Academic Journal
Linear-Aware Attention: Enhancing Art Style Classification with Structural Edge Priors.
| Τίτλος: | Linear-Aware Attention: Enhancing Art Style Classification with Structural Edge Priors. |
|---|---|
| Συγγραφείς: | Yu, Wanglong, Liu, Xuefeng |
| Πηγή: | Electronics (2079-9292); Jun2026, Vol. 15 Issue 11, p2314, 26p |
| Θεματικοί όροι: | Convolutional neural networks, Edge detection (Image processing), Computer vision, Attention, Art movements, Deep learning |
| Περίληψη: | While deep learning has achieved impressive success in art style classification, standard convolutional neural networks (CNNs) often exhibit a "texture bias", prioritizing local brushstrokes and color patterns over the global structural logic essential for stylistic identification. Drawing inspiration from Heinrich Wölfflin's "Linear and Painterly" theory, we propose the Edge-Guided Spatial Attention Network (ESA-Net) to bridge the gap between feature extraction and aesthetic structure. ESA-Net utilizes a dual-stream architecture that decouples artistic representation into semantic textures and structural contours. As its core, the proposed Edge-Guided Convolutional Block Attention Module (EG-CBAM) treats exogenous edge maps as spatial gates, recalibrating the model's focus toward salient outlines while suppressing textural noise. The experimental results on the WikiArt dataset demonstrate that ESA-Net achieves a state-of-the-art top 1 accuracy of 69.40%. Qualitative visualizations via Grad-CAM further confirm that our model effectively aligns its decision-making process with the structural layouts which are favored by human experts, providing a theoretically grounded approach to computational connoisseurship. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=20799292&ISBN=&volume=15&issue=11&date=20260601&spage=2314&pages=2314-2339&title=Electronics (2079-9292)&atitle=Linear-Aware%20Attention%3A%20Enhancing%20Art%20Style%20Classification%20with%20Structural%20Edge%20Priors.&aulast=Yu%2C%20Wanglong&id=DOI:10.3390/electronics15112314 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: Linear-Aware Attention: Enhancing Art Style Classification with Structural Edge Priors. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yu%2C+Wanglong%22">Yu, Wanglong</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Xuefeng%22">Liu, Xuefeng</searchLink> – Name: TitleSource Label: Source Group: Src Data: Electronics (2079-9292); Jun2026, Vol. 15 Issue 11, p2314, 26p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Edge+detection+%28Image+processing%29%22">Edge detection (Image processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Attention%22">Attention</searchLink><br /><searchLink fieldCode="DE" term="%22Art+movements%22">Art movements</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: While deep learning has achieved impressive success in art style classification, standard convolutional neural networks (CNNs) often exhibit a "texture bias", prioritizing local brushstrokes and color patterns over the global structural logic essential for stylistic identification. Drawing inspiration from Heinrich Wölfflin's "Linear and Painterly" theory, we propose the Edge-Guided Spatial Attention Network (ESA-Net) to bridge the gap between feature extraction and aesthetic structure. ESA-Net utilizes a dual-stream architecture that decouples artistic representation into semantic textures and structural contours. As its core, the proposed Edge-Guided Convolutional Block Attention Module (EG-CBAM) treats exogenous edge maps as spatial gates, recalibrating the model's focus toward salient outlines while suppressing textural noise. The experimental results on the WikiArt dataset demonstrate that ESA-Net achieves a state-of-the-art top 1 accuracy of 69.40%. Qualitative visualizations via Grad-CAM further confirm that our model effectively aligns its decision-making process with the structural layouts which are favored by human experts, providing a theoretically grounded approach to computational connoisseurship. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Electronics (2079-9292) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/electronics15112314 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 2314 Subjects: – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Edge detection (Image processing) Type: general – SubjectFull: Computer vision Type: general – SubjectFull: Attention Type: general – SubjectFull: Art movements Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: Linear-Aware Attention: Enhancing Art Style Classification with Structural Edge Priors. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yu, Wanglong – PersonEntity: Name: NameFull: Liu, Xuefeng IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20799292 Numbering: – Type: volume Value: 15 – Type: issue Value: 11 Titles: – TitleFull: Electronics (2079-9292) Type: main |
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