Paper Citation Record · LEDGER

Are we done with ImageNet?

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 41 inbound Pith citation observations for arXiv:2006.07159.

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

pith.paper-citation-record.v1
2006.07159 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 41 of 41 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:52:51.892234Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

75
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 1acb96f4-5124-4001-b998-e6c3c69eba19 · inbound

PaLI: A Jointly-Scaled Multilingual Language-Image Model cites this paper.

PaLI: A Jointly-Scaled Multilingual Language-Image Model Are we done with ImageNet?

Reference 189

Resolution: verified exact; arxiv_id, observed 2026-05-16T09:29:06.171984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9eedfa5d-24d6-4060-bff7-f5a2449f2dc5 · inbound

Sigmoid Loss for Language Image Pre-Training cites this paper.

Sigmoid Loss for Language Image Pre-Training Are we done with ImageNet?

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T13:05:36.552655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation af299ae4-93fd-4081-9dad-3e486c880ac6 · inbound

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

DINOv2: Learning Robust Visual Features without Supervision Are we done with ImageNet?

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-09T04:17:20.394194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9e0f58ee-3d8d-482d-a335-20dab654d1f1 · inbound

PaLI-X: On Scaling up a Multilingual Vision and Language Model cites this paper.

PaLI-X: On Scaling up a Multilingual Vision and Language Model Are we done with ImageNet?

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-05-17T14:36:10.028979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d2a15c82-3776-4014-99da-6fccae660361 · inbound

Vision Transformers Need Registers cites this paper.

Vision Transformers Need Registers Are we done with ImageNet?

Reference 111

Resolution: verified exact; arxiv_id, observed 2026-05-13T09:41:38.082214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5f7f42a2-b373-4b71-a3fd-7ab5f62e0819 · inbound

InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks cites this paper.

InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks Are we done with ImageNet?

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-13T22:46:09.795868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation de8e5865-e5b5-4204-9b41-2524dd3a1659 · inbound

Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications cites this paper.

Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications Are we done with ImageNet?

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-24T01:08:41.958239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2397797f-95ee-409c-9da9-77d99bd6f978 · inbound

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions cites this paper.

LibraGrad: Balancing Gradient Flow for Universally Better Vision Transformer Attributions Are we done with ImageNet?

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T13:52:51.892234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea95d6a2-7514-4ea7-a452-c8045913625b · inbound

Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling cites this paper.

Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling Are we done with ImageNet?

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-10T13:23:58.188150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 18141e32-d7a4-4332-bf00-3e02ca0afb0a · inbound

Double-Exponential Increases in Inference Energy: The Cost of the Race for Accuracy cites this paper.

Double-Exponential Increases in Inference Energy: The Cost of the Race for Accuracy Are we done with ImageNet?

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T16:51:46.273727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18a405b1-715b-42be-9a78-59a869786e88 · inbound

MAL: Cluster-Masked and Multi-Task Pretraining for Enhanced xLSTM Vision Performance cites this paper.

MAL: Cluster-Masked and Multi-Task Pretraining for Enhanced xLSTM Vision Performance Are we done with ImageNet?

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T15:45:21.841657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e00fd692-b307-4b88-a5eb-9086317d8b11 · inbound

The Impact of the Single-Label Assumption in Image Recognition Benchmarking cites this paper.

The Impact of the Single-Label Assumption in Image Recognition Benchmarking Are we done with ImageNet?

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T04:46:35.223174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26115eca-d13d-4b77-a579-f19d226b6155 · inbound

Learning from Ambiguous Data with Hard Labels cites this paper.

Learning from Ambiguous Data with Hard Labels Are we done with ImageNet?

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-10T22:26:58.363303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d22f92a-82fc-40a4-8f19-f0a8f945016b · inbound

SimLabel: Consistency-Guided OOD Detection with Pretrained Vision-Language Models cites this paper.

SimLabel: Consistency-Guided OOD Detection with Pretrained Vision-Language Models Are we done with ImageNet?

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T18:17:23.828359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57897ee0-9073-479c-9b9f-554d4a1f74f3 · inbound

With Great Backbones Comes Great Adversarial Transferability cites this paper.

With Great Backbones Comes Great Adversarial Transferability Are we done with ImageNet?

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T17:24:21.476162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14721a92-0cfb-40d9-bb65-71a8069a5139 · inbound

Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? cites this paper.

Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Are we done with ImageNet?

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-10T14:22:24.579251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee0fbd76-015e-464d-b862-02753ebe7013 · inbound

Cluster and Predict Latent Patches for Improved Masked Image Modeling cites this paper.

Cluster and Predict Latent Patches for Improved Masked Image Modeling Are we done with ImageNet?

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T23:50:07.405408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85a8a5a2-68a2-4840-8a23-338078eff7e2 · inbound

SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features cites this paper.

SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features Are we done with ImageNet?

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-10T15:49:22.327294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation dcebfebb-d997-4b6a-8655-b4a7189c2013 · inbound

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks cites this paper.

SNAP: A Benchmark for Testing the Effects of Capture Conditions on Fundamental Vision Tasks Are we done with ImageNet?

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T15:19:19.411111Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8ae5879-52e0-4302-b0dd-7353b7da03df · inbound

Multiplicity is an Inevitable and Inherent Challenge in Multimodal Learning cites this paper.

Multiplicity is an Inevitable and Inherent Challenge in Multimodal Learning Are we done with ImageNet?

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T14:16:06.500633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d568042-9641-4a89-b56c-cdc8185e47ea · inbound

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets cites this paper.

Bridging Annotation Gaps: Transferring Labels to Align Object Detection Datasets Are we done with ImageNet?

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T10:40:46.473215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4e66b74-3c82-41a3-91cd-9a3d2daea8e2 · inbound

Object-level Self-Distillation for Vision Pretraining cites this paper.

Object-level Self-Distillation for Vision Pretraining Are we done with ImageNet?

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T10:52:54.802249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bbf0430-80c3-42a7-9c83-43b6ff75dcb2 · inbound

AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions cites this paper.

AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions Are we done with ImageNet?

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-07T05:01:08.336805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee948ce8-15b9-4e04-bbec-1567b84ed3e7 · inbound

Canonical Latent Representations in Conditional Diffusion Models cites this paper.

Canonical Latent Representations in Conditional Diffusion Models Are we done with ImageNet?

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T04:43:05.840404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62718e30-1a98-404c-a068-a06d11346922 · inbound

LAION-C: An Out-of-Distribution Benchmark for Web-Scale Vision Models cites this paper.

LAION-C: An Out-of-Distribution Benchmark for Web-Scale Vision Models Are we done with ImageNet?

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-22T00:54:31.259073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4844f0b5-7b05-424c-a6a9-74e2b21f1797 · inbound

Deprecating Benchmarks: Criteria and Framework cites this paper.

Deprecating Benchmarks: Criteria and Framework Are we done with ImageNet?

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:40.395204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d720a27-962c-4d88-ba2f-a649c8b5df4f · inbound

SCOOTER: A Human Evaluation Framework for Unrestricted Adversarial Examples cites this paper.

SCOOTER: A Human Evaluation Framework for Unrestricted Adversarial Examples Are we done with ImageNet?

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-19T05:37:05.601228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cc9f1caa-8246-406c-b3c4-419cd41fbbaf · inbound

Franca: Nested Matryoshka Clustering for Scalable Visual Representation Learning cites this paper.

Franca: Nested Matryoshka Clustering for Scalable Visual Representation Learning Are we done with ImageNet?

Reference 62

Resolution: verified exact; arxiv_id, observed 2026-05-19T03:42:01.378630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bbb27a14-c4df-43b8-95ed-1ca50f420058 · inbound

Token-Based Detection of Spurious Correlations in Vision Transformers cites this paper.

Token-Based Detection of Spurious Correlations in Vision Transformers Are we done with ImageNet?

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T10:31:37.200404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79b454bc-263c-4864-bfb1-dc73f3bc623d · inbound

Image Recognition with Vision and Language Embeddings of VLMs cites this paper.

Image Recognition with Vision and Language Embeddings of VLMs Are we done with ImageNet?

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-04T19:24:21.435965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bf46e6e-6ec4-4f98-9bef-a4a8c3451125 · inbound

CanViT: Toward Active-Vision Foundation Models cites this paper.

CanViT: Toward Active-Vision Foundation Models Are we done with ImageNet?

Reference 63

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T10:34:06.902134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c8a192ee-f397-415b-91b3-a7731281a94f · inbound

Hierarchical Pre-Training of Vision Encoders with Large Language Model cites this paper.

Hierarchical Pre-Training of Vision Encoders with Large Language Model Are we done with ImageNet?

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-04T05:37:50.137745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a071b17-f055-4e36-9be6-24435f112944 · inbound

Elastic Attention Cores for Scalable Vision Transformers cites this paper.

Elastic Attention Cores for Scalable Vision Transformers Are we done with ImageNet?

Reference 148

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:07:22.652059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7738d6cf-e800-4532-a0ad-db39b446e227 · inbound

Position: Early-Stage Quality Assurance in Annotation Pipelines Is More Cost-Effective Than Late-Stage Validation cites this paper.

Position: Early-Stage Quality Assurance in Annotation Pipelines Is More Cost-Effective Than Late-Stage Validation Are we done with ImageNet?

Reference 2

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T17:33:36.633269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bbf38b34-ac84-473c-bd05-06a99be126ff · inbound

From Uncertain Judgments to Calibrated Rankings: Conformal Elo Estimation for LLM Evaluation cites this paper.

From Uncertain Judgments to Calibrated Rankings: Conformal Elo Estimation for LLM Evaluation Are we done with ImageNet?

Reference 44

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T13:38:19.231041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6a491112-b539-43a2-bb04-aa97c3009ced · inbound

Unmasking LAION-5B: Age, Gender, Race, and Emotion Biases in Large-Scale Image Datasets cites this paper.

Unmasking LAION-5B: Age, Gender, Race, and Emotion Biases in Large-Scale Image Datasets Are we done with ImageNet?

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-07-04T09:59:45.828077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c679a254-b026-40f7-a032-3fca8010f538 · inbound

Mixture-of-Control: State-Aware Fine-Tuning for Transformer-based Models cites this paper.

Mixture-of-Control: State-Aware Fine-Tuning for Transformer-based Models Are we done with ImageNet?

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-07-01T06:55:28.748174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b7846746-eb07-4459-aea4-9c3220f7b82a · inbound

Evaluating Epistemic Uncertainty: Beyond OOD Detection and Active Learning cites this paper.

Evaluating Epistemic Uncertainty: Beyond OOD Detection and Active Learning Are we done with ImageNet?

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-02T01:03:58.949279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d44f667c-18a8-4079-93ea-3529d5273f32 · inbound

Riemannian Deep Learning: Modules, Networks, and Geometries cites this paper.

Riemannian Deep Learning: Modules, Networks, and Geometries Are we done with ImageNet?

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-01T12:55:18.601001Z

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Observation 102db97d-c0fb-4f68-ac02-554b4a632b0c · inbound

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations cites this paper.

Consistent Evidence, Robust Recognition: Faithful Attribution Regularization under Geometric Transformations Are we done with ImageNet?

Reference 39

Resolution: unresolved; no resolver link, observed 2026-07-30T11:00:16.150623Z

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Unavailable: canonical work link unavailable.

Observation 675b06ff-38b1-49d0-904c-663fd557d613 · inbound

Sharpness-Aware Minimization and Muon: Robustness under the Spectral Norm cites this paper.

Sharpness-Aware Minimization and Muon: Robustness under the Spectral Norm Are we done with ImageNet?

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T00:57:38.003121Z

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Unavailable: canonical work link unavailable.