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Paper Citation Record · LEDGER

Online Adaptation of Convolutional Neural Networks for Video Object Segmentation

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1706.09364.

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

pith.paper-citation-record.v1
1706.09364 v2

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 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:40:45.259576Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T13:56:25.456836Z

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 6b16e352-f583-439b-9b55-9cd8b1435b75 · inbound

In defense of OSVOS cites this paper.

In defense of OSVOS Online Adaptation of Convolutional Neural Networks for Video Object Segmentation

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-14T12:40:45.259576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T12:40:45.259576Z digest=sha256:d1b5d507c0e55415acd205cf3b96c8b733a40006836037f72d8a19e07147ad9a

Observation 9b0a69ba-9832-4e0e-a8cb-79187030c2e0 · inbound

SAM 2: Segment Anything in Images and Videos cites this paper.

SAM 2: Segment Anything in Images and Videos Online Adaptation of Convolutional Neural Networks for Video Object Segmentation

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-07-04T22:06:08.609829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-10T13:56:25.331304Z digest=sha256:04d446f463e819edff21a57043f1a5815d01c1ef87d5088cffe93ec6694502b1