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Paper Citation Record · LEDGER

SeqFormer: Sequential Transformer for Video Instance Segmentation

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2112.08275.

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

pith.paper-citation-record.v1
2112.08275 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 2026-08-06T06:34:29.942622+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T10:45:35.568212Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T03:09:30.485482Z

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 47e37d58-058a-475f-941f-223a3dbee789 · inbound

SA-VIS: Sparse frame Annotations for training Video Instance Segmentation cites this paper.

SA-VIS: Sparse frame Annotations for training Video Instance Segmentation SeqFormer: Sequential Transformer for Video Instance Segmentation

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-07-04T03:09:30.487391Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-06-26T18:19:56.997875Z digest=sha256:c2b2528a5ae9b479210920ba1610bf4edee3f381b94f49c3233e79ea33bded04

Observation 552b16d1-2162-484e-813e-ce3396911b9f · inbound

SA-VIS: Sparse frame Annotations for training Video Instance Segmentation cites this paper.

SA-VIS: Sparse frame Annotations for training Video Instance Segmentation SeqFormer: Sequential Transformer for Video Instance Segmentation

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-06-30T10:54:37.004244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-06-30T10:45:35.568212Z digest=sha256:bad45ef298a4d3746d26d6bc64d4951bd8c6d956d5abb96b04582712ea0ed663