Paper Citation Record · LEDGER

Are Convolutional Neural Networks or Transformers more like human vision?

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2105.07197.

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

pith.paper-citation-record.v1
2105.07197 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T16:52:46.835836Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T19:08:49.764132Z

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 9bfef5fc-af28-49ac-89cf-d8e71111d986 · inbound

Accuracy Improvement of Cell Image Segmentation Using Feedback Former cites this paper.

Accuracy Improvement of Cell Image Segmentation Using Feedback Former Are Convolutional Neural Networks or Transformers more like human vision?

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-23T21:53:29.731549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1e7e8b1f-d9ee-4b71-977d-dc4f196d5665 · inbound

Explaining Object Detectors via Collective Contribution of Pixels cites this paper.

Explaining Object Detectors via Collective Contribution of Pixels Are Convolutional Neural Networks or Transformers more like human vision?

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-23T08:22:44.452127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dd5336d2-6729-4a40-a851-271aeb465d43 · inbound

Spectral-Adaptive Modulation Networks for Visual Perception cites this paper.

Spectral-Adaptive Modulation Networks for Visual Perception Are Convolutional Neural Networks or Transformers more like human vision?

Reference 20

Resolution: metadata mismatch; arxiv_id, observed 2026-05-22T22:35:11.634584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e8f01e71-959c-41dd-b787-3174463ef6a9 · inbound

Foundations and Models in Modern Computer Vision: Key Building Blocks in Landmark Architectures cites this paper.

Foundations and Models in Modern Computer Vision: Key Building Blocks in Landmark Architectures Are Convolutional Neural Networks or Transformers more like human vision?

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T10:51:14.728876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation befb1d77-a96b-4609-8847-2d67ff2e3cf4 · inbound

Zero-Shot Textual Explanations via Translating Decision-Critical Features cites this paper.

Zero-Shot Textual Explanations via Translating Decision-Critical Features Are Convolutional Neural Networks or Transformers more like human vision?

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-21T18:10:27.642894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5479a122-d03e-431d-9efa-44ccd8c5218b · inbound

Do Machines Fail Like Humans? A Human-Centred Out-of-Distribution Spectrum for Mapping Error Alignment cites this paper.

Do Machines Fail Like Humans? A Human-Centred Out-of-Distribution Spectrum for Mapping Error Alignment Are Convolutional Neural Networks or Transformers more like human vision?

Reference 35

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T15:16:09.619962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 907dd081-04e9-48bb-a669-8045fe3597ec · inbound

MAPS: A Synthetic Dataset for Probing Vision Models in a Controlled 3D Scene Space cites this paper.

MAPS: A Synthetic Dataset for Probing Vision Models in a Controlled 3D Scene Space Are Convolutional Neural Networks or Transformers more like human vision?

Reference 61

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T06:34:00.713432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c2e63b4f-fff2-4b40-91da-858d8ff98044 · inbound

Attention Alignment Between Humans and Vision-Language Models cites this paper.

Attention Alignment Between Humans and Vision-Language Models Are Convolutional Neural Networks or Transformers more like human vision?

Reference 23

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T19:08:49.766456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0ffae1c9-fa33-4644-b129-b3fa49331384 · inbound

IRIS: A Visual Cortex-Inspired Framework for Analyzing Orientation Selectivity in Vision Transformers cites this paper.

IRIS: A Visual Cortex-Inspired Framework for Analyzing Orientation Selectivity in Vision Transformers Are Convolutional Neural Networks or Transformers more like human vision?

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T04:50:37.276679Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9c7737c-bcd8-4321-b43f-458171097794 · inbound

IRIS: A Visual Cortex-Inspired Framework for Analyzing Orientation Selectivity in Vision Transformers cites this paper.

IRIS: A Visual Cortex-Inspired Framework for Analyzing Orientation Selectivity in Vision Transformers Are Convolutional Neural Networks or Transformers more like human vision?

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-08T16:52:46.835836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.