Papers with Code 아카이브
Image Manipulation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 1
- 보존된 평가 기록
- 2
Image Manipulation is the process of altering or transforming an existing image to achieve a desired effect or to modify its content. This can involve various techniques and tools to enhance, modify, or create images based on specific requirements.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
S2FGAN: Semantically Aware Interactive Sketch-to-Face Translation
Differentiable Data Augmentation with Kornia
Fooling the primate brain with minimal, targeted image manipulation
Representation Decomposition for Image Manipulation and Beyond
Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation
A Multi-Modal Method for Satire Detection using Textual and Visual Cues
Generative Autoregressive Ensembles for Satellite Imagery Manipulation Detection
SMILE: Semantically-guided Multi-attribute Image and Layout Editing
RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse prior
Two-stream Encoder-Decoder Network for Localizing Image Forgeries
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
WDRN : A Wavelet Decomposed RelightNet for Image Relighting
L2-Constrained RemNet for Camera Model Identification and Image Manipulation Detection
Text as Neural Operator: Image Manipulation by Text Instruction
Open-Edit: Open-Domain Image Manipulation with Open-Vocabulary Instructions
Seeing eye-to-eye? A comparison of object recognition performance in humans and deep convolutional neural networks under image manipulation
Closed-Form Factorization of Latent Semantics in GANs
Image Shape Manipulation from a Single Augmented Training Sample
Swapping Autoencoder for Deep Image Manipulation
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
Perspective Plane Program Induction from a Single Image
Conditional Image Generation and Manipulation for User-Specified Content
Fake face detection via adaptive manipulation traces extraction network
High Resolution Face Age Editing
GIMP-ML: Python Plugins for using Computer Vision Models in GIMP
A Disentangling Invertible Interpretation Network for Explaining Latent Representations
Printing and Scanning Attack for Image Counter Forensics
SESAME: Semantic Editing of Scenes by Adding, Manipulating or Erasing Objects
Semantic Image Manipulation Using Scene Graphs
Self-Supervised Scene De-occlusion
Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation
Lifespan Age Transformation Synthesis
High-Resolution Daytime Translation Without Domain Labels
StyleGAN2 Distillation for Feed-forward Image Manipulation
multi-patch aggregation models for resampling detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.