Paper Citation Record · LEDGER

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models

As of 15 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 3 inbound Pith citation observations for arXiv:2506.00718.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.00718 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

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measured 46 of 46 standing notices

One-hop event checks from named stored sources.

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measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:14:37.988960Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-14T20:39:27.701693Z

Reference resolution

43 of 43 outbound references displayed

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External citation measurements

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Outbound references

Observation 0d7c9f32-8a05-4bb1-9129-cfa8e792641a · outbound

This paper cites A century of gestalt psychology in visual perception: I.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models A century of gestalt psychology in visual perception: I

Reference 1

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Observation e7ed026d-7165-4721-81ed-e4a786cdb43a · outbound

This paper cites A century of gestalt psychology in visual perception: Ii.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models A century of gestalt psychology in visual perception: Ii

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:04:04.915639Z

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Observation 2538ede7-4e67-4072-b59f-692098689aff · outbound

This paper cites Untersuchungen zur lehre von der gestalt, ii.Psychologische Forschung, 4:301–350, 1923.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Untersuchungen zur lehre von der gestalt, ii.Psychologische Forschung, 4:301–350, 1923

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:04:04.576852Z

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Observation 0c88592d-d919-498d-9073-29384da002ec · outbound

This paper cites Subjective contours.Scientific American, 234(4):48–52, 1976.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Subjective contours.Scientific American, 234(4):48–52, 1976

Reference 4

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Observation 538fb759-d63c-44a6-8f6f-008e02e137bd · outbound

This paper cites Masked autoencoders are scalable vision learners.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Masked autoencoders are scalable vision learners

Reference 5

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Observation f9086773-e385-4dda-90ec-48d59edd84ab · outbound

This paper cites Univ of California Press, 1972.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Univ of California Press, 1972

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:04:03.843622Z

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Observation 55c7dd91-28d9-4e83-bdfd-c960e6d0bd03 · outbound

This paper cites Convexity and symmetry in figure-ground organization.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Convexity and symmetry in figure-ground organization

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:04:03.484051Z

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Observation 99ac48d1-f1d2-4753-9879-6bff55147f4f · outbound

This paper cites Inhibitory competition in figure-ground perception: Context and convexity.Journal of Vision, 8(16):4–4, 2008.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Inhibitory competition in figure-ground perception: Context and convexity.Journal of Vision, 8(16):4–4, 2008

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:04:03.155200Z

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Observation 00aac2fa-01e9-4232-b8f1-a106f43da6cd · outbound

This paper cites Who owns the contour of a visual hole?Perception, 35(7):883–894, 2006.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Who owns the contour of a visual hole?Perception, 35(7):883–894, 2006

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:04:02.921121Z

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Observation 077f4ac2-f175-4394-8496-171d996472ed · outbound

This paper cites A convnet for the 2020s.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models A convnet for the 2020s

Reference 10

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Observation c5a15848-4579-445e-94b4-12edab864444 · outbound

This paper cites Convnext v2: Co-designing and scaling convnets with masked autoencoders.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Convnext v2: Co-designing and scaling convnets with masked autoencoders

Reference 11

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Observation ff8ee730-0f15-4d10-8d6b-c343a8efd1a8 · outbound

This paper cites Texture synthesis using convolutional neural networks.Advances in neural information processing systems, 28, 2015.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Texture synthesis using convolutional neural networks.Advances in neural information processing systems, 28, 2015

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:04:02.650173Z

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Observation 0c345c29-98c5-46dc-8e4f-9bc8092df174 · outbound

This paper cites Learning transferable visual models from natural language supervision.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Learning transferable visual models from natural language supervision

Reference 13

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Observation 4347e6e6-241b-4535-834a-bea59983e441 · outbound

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

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models DINOv2: Learning Robust Visual Features without Supervision

Reference 14

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Observation 21d5b03d-47ce-423f-916d-6834d1354101 · outbound

This paper cites When and why vision-language models behave like bags-of-words, and what to do about it?.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models When and why vision-language models behave like bags-of-words, and what to do about it?

Reference 15

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Observation 39cfa9f6-2b86-4a72-9823-c7e3d4a0b01e · outbound

This paper cites Emergence of shape bias in convolutional neural networks through activation sparsity.Advances in Neural Information Processing Systems, 36:71755–71766, 2023.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Emergence of shape bias in convolutional neural networks through activation sparsity.Advances in Neural Information Processing Systems, 36:71755–71766, 2023

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:04:02.424348Z

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Observation 6721d0cb-8bd3-4920-a15a-6f39d1d4bc8c · outbound

This paper cites Large-scale two-photon imaging revealed super-sparse population codes in the v1 superficial layer of awake monkeys.Elife, 7:e33370, 2018.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Large-scale two-photon imaging revealed super-sparse population codes in the v1 superficial layer of awake monkeys.Elife, 7:e33370, 2018

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:04:02.261629Z

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Observation c43a076c-04dd-4e6c-b07a-cea0e2935cc7 · outbound

This paper cites Surfgen: Adversarial 3d shape synthesis with explicit surface discriminators.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Surfgen: Adversarial 3d shape synthesis with explicit surface discriminators

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:04:02.095770Z

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Observation 0d418c06-aa1d-4e59-99aa-43e89a578c19 · outbound

This paper cites Deep residual learning for image recognition.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Deep residual learning for image recognition

Reference 19

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Observation 954c0556-a0ad-4b7c-a348-19db48bfc5fd · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020

Reference 20

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Observation d6a294e7-8987-4789-b884-c00d77f0d087 · outbound

This paper cites Robust category-level 6d pose estimation with coarse-to-fine rendering of neural features.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Robust category-level 6d pose estimation with coarse-to-fine rendering of neural features

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:04:01.817405Z

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Observation 89217749-676f-47ba-903c-6014fedd3963 · outbound

This paper cites Hallucination improves the performance of unsupervised visual representation learning.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Hallucination improves the performance of unsupervised visual representation learning

Reference 22

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Observation d8212223-5aa7-493f-92b8-10697e9dc6eb · outbound

This paper cites Integrating Auxiliary Information in Self-supervised Learning.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Integrating Auxiliary Information in Self-supervised Learning

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:03:58.729453Z

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Observation a994a18d-b15d-432c-b29e-f58dd1b70b7d · outbound

This paper cites Hierarchical interdisciplinary topic detection model for research proposal classification.IEEE Transactions on Knowledge and Data Engineering, 35(9):9685–9699, 2023.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Hierarchical interdisciplinary topic detection model for research proposal classification.IEEE Transactions on Knowledge and Data Engineering, 35(9):9685–9699, 2023

Reference 24

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Observation 4a0adae1-5d88-4137-aa88-6083c33b40d0 · outbound

This paper cites A deep learning framework based on dynamic channel selection for early classification of left and right hand motor imagery tasks.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models A deep learning framework based on dynamic channel selection for early classification of left and right hand motor imagery tasks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:04:00.997181Z

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Observation 907eb8a4-d560-4b04-b76d-2837b61a57a5 · outbound

This paper cites Learning Weakly-Supervised Contrastive Representations.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Learning Weakly-Supervised Contrastive Representations

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:03:58.457846Z

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Observation cc5254dc-2458-43de-b496-d4ac750c6846 · outbound

This paper cites Prototype memory and attention mechanisms for few shot image generation.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Prototype memory and attention mechanisms for few shot image generation

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:04:00.727497Z

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Observation 711472b3-a901-4e48-bbd6-57da38cc8358 · outbound

This paper cites Conditional Contrastive Learning with Kernel.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Conditional Contrastive Learning with Kernel

Reference 28

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This paper cites A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?

Reference 29

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Observation cba2da16-f7e0-4895-b97e-fcf7b311e157 · outbound

This paper cites Opening the black box: the promise and limitations of explainable machine learning in cardiology.Canadian Journal of Cardiology, 38(2):204–213, 2022.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Opening the black box: the promise and limitations of explainable machine learning in cardiology.Canadian Journal of Cardiology, 38(2):204–213, 2022

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:04:00.444146Z

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Observation d0f334b9-4a85-4b74-95a8-ce8564650101 · outbound

This paper cites Explaining explanations: An overview of interpretability of machine learning.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Explaining explanations: An overview of interpretability of machine learning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:04:00.250211Z

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This paper cites Assessing neural network representations during training using data diffusion spectra.ICML workshop on TAG-ML, 2023.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Assessing neural network representations during training using data diffusion spectra.ICML workshop on TAG-ML, 2023

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:59.949980Z

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Observation d2abc5e3-5234-4f77-b52a-da1add6e06a3 · outbound

This paper cites Neural networks trained on natural scenes exhibit gestalt closure.Computational Brain & Behavior, 4(3):251–263, 2021.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Neural networks trained on natural scenes exhibit gestalt closure.Computational Brain & Behavior, 4(3):251–263, 2021

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:59.732821Z

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This paper cites Evaluating Machine Learning Models with NERO: Non-Equivariance Revealed on Orbits.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Evaluating Machine Learning Models with NERO: Non-Equivariance Revealed on Orbits

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:03:58.100010Z

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This paper cites Vision transformers with self-distilled registers.arXiv preprint arXiv:2505.21501, 2025.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Vision transformers with self-distilled registers.arXiv preprint arXiv:2505.21501, 2025

Reference 35

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This paper cites Visualizing and understanding convolutional networks.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Visualizing and understanding convolutional networks

Reference 36

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

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness

Reference 37

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This paper cites Partial success in closing the gap between human and machine vision.Advances in Neural Information Processing Systems, 34:23885– 23899, 2021.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Partial success in closing the gap between human and machine vision.Advances in Neural Information Processing Systems, 34:23885– 23899, 2021

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:59.539768Z

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Observation 85837ed1-1b79-410d-a789-de192f04b193 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 39

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Observation 452b9f90-a144-4b1d-bcdc-7bddcf8ebb58 · outbound

This paper cites Vision transformers are robust learners.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Vision transformers are robust learners

Reference 40

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This paper cites Imagenet: A large- scale hierarchical image database.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Imagenet: A large- scale hierarchical image database

Reference 41

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This paper cites Deit iii: Revenge of the vit.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models Deit iii: Revenge of the vit

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:03:59.272200Z

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Observation 1f165c69-9e5f-44c1-84f3-f21900e71df4 · outbound

This paper cites given-up.

From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models given-up

Reference 43

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Pith citing papers

Observation 136f44b0-0019-4248-a29c-237c99b2deb2 · inbound

Human-like Object Grouping in Self-supervised Vision Transformers cites this paper.

Human-like Object Grouping in Self-supervised Vision Transformers From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models

Reference 25

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Revealing the Gap in Human and VLM Scene Perception through Counterfactual Semantic Saliency cites this paper.

Revealing the Gap in Human and VLM Scene Perception through Counterfactual Semantic Saliency From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models

Reference 37

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More Accurate, Less Human: Gestalt Grouping in Vision Models cites this paper.

More Accurate, Less Human: Gestalt Grouping in Vision Models From Local Cues to Global Percepts: Emergent Gestalt Organization in Self-Supervised Vision Models

Reference 16

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