Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
SEAGULL: No-reference Image Quality Assessment for Regions of Interest via Vision-Language Instruction Tuning
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2411.10161.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:09:42.405928Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-16T19:01:11.706698Z
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 4a662c05-09dd-41e8-b956-9eef91aacce1 · inbound
NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment cites this paper.
NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment SEAGULL: No-reference Image Quality Assessment for Regions of Interest via Vision-Language Instruction Tuning
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:09:42.405928Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f250c247-cb08-44dc-a9b6-eeedb1f6ef44 · inbound
Q-Ponder: A Unified Training Pipeline for Reasoning-based Visual Quality Assessment cites this paper.
Q-Ponder: A Unified Training Pipeline for Reasoning-based Visual Quality Assessment SEAGULL: No-reference Image Quality Assessment for Regions of Interest via Vision-Language Instruction Tuning
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:22:43.430265Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c8c8b3be-d2cc-4ddd-bc6c-a285d744d9cd · inbound
FinPercep-RM: A Fine-grained Reward Model and Co-evolutionary Curriculum for RL-based Real-world Super-Resolution cites this paper.
FinPercep-RM: A Fine-grained Reward Model and Co-evolutionary Curriculum for RL-based Real-world Super-Resolution SEAGULL: No-reference Image Quality Assessment for Regions of Interest via Vision-Language Instruction Tuning
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T19:01:11.709399Z
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.