https://github.com/sainisatish/News_virality
Deep Feelings: A Massive Cross-Lingual Study on the Relation between Emotions and Virality
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 Feelings: A Massive Cross-Lingual Study on the Relation between Emotions and Virality
EDICT: Exact Diffusion Inversion via Coupled Transformations
Instance Normalization: The Missing Ingredient for Fast Stylization
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Deep High-Resolution Representation Learning for Human Pose Estimation
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Personal Protective Equipment Detection in Extreme Construction Conditions
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
Exploring the structure of a real-time, arbitrary neural artistic stylization network
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.