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Compositional concept extraction with multimodal large models: A unified framework with thought chain optimization.

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Title: Compositional concept extraction with multimodal large models: A unified framework with thought chain optimization.
Authors: Wu, Yuxin1, wuyx2021@lzu.edu.cn, Song, Zichen1,2, songzch21@lzu.edu.cn, Huang, Sitan2, 320220937820@lzu.edu.cn, Kang, Zhongfeng1,2, kangzf@lzu.edu.cn
Source: Expert Systems with Applications; Apr2026, Vol. 306, pN.PAG-N.PAG, 1p
Database: Applied Science & Technology Source
FullText Text:
  Availability: 0
Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 191815152
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Compositional concept extraction with multimodal large models: A unified framework with thought chain optimization.
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  Data: <searchLink fieldCode="JN" term="%22Expert+Systems+with+Applications%22">Expert Systems with Applications</searchLink>; Apr2026, Vol. 306, pN.PAG-N.PAG, 1p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=191815152
RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1016/j.eswa.2025.130925
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      – Code: eng
        Text: English
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        PageCount: 1
        StartPage: N.PAG
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      – TitleFull: Compositional concept extraction with multimodal large models: A unified framework with thought chain optimization.
        Type: main
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            NameFull: Wu, Yuxin
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            NameFull: Song, Zichen
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            NameFull: Huang, Sitan
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            – D: 15
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              Text: Apr2026
              Type: published
              Y: 2026
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              Value: 306
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            – TitleFull: Expert Systems with Applications
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