Λεπτομέρειες βιβλιογραφικής εγγραφής
| 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 |