CM-GAN: Image Inpainting with Cascaded Modulation GAN and Object-Aware Training Article Swipe

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Haitian Zheng , Zhe Lin , Jingwan Lu , Scott Cohen , Eli Shechtman , Connelly Barnes , Zhang, Jianming , Ning Xu , Sohrab Amirghodsi , Jiebo Luo ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2203.11947

Recent image inpainting methods have made great progress but often struggle to generate plausible image structures when dealing with large holes in complex images. This is partially due to the lack of effective network structures that can capture both the long-range dependency and high-level semantics of an image. We propose cascaded modulation GAN (CM-GAN), a new network design consisting of an encoder with Fourier convolution blocks that extract multi-scale feature representations from the input image with holes and a dual-stream decoder with a novel cascaded global-spatial modulation block at each scale level. In each decoder block, global modulation is first applied to perform coarse and semantic-aware structure synthesis, followed by spatial modulation to further adjust the feature map in a spatially adaptive fashion. In addition, we design an object-aware training scheme to prevent the network from hallucinating new objects inside holes, fulfilling the needs of object removal tasks in real-world scenarios. Extensive experiments are conducted to show that our method significantly outperforms existing methods in both quantitative and qualitative evaluation. Please refer to the project page: \url{https://github.com/htzheng/CM-GAN-Inpainting}.

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Inpainting Computer science Block (permutation group theory) Encoder Feature (linguistics) Artificial intelligence Modulation (music) Upsampling Image (mathematics) Convolution (computer science) Computer vision Object (grammar) Pattern recognition (psychology) Artificial neural network Mathematics Operating system Geometry Aesthetics Philosophy Linguistics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2203.11947
PDF: https://arxiv.org/pdf/2203.11947
OA Status: green
Cited By: 1
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2203.11947

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Title: CM-GAN: Image Inpainting with Cascaded Modulation GAN and Object-Aware Training

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Language: en

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Publication year: 2022

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Publication date: 2022-03-22

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Authors: Haitian Zheng, Zhe Lin, Jingwan Lu, Scott Cohen, Eli Shechtman, Connelly Barnes, Zhang, Jianming, Ning Xu, Sohrab Amirghodsi, Jiebo Luo

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Landing page: https://arxiv.org/abs/2203.11947

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Concepts: Inpainting, Computer science, Block (permutation group theory), Encoder, Feature (linguistics), Artificial intelligence, Modulation (music), Upsampling, Image (mathematics), Convolution (computer science), Computer vision, Object (grammar), Pattern recognition (psychology), Artificial neural network, Mathematics, Operating system, Geometry, Aesthetics, Philosophy, Linguistics

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Cited by: 1

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abstract_inverted_index.methods	3, 163
abstract_inverted_index.network	33, 56, 134
abstract_inverted_index.objects	138
abstract_inverted_index.perform	102
abstract_inverted_index.prevent	132
abstract_inverted_index.project	174
abstract_inverted_index.propose	49
abstract_inverted_index.removal	146
abstract_inverted_index.spatial	110
abstract_inverted_index.adaptive	121
abstract_inverted_index.cascaded	50, 84
abstract_inverted_index.existing	162
abstract_inverted_index.fashion.	122
abstract_inverted_index.followed	108
abstract_inverted_index.generate	12
abstract_inverted_index.progress	7
abstract_inverted_index.struggle	10
abstract_inverted_index.training	129
abstract_inverted_index.(CM-GAN),	53
abstract_inverted_index.Extensive	151
abstract_inverted_index.addition,	124
abstract_inverted_index.conducted	154
abstract_inverted_index.effective	32
abstract_inverted_index.partially	26
abstract_inverted_index.plausible	13
abstract_inverted_index.semantics	44
abstract_inverted_index.spatially	120
abstract_inverted_index.structure	106
abstract_inverted_index.consisting	58
abstract_inverted_index.dependency	41
abstract_inverted_index.fulfilling	141
abstract_inverted_index.high-level	43
abstract_inverted_index.inpainting	2
abstract_inverted_index.long-range	40
abstract_inverted_index.modulation	51, 86, 97, 111
abstract_inverted_index.real-world	149
abstract_inverted_index.scenarios.	150
abstract_inverted_index.structures	15, 34
abstract_inverted_index.synthesis,	107
abstract_inverted_index.convolution	64
abstract_inverted_index.dual-stream	79
abstract_inverted_index.evaluation.	169
abstract_inverted_index.experiments	152
abstract_inverted_index.multi-scale	68
abstract_inverted_index.outperforms	161
abstract_inverted_index.qualitative	168
abstract_inverted_index.object-aware	128
abstract_inverted_index.quantitative	166
abstract_inverted_index.hallucinating	136
abstract_inverted_index.significantly	160
abstract_inverted_index.global-spatial	85
abstract_inverted_index.semantic-aware	105
abstract_inverted_index.representations	70
abstract_inverted_index.\url{https://github.com/htzheng/CM-GAN-Inpainting}.	176
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	10
citation_normalized_percentile