Multi-Task CNN Model for Attribute Prediction
This paper proposes a joint multi-task learning algorithm to better predict attributes in images using deep convolutional neural networks (CNN). We consider learning binary semantic attributes through a multi-task CNN model, where each CNN will predict one binary attribute. The multitask learni...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/82925 http://hdl.handle.net/10220/40352 |
| _version_ | 1826129802695802880 |
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| author | Abdulnabi, Abrar H. Wang, Gang Lu, Jiwen Jia, Kui |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Abdulnabi, Abrar H. Wang, Gang Lu, Jiwen Jia, Kui |
| author_sort | Abdulnabi, Abrar H. |
| collection | NTU |
| description | This paper proposes a joint multi-task learning
algorithm to better predict attributes in images using deep
convolutional neural networks (CNN). We consider learning
binary semantic attributes through a multi-task CNN model,
where each CNN will predict one binary attribute. The multitask
learning allows CNN models to simultaneously share visual
knowledge among different attribute categories. Each CNN
will generate attribute-specific feature representations, and then
we apply multi-task learning on the features to predict their
attributes. In our multi-task framework, we propose a method
to decompose the overall model’s parameters into a latent task
matrix and combination matrix. Furthermore, under-sampled
classifiers can leverage shared statistics from other classifiers
to improve their performance. Natural grouping of attributes is
applied such that attributes in the same group are encouraged to
share more knowledge. Meanwhile, attributes in different groups
will generally compete with each other, and consequently share
less knowledge. We show the effectiveness of our method on two
popular attribute datasets. |
| first_indexed | 2024-10-01T07:46:30Z |
| format | Journal Article |
| id | ntu-10356/82925 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:46:30Z |
| publishDate | 2016 |
| record_format | dspace |
| spelling | ntu-10356/829252020-03-07T13:57:24Z Multi-Task CNN Model for Attribute Prediction Abdulnabi, Abrar H. Wang, Gang Lu, Jiwen Jia, Kui School of Electrical and Electronic Engineering Deep CNN Latent tasks matrix Multi-task learning Semantic attributes This paper proposes a joint multi-task learning algorithm to better predict attributes in images using deep convolutional neural networks (CNN). We consider learning binary semantic attributes through a multi-task CNN model, where each CNN will predict one binary attribute. The multitask learning allows CNN models to simultaneously share visual knowledge among different attribute categories. Each CNN will generate attribute-specific feature representations, and then we apply multi-task learning on the features to predict their attributes. In our multi-task framework, we propose a method to decompose the overall model’s parameters into a latent task matrix and combination matrix. Furthermore, under-sampled classifiers can leverage shared statistics from other classifiers to improve their performance. Natural grouping of attributes is applied such that attributes in the same group are encouraged to share more knowledge. Meanwhile, attributes in different groups will generally compete with each other, and consequently share less knowledge. We show the effectiveness of our method on two popular attribute datasets. ASTAR (Agency for Sci., Tech. and Research, S’pore) MOE (Min. of Education, S’pore) Accepted version 2016-03-31T07:45:42Z 2019-12-06T15:08:21Z 2016-03-31T07:45:42Z 2019-12-06T15:08:21Z 2015 Journal Article Abdulnabi, A. H., Wang, G., Lu, J., & Jia, K. (2015). Multi-Task CNN Model for Attribute Prediction. IEEE Transactions on Multimedia, 17(11), 1949-1959. 1520-9210 https://hdl.handle.net/10356/82925 http://hdl.handle.net/10220/40352 10.1109/TMM.2015.2477680 en IEEE Transactions on Multimedia © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TMM.2015.2477680]. 11 p. application/pdf |
| spellingShingle | Deep CNN Latent tasks matrix Multi-task learning Semantic attributes Abdulnabi, Abrar H. Wang, Gang Lu, Jiwen Jia, Kui Multi-Task CNN Model for Attribute Prediction |
| title | Multi-Task CNN Model for Attribute Prediction |
| title_full | Multi-Task CNN Model for Attribute Prediction |
| title_fullStr | Multi-Task CNN Model for Attribute Prediction |
| title_full_unstemmed | Multi-Task CNN Model for Attribute Prediction |
| title_short | Multi-Task CNN Model for Attribute Prediction |
| title_sort | multi task cnn model for attribute prediction |
| topic | Deep CNN Latent tasks matrix Multi-task learning Semantic attributes |
| url | https://hdl.handle.net/10356/82925 http://hdl.handle.net/10220/40352 |
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