Learning a Discriminative Feature Network for Semantic Segmentation Article Swipe

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Changqian Yu , Jingbo Wang , Chao Peng , Changxin Gao , Gang Yu , Nong Sang ·

YOU? · · 2018 · Open Access · · DOI: https://doi.org/10.48550/arxiv.1804.09337

Most existing methods of semantic segmentation still suffer from two aspects of challenges: intra-class inconsistency and inter-class indistinction. To tackle these two problems, we propose a Discriminative Feature Network (DFN), which contains two sub-networks: Smooth Network and Border Network. Specifically, to handle the intra-class inconsistency problem, we specially design a Smooth Network with Channel Attention Block and global average pooling to select the more discriminative features. Furthermore, we propose a Border Network to make the bilateral features of boundary distinguishable with deep semantic boundary supervision. Based on our proposed DFN, we achieve state-of-the-art performance 86.2% mean IOU on PASCAL VOC 2012 and 80.3% mean IOU on Cityscapes dataset.

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Pascal (Unit)

Computer Science

Artificial Intelligence

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Discriminative model Pooling Pascal (unit) Computer science Artificial intelligence Segmentation Feature (linguistics) Pattern recognition (psychology) Class (philosophy) Backbone network Semantic feature Machine learning Computer network Philosophy Programming language Linguistics

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/1804.09337
PDF: https://arxiv.org/pdf/1804.09337
OA Status: green
Cited By: 88
References: 33
Related Works: 10
OpenAlex ID: https://openalex.org/W2952577426

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DOI: https://doi.org/10.48550/arxiv.1804.09337

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Title: Learning a Discriminative Feature Network for Semantic Segmentation

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Type: preprint

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Language: en

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Publication year: 2018

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Publication date: 2018-04-25

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Authors: Changqian Yu, Jingbo Wang, Chao Peng, Changxin Gao, Gang Yu, Nong Sang

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Landing page: https://arxiv.org/abs/1804.09337

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PDF URL: https://arxiv.org/pdf/1804.09337

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Concepts: Discriminative model, Pooling, Pascal (unit), Computer science, Artificial intelligence, Segmentation, Feature (linguistics), Pattern recognition (psychology), Class (philosophy), Backbone network, Semantic feature, Machine learning, Computer network, Philosophy, Programming language, Linguistics

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Cited by: 88

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Citations by year (recent): 2024: 2, 2023: 2, 2022: 8, 2021: 16, 2020: 21

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References (count): 33

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referenced_works_count	33
abstract_inverted_index.a	25, 49, 69
abstract_inverted_index.To	18
abstract_inverted_index.of	3, 11, 77
abstract_inverted_index.on	86, 97, 105
abstract_inverted_index.to	40, 60, 72
abstract_inverted_index.we	23, 46, 67, 90
abstract_inverted_index.IOU	96, 104
abstract_inverted_index.VOC	99
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abstract_inverted_index.our	87
abstract_inverted_index.the	42, 62, 74
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abstract_inverted_index.86.2%	94
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abstract_inverted_index.these	20
abstract_inverted_index.which	30
abstract_inverted_index.(DFN),	29
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abstract_inverted_index.methods	2
abstract_inverted_index.pooling	59
abstract_inverted_index.propose	24, 68
abstract_inverted_index.Network.	38
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abstract_inverted_index.dataset.	107
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abstract_inverted_index.problem,	45
abstract_inverted_index.proposed	88
abstract_inverted_index.semantic	4, 82
abstract_inverted_index.Attention	54
abstract_inverted_index.bilateral	75
abstract_inverted_index.features.	65
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abstract_inverted_index.indistinction.	17
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cited_by_percentile_year
countries_distinct_count	1
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sustainable_development_goals[0].id	https://metadata.un.org/sdg/10
sustainable_development_goals[0].score	0.7799999713897705
sustainable_development_goals[0].display_name	Reduced inequalities
citation_normalized_percentile