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Paper Citation Record · LEDGER

PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2111.12710.

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

pith.paper-citation-record.v1
2111.12710 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:28:54.644923Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T09:41:38.285744Z

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 fbd2095e-a186-432f-8f44-a6ab31cc6ba8 · inbound

Vision Transformers Need Registers cites this paper.

Vision Transformers Need Registers PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers

Reference 79

Resolution
verified exact
arxiv_id, observed 2026-05-13T09:41:38.287094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-05-13T09:41:37.937046Z digest=sha256:1aee15798e28f39ce562aa880d36ef3f5b8d2673ef2fc44ef00c9befd433a920

Observation 1827dfdb-0312-4da4-8d63-403c67c30525 · inbound

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation cites this paper.

Incorporating Feature Pyramid Tokenization and Open Vocabulary Semantic Segmentation PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-11T12:28:54.644923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T12:28:54.644923Z digest=sha256:8ae9120ccf039a1c1483b7a438e372eb21d9f5b3e12b6944cadda476462f514f

Observation b03dc2d5-f2f7-46f0-8c5a-8c3d5866db2f · inbound

From Pixels to Components: Eigenvector Masking for Visual Representation Learning cites this paper.

From Pixels to Components: Eigenvector Masking for Visual Representation Learning PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-08T15:53:51.468757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T15:53:51.468757Z digest=sha256:fdf01987706995a66b77d4f772ce232282bdbf20f586171f81e5f52b3282dff3

Observation 31a01e64-f2d5-46e7-9d61-b71d7bac0fe6 · inbound

Beginning with You: Perceptual-Initialization Improves Vision-Language Representation and Alignment cites this paper.

Beginning with You: Perceptual-Initialization Improves Vision-Language Representation and Alignment PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-07T15:42:31.400258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:42:31.400258Z digest=sha256:a3c86faaa12c1f6c618592ffd91fcee2e54e5d17470098d8746226832afb8e00

Observation 8c5100ec-fa56-4161-a5c2-63b2df905f8f · inbound

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking cites this paper.

MaskAdapt: Unsupervised Geometry-Aware Domain Adaptation Using Multimodal Contextual Learning and RGB-Depth Masking PeCo: Perceptual Codebook for BERT Pre-training of Vision Transformers

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T12:44:28.356359Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:44:28.356359Z digest=sha256:ebb3ee5e0ddbc4334cb057b9ffb9d797294bbd77068de1329832d3d9f882745d