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ARXIV:2604.01341 · COMPUTER VISION RESEARCH · SUBMITTED 03 APR · 20:20 UTC · FRESHNESS STALE
ARXIV:2604.01341COMPUTER VISION RESEARCHSUBMITTED 03 APR · 20:20 UTCFRESHNESS STALELudovica de Paolis · Fabio Anselmi · Alessio Ansuini · Eugenio Piasini · arXiv
This research investigates the disconnect between how convolutional neural networks represent textures and human perception, suggesting current object recognition models are insufficient for understanding texture perception.
Opportunity summary
Pain This research investigates the disconnect between how convolutional neural networks represent textures and human perception, suggesting current object recognition models are insufficient for understanding texture perception.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
This research investigates the disconnect between how convolutional neural networks represent textures and human perception, suggesting current object recognition models are insufficient for understanding texture perception. An influential approach for texture analysis and generation…
Mathematical modeling of visual textures traces back to Julesz's intuition that texture perception in humans is based on local correlations between image features. An influential approach for texture analysis and generation generalizes this notion…
ScienceToStartup currently rates this 2.0/10 on the public viability pass. We conclude that texture perception involves mechanisms that are distinct from those that are commonly modeled using approaches based on CNNs trained on object…
Computer Vision Research moved forward this cycle; last verified April 2026. Public score 2.0/10.
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This research investigates the disconnect between how convolutional neural networks represent textures and human perception, suggesting current object recognition models are insufficient for understanding texture perception.
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10.48550/arXiv.2604.01341This research investigates the disconnect between how convolutional neural networks represent textures and human perception, suggesting current object recognition models are insufficient for understanding texture perception.
Abstract
Mathematical modeling of visual textures traces back to Julesz's intuition that texture perception in humans is based on local correlations between image features. An influential approach for texture analysis and generation generalizes this notion to linear correlations between the nonlinear features computed by convolutional neural networks (CNNs), compiled into Gram matrices. Given that CNNs are often used as models for the visual system, it is natural to ask whether such "texture representations" spontaneously align with the textures' perceptual content, and in particular whether those CNNs that are regarded as better models for the visual system also possess more human-like texture representations. Here we compare the perceptual content captured by feature correlations computed for a diverse pool of CNNs, and we compare it to the models' perceptual alignment with the mammalian visual system as measured by Brain-Score. Surprisingly, we find that there is no connection between conventional measures of CNN quality as a model of the visual system and its alignment with human texture perception. We conclude that texture perception involves mechanisms that are distinct from those that are commonly modeled using approaches based on CNNs trained on object recognition, possibly depending on the integration of contextual information.
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PROBLEM
This research investigates the disconnect between how convolutional neural networks represent textures and human perception, suggesting current object recognition models are insufficient for understanding texture perception. An influential approach for texture analysis and gener...
METHOD
Mathematical modeling of visual textures traces back to Julesz's intuition that texture perception in humans is based on local correlations between image features. An influential approach for texture analysis and generation generalizes this notion to linear correlations between...
RESULT
ScienceToStartup currently rates this 2.0/10 on the public viability pass. We conclude that texture perception involves mechanisms that are distinct from those that are commonly modeled using approaches based on CNNs trained on object recognition, possibly depending on the integ...
WHY NOW
Computer Vision Research moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed public claims while anchored extraction refreshes.
This research investigates the disconnect between how convolutional neural networks represent textures and human perception, suggesting current object recognition models are insufficient for understanding texture perception. An influential approach for texture analysis and generation generalizes this notion to linear correlations between the nonlinear features computed by convolutional neural networks (CNNs), compiled into Gram matrices.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Mathematical modeling of visual textures traces back to Julesz's intuition that texture perception in humans is based on local correlations between image features. An influential approach for texture analysis and generation generalizes this notion to linear correlations between the nonlinear features computed by convolutional neural networks (CNNs), compiled into Gram matrices.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 2.0/10 on the public viability pass. We conclude that texture perception involves mechanisms that are distinct from those that are commonly modeled using approaches based on CNNs trained on object recognition, possibly depending on the integration of contextual information.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Computer Vision Research moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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This research investigates the disconnect between how convolutional neural networks represent textures and human perception, suggesting current object recognition models are insufficient for understanding texture perception.
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Computer Vision Research
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