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Image Stylization
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Image stylization is a task that involves transforming an input image into a new image that has a different style, while preserving the content of the original image. The goal of image stylization is to create visually appealing images with a specific style or aesthetic, such as impressionism, cubism, or surrealism. It can also be used to make images more visually appealing for specific applications, such as social media or advertising.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning
Learning Visual Styles from Audio-Visual Associations
Image Steganography based on Style Transfer
JoJoGAN: One Shot Face Stylization
Digging Into Self-Supervised Learning of Feature Descriptors
UMFA: A photorealistic style transfer method based on U-Net and multi-layer feature aggregation
Foreground-Aware Stylization and Consensus Pseudo-Labeling for Domain Adaptation of First-Person Hand Segmentation
Achieving Domain Generalization in Underwater Object Detection by Domain Mixup and Contrastive Learning
Image Stylization for Robust Features
Co-Attention for Conditioned Image Matching
Frustratingly Simple Domain Generalization via Image Stylization
Distribution Aligned Multimodal and Multi-Domain Image Stylization
Multi-Attribute Guided Painting Generation
Image Stylization: From Predefined to Personalized
CartoonRenderer: An Instance-based Multi-Style Cartoon Image Translator
Texture Underfitting for Domain Adaptation
TileGAN: Synthesis of Large-Scale Non-Homogeneous Textures
Aesthetics of Neural Network Art
Image Super-Resolution by Neural Texture Transfer
Automatic Image Stylization Using Deep Fully Convolutional Networks
Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization
Context-Aware Text-Based Binary Image Stylization and Synthesis
A Style-Aware Content Loss for Real-time HD Style Transfer
Reference-Conditioned Super-Resolution by Neural Texture Transfer
A Closed-form Solution to Photorealistic Image Stylization
Coherent Online Video Style Transfer
Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis
Instance Normalization: The Missing Ingredient for Fast Stylization
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