AI RESEARCH
Cognitive Alignment At No Cost: Inducing Human Attention Biases For Interpretable Vision Transformers
arXiv CS.CV
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ArXi:2604.20027v1 Announce Type: new For state-of-the-art image understanding, Vision Transformers (ViTs) have become the standard architecture but their processing diverges substantially from human attentional characteristics. We investigate whether this cognitive gap can be shrunk by fine-tuning the self-attention weights of Google's ViT-B/16 on human saliency fixation maps. To isolate the effects of semantically relevant signals from generic human supervision, the tuned model is compared against a shuffled control.