Semantic-aware multi-task learning for image aesthetic quality assessment
In recent years, image aesthetic quality assessment has attracted considerable attention due to the massive growth of digital images in social platforms and the Internet. However, automatically assessing aesthetic quality of an image is a challenging task, because image aesthetic is affected by vari...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2022.2147902 |
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| _version_ | 1850168165662720000 |
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| author | Weiliang Yan Yuqing Li Huan Yang Baoxiang Huang Zhenkuan Pan |
| author_facet | Weiliang Yan Yuqing Li Huan Yang Baoxiang Huang Zhenkuan Pan |
| author_sort | Weiliang Yan |
| collection | DOAJ |
| description | In recent years, image aesthetic quality assessment has attracted considerable attention due to the massive growth of digital images in social platforms and the Internet. However, automatically assessing aesthetic quality of an image is a challenging task, because image aesthetic is affected by various factors, and the criteria for judging the aesthetic of images with diverse semantic information are different. To this end, a Semantic-Aware Multi-task convolution neural network (SAM-CNN) for evaluating image aesthetic quality is proposed in this paper. The network can fuse intermediate features of different layers at different scales in CNN to obtain a more comprehensive and accurate aesthetic expression, under the joint supervision of image aesthetic quality assessment task and semantic classification task in a multi-task learning manner. Besides, by applying the attention mechanism, semantic information with a large receptive field extracted from deep layers is utilised to guide the network to focus on the key parts of features to be fused, to improve the effectiveness of feature fusion. Experimental results on the AVA dataset and Photo.net dataset demonstrate the effectiveness and superiority of the proposed SAM-CNN. |
| format | Article |
| id | doaj-art-e61998bd3ba24d84a01f5b6263f7ed13 |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-e61998bd3ba24d84a01f5b6263f7ed132025-08-20T02:21:02ZengTaylor & Francis GroupConnection Science0954-00911360-04942022-12-013412689271310.1080/09540091.2022.21479022147902Semantic-aware multi-task learning for image aesthetic quality assessmentWeiliang Yan0Yuqing Li1Huan Yang2Baoxiang Huang3Zhenkuan Pan4College of Computer Science and Technology, Qingdao UniversityCollege of Computer Science and Technology, Qingdao UniversityCollege of Computer Science and Technology, Qingdao UniversityCollege of Computer Science and Technology, Qingdao UniversityCollege of Computer Science and Technology, Qingdao UniversityIn recent years, image aesthetic quality assessment has attracted considerable attention due to the massive growth of digital images in social platforms and the Internet. However, automatically assessing aesthetic quality of an image is a challenging task, because image aesthetic is affected by various factors, and the criteria for judging the aesthetic of images with diverse semantic information are different. To this end, a Semantic-Aware Multi-task convolution neural network (SAM-CNN) for evaluating image aesthetic quality is proposed in this paper. The network can fuse intermediate features of different layers at different scales in CNN to obtain a more comprehensive and accurate aesthetic expression, under the joint supervision of image aesthetic quality assessment task and semantic classification task in a multi-task learning manner. Besides, by applying the attention mechanism, semantic information with a large receptive field extracted from deep layers is utilised to guide the network to focus on the key parts of features to be fused, to improve the effectiveness of feature fusion. Experimental results on the AVA dataset and Photo.net dataset demonstrate the effectiveness and superiority of the proposed SAM-CNN.http://dx.doi.org/10.1080/09540091.2022.2147902image aesthetic quality assessmentdeep learningfeature fusionattention mechanismsemantic aware |
| spellingShingle | Weiliang Yan Yuqing Li Huan Yang Baoxiang Huang Zhenkuan Pan Semantic-aware multi-task learning for image aesthetic quality assessment Connection Science image aesthetic quality assessment deep learning feature fusion attention mechanism semantic aware |
| title | Semantic-aware multi-task learning for image aesthetic quality assessment |
| title_full | Semantic-aware multi-task learning for image aesthetic quality assessment |
| title_fullStr | Semantic-aware multi-task learning for image aesthetic quality assessment |
| title_full_unstemmed | Semantic-aware multi-task learning for image aesthetic quality assessment |
| title_short | Semantic-aware multi-task learning for image aesthetic quality assessment |
| title_sort | semantic aware multi task learning for image aesthetic quality assessment |
| topic | image aesthetic quality assessment deep learning feature fusion attention mechanism semantic aware |
| url | http://dx.doi.org/10.1080/09540091.2022.2147902 |
| work_keys_str_mv | AT weiliangyan semanticawaremultitasklearningforimageaestheticqualityassessment AT yuqingli semanticawaremultitasklearningforimageaestheticqualityassessment AT huanyang semanticawaremultitasklearningforimageaestheticqualityassessment AT baoxianghuang semanticawaremultitasklearningforimageaestheticqualityassessment AT zhenkuanpan semanticawaremultitasklearningforimageaestheticqualityassessment |