Linear-Aware Attention: Enhancing Art Style Classification with Structural Edge Priors

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.