Art design integrating visual relation and affective semantics based on Convolutional Block Attention Mechanism-generative adversarial network model
Scene-based image semantic extraction and its precise sentiment expression significantly enhance artistic design. To address the incongruity between image features and sentiment features caused by non-bilinear pooling, this study introduces a generative adversarial network (GAN) model that integrate...
Uloženo v:
| Vydáno v: | PeerJ. Computer science Ročník 10; s. e2274 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
PeerJ. Ltd
30.08.2024
PeerJ Inc |
| Témata: | |
| ISSN: | 2376-5992, 2376-5992 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Scene-based image semantic extraction and its precise sentiment expression significantly enhance artistic design. To address the incongruity between image features and sentiment features caused by non-bilinear pooling, this study introduces a generative adversarial network (GAN) model that integrates visual relationships with sentiment semantics. The GAN-based regularizer is utilized during training to incorporate target information derived from the contextual information into the process. This regularization mechanism imposes stronger penalties for inaccuracies in subject-object type predictions and integrates a sentiment corpus to generate more human-like descriptive statements. The capsule network is employed to reconstruct sentences and predict probabilities in the discriminator. To preserve crucial focal points in feature extraction, the Convolutional Block Attention Mechanism (CBAM) is introduced. Furthermore, two bidirectional long short-term memory (LSTM) modules are used to model both target and relational contexts, thereby refining target labels and inter-target relationships. Experimental results highlight the model’s superiority over comparative models in terms of accuracy, BiLingual Evaluation Understudy (BLEU) score, and text preservation rate. The proposed model achieves an accuracy of 95.40% and the highest BLEU score of 16.79, effectively capturing both the label content and the emotional nuances within the image. |
|---|---|
| AbstractList | Scene-based image semantic extraction and its precise sentiment expression significantly enhance artistic design. To address the incongruity between image features and sentiment features caused by non-bilinear pooling, this study introduces a generative adversarial network (GAN) model that integrates visual relationships with sentiment semantics. The GAN-based regularizer is utilized during training to incorporate target information derived from the contextual information into the process. This regularization mechanism imposes stronger penalties for inaccuracies in subject-object type predictions and integrates a sentiment corpus to generate more human-like descriptive statements. The capsule network is employed to reconstruct sentences and predict probabilities in the discriminator. To preserve crucial focal points in feature extraction, the Convolutional Block Attention Mechanism (CBAM) is introduced. Furthermore, two bidirectional long short-term memory (LSTM) modules are used to model both target and relational contexts, thereby refining target labels and inter-target relationships. Experimental results highlight the model’s superiority over comparative models in terms of accuracy, BiLingual Evaluation Understudy (BLEU) score, and text preservation rate. The proposed model achieves an accuracy of 95.40% and the highest BLEU score of 16.79, effectively capturing both the label content and the emotional nuances within the image. Scene-based image semantic extraction and its precise sentiment expression significantly enhance artistic design. To address the incongruity between image features and sentiment features caused by non-bilinear pooling, this study introduces a generative adversarial network (GAN) model that integrates visual relationships with sentiment semantics. The GAN-based regularizer is utilized during training to incorporate target information derived from the contextual information into the process. This regularization mechanism imposes stronger penalties for inaccuracies in subject-object type predictions and integrates a sentiment corpus to generate more human-like descriptive statements. The capsule network is employed to reconstruct sentences and predict probabilities in the discriminator. To preserve crucial focal points in feature extraction, the Convolutional Block Attention Mechanism (CBAM) is introduced. Furthermore, two bidirectional long short-term memory (LSTM) modules are used to model both target and relational contexts, thereby refining target labels and inter-target relationships. Experimental results highlight the model's superiority over comparative models in terms of accuracy, BiLingual Evaluation Understudy (BLEU) score, and text preservation rate. The proposed model achieves an accuracy of 95.40% and the highest BLEU score of 16.79, effectively capturing both the label content and the emotional nuances within the image.Scene-based image semantic extraction and its precise sentiment expression significantly enhance artistic design. To address the incongruity between image features and sentiment features caused by non-bilinear pooling, this study introduces a generative adversarial network (GAN) model that integrates visual relationships with sentiment semantics. The GAN-based regularizer is utilized during training to incorporate target information derived from the contextual information into the process. This regularization mechanism imposes stronger penalties for inaccuracies in subject-object type predictions and integrates a sentiment corpus to generate more human-like descriptive statements. The capsule network is employed to reconstruct sentences and predict probabilities in the discriminator. To preserve crucial focal points in feature extraction, the Convolutional Block Attention Mechanism (CBAM) is introduced. Furthermore, two bidirectional long short-term memory (LSTM) modules are used to model both target and relational contexts, thereby refining target labels and inter-target relationships. Experimental results highlight the model's superiority over comparative models in terms of accuracy, BiLingual Evaluation Understudy (BLEU) score, and text preservation rate. The proposed model achieves an accuracy of 95.40% and the highest BLEU score of 16.79, effectively capturing both the label content and the emotional nuances within the image. |
| ArticleNumber | e2274 |
| Audience | Academic |
| Author | Shen, Jiadong Wang, Jian |
| Author_xml | – sequence: 1 givenname: Jiadong surname: Shen fullname: Shen, Jiadong organization: School of Design and Art, Changsha University of Science and Technology, Changsha, Hunan, China – sequence: 2 givenname: Jian surname: Wang fullname: Wang, Jian organization: School of Design and Art, Changsha University of Science and Technology, Changsha, Hunan, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39314726$$D View this record in MEDLINE/PubMed |
| BookMark | eNptks1u1DAUhSNUREvpki2KxAYWGew4iZMVmlb8jFSEBN1bN_Z16mliD7YzwHvwwHhmStWRSBaxne8c3yOd59mJdRaz7CUlC84pf7dB9OtChkVZ8upJdlYy3hR115Unj9an2UUIa0IIrWl6umfZKesYrXjZnGV_lj7mCoMZbG5sxMFDNHbItybMMOYex7R3NgerctAaZTRbzANOYKORIe8hoMoTcOXs1o3zDk66y9HJu3wZI9q9_AvKW7AmTMWAFnd3JBdQW_QBvEkCi_Gn83f55BSOL7KnGsaAF_ff8-zm44ebq8_F9ddPq6vldSFTklggU7QDhpT2vCaqbBSBVjcVx0piTesOaEO05EwrUmnSta2sAWnTyJ5WirLzbHWwVQ7WYuPNBP63cGDE_sD5QYBPKUcUTHesoZT0VVlXPUfguuVUlkT2pNG9TF7vD16buZ9QyZTbw3hkevzHmlsxuK2gtKJdU5bJ4c29g3c_ZgxRTCZIHEew6OYgGCUtb-qStQl9fUAHSLMZq12ylDtcLFvKOKnTrIla_IdKr8LJyFQkbdL5keDtkSAxEX_FAeYQxOr7t2P21eO8D0H_NSsBxQGQ3oXgUT8glIhdecW-vEIGsSsv-wtmv-Tk |
| Cites_doi | 10.3390/app12010527 10.3390/ijgi11040245 10.1016/j.matpr.2020.10.148 10.1016/j.addma.2020.101538 10.3390/math7100883 10.1021/acs.molpharmaceut.9b00500 10.1109/TCYB.2021.3052522 10.1007/s11263-019-01265-2 10.1109/TPAMI.2020.2992222 10.1007/s00521-023-08584-z 10.1109/ACCESS.2020.2988550 10.1609/aaai.v30i1.10475 10.1109/TPAMI.2021.3137605 10.13140/RG.2.2.11161.57446 10.1007/s10844-021-00660-x |
| ContentType | Journal Article |
| Copyright | 2024 Shen and Wang. COPYRIGHT 2024 PeerJ. Ltd. 2024 Shen and Wang 2024 Shen and Wang |
| Copyright_xml | – notice: 2024 Shen and Wang. – notice: COPYRIGHT 2024 PeerJ. Ltd. – notice: 2024 Shen and Wang 2024 Shen and Wang |
| DBID | AAYXX CITATION NPM ISR 7X8 5PM DOA |
| DOI | 10.7717/peerj-cs.2274 |
| DatabaseName | CrossRef PubMed Gale In Context: Science MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | CrossRef MEDLINE - Academic PubMed |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 2376-5992 |
| ExternalDocumentID | oai_doaj_org_article_3f936110b4254b7ea7f871c20cb06fbc PMC11419622 A813705611 39314726 10_7717_peerj_cs_2274 |
| Genre | Journal Article |
| GroupedDBID | 53G 5VS 8FE 8FG AAFWJ AAYXX ABUWG ADBBV AFFHD AFKRA AFPKN ALMA_UNASSIGNED_HOLDINGS ARAPS ARCSS AZQEC BCNDV BENPR BGLVJ BPHCQ CCPQU CITATION DWQXO FRP GNUQQ GROUPED_DOAJ HCIFZ IAO ICD IEA ISR ITC K6V K7- M~E OK1 P62 PHGZM PHGZT PIMPY PQGLB PQQKQ PROAC RPM 3V. H13 M0N NPM 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c511t-e3d19a3e11b750d26d0a8f647e4ce5159a160fc73fd04f0988c5ae166cb14d13 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001305547800005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2376-5992 |
| IngestDate | Mon Nov 10 04:24:58 EST 2025 Tue Nov 04 02:04:56 EST 2025 Thu Sep 04 18:16:27 EDT 2025 Tue Nov 11 10:54:12 EST 2025 Tue Nov 04 18:18:33 EST 2025 Thu Nov 13 16:11:40 EST 2025 Thu Jan 02 22:37:03 EST 2025 Sat Nov 29 06:22:54 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Art design LSTM Visual communication GAN CBEAM |
| Language | English |
| License | https://creativecommons.org/licenses/by/4.0 2024 Shen and Wang. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c511t-e3d19a3e11b750d26d0a8f647e4ce5159a160fc73fd04f0988c5ae166cb14d13 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | https://doaj.org/article/3f936110b4254b7ea7f871c20cb06fbc |
| PMID | 39314726 |
| PQID | 3108765238 |
| PQPubID | 23479 |
| PageCount | e2274 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_3f936110b4254b7ea7f871c20cb06fbc pubmedcentral_primary_oai_pubmedcentral_nih_gov_11419622 proquest_miscellaneous_3108765238 gale_infotracmisc_A813705611 gale_infotracacademiconefile_A813705611 gale_incontextgauss_ISR_A813705611 pubmed_primary_39314726 crossref_primary_10_7717_peerj_cs_2274 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-08-30 |
| PublicationDateYYYYMMDD | 2024-08-30 |
| PublicationDate_xml | – month: 08 year: 2024 text: 2024-08-30 day: 30 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States – name: San Diego, USA |
| PublicationTitle | PeerJ. Computer science |
| PublicationTitleAlternate | PeerJ Comput Sci |
| PublicationYear | 2024 |
| Publisher | PeerJ. Ltd PeerJ Inc |
| Publisher_xml | – name: PeerJ. Ltd – name: PeerJ Inc |
| References | Raju (10.7717/peerj-cs.2274/ref-22) 2019; 18 Tan (10.7717/peerj-cs.2274/ref-23) 2022 Kim (10.7717/peerj-cs.2274/ref-11) 2022; 11 Guo (10.7717/peerj-cs.2274/ref-5) 2021; 52 Qi (10.7717/peerj-cs.2274/ref-21) 2020; 128 Dennis (10.7717/peerj-cs.2274/ref-3) 2019; 30 Hung (10.7717/peerj-cs.2274/ref-8) 2020; 43 Xu (10.7717/peerj-cs.2274/ref-30) 2020; 1 Bian (10.7717/peerj-cs.2274/ref-1) 2019; 16 Manessi (10.7717/peerj-cs.2274/ref-17) 2018 Gulrajani (10.7717/peerj-cs.2274/ref-4) 2017; 30 Hudson (10.7717/peerj-cs.2274/ref-7) 2019 Wu (10.7717/peerj-cs.2274/ref-28) 2021 Xu (10.7717/peerj-cs.2274/ref-29) 2017 Manieniyan (10.7717/peerj-cs.2274/ref-18) 2021; 37 Wang (10.7717/peerj-cs.2274/ref-26) 2020; 36 Zhao (10.7717/peerj-cs.2274/ref-32) 2023; 35 Hameed (10.7717/peerj-cs.2274/ref-6) 2020; 8 Jin (10.7717/peerj-cs.2274/ref-10) 2020 Yang (10.7717/peerj-cs.2274/ref-31) 2020 Jin (10.7717/peerj-cs.2274/ref-9) 2023 Wei (10.7717/peerj-cs.2274/ref-27) 2022 Li (10.7717/peerj-cs.2274/ref-13) 2019; 7 Liu (10.7717/peerj-cs.2274/ref-15) 2021 Mathews (10.7717/peerj-cs.2274/ref-19) 2016 Tang (10.7717/peerj-cs.2274/ref-25) 2019 Chang (10.7717/peerj-cs.2274/ref-2) 2021; 45 Liang (10.7717/peerj-cs.2274/ref-14) 2017 Ma (10.7717/peerj-cs.2274/ref-16) 2022; 12 Tang (10.7717/peerj-cs.2274/ref-24) 2020 Powell (10.7717/peerj-cs.2274/ref-20) 2021; 57 Kolesnyk (10.7717/peerj-cs.2274/ref-12) 2022; 31 |
| References_xml | – start-page: 6619 year: 2019 ident: 10.7717/peerj-cs.2274/ref-25 article-title: Learning to compose dynamic tree structures for visual contexts – start-page: 11546 year: 2021 ident: 10.7717/peerj-cs.2274/ref-15 article-title: Fully convolutional scene graph generation – start-page: 5410 year: 2017 ident: 10.7717/peerj-cs.2274/ref-29 article-title: Scene graph generation by iterative message passing – volume: 12 start-page: 527 issue: 1 year: 2022 ident: 10.7717/peerj-cs.2274/ref-16 article-title: Data augmentation for audio-visual emotion recognition with an efficient multimodal conditional GAN publication-title: Applied Sciences doi: 10.3390/app12010527 – start-page: 1 year: 2022 ident: 10.7717/peerj-cs.2274/ref-27 article-title: Visual descriptor extraction from patent figure captions: a case study of data efficiency between BiLSTM and transformer – volume: 11 start-page: 245 issue: 4 year: 2022 ident: 10.7717/peerj-cs.2274/ref-11 article-title: Automatic classification of photos by tourist attractions using deep learning model and image feature vector clustering publication-title: ISPRS International Journal of Geo-Information doi: 10.3390/ijgi11040245 – start-page: 2305 year: 2020 ident: 10.7717/peerj-cs.2274/ref-10 article-title: Image restoration method based on GAN and multi-scale feature fusion – start-page: 106 year: 2023 ident: 10.7717/peerj-cs.2274/ref-9 article-title: Independent relationship detection for real-time scene graph generation – start-page: 61 year: 2018 ident: 10.7717/peerj-cs.2274/ref-17 article-title: Learning combinations of activation functions – volume: 30 start-page: 593 year: 2019 ident: 10.7717/peerj-cs.2274/ref-3 article-title: AI-Generated fashion designs: who or what owns the goods publication-title: Fordham Intellectual Property, Media & Entertainment Law Journal – volume: 37 start-page: 3665 year: 2021 ident: 10.7717/peerj-cs.2274/ref-18 article-title: Study on diesel engine characteristics using multi-walled carbon nanotubes blended thermal cracked vegetable oil refining waste publication-title: Materials Today. Proceedings doi: 10.1016/j.matpr.2020.10.148 – volume: 36 start-page: 101538 year: 2020 ident: 10.7717/peerj-cs.2274/ref-26 article-title: Machine learning in additive manufacturing: State-of-the-art and perspectives publication-title: Additive Manufacturing doi: 10.1016/j.addma.2020.101538 – volume: 30 year: 2017 ident: 10.7717/peerj-cs.2274/ref-4 article-title: Improved training of Wasserstein gans publication-title: Advances in Neural Information Processing Systems – volume: 7 start-page: 883 issue: 10 year: 2019 ident: 10.7717/peerj-cs.2274/ref-13 article-title: Automatic melody composition using enhanced GAN publication-title: Mathematics doi: 10.3390/math7100883 – start-page: 168 year: 2021 ident: 10.7717/peerj-cs.2274/ref-28 article-title: On GANs art in context of artificial intelligence art – volume: 16 start-page: 4451 issue: 11 year: 2019 ident: 10.7717/peerj-cs.2274/ref-1 article-title: Deep convolutional generative adversarial network (dcGAN) models for screening and design of small molecules targeting cannabinoid receptors publication-title: Molecular Pharmaceutics doi: 10.1021/acs.molpharmaceut.9b00500 – volume: 52 start-page: 5961 issue: 7 year: 2021 ident: 10.7717/peerj-cs.2274/ref-5 article-title: Relation regularized scene graph generation publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2021.3052522 – volume: 18 start-page: 1229 year: 2019 ident: 10.7717/peerj-cs.2274/ref-22 article-title: Experimental investigation of alumina oxide nanoparticles effects on the performance and emission characteristics of tamarind seed biodiesel fuelled diesel engine publication-title: Materials Today: Proceedings – volume: 128 start-page: 1118 issue: 5 year: 2020 ident: 10.7717/peerj-cs.2274/ref-21 article-title: Loss-sensitive generative adversarial networks on lipschitz densities publication-title: International Journal of Computer Vision doi: 10.1007/s11263-019-01265-2 – volume: 43 start-page: 3820 issue: 11 year: 2020 ident: 10.7717/peerj-cs.2274/ref-8 article-title: Contextual translation embedding for visual relationship detection and scene graph generation publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence doi: 10.1109/TPAMI.2020.2992222 – volume: 35 start-page: 24565 year: 2023 ident: 10.7717/peerj-cs.2274/ref-32 article-title: Computer-aided digital media art creation based on artificial intelligence publication-title: Neural Computing and Applications doi: 10.1007/s00521-023-08584-z – start-page: 244 year: 2020 ident: 10.7717/peerj-cs.2274/ref-31 article-title: Triple-GAN with variable fractional order gradient descent method and mish activation function – volume: 8 start-page: 73992 year: 2020 ident: 10.7717/peerj-cs.2274/ref-6 article-title: Sentiment classification using a single-layered BiLSTM model publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2988550 – volume: 31 start-page: 128 issue: 12 year: 2022 ident: 10.7717/peerj-cs.2274/ref-12 article-title: Digital art in designing an artistic image publication-title: Ad Alta – start-page: 3716 year: 2020 ident: 10.7717/peerj-cs.2274/ref-24 article-title: Unbiased scene graph generation from biased training – year: 2016 ident: 10.7717/peerj-cs.2274/ref-19 article-title: Senticap: generating image descriptions with sentiments doi: 10.1609/aaai.v30i1.10475 – volume: 45 start-page: 1 issue: 1 year: 2021 ident: 10.7717/peerj-cs.2274/ref-2 article-title: A comprehensive survey of scene graphs: generation and application publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence doi: 10.1109/TPAMI.2021.3137605 – year: 2022 ident: 10.7717/peerj-cs.2274/ref-23 article-title: DR-GAN: distribution regularization for text-to-image generation publication-title: IEEE Transactions on Neural Networks and Learning Systems – start-page: 3362 year: 2017 ident: 10.7717/peerj-cs.2274/ref-14 article-title: Recurrent topic-transition gan for visual paragraph generation – start-page: 6700 year: 2019 ident: 10.7717/peerj-cs.2274/ref-7 article-title: Gqa: a new dataset for real-world visual reasoning and compositional question answering – volume: 1 year: 2020 ident: 10.7717/peerj-cs.2274/ref-30 article-title: A survey of scene graph: generation and application publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.13140/RG.2.2.11161.57446 – volume: 57 start-page: 583 issue: 3 year: 2021 ident: 10.7717/peerj-cs.2274/ref-20 article-title: How to raise artwork prices using action rules, personalization and artwork visual features publication-title: Journal of Intelligent Information Systems doi: 10.1007/s10844-021-00660-x |
| SSID | ssj0001511119 |
| Score | 2.26621 |
| Snippet | Scene-based image semantic extraction and its precise sentiment expression significantly enhance artistic design. To address the incongruity between image... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database |
| StartPage | e2274 |
| SubjectTerms | Adaptive and Self-Organizing Systems Algorithms and Analysis of Algorithms Art design Artificial Intelligence CBEAM GAN Liquors LSTM Neural Networks Semantics Social Computing Visual communication |
| Title | Art design integrating visual relation and affective semantics based on Convolutional Block Attention Mechanism-generative adversarial network model |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/39314726 https://www.proquest.com/docview/3108765238 https://pubmed.ncbi.nlm.nih.gov/PMC11419622 https://doaj.org/article/3f936110b4254b7ea7f871c20cb06fbc |
| Volume | 10 |
| WOSCitedRecordID | wos001305547800005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: DOA dateStart: 20150101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: M~E dateStart: 20150101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: P5Z dateStart: 20150527 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: K7- dateStart: 20150527 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: BENPR dateStart: 20150527 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: PIMPY dateStart: 20150527 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nj9MwELVg4cBl-WYDS2UQglNYJ07j5NiuumKFWkXLHgoXy7GdJaBNV3XaI7-CH8yMk64aceDCxYd6UtUzz56ZdPyGkHdcj7mJmQ2Z1ZCgGIBxrnUa5thyOhvrKreVbzYhFotsucyLvVZfWBPW0QN3ijvhVc5T8FElgCsphVWighhfx0yXLK1KjacvRD17yVR3PxiPgrwj1RSQspzcWLv-EWr3MY5FMnBCnqv_7xN5zyUNyyX3_M_ZI3LYB4500v3gx-SObZ6Qh7umDLTfo0_Jb5Cgxtdl0B0XBLgnuq3dBp5f98VvVDWGKl_MAecddfYaVFxrR9GtGQoCp6tm2-MSnpuC0_tJJ23blUfSucUrw7W7Dq88cbX_FoXdnZ1CTNOmqy-nvtXOM3J5Nrs8_RT2rRdCDZprQ8tNlCtuo6iEkMLEqWEqq9JE2ERbDIFUlLJKC14ZllQszzI9VjZKU11GiYn4c3LQrBp7RCgEgKlJmMVAI0nKvBTGcgFJYYl_kcYqIO93ppA3HcGGhMQEbSa9zaR2Em0WkCka6lYIebH9B4AW2aNF_gstAXmLZpbIfNFgac2V2jgnz79cyEkWcYH5VBSQD71QtQKDa9XfVIAFIVnWQPJ4IAlbUw-m3-zQJHEK69kau9o4CUE1uKExxEsBedGh63ZhPOdRIuI0INkAd4OVD2ea-rtnBgcVw4kaxy__h65ekQcxRHD-BTo7JgftemNfk_t629ZuPSJ3xTIbkXvT2aK4GPndB-NnEcI4_zWDsRh_g_nifF58_QM1mzv4 |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Art+design+integrating+visual+relation+and+affective+semantics+based+on+Convolutional+Block+Attention+Mechanism-generative+adversarial+network+model&rft.jtitle=PeerJ.+Computer+science&rft.au=Shen%2C+Jiadong&rft.au=Wang%2C+Jian&rft.date=2024-08-30&rft.pub=PeerJ+Inc&rft.eissn=2376-5992&rft.volume=10&rft_id=info:doi/10.7717%2Fpeerj-cs.2274&rft.externalDocID=PMC11419622 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2376-5992&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2376-5992&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2376-5992&client=summon |