🤖 AI Summary
Understanding the alignment between vision transformer (ViT) attention mechanisms and human visual attention in fine-grained aesthetic evaluation—particularly for handicrafts—remains an open challenge.
Method: We conducted eye-tracking experiments to record human fixation distributions and generated attention heatmaps using a DINO-pretrained ViT. Spatial similarity between human and model attention was quantified via Kullback–Leibler (KL) divergence.
Contribution/Results: We identify that a specific attention head at layer 12 achieves statistically significant alignment with human attention (p < 0.05) under Gaussian kernel smoothing (σ = 2.4), demonstrating both global coverage and selective sensitivity to salient local features. In contrast, other heads exhibit systematic deviations, revealing inherent architectural biases. This is the first empirical evidence that ViT attention can approximate human perceptual mechanisms in domain-specific aesthetic judgment. The findings establish a foundation for interpretable, AI-driven design assessment and introduce a new paradigm for human-aligned visual modeling in computational aesthetics.
📝 Abstract
Visual attention mechanisms play a crucial role in human perception and aesthetic evaluation. Recent advances in Vision Transformers (ViTs) have demonstrated remarkable capabilities in computer vision tasks, yet their alignment with human visual attention patterns remains underexplored, particularly in aesthetic contexts. This study investigates the correlation between human visual attention and ViT attention mechanisms when evaluating handcrafted objects. We conducted an eye-tracking experiment with 30 participants (9 female, 21 male, mean age 24.6 years) who viewed 20 artisanal objects comprising basketry bags and ginger jars. Using a Pupil Labs eye-tracker, we recorded gaze patterns and generated heat maps representing human visual attention. Simultaneously, we analyzed the same objects using a pre-trained ViT model with DINO (Self-DIstillation with NO Labels), extracting attention maps from each of the 12 attention heads. We compared human and ViT attention distributions using Kullback-Leibler divergence across varying Gaussian parameters (sigma=0.1 to 3.0). Statistical analysis revealed optimal correlation at sigma=2.4 +-0.03, with attention head #12 showing the strongest alignment with human visual patterns. Significant differences were found between attention heads, with heads #7 and #9 demonstrating the greatest divergence from human attention (p< 0.05, Tukey HSD test). Results indicate that while ViTs exhibit more global attention patterns compared to human focal attention, certain attention heads can approximate human visual behavior, particularly for specific object features like buckles in basketry items. These findings suggest potential applications of ViT attention mechanisms in product design and aesthetic evaluation, while highlighting fundamental differences in attention strategies between human perception and current AI models.