Papers with Code 아카이브

Image Inpainting

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

3

평가표

12

보존된 평가 기록

48

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/7201krap/CSCE748_Computational-Photography

공식 코드

Leveraging Depth Maps and Attention Mechanisms for Enhanced Image Inpainting

https://github.com/AKASH2907/Forgery-Classification-via-Domain-Adaptation

공식 코드

Syn2Real: Forgery Classification via Unsupervised Domain Adaptation

https://github.com/ArshockAbedan/CS_MSc_Codes

공식 코드

Multi-Modality Image Inpainting using Generative Adversarial Networks

https://github.com/Aswhalefall/VISTA

공식 코드

Visibility-Uncertainty-guided 3D Gaussian Inpainting via Scene Conceptional Learning

https://github.com/Atlas200dk/sample-imageinpainting-HiFill

공식 코드

Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting

https://github.com/BUPTLdy/RICE_DATASET

공식 코드

A Remote Sensing Image Dataset for Cloud Removal

https://github.com/CUG-BEODL/MS2TAN

공식 코드

Multi-scale Restoration of Missing Data in Optical Time-series Images with Masked Spatial-Temporal Attention Network

https://github.com/Evan1578/BIPSDA

공식 코드

Can Diffusion Models Provide Rigorous Uncertainty Quantification for Bayesian Inverse Problems?

https://github.com/GouravWadhwa/Hypergraphs-Image-Inpainting

공식 코드

Hyperrealistic Image Inpainting with Hypergraphs

https://github.com/GuHuangAI/ADM-Public

공식 코드

Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image Attenuation

https://github.com/Hammour-steak/GOUB

공식 코드

Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge

https://github.com/HighwayWu/ImageInpainting

공식 코드

Deep Generative Model for Image Inpainting with Local Binary Pattern Learning and Spatial Attention

https://github.com/HighwayWu/InpaintingForensics

공식 코드

GIID-Net: Generalizable Image Inpainting Detection via Neural Architecture Search and Attention

https://github.com/HuiSiqi/Auxiliary-Loss-Reweighting-for-Image-Inpainting

공식 코드

Auxiliary Loss Reweighting for Image Inpainting

https://github.com/IamShubhamGupto/WSSL-Weighted-Self-Supervised-Learning-for-Image-Inpainting

공식 코드

WSSL: Weighted Self-supervised Learning Framework For Image-inpainting

https://github.com/JiahuiYu/generative_inpainting

공식 코드

Generative Image Inpainting with Contextual Attention

https://github.com/Jireh-Jam/R-MNet-Inpainting-keras

공식 코드

R-MNet: A Perceptual Adversarial Network for Image Inpainting

https://github.com/KumapowerLIU/CSA-inpainting

공식 코드

Coherent Semantic Attention for Image Inpainting

https://github.com/KumapowerLIU/PD-GAN

공식 코드

PD-GAN: Probabilistic Diverse GAN for Image Inpainting

https://github.com/KumapowerLIU/Rethinking-Inpainting-MEDFE

공식 코드

Rethinking Image Inpainting via a Mutual Encoder-Decoder with Feature Equalizations

https://github.com/Lingzhi-Pan/PILOT

공식 코드

Coherent and Multi-modality Image Inpainting via Latent Space Optimization

https://github.com/LonglongaaaGo/EXE-GAN

공식 코드

Do Inpainting Yourself: Generative Facial Inpainting Guided by Exemplars

https://github.com/MADF-inpainting/Pytorch-MADF

공식 코드

Image Inpainting by End-to-End Cascaded Refinement with Mask Awareness

https://github.com/MLI-lab/ConvDecoder

공식 코드

Accelerated MRI with Un-trained Neural Networks

https://github.com/MengyingLei/GLSKF

공식 코드

Generalized Least Squares Kernelized Tensor Factorization

https://github.com/Mosasaur5526/BCM-iCT-torch

공식 코드

Bidirectional Consistency Models

https://github.com/NVIDIA/partialconv

공식 코드

Image Inpainting for Irregular Holes Using Partial Convolutions

https://github.com/OpenGVLab/Diffree

공식 코드

Diffree: Text-Guided Shape Free Object Inpainting with Diffusion Model

https://github.com/PatrickTUM/SEN12MS-CR-TS

공식 코드

Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery

https://github.com/Penn000/SpA-GAN_for_cloud_removal

공식 코드

Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network

https://github.com/PierfrancescoArdino/SGINet

공식 코드

Semantic-Guided Inpainting Network for Complex Urban Scenes Manipulation

https://github.com/QixuanJin99/generative_validation

공식 코드

MaskMedPaint: Masked Medical Image Inpainting with Diffusion Models for Mitigation of Spurious Correlations

https://github.com/RenYurui/StructureFlow

공식 코드

StructureFlow: Image Inpainting via Structure-aware Appearance Flow

https://github.com/RotsteinNoam/Paint-by-Inpaint

공식 코드

Paint by Inpaint: Learning to Add Image Objects by Removing Them First

https://github.com/SHI-Labs/FcF-Inpainting

공식 코드

Keys to Better Image Inpainting: Structure and Texture Go Hand in Hand

https://github.com/Scientific-Computing-Lab-NRCN/MLography

공식 코드

An End-to-End Computer Vision Methodology for Quantitative Metallography

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.