Generating Scenery Images with Larger Variety According to User Descriptions Article Swipe

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YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.3390/app112110224

In this paper, a framework based on generative adversarial networks is proposed to perform nature-scenery generation according to descriptions from the users. The desired place, time and seasons of the generated scenes can be specified with the help of text-to-image generation techniques. The framework improves and modifies the architecture of a generative adversarial network with attention models by adding the imagination models. The proposed attentional and imaginative generative network uses the hidden layer information to initialize the memory cell of the recurrent neural network to produce the desired photos. A data set containing different categories of scenery images is established to train the proposed system. The experiments validate that the proposed method is able to increase the quality and diversity of the generated images compared to the existing method. A possible application of road image generation for data augmentation is also demonstrated in the experimental results.

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Computer Science

Generative Grammar

Artificial Intelligence

Generative Adversarial Network

Concepts

Computer science Generative grammar Artificial intelligence Image (mathematics) Set (abstract data type) Variety (cybernetics) Adversarial system Generative adversarial network Layer (electronics) Artificial neural network Generative model Pattern recognition (psychology) Computer vision Organic chemistry Chemistry Programming language

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Type: article
Language: en
Landing Page: https://doi.org/10.3390/app112110224
PDF: https://www.mdpi.com/2076-3417/11/21/10224/pdf?version=1635759212
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References: 38
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DOI: https://doi.org/10.3390/app112110224

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Title: Generating Scenery Images with Larger Variety According to User Descriptions

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Type: article

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Language: en

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Publication year: 2021

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Publication date: 2021-11-01

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Authors: Hsu-Yung Cheng, Chih-Chang Yu

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Landing page: https://doi.org/10.3390/app112110224

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Concepts: Computer science, Generative grammar, Artificial intelligence, Image (mathematics), Set (abstract data type), Variety (cybernetics), Adversarial system, Generative adversarial network, Layer (electronics), Artificial neural network, Generative model, Pattern recognition (psychology), Computer vision, Organic chemistry, Chemistry, Programming language

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abstract_inverted_index.layer	72
abstract_inverted_index.train	101
abstract_inverted_index.adding	58
abstract_inverted_index.hidden	71
abstract_inverted_index.images	97, 123
abstract_inverted_index.memory	77
abstract_inverted_index.method	111
abstract_inverted_index.models	56
abstract_inverted_index.neural	82
abstract_inverted_index.paper,	2
abstract_inverted_index.place,	24
abstract_inverted_index.scenes	31
abstract_inverted_index.users.	21
abstract_inverted_index.desired	23, 87
abstract_inverted_index.method.	128
abstract_inverted_index.models.	61
abstract_inverted_index.network	53, 68, 83
abstract_inverted_index.perform	13
abstract_inverted_index.photos.	88
abstract_inverted_index.produce	85
abstract_inverted_index.quality	117
abstract_inverted_index.scenery	96
abstract_inverted_index.seasons	27
abstract_inverted_index.system.	104
abstract_inverted_index.compared	124
abstract_inverted_index.existing	127
abstract_inverted_index.improves	44
abstract_inverted_index.increase	115
abstract_inverted_index.modifies	46
abstract_inverted_index.networks	9
abstract_inverted_index.possible	130
abstract_inverted_index.proposed	11, 63, 103, 110
abstract_inverted_index.results.	145
abstract_inverted_index.validate	107
abstract_inverted_index.according	16
abstract_inverted_index.attention	55
abstract_inverted_index.different	93
abstract_inverted_index.diversity	119
abstract_inverted_index.framework	4, 43
abstract_inverted_index.generated	30, 122
abstract_inverted_index.recurrent	81
abstract_inverted_index.specified	34
abstract_inverted_index.categories	94
abstract_inverted_index.containing	92
abstract_inverted_index.generation	15, 40, 135
abstract_inverted_index.generative	7, 51, 67
abstract_inverted_index.initialize	75
abstract_inverted_index.adversarial	8, 52
abstract_inverted_index.application	131
abstract_inverted_index.attentional	64
abstract_inverted_index.established	99
abstract_inverted_index.experiments	106
abstract_inverted_index.imagination	60
abstract_inverted_index.imaginative	66
abstract_inverted_index.information	73
abstract_inverted_index.techniques.	41
abstract_inverted_index.architecture	48
abstract_inverted_index.augmentation	138
abstract_inverted_index.demonstrated	141
abstract_inverted_index.descriptions	18
abstract_inverted_index.experimental	144
abstract_inverted_index.text-to-image	39
abstract_inverted_index.nature-scenery	14
cited_by_percentile_year
corresponding_author_ids	https://openalex.org/A5028504321
countries_distinct_count	1
institutions_distinct_count	2
corresponding_institution_ids	https://openalex.org/I22265921
sustainable_development_goals[0].id	https://metadata.un.org/sdg/11
sustainable_development_goals[0].score	0.6100000143051147
sustainable_development_goals[0].display_name	Sustainable cities and communities
citation_normalized_percentile.value	0.13446836
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False