Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Rethinking RGB-D Fusion for Semantic Segmentation in Surgical Datasets
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2407.19714.
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 2026-08-06T06:34:29.942622+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T07:53:48.550316Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T22:45:50.873798Z
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 9b2b4dfc-18a2-46b3-9ddc-8988d095d6ef · inbound
SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge cites this paper.
SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge Rethinking RGB-D Fusion for Semantic Segmentation in Surgical Datasets
Reference 47
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T22:45:50.877561Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.
Observation 48a0b7ea-bd98-4437-8653-7e09e0543939 · inbound
On the Role of Depth in Surgical Vision Foundation Models: An Empirical Study of RGB-D Pre-training cites this paper.
On the Role of Depth in Surgical Vision Foundation Models: An Empirical Study of RGB-D Pre-training Rethinking RGB-D Fusion for Semantic Segmentation in Surgical Datasets
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T07:53:48.550316Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.