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Paper Citation Record · LEDGER

A Deep Learning based No-reference Quality Assessment Model for UGC Videos

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2204.14047.

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

pith.paper-citation-record.v1
2204.14047 v2

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 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:52:56.789780Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T16:35:48.069025Z

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 898ab87a-4d87-4936-b1b1-b9515fc93393 · inbound

Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels cites this paper.

Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels A Deep Learning based No-reference Quality Assessment Model for UGC Videos

Reference 205

Resolution
verified exact
arxiv_id, observed 2026-05-15T16:35:48.073092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-05-15T16:35:47.826165Z digest=sha256:d6d9ccd04b408fc2d6203b82c495efef1f7475811839ec1de3ec138d9ebc8279

Observation 73054bf1-506a-4c4d-b789-aa5bfdd6f7f2 · inbound

Video Quality Assessment: A Comprehensive Survey cites this paper.

Video Quality Assessment: A Comprehensive Survey A Deep Learning based No-reference Quality Assessment Model for UGC Videos

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-11T22:52:56.789780Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T22:52:56.789780Z digest=sha256:87e8950b0043ae17f5a4b3ebf5e03da5ea3d39af31e04aa5920ef418259724e2