Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2306.16269.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T11:16:30.027074Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-01T14:25:46.694841Z
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 9dcbade4-619b-4277-b743-b2ad06515aec · inbound
SOPSeg: Prompt-based Small Object Instance Segmentation in Remote Sensing Imagery cites this paper.
SOPSeg: Prompt-based Small Object Instance Segmentation in Remote Sensing Imagery RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T11:16:30.027074Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2671bec4-cc09-4207-b723-6a202d83ad42 · inbound
HiSem: Hierarchical Semantic Disentangling for Remote Sensing Image Change Captioning cites this paper.
HiSem: Hierarchical Semantic Disentangling for Remote Sensing Image Change Captioning RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-07-01T14:25:46.696496Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 540887d5-0d06-43f9-9e6c-6cbdddfb8887 · inbound
GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation cites this paper.
GeoSAM-Lite: A Lightweight Foundation Model for Onboard Remote Sensing Segmentation RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T00:08:46.276767Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.