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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Vision Transformer attention alignment with human visual perception in aesthetic object evaluation.

Miguel Carrasco1, César González-Martín2, José Aranda3

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This study compared human visual attention with Vision Transformer (ViT) attention mechanisms for handcrafted objects. Certain ViT attention heads showed alignment with human gaze patterns, suggesting AI potential in aesthetic evaluation.

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Cognitive Science

Background:

  • Visual attention is key to human perception and aesthetic judgment.
  • Vision Transformers (ViTs) excel in computer vision but their attention alignment with humans is unclear, especially for aesthetics.
  • Understanding this alignment can inform AI development for design and evaluation.

Purpose of the Study:

  • To investigate the correlation between human visual attention and ViT attention mechanisms.
  • To evaluate ViT attention patterns on handcrafted objects using eye-tracking and AI analysis.
  • To identify specific ViT attention heads that best mimic human visual behavior.

Main Methods:

  • An eye-tracking experiment recorded 30 participants' gaze on 20 artisanal objects.
  • Pre-trained ViT with DINO (Self-DIstillation with NO Labels) generated attention maps from 12 heads.
  • Human and ViT attention maps were compared using KL divergence, SSIM, CC, and SIM metrics, alongside AOI analysis.

Main Results:

  • Optimal correlation between human and ViT attention occurred at specific Gaussian parameters, with head #12 showing strongest alignment.
  • Attention heads #7 and #9 diverged most significantly from human attention patterns.
  • ViT heads concentrated attention on objects more than backgrounds, with heads #12, #1, and #3 showing high lift values.

Conclusions:

  • ViTs exhibit more global attention than human focal attention, but specific heads approximate human visual behavior.
  • ViT attention mechanisms show promise for applications in product design and aesthetic evaluation.
  • Fundamental differences in attention strategies exist between human perception and current AI models.