Multi-Label Image Classification by Feature Attention Network
<p dir="ltr">Learning the correlation among labels is a standing-problem in the multi-label image recognition task. The label correlation is the key to solve the multi-label classification but it is too abstract to model. Most solutions try to learn image label dependencies to improv...
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| مؤلفون آخرون: | , , |
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2019
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| _version_ | 1864513505623801856 |
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| author | Zheng Yan (194231) |
| author2 | Weiwei Liu (341566) Shiping Wen (7168688) Yin Yang (35103) |
| author2_role | author author author |
| author_facet | Zheng Yan (194231) Weiwei Liu (341566) Shiping Wen (7168688) Yin Yang (35103) |
| author_role | author |
| dc.creator.none.fl_str_mv | Zheng Yan (194231) Weiwei Liu (341566) Shiping Wen (7168688) Yin Yang (35103) |
| dc.date.none.fl_str_mv | 2019-07-18T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2019.2929512 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Multi-Label_Image_Classification_by_Feature_Attention_Network/27003817 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Machine learning Correlation Neural networks Task analysis Convolution Semantics Transforms Object detection Deep neural network multi-label recognition label correlation attention |
| dc.title.none.fl_str_mv | Multi-Label Image Classification by Feature Attention Network |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Learning the correlation among labels is a standing-problem in the multi-label image recognition task. The label correlation is the key to solve the multi-label classification but it is too abstract to model. Most solutions try to learn image label dependencies to improve multi-label classification performance. However, they have ignored two more realistic problems: object scale inconsistent and label tail (category imbalance). These two problems will impact the bad influence on the classification model. To tackle these two problems and learn the label correlations, we propose feature attention network (FAN) which contains feature refinement network and correlation learning network. FAN builds top-down feature fusion mechanism to refine more important features and learn the correlations among convolutional features from FAN to indirect learn the label dependencies. Following our proposed solution, we achieve performed classification accuracy on MSCOCO 2014 and VOC 2007 dataset.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" rel="noreferrer" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2019.2929512" target="_blank">https://dx.doi.org/10.1109/access.2019.2929512</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_d36992f4e9e87958efc520a89f1f3005 |
| identifier_str_mv | 10.1109/access.2019.2929512 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/27003817 |
| publishDate | 2019 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Multi-Label Image Classification by Feature Attention NetworkZheng Yan (194231)Weiwei Liu (341566)Shiping Wen (7168688)Yin Yang (35103)Information and computing sciencesMachine learningCorrelationNeural networksTask analysisConvolutionSemanticsTransformsObject detectionDeep neural networkmulti-label recognitionlabel correlationattention<p dir="ltr">Learning the correlation among labels is a standing-problem in the multi-label image recognition task. The label correlation is the key to solve the multi-label classification but it is too abstract to model. Most solutions try to learn image label dependencies to improve multi-label classification performance. However, they have ignored two more realistic problems: object scale inconsistent and label tail (category imbalance). These two problems will impact the bad influence on the classification model. To tackle these two problems and learn the label correlations, we propose feature attention network (FAN) which contains feature refinement network and correlation learning network. FAN builds top-down feature fusion mechanism to refine more important features and learn the correlations among convolutional features from FAN to indirect learn the label dependencies. Following our proposed solution, we achieve performed classification accuracy on MSCOCO 2014 and VOC 2007 dataset.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" rel="noreferrer" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2019.2929512" target="_blank">https://dx.doi.org/10.1109/access.2019.2929512</a></p>2019-07-18T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2019.2929512https://figshare.com/articles/journal_contribution/Multi-Label_Image_Classification_by_Feature_Attention_Network/27003817CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/270038172019-07-18T06:00:00Z |
| spellingShingle | Multi-Label Image Classification by Feature Attention Network Zheng Yan (194231) Information and computing sciences Machine learning Correlation Neural networks Task analysis Convolution Semantics Transforms Object detection Deep neural network multi-label recognition label correlation attention |
| status_str | publishedVersion |
| title | Multi-Label Image Classification by Feature Attention Network |
| title_full | Multi-Label Image Classification by Feature Attention Network |
| title_fullStr | Multi-Label Image Classification by Feature Attention Network |
| title_full_unstemmed | Multi-Label Image Classification by Feature Attention Network |
| title_short | Multi-Label Image Classification by Feature Attention Network |
| title_sort | Multi-Label Image Classification by Feature Attention Network |
| topic | Information and computing sciences Machine learning Correlation Neural networks Task analysis Convolution Semantics Transforms Object detection Deep neural network multi-label recognition label correlation attention |