https://github.com/SunnerLi/Cross-you-in-style
Crossing You in Style: Cross-modal Style Transfer from Music to Visual Arts
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.
Crossing You in Style: Cross-modal Style Transfer from Music to Visual Arts
Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
J-Guard: Journalism Guided Adversarially Robust Detection of AI-generated News
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
EfficientHRNet: Efficient Scaling for Lightweight High-Resolution Multi-Person Pose Estimation
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
A Contextual-Bandit Approach to Personalized News Article Recommendation
MUST-GAN: Multi-level Statistics Transfer for Self-driven Person Image Generation
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
TongUI: Building Generalized GUI Agents by Learning from Multimodal Web Tutorials
Improving the Transferability of Adversarial Examples with Arbitrary Style Transfer
A Contextual-Bandit Approach to Personalized News Article Recommendation
Style Transfer for Texts: Retrain, Report Errors, Compare with Rewrites
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.