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Paper Citation Record · LEDGER

Underwater Camouflaged Object Tracking Meets Vision-Language SAM2

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2409.16902.

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

pith.paper-citation-record.v1
2409.16902 v5

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 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T10:14:04.075277Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T09:00:58.766106Z

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 c9439111-3308-4051-ad60-7ea4b0c8d0a5 · inbound

UIS-Mamba: Exploring Mamba for Underwater Instance Segmentation via Dynamic Tree Scan and Hidden State Weaken cites this paper.

UIS-Mamba: Exploring Mamba for Underwater Instance Segmentation via Dynamic Tree Scan and Hidden State Weaken Underwater Camouflaged Object Tracking Meets Vision-Language SAM2

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-06T10:14:04.075277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T10:14:04.075277Z digest=sha256:c0183cc9ca3af854c829daa85dbc4614a482209e5960e67ddc0e28be2ed358b8

Observation 9c529518-e553-460c-8a8f-e7852100e0ea · inbound

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation cites this paper.

Bootstrapping Video Semantic Segmentation Model via Distillation-assisted Test-Time Adaptation Underwater Camouflaged Object Tracking Meets Vision-Language SAM2

Reference 67

Resolution
verified exact
arxiv_id, observed 2026-05-11T09:00:58.768562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-10T16:22:03.651594Z digest=sha256:8dc01bccbaf7218fd9ca36f530a58f09ea6cb9bda10944b7c961c17ca0deeab9