https://github.com/Yingqiancui/FT-Shield
FT-Shield: A Watermark Against Unauthorized Fine-tuning in Text-to-Image Diffusion Models
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.
FT-Shield: A Watermark Against Unauthorized Fine-tuning in Text-to-Image Diffusion Models
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
Deep High-Resolution Representation Learning for Human Pose Estimation
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
Domain-Specific Mappings for Generative Adversarial Style Transfer
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
PubLayNet: largest dataset ever for document layout analysis
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.