Paper Citation Record · LEDGER

Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2207.05501.

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

pith.paper-citation-record.v1
2207.05501 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:16:43.629784Z

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

138
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 46d3185b-4bdc-4a68-950e-132df9a8a925 · inbound

Faster Segment Anything: Towards Lightweight SAM for Mobile Applications cites this paper.

Faster Segment Anything: Towards Lightweight SAM for Mobile Applications Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-17T22:41:43.523599Z

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.

Observation 5fdaa095-ca5e-4259-a662-9a9aba734c93 · inbound

Parallel Sequence Modeling via Generalized Spatial Propagation Network cites this paper.

Parallel Sequence Modeling via Generalized Spatial Propagation Network Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-10T17:16:43.629784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f70342b7-0247-445e-a0e0-ef1df39084f1 · inbound

Illicit object detection in X-ray imaging using deep learning techniques: A comparative evaluation cites this paper.

Illicit object detection in X-ray imaging using deep learning techniques: A comparative evaluation Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T14:50:15.345499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd415c8f-6075-4e97-98a1-95b276c6817a · inbound

Insights from Visual Cognition: Understanding Human Action Dynamics with Overall Glance and Refined Gaze Transformer cites this paper.

Insights from Visual Cognition: Understanding Human Action Dynamics with Overall Glance and Refined Gaze Transformer Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:26:01.540334Z

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.

Observation 0294f178-87d3-4203-b401-6c003b59f8ad · inbound

ShellfishNet: A Domain-Specific Benchmark for Visual Recognition of Marine Molluscs cites this paper.

ShellfishNet: A Domain-Specific Benchmark for Visual Recognition of Marine Molluscs Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:40:58.274219Z

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.

Observation eb90327f-2c86-40d9-ba6a-d13a9de3c97d · inbound

TextTeacher: What Can Language Teach About Images? cites this paper.

TextTeacher: What Can Language Teach About Images? Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:26:12.680735Z

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.

Observation 2c0f9cbf-c362-4b18-a234-caeb4dc91e84 · inbound

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders cites this paper.

Scaling Parallel Sequence Models to Foundation-Scale Vision Encoders Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios

Reference 54

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T19:32:35.294812Z

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.