Papers with Code 아카이브

Image Inpainting

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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/Sebbyraft/Spatio-Temporal_SAR-Optical_Data_Fusion_for_Cloud_Removal

공식 코드

Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model

https://github.com/ShinyCode/image-outpainting

공식 코드

Painting Outside the Box: Image Outpainting with GANs

https://github.com/SteveImmanuel/OEM-Few-Shot-Learnable-Prompt

공식 코드

Learnable Prompt for Few-Shot Semantic Segmentation in Remote Sensing Domain

https://github.com/SteveImmanuel/rs-paint

공식 코드

Tackling Few-Shot Segmentation in Remote Sensing via Inpainting Diffusion Model

https://github.com/Stillrot/RGINP

공식 코드

Reference Guided Image Inpainting using Facial Attributes

https://github.com/TencentARC/VideoPainter

공식 코드

VideoPainter: Any-length Video Inpainting and Editing with Plug-and-Play Context Control

https://github.com/Tengfei-Wang/external-internal-inpainting

공식 코드

Image Inpainting with External-internal Learning and Monochromic Bottleneck

https://github.com/USTC-JialunPeng/Diverse-Structure-Inpainting

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Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAE

https://github.com/VRI-UFPR/IDA

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IDA: Improved Data Augmentation Applied to Salient Object Detection

https://github.com/VSainteuf/utae-paps

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Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks

https://github.com/Vious/LBAM_inpainting

공식 코드

Image Inpainting with Learnable Bidirectional Attention Maps

https://github.com/WHUQZhang/STS-CNN

공식 코드

Missing Data Reconstruction in Remote Sensing image with a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Network

https://github.com/WendongZh/SPL

공식 코드

Context-Aware Image Inpainting with Learned Semantic Priors

https://github.com/XuFengthucs/fSVT

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Faster Matrix Completion Using Randomized SVD

https://github.com/YaN9-Y/lafin

공식 코드

LaFIn: Generative Landmark Guided Face Inpainting

https://github.com/YilinLiu97/Untrained-Recon

공식 코드

Towards Architecture-Agnostic Untrained Network Priors for Image Reconstruction with Frequency Regularization

https://github.com/YoungGod/sturcture-inpainting

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Learning to Incorporate Structure Knowledge for Image Inpainting

https://github.com/YuvalNirkin/fsgan

공식 코드

FSGAN: Subject Agnostic Face Swapping and Reenactment

https://github.com/ZAL-NASK/CSMC

공식 코드

Randomized Approach to Matrix Completion: Applications in Collaborative Filtering and Image Inpainting

https://github.com/Zhaoyi-Yan/Shift-Net_pytorch

공식 코드

Shift-Net: Image Inpainting via Deep Feature Rearrangement

https://github.com/Zheng222/DMFN

공식 코드

Image Fine-grained Inpainting

https://github.com/ZongyuGuo/Inpainting_FRRN

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Progressive Image Inpainting with Full-Resolution Residual Network

https://github.com/a-mos/High_Resolution_Image_Inpainting

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Deep Two-Stage High-Resolution Image Inpainting

https://github.com/aaberdam/AdaLISTA

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Ada-LISTA: Learned Solvers Adaptive to Varying Models

https://github.com/abyildirim/inst-inpaint

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Inst-Inpaint: Instructing to Remove Objects with Diffusion Models

https://github.com/advaitkumar3107/rsinet

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RSINet: Inpainting Remotely Sensed Images Using Triple GAN Framework

https://github.com/akmtn/OmniDreamer

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Diverse Plausible 360-Degree Image Outpainting for Efficient 3DCG Background Creation

https://github.com/algolzw/daclip-uir

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Controlling Vision-Language Models for Multi-Task Image Restoration

https://github.com/algolzw/image-restoration-sde

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Image Restoration with Mean-Reverting Stochastic Differential Equations

https://github.com/alonzoleeeooo/sketchrefiner

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Towards Interactive Image Inpainting via Sketch Refinement

https://github.com/alvinzhuyx/cdrl

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Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood

https://github.com/amirbar/visual_prompting

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Visual Prompting via Image Inpainting

https://github.com/amjltc295/Free-Form-Video-Inpainting

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Learnable Gated Temporal Shift Module for Deep Video Inpainting

https://github.com/andreas128/RePaint

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RePaint: Inpainting using Denoising Diffusion Probabilistic Models

https://github.com/andrefagereng/polyp-gan

공식 코드

PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps

https://github.com/andyshih12/mac

공식 코드

Training and Inference on Any-Order Autoregressive Models the Right Way

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