Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
One-Shot Instance Segmentation
As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1811.11507.
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 2026-08-03T06:30:56.289259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-30T10:38:38.373058Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-04T10:19:48.012372Z
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 2d9ab461-d1db-4e03-adbe-c457f959bea1 · inbound
From Pixels to Concepts: Do Segmentation Models Understand What They Segment? cites this paper.
From Pixels to Concepts: Do Segmentation Models Understand What They Segment? One-Shot Instance Segmentation
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T03:16:19.445479Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 5da89504-692e-4d38-826b-916c3c56040f · inbound
Boundary-by-Mask: Few-Shot Instance Segmentation with Mask-Conditioned Boundary Learning for Texture-Poor Industrial Parts cites this paper.
Boundary-by-Mask: Few-Shot Instance Segmentation with Mask-Conditioned Boundary Learning for Texture-Poor Industrial Parts One-Shot Instance Segmentation
Reference 11
- Resolution
- verified exact
- local_arxiv, observed 2026-07-04T06:09:36.729235Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 8c2737d5-fceb-4e41-bd82-8860e83b1f6a · inbound
Rethinking Prototype-based Similarity Learning for Few-Shot Object Detection cites this paper.
Rethinking Prototype-based Similarity Learning for Few-Shot Object Detection One-Shot Instance Segmentation
Reference 26
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-04T10:19:48.013517Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation d33de28d-6df5-4e96-a5a5-3939d39718f0 · inbound
Rethinking Prototype-based Similarity Learning for Few-Shot Object Detection cites this paper.
Rethinking Prototype-based Similarity Learning for Few-Shot Object Detection One-Shot Instance Segmentation
Reference 26
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-06-30T10:44:36.594886Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.