@inproceedings{0e358d14b9ee41d9b973ec240b741831,
title = "DeepGIN: Deep Generative Inpainting Network for Extreme Image Inpainting",
abstract = "The degree of difficulty in image inpainting depends on the types and sizes of the missing parts. Existing image inpainting approaches usually encounter difficulties in completing the missing parts in the wild with pleasing visual and contextual results as they are trained for either dealing with one specific type of missing patterns (mask) or unilaterally assuming the shapes and/or sizes of the masked areas. We propose a deep generative inpainting network, named DeepGIN, to handle various types of masked images. We design a Spatial Pyramid Dilation (SPD) ResNet block to enable the use of distant features for reconstruction. We also employ Multi-Scale Self-Attention (MSSA) mechanism and Back Projection (BP) technique to enhance our inpainting results. Our DeepGIN outperforms the state-of-the-art approaches generally, including two publicly available datasets (FFHQ and Oxford Buildings), both quantitatively and qualitatively. We also demonstrate that our model is capable of completing masked images in the wild.",
keywords = "Attention, Back projection, Image inpainting",
author = "Li, {Chu Tak} and Siu, {Wan Chi} and Liu, {Zhi Song} and Wang, {Li Wen} and Lun, {Daniel Pak Kong}",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; Workshops held at the 16th European Conference on Computer Vision, ECCV 2020 ; Conference date: 23-08-2020 Through 28-08-2020",
year = "2021",
month = jan,
doi = "10.1007/978-3-030-66823-5_1",
language = "English",
isbn = "9783030668228",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "5--22",
editor = "Adrien Bartoli and Andrea Fusiello",
booktitle = "Computer Vision – ECCV 2020 Workshops, Proceedings",
address = "Germany",
}