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Paper Citation Record · LEDGER

R-FCN: Object Detection via Region-based Fully Convolutional Networks

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1605.06409.

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

pith.paper-citation-record.v1
1605.06409 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T23:21:43.055970Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T00:28:45.716913Z

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 76b88e54-a647-4971-a863-5665e15810dc · inbound

Rethinking Atrous Convolution for Semantic Image Segmentation cites this paper.

Rethinking Atrous Convolution for Semantic Image Segmentation R-FCN: Object Detection via Region-based Fully Convolutional Networks

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-12T00:28:45.721530Z

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.

source=pdf_text observed=2026-05-12T00:28:45.260361Z digest=sha256:9b39d0988480bfd272af507fb2f674ac859b096ffee5850bba2623c874d2017b

Observation 26bdf4cf-55d7-4124-b08b-581d8339d7b5 · inbound

An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras cites this paper.

An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras R-FCN: Object Detection via Region-based Fully Convolutional Networks

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-08T22:21:36.310475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T22:21:36.310475Z digest=sha256:8f52e14d00dd0ff76c5e6664f8b08fc2d0c388defe434dfab6564312ba87baf6

Observation 7f7e8c27-0669-4d86-988a-ab5fdea675e2 · inbound

Set Visualizations for Comparing and Evaluating Machine Learning Models cites this paper.

Set Visualizations for Comparing and Evaluating Machine Learning Models R-FCN: Object Detection via Region-based Fully Convolutional Networks

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-09T23:21:43.055970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T23:21:43.055970Z digest=sha256:802eabd7ac9e24512dce195a60167999ad3662939e47e772929113173231cd46