the structure of a real-time, arbitrary neural artistic stylization network
Language agents achieve superhuman synthesis of scientific knowledge
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.