Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception | Paper Citation Record

Observation 9226c784-f051-4547-8f85-2e0e19f008b4 · outbound

This paper cites Frontiers in Cellular Neuroscience , volume =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception Frontiers in Cellular Neuroscience , volume =

Reference 1

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

Reference 2

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This paper cites Proceedings of the Royal Society B: Biological Sciences , volume =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception Proceedings of the Royal Society B: Biological Sciences , volume =

Reference 3

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This paper cites doi:10.1073/pnas.91.18.8482 , url =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception doi:10.1073/pnas.91.18.8482 , url =

Reference 4

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This paper cites Visual Processing of Informative Multipoint Correlations Arises Primarily in.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception Visual Processing of Informative Multipoint Correlations Arises Primarily in

Reference 5

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This paper cites IRE Transactions on Information Theory , year =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception IRE Transactions on Information Theory , year =

Reference 6

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This paper cites and Julesz, B.

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

Reference 7

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This paper cites Nature , year =.

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

Reference 8

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

Reference 9

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

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This paper cites Journal of the Optical Society of America A , year =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception Journal of the Optical Society of America A , year =

Reference 11

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This paper cites , title =.

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

Reference 12

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

Reference 13

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This paper cites IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , year =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , year =

Reference 14

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This paper cites Local Statistics in Natural Scenes Predict the Saliency of Synthetic Textures , journal =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception Local Statistics in Natural Scenes Predict the Saliency of Synthetic Textures , journal =

Reference 15

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This paper cites , title =.

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

Reference 16

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This paper cites and Conte, Mary M.

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

Reference 17

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This paper cites and Zoccolan, Davide and Rust, Nicole C.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception and Zoccolan, Davide and Rust, Nicole C

Reference 18

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This paper cites Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , year =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , year =

Reference 19

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This paper cites and Hong, Ha and Yamins, Daniel L.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception and Hong, Ha and Yamins, Daniel L

Reference 20

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This paper cites Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream , journal =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream , journal =

Reference 21

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This paper cites and Victor, Jonathan D.

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

Reference 22

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This paper cites and Kaiser.

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

Reference 23

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This paper cites and Ecker, Alexander S.

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

Reference 24

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This paper cites and Ecker, Alexander S.

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

Reference 25

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This paper cites and Bashivan, Pouya and Kar, Kohitij and Schmidt, Kailyn and DiCarlo, James J.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception and Bashivan, Pouya and Kar, Kohitij and Schmidt, Kailyn and DiCarlo, James J

Reference 26

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This paper cites and Briguglio, John J.

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

Reference 27

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

Reference 28

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

Reference 29

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This paper cites and Conte, Mary M.

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

Reference 30

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

Reference 31

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Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception and Freeman, Jeremy and Movshon, J

Reference 32

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This paper cites Cerebral Cortex , year =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception Cerebral Cortex , year =

Reference 33

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

Reference 34

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This paper cites and Tarr, Michael J.

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

Reference 35

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

Reference 36

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

Reference 37

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This paper cites and Geirhos, Robert , title =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception and Geirhos, Robert , title =

Reference 38

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This paper cites Model Metamers Reveal Divergent Invariances between Biological and Artificial Neural Networks , journal =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception Model Metamers Reveal Divergent Invariances between Biological and Artificial Neural Networks , journal =

Reference 39

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Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception and Brendel, Wieland , title =

Reference 40

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Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception and Rajalingham, Rishi and Issa, Elias B

Reference 41

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

Reference 42

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This paper cites Proceedings of the 38th International Conference on Machine Learning , year =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception Proceedings of the 38th International Conference on Machine Learning , year =

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T09:26:59.732926Z

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This paper cites From BoW to CNN: Two Decades of Texture Representation for Texture Classification , journal =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception From BoW to CNN: Two Decades of Texture Representation for Texture Classification , journal =

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T09:26:59.775823Z

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

Reference 45

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

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T09:26:59.829040Z

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This paper cites Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , year =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , year =

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T09:26:59.780741Z

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Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception and Fei-Fei, Li , title =

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T09:26:59.816457Z

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This paper cites International Conference on Learning Representations (ICLR) , year =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception International Conference on Learning Representations (ICLR) , year =

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T09:26:59.773456Z

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Observation b397e8db-de97-4d84-92d2-03a8e5ae5ede · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision , journal =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception DINOv2: Learning Robust Visual Features without Supervision , journal =

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T09:26:59.802251Z

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This paper cites Proceedings of the 37th International Conference on Machine Learning , year =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception Proceedings of the 37th International Conference on Machine Learning , year =

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T09:26:59.839864Z

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Observation 0a9e9112-e864-490e-a2ec-df51d88642b2 · outbound

This paper cites PNAS Nexus , year =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception PNAS Nexus , year =

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T09:26:59.828043Z

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Observation ff1d911d-01b5-4242-92ea-a1f58ec0a78c · outbound

This paper cites Perceptual misalignment of texture representations in convolutional neural networks.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception Perceptual misalignment of texture representations in convolutional neural networks

Reference 53

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Observation a1a04a33-d0fd-4200-9780-79ab06a65c5c · outbound

This paper cites Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP) , year =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP) , year =

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T09:26:59.824759Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f0c677a8-5a54-4872-9cbd-79e1d2f4bb26 · outbound

This paper cites International Conference on Learning Representations (ICLR) , year =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception International Conference on Learning Representations (ICLR) , year =

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T09:26:59.784762Z

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Observation 568d6687-c124-4d99-b9ea-7df3f2b4f266 · outbound

This paper cites International Conference on Learning Representations (ICLR) , year =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception International Conference on Learning Representations (ICLR) , year =

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T09:26:59.801927Z

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Observation 260e504b-7048-4330-9110-b8f483f0ccce · outbound

This paper cites Ravishankar and Lohse, Gerald L.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception Ravishankar and Lohse, Gerald L

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T09:26:59.813169Z

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Observation 061c0d74-f02c-4407-9b53-717b0103325a · outbound

This paper cites Self-Guided Contrastive Learning for BERT Sentence Representations.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception Self-Guided Contrastive Learning for BERT Sentence Representations

Reference 58

Resolution: verified exact; doi, observed 2026-07-10T09:26:59.494253Z

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Observation 125dcd28-e0ed-4253-84b6-432dd21074a0 · outbound

This paper cites Universal Text Representation from BERT: An Empirical Study.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception Universal Text Representation from BERT: An Empirical Study

Reference 59

Resolution: metadata mismatch; local_arxiv, observed 2026-07-10T09:26:59.503619Z

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Observation fe2443bd-8fc2-41cb-8ada-62c99b498ee5 · outbound

This paper cites The geometry of hidden representations of large transformer models.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception The geometry of hidden representations of large transformer models

Reference 60

Resolution: metadata mismatch; local_arxiv, observed 2026-07-10T09:26:59.666070Z

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Observation ace9cc9e-e4d1-4eb4-a11b-b92318490629 · outbound

This paper cites doi:10.48550/arXiv.2601.21621 , year =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception doi:10.48550/arXiv.2601.21621 , year =

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-07-10T09:26:59.484683Z

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Observation dabae3d6-de71-4f4b-a624-cc870bc8b5d6 · outbound

This paper cites doi:doi:10.5281/zenodo.5561807 , url =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception doi:doi:10.5281/zenodo.5561807 , url =

Reference 62

Resolution: verified exact; doi, observed 2026-07-10T09:26:59.489665Z

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Observation 39b8842a-a188-4587-ad26-9ffcc6ecef86 · outbound

This paper cites , title =.

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

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T09:26:59.814918Z

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Observation 0030f0ba-709d-41ce-b18f-6159266c62e8 · outbound

This paper cites 2026 , eprint =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception 2026 , eprint =

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T09:26:59.744463Z

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Observation 7556e17a-7971-44b8-9779-d9e595aeae00 · outbound

This paper cites 2022 , issn =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception 2022 , issn =

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-07-10T09:26:59.494523Z

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Observation 2d86cfb5-c218-4858-afb1-97d9241857ea · outbound

This paper cites ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

Reference 66

Resolution: metadata mismatch; local_arxiv, observed 2026-07-10T09:26:59.507665Z

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Observation daf88431-5ea1-4b6b-81ed-17be16633d46 · outbound

This paper cites The Origins and Prevalence of Texture Bias in Convolutional Neural Networks , booktitle =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception The Origins and Prevalence of Texture Bias in Convolutional Neural Networks , booktitle =

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T09:26:59.737637Z

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Observation cd350eae-a0f9-4ae6-b15a-9efd604a25e6 · outbound

This paper cites Shape or Texture: Understanding Discriminative Features in CNNs.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception Shape or Texture: Understanding Discriminative Features in CNNs

Reference 68

Resolution: metadata mismatch; local_arxiv, observed 2026-07-10T09:26:59.475040Z

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Observation a7b7a420-6113-4c91-8e14-08ffba1f539b · outbound

This paper cites COR- net: Modeling the Neural Mecha- nisms of Core Object Recognition.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception COR- net: Modeling the Neural Mecha- nisms of Core Object Recognition

Reference 69

Resolution: verified exact; doi, observed 2026-07-10T09:26:59.507474Z

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Observation 8281b8a5-71d5-4187-bb86-a552785a3734 · outbound

This paper cites Proceedings of the 41st International Conference on Machine Learning , pages =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception Proceedings of the 41st International Conference on Machine Learning , pages =

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T09:26:59.796530Z

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Observation aff066ed-9114-4787-b602-4a4fea51975b · outbound

This paper cites Nature Neuroscience , year =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception Nature Neuroscience , year =

Reference 71

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Observation 13988c37-3fc2-4f63-97e6-c4fa66d78160 · outbound

This paper cites Neocognitron: A self-organizing neural network model for a mecha- nism of pattern recognition unaffected by shift in position.Biological Cybernetics, 36(4): 193–202.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception Neocognitron: A self-organizing neural network model for a mecha- nism of pattern recognition unaffected by shift in position.Biological Cybernetics, 36(4): 193–202

Reference 72

Resolution: metadata mismatch; doi, observed 2026-07-10T09:26:59.491682Z

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Observation a18e2408-9a7f-4dbe-a6ec-a362891a304e · outbound

This paper cites the visual cortex.

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

Reference 73

Resolution: metadata mismatch; doi, observed 2026-07-10T09:26:59.496978Z

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Observation f9e9952b-18a3-4c66-ac91-f0c2f70e8bca · outbound

This paper cites , title =.

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

Reference 74

Resolution: verified exact; doi, observed 2026-07-10T09:26:59.487349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 59744830-6b51-4394-b9d0-edde7d929ed8 · outbound

This paper cites , title =.

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

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T09:26:59.815131Z

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Observation 031790e7-0da2-4f58-83e9-8dc70591ac60 · outbound

This paper cites Annual Review of Vision Science , year =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception Annual Review of Vision Science , year =

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T09:26:59.837973Z

Source-reported events for the cited work

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Observation eacc83ea-0ad8-46e8-b631-436627a42161 · outbound

This paper cites , title =.

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

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T09:26:59.834324Z

Source-reported events for the cited work

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Observation 348331d9-956a-4929-947a-d0e43908b31d · outbound

This paper cites , title =.

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

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T09:26:59.819185Z

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Observation 365626e3-94c6-4baa-81f3-c7e584694aa1 · outbound

This paper cites International Conference on Learning Representations , year =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception International Conference on Learning Representations , year =

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T09:26:59.777574Z

Source-reported events for the cited work

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Observation 216afca4-1e55-4ab9-b65b-f66ba6b830a0 · outbound

This paper cites 2026 , eprint=.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception 2026 , eprint=

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T09:26:59.821227Z

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Observation aa1633aa-6007-48e8-bb0a-6f6f0d0611b0 · outbound

This paper cites Of Human Observers and Deep Neural Networks: A Detailed Psychophysical Comparison , journal =.

Texture Representations in Deep Vision Models: Comparing CNNs, Vision Transformers, and Human Perception Of Human Observers and Deep Neural Networks: A Detailed Psychophysical Comparison , journal =

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-07-10T09:26:59.807534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Reference resolution

Outbound references

Pith citing papers