Attention Feature Network Extraction Combined with the Generation Algorithm of Multimedia Image Description
In view of the issue that the features of the images in the shallow layer cannot be fully utilized when the image description is generated and the target association of the image cannot be sufficiently obtained, a generation method for the description of the acquisition of attention images is put fo...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2021/6484128 |
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| _version_ | 1832561492469219328 |
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| author | Beibei Sun |
| author_facet | Beibei Sun |
| author_sort | Beibei Sun |
| collection | DOAJ |
| description | In view of the issue that the features of the images in the shallow layer cannot be fully utilized when the image description is generated and the target association of the image cannot be sufficiently obtained, a generation method for the description of the acquisition of attention images is put forward in this paper. The proportions of the features of images at various depths are autonomously assigned based on the content data of the language model, and the images thus generated are all pictures with image features with attention. In this way, the effect of description generation of images has been improved. After the testing of the database, the results indicate that the calculation method of the algorithm put forward in this paper is more accurate than the top-down multimedia image algorithm generated by a single attention. |
| format | Article |
| id | doaj-art-47260699767542cc971f1ff51bfff4c6 |
| institution | Kabale University |
| issn | 1687-5680 1687-5699 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-47260699767542cc971f1ff51bfff4c62025-02-03T01:24:52ZengWileyAdvances in Multimedia1687-56801687-56992021-01-01202110.1155/2021/64841286484128Attention Feature Network Extraction Combined with the Generation Algorithm of Multimedia Image DescriptionBeibei Sun0Department of Information Engineering, Zibo Vocational Institute, Zibo 255314, Shandong, ChinaIn view of the issue that the features of the images in the shallow layer cannot be fully utilized when the image description is generated and the target association of the image cannot be sufficiently obtained, a generation method for the description of the acquisition of attention images is put forward in this paper. The proportions of the features of images at various depths are autonomously assigned based on the content data of the language model, and the images thus generated are all pictures with image features with attention. In this way, the effect of description generation of images has been improved. After the testing of the database, the results indicate that the calculation method of the algorithm put forward in this paper is more accurate than the top-down multimedia image algorithm generated by a single attention.http://dx.doi.org/10.1155/2021/6484128 |
| spellingShingle | Beibei Sun Attention Feature Network Extraction Combined with the Generation Algorithm of Multimedia Image Description Advances in Multimedia |
| title | Attention Feature Network Extraction Combined with the Generation Algorithm of Multimedia Image Description |
| title_full | Attention Feature Network Extraction Combined with the Generation Algorithm of Multimedia Image Description |
| title_fullStr | Attention Feature Network Extraction Combined with the Generation Algorithm of Multimedia Image Description |
| title_full_unstemmed | Attention Feature Network Extraction Combined with the Generation Algorithm of Multimedia Image Description |
| title_short | Attention Feature Network Extraction Combined with the Generation Algorithm of Multimedia Image Description |
| title_sort | attention feature network extraction combined with the generation algorithm of multimedia image description |
| url | http://dx.doi.org/10.1155/2021/6484128 |
| work_keys_str_mv | AT beibeisun attentionfeaturenetworkextractioncombinedwiththegenerationalgorithmofmultimediaimagedescription |