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Cited by 2,527

The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

Generative Image Inpainting with Contextual Attention

Open access
Yu, Jiahui, Lin, Zhe, Yang, Jimei, Shen, Xiaohui, Lu, Xin, Huang, Thomas S.·2018·2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
DOI 10.1109/cvpr.2018.00577
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Abstract

Recent deep learning based approaches have shown promising results for the challenging task of inpainting large missing regions in an image. These methods can generate visually plausible image structures and textures, but often create distorted structures or blurry textures inconsistent with surrounding areas. This is mainly due to ineffectiveness of convolutional neural networks in explicitly borrowing or copying information from distant spatial locations. On the other hand, traditional texture and patch synthesis approaches are particularly suitable when it needs to borrow textures from the surrounding regions. Motivated by these observations, we propose a new deep generative model-based approach which can not only synthesize novel image structures but also explicitly utilize surrounding image features as references during network training to make better predictions. The model is a feedforward, fully convolutional neural network which can process images with multiple holes at arbitrary locations and with variable sizes during the test time. Experiments on multiple datasets including faces (CelebA, CelebA-HQ), textures (DTD) and natural images (ImageNet, Places2) demonstrate that our proposed approach generates higher-quality inpainting results than existing ones. Code, demo and models are available at: https://github.com/JiahuiYu/generative_inpainting.

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Citations

Cites

Research works printed but not linked: 31

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Metadata

Authors
Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., & Huang, T. S.
Venue
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Publisher
IEEE
Published
2018-06
Pages
5505-5514
ISSN
1063-6919 (ISSN-L)
DOI
10.1109/cvpr.2018.00577
OpenAlex
W3043547428
Cited by
2,527
References
11
FWCI
87.08
Type
conference paper
Language
en
Open access
green

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