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Paper Citation Record · LEDGER

Virtual-to-Real: Learning to Control in Visual Semantic Segmentation

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1802.00285.

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

pith.paper-citation-record.v1
1802.00285 v4

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 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T11:29:13.709430Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-14T11:29:14.103740Z

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 49f9e5f6-a510-44ba-87ac-1159424aa44e · inbound

Situational Fusion of Visual Representation for Visual Navigation cites this paper.

Situational Fusion of Visual Representation for Visual Navigation Virtual-to-Real: Learning to Control in Visual Semantic Segmentation

Reference 14

Resolution
verified exact
local_arxiv, observed 2026-08-14T11:29:14.108171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-14T11:29:13.709430Z digest=sha256:f14eb09e354257487fef73d777e99e0cfce02c8fa9c951d33df2d6b360eb8947

Observation e2d4069f-9db0-4e09-8e8e-74efd717fe51 · inbound

Enhancing Reinforcement Learning in 3D Environments through Semantic Segmentation: A Case Study in ViZDoom cites this paper.

Enhancing Reinforcement Learning in 3D Environments through Semantic Segmentation: A Case Study in ViZDoom Virtual-to-Real: Learning to Control in Visual Semantic Segmentation

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-03T22:43:28.156963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T22:43:28.156963Z digest=sha256:1f2d51042d9c2d1fb037152444398e4175baab68781b3807d83ecee92aaeaf20