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Vision Transformers better model human texture perception than CNNs, study finds

By PulseAugur Editorial · [2 sources] ·

A new arXiv paper by Ludovica De Paolis compares how Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) represent textures, a key aspect of visual perception. The study found that ViTs create similar texture representations regardless of complexity and that human performance in recognizing textures is better predicted by ViT representations than CNNs. These findings suggest that ViTs may more accurately model human texture processing, with network architecture playing a significant role. AI

IMPACT Suggests Vision Transformers may offer a more accurate computational model for human texture perception compared to traditional CNNs.

RANK_REASON The cluster contains a research paper published on arXiv comparing different AI model architectures.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Vision Transformers better model human texture perception than CNNs, study finds

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The cluster contains a research paper published on arXiv comparing different AI model architectures.
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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Ludovica de Paolis, Marco Baroni, Alessandro Laio, Eugenio Piasini ·

    Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception

    arXiv:2607.08321v1 Announce Type: new Abstract: In computational vision science, Convolutional Neural Networks (CNNs) have emerged as a popular model of biological vision because of the alignment they can exhibit with neural and behavioral data in humans and animals. However, it …

  2. arXiv cs.CV TIER_1 English(EN) · Eugenio Piasini ·

    Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception

    In computational vision science, Convolutional Neural Networks (CNNs) have emerged as a popular model of biological vision because of the alignment they can exhibit with neural and behavioral data in humans and animals. However, it remains unclear to what extent this alignment pe…

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