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.
StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators
Open-World Entity Segmentation
Remember What You have drawn: Semantic Image Manipulation with Memory
An Image is Worth More Than a Thousand Words: Towards Disentanglement in the Wild
Attack to Fool and Explain Deep Networks
SinIR: Efficient General Image Manipulation with Single Image Reconstruction
Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs
D2C: Diffusion-Denoising Models for Few-shot Conditional Generation
Pivotal Tuning for Latent-based Editing of Real Images
FReTAL: Generalizing Deepfake Detection using Knowledge Distillation and Representation Learning
ExSinGAN: Learning an Explainable Generative Model from a Single Image
Manipulation Detection in Satellite Images Using Vision Transformer
Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing
SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models
Cross-Domain and Disentangled Face Manipulation with 3D Guidance
Image Manipulation Detection by Multi-View Multi-Scale Supervision
Holistic Image Manipulation Detection using Pixel Co-occurrence Matrices
Content-Aware GAN Compression
StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery
Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image Generative Models
PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization
Detection and Localization of Facial Expression Manipulations
Learning Long-Term Style-Preserving Blind Video Temporal Consistency
Effects of Image Compression on Face Image Manipulation Detection: A Case Study on Facial Retouching
Interpreting the Latent Space of Generative Adversarial Networks using Supervised Learning
Only a Matter of Style: Age Transformation Using a Style-Based Regression Model
Designing an Encoder for StyleGAN Image Manipulation
Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigation
VIPPrint: A Large Scale Dataset of Printed and Scanned Images for Synthetic Face Images Detection and Source Linking
The Effect of Class Definitions on the Transferability of Adversarial Attacks Against Forensic CNNs
GAN Inversion: A Survey
Generative Adversarial U-Net for Domain-free Medical Image Augmentation
Learning Accurate Dense Correspondences and When to Trust Them
Latent Compass: Creation by Navigation
Positional Encoding as Spatial Inductive Bias in GANs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.