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Paper Citation Record · LEDGER

Segment Any Motion in Videos

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2503.22268.

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

pith.paper-citation-record.v1
2503.22268 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 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T00:08:23.125069Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T16:41:08.689917Z

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 8a4d1647-1cab-484b-b7c7-49b81f032a82 · inbound

ViPE: Video Pose Engine for 3D Geometric Perception cites this paper.

ViPE: Video Pose Engine for 3D Geometric Perception Segment Any Motion in Videos

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-05-16T16:41:08.691831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-16T16:41:08.620285Z digest=sha256:8adc6481360a7443645ac156d0b094e750828d7ab42568a8ce13b4146a6c42d8

Observation 3da10449-a9ad-4ec6-ac8b-2d0236ce4151 · inbound

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision cites this paper.

STAR-VLM: Spatiotemporal Grounding Vision-Language Models for Motion and Velocity Estimation via Automotive Radar Supervision Segment Any Motion in Videos

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-06T00:08:23.125069Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T00:08:23.125069Z digest=sha256:b71a0f45312bc6bef2ede73e2766addef3296a10d008d1a50f68069a0d2a65da