Paper Citation Record · LEDGER

CoAtNet: Marrying Convolution and Attention for All Data Sizes

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2106.04803.

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

pith.paper-citation-record.v1
2106.04803 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T11:19:33.862479Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-05T11:41:02.799655Z

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 1e08dccc-3a69-42d3-b1e7-7942c5e5b7ba · inbound

MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer cites this paper.

MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer CoAtNet: Marrying Convolution and Attention for All Data Sizes

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-20T20:46:35.211861Z

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 bd6b2b45-d640-41e6-972f-b6f4603c7867 · inbound

Florence: A New Foundation Model for Computer Vision cites this paper.

Florence: A New Foundation Model for Computer Vision CoAtNet: Marrying Convolution and Attention for All Data Sizes

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-16T09:38:09.485888Z

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 07410519-75ec-4f48-99b6-0727dd33450b · inbound

DFCon: Attention-Driven Supervised Contrastive Learning for Robust Deepfake Detection cites this paper.

DFCon: Attention-Driven Supervised Contrastive Learning for Robust Deepfake Detection CoAtNet: Marrying Convolution and Attention for All Data Sizes

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T11:19:33.862479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c9c4fae-ac83-4d4e-8b8e-4d89912c42c4 · inbound

SIM-Net: A Multimodal Fusion Network Using Inferred 3D Object Shape Point Clouds from RGB Images for 2D Classification cites this paper.

SIM-Net: A Multimodal Fusion Network Using Inferred 3D Object Shape Point Clouds from RGB Images for 2D Classification CoAtNet: Marrying Convolution and Attention for All Data Sizes

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T23:21:45.974828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c15361b-e1a1-4a4e-bef6-28156de9a622 · inbound

ClawEnvKit: Automatic Environment Generation for Claw-Like Agents cites this paper.

ClawEnvKit: Automatic Environment Generation for Claw-Like Agents CoAtNet: Marrying Convolution and Attention for All Data Sizes

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-07-05T11:41:02.801223Z

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 30f27239-2882-49e5-bec0-8f7fd7a4ed2c · inbound

Advancing Vision Transformer with Enhanced Spatial Priors cites this paper.

Advancing Vision Transformer with Enhanced Spatial Priors CoAtNet: Marrying Convolution and Attention for All Data Sizes

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:23:37.664602Z

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.