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Paper Citation Record · LEDGER

GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2210.02025.

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

pith.paper-citation-record.v1
2210.02025 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T10:55:15.367338Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T17:08:40.566960Z

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 d1f235c3-82ad-433f-b1ac-aaa1c23803fa · inbound

Segmentation of arbitrary features in very high resolution remote sensing imagery cites this paper.

Segmentation of arbitrary features in very high resolution remote sensing imagery GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-11T10:55:15.367338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T10:55:15.367338Z digest=sha256:12213d70c85eae6a6f633e2a581af6c23b58b91c67f32f0f14b9f36cb9d51b58

Observation 7dcd9fac-9fcc-4249-83df-ea822c36b122 · inbound

A Novel Scene Coupling Semantic Mask Network for Remote Sensing Image Segmentation cites this paper.

A Novel Scene Coupling Semantic Mask Network for Remote Sensing Image Segmentation GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models

Reference 53

Resolution
verified exact
local_arxiv, observed 2026-08-10T17:08:40.616265Z

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=pdf_text observed=2026-08-10T17:08:39.215468Z digest=sha256:a63b6956145e39bf83d342de0b43a60f832617fc385e6f8a6c8f6a81e0834946