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Paper Citation Record · LEDGER

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

As of 8 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 2 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.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:03:57.574610Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T21:34:48.709465Z

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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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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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

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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

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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

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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

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Source-reported events for the cited work

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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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Observation b8e79915-b529-4624-bef2-114a8d6db8d1 · outbound

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

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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

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Observation f064788f-6f91-45a8-98f1-3ff994bff1c8 · outbound

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

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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

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raw_fallback, observed 2026-08-07T12:03:59.732821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:03:56.048059Z digest=sha256:3b263e072b47cba156ff3b07def7efd743204579aaf0aa382d74246c8c9eb91a

Observation 7edb45e1-d174-4e43-b91b-df03c0e165e1 · outbound

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:03:56.233722Z digest=sha256:d1284fbcf0840afbc03fa118111b5ff0cd61ecda404a9dc3e084b777bd90c766

Observation f51480cd-2698-466a-a80c-47e32d46c229 · outbound

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

Resolution
unresolved
no resolver link, observed 2026-08-07T12:03:56.444955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:03:56.444955Z digest=sha256:a796db58250441cd608d731ae7c72b8b9e31a4392916e6f31a0b3abf0c3c478f

Observation 6abc1f00-e7f8-4b64-bc15-901db9e88320 · outbound

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

Resolution
unresolved
no resolver link, observed 2026-08-07T12:03:56.642954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:03:56.642954Z digest=sha256:4190435f93667de11e7482ec2e1dd0c311b727bfcffbedc9df5dd674cfdf3934

Observation 49cd2271-cb87-49a3-8835-00895539d129 · outbound

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

Resolution
unresolved
no resolver link, observed 2026-08-07T12:03:56.785903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:03:56.785903Z digest=sha256:3cf98692f10a984376ebad2a61eb920358297fb9553b43d9fb8b08af5751ed66

Observation 1cbebdbd-58c4-43b7-aa90-5d5c7186448c · outbound

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:03:56.895420Z digest=sha256:1bace127ddd30eec4aa43b7a76307ecf1d23e3af30fdf2cec2f3fe298fe22bb4

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

Resolution
unresolved
no resolver link, observed 2026-08-07T12:03:56.976332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:03:56.976332Z digest=sha256:fa471940b7bc3f214f5656b8f9605ecbd7528619276c451e9cd4f86ddca758a0

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

Resolution
unresolved
no resolver link, observed 2026-08-07T12:03:57.098968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:03:57.098968Z digest=sha256:2a02e80841fb7c825064f5734fdf0596a68e545f67baa8c7aa9f8ef1c3dd9387

Observation d1479110-2c12-49d6-b298-4dd54d0de2a2 · outbound

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

Resolution
unresolved
no resolver link, observed 2026-08-07T12:03:57.237121Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:03:57.237121Z digest=sha256:eeea0ea7992f774d9d962ab71341df3d720135f52a554c0c29faf4f469ad903f

Observation ad57b297-cd41-40bd-bbd8-c5158bcf6532 · outbound

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:03:57.403064Z digest=sha256:bbfbe0f6b7920934f11b8ff0e13e22ae942728bb5410a9817ef44dad5ef1d12b

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T12:03:59.079968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T12:03:57.574610Z digest=sha256:b9b23bf89bbc6f2af415377954b9ede6c57266fc7327be5a3753e7a3f6edd007

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

Resolution
unresolved
no resolver link, observed 2026-07-14T21:34:48.709465Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T21:34:48.709465Z digest=sha256:be2b9d7ce5ea3f6b2f98e42e85c39c4253b7c8c28262f4fff03625adca872327

Observation 0baa2d9b-f328-472b-87cb-11e3afeae8e8 · inbound

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

Resolution
verified exact
arxiv_id, observed 2026-05-14T20:39:27.707162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-14T20:35:08.286755Z digest=sha256:3c97377cf9d7a3cb4ad581f5e5fd3349ee0ee372c1bc5b1cd37e11f8d877276b