Image Inpainting is a task of reconstructing missing regions in an image. It is an important problem in computer vision and an essential functionality in many imaging and graphics applications, e.g. object removal, image restoration, manipulation, re-targeting, compositing, and image-based rendering. Source: High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling Image source: High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling
10-shot-image-generation-1
Image Inpainting
3260 papers • 126 benchmarks • 313 datasets
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in 10-shot-image-generation-1
Trend
Dataset
Best Model
Actions
Places2
Places2
Places2 val
Places2 val
CelebA-HQ
CelebA-HQ
Libraries
iUse these libraries to find 10-shot-image-generation-1 models and implementations
3 papers 6,302
Datasets
Subtasks
Most implemented papers
Image Inpainting for Irregular Holes Using Partial Convolutions
Bryan Catanzaro, Ting-Chun Wang, Guilin Liu, Andrew Tao, F. Reda, Kevin J. Shih•Thu Apr 19 2018
This work proposes the use of partial convolutions, where the convolution is masked and renormalized to be conditioned on only valid pixels, and outperforms other methods for irregular masks.
2154
