Pluralistic Image Completion for Anomaly Detection (Med. Image Anal. 2023)
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Updated
Mar 12, 2024 - Python
Pluralistic Image Completion for Anomaly Detection (Med. Image Anal. 2023)
A High-Quality PyTorch Implementation of "Globally and Locally Consistent Image Completion".
[CVPR 2022]: Bridging Global Context Interactions for High-Fidelity Image Completion
🎨 Deep Fusion Network for Image Completion - ACMMM 2019
High-Fidelity Pluralistic Image Completion with Transformers (ICCV 2021)
Globally and Locally Consistent Image Completion using CNNs and GANs
The pytorch implementation of the paper "text-guided neural image inpainting" at MM'2020 (oral)
Want to remove something(someone) from a photo as it never was there? This is .NET implementation of content-aware fill. It smartly fills in unwanted or missing areas of photographs.
[CVPR 2019]: Pluralistic Image Completion
NTIRE 2022 - Image Inpainting Challenge
pytorch implementation of the paper ``Large Scale Image Completion via Co-Modulated Generative Adversarial Networks"
CR-Fill: Generative Image Inpainting with Auxiliary Contextual Reconstruction. ICCV 2021
A Deep-learning Project to remove human face masks and generate the left behind region (facial inpainting)
[ICLR 2021, Spotlight] Large Scale Image Completion via Co-Modulated Generative Adversarial Networks
Source code of AAAI 2020 paper 'Learning to Incorporate Structure Knowledge for Image Inpainting'
The main objective of this project is to present interactive image editing tools using a new randomized algorithm for quickly finding approximate nearest neighbor matches between image patches. This algorithm forms the basis for a variety of tools – that can be used together in the context of a high-level image editing application.
In this project I will be using 3 inpainting methods on MIT Places Dataset.
"Globally and Locally Consistent Image Completion" with Tensorflow2 Keras
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