Paper Citation Record · LEDGER

Do Vision Transformers See Like Convolutional Neural Networks?

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2108.08810.

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

pith.paper-citation-record.v1
2108.08810 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:24:26.087105Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T00:39:16.593635Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 92806ed5-88f7-47fe-9e93-4c476e05ff34 · inbound

Revisiting Simple Baselines for In-The-Wild Deepfake Detection cites this paper.

Revisiting Simple Baselines for In-The-Wild Deepfake Detection Do Vision Transformers See Like Convolutional Neural Networks?

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-05T10:24:26.087105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c597e4e0-2647-4870-a5b8-f902c3733fe3 · inbound

GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA cites this paper.

GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA Do Vision Transformers See Like Convolutional Neural Networks?

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-09T22:29:06.782002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 85d85d0e-429c-49fe-9cc7-9f4f2c358cef · inbound

Beyond Compression: Quantifying Spectral Accessibility in Vision Representations cites this paper.

Beyond Compression: Quantifying Spectral Accessibility in Vision Representations Do Vision Transformers See Like Convolutional Neural Networks?

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T02:46:27.696682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ffe7dbbc-e8c0-49eb-9f0a-932df4c5a575 · inbound

LEAP: Layer-skipping Efficiency via Adaptive Progression for Vision Transformer Distillation cites this paper.

LEAP: Layer-skipping Efficiency via Adaptive Progression for Vision Transformer Distillation Do Vision Transformers See Like Convolutional Neural Networks?

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:39:16.595153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.