@inproceedings{55df531e87284cc7baee7a8152111a2e,
title = "SCAN: Spatial and Channel Attention Normalization for Image Inpainting",
abstract = "Image inpainting focuses on predicting contents with shape structure and consistent details in damaged regions. Recent approaches based on convolutional neural network (CNN) have shown promising results via adversarial learning, attention mechanism, and various loss functions. This paper introduces a novel module named Spatial and Channel Attention Normalization (SCAN), combining attention mechanisms in spatial and channel dimension and normalization to handle complex information of known regions while avoiding its misuse. Experiments on the varies datasets indicate that the performance of the proposed method outperforms the current state-of-the-art (SOTA) inpainting approaches.",
keywords = "Attention mechanism, Deep learning, Image inpainting, Normalization",
author = "Shiyu Chen and Wenxin Yu and Liang Nie and Xuewen Zhang and Siyuan Li and Zhiqiang Zhang and Jun Gong",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 28th International Conference on Neural Information Processing, ICONIP 2021 ; Conference date: 08-12-2021 Through 12-12-2021",
year = "2021",
doi = "10.1007/978-3-030-92310-5_78",
language = "English",
isbn = "9783030923099",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "674--682",
editor = "Teddy Mantoro and Minho Lee and Ayu, {Media Anugerah} and Wong, {Kok Wai} and Hidayanto, {Achmad Nizar}",
booktitle = "Neural Information Processing - 28th International Conference, ICONIP 2021, Proceedings",
address = "Germany",
}