Paper Citation Record · LEDGER

FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2310.15160.

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

pith.paper-citation-record.v1
2310.15160 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T18:11:54.565504Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

7
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation cb24d4eb-5337-48b7-97d5-be80ac94b343 · inbound

How Vision-Language Tasks Benefit from Large Pre-trained Models: A Survey cites this paper.

How Vision-Language Tasks Benefit from Large Pre-trained Models: A Survey FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-11T18:11:54.565504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31cb0c36-758b-48ad-96ec-a9cd1dcceffa · inbound

Stylistic Attribute Control in Latent Diffusion Models cites this paper.

Stylistic Attribute Control in Latent Diffusion Models FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-08T18:44:00.058951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fcd3832d-49fa-4301-bed1-70c7395e087c · inbound

Leveraging Image Generators to Address Data Scarcity: The Gen4Regen Dataset for Forest Regeneration Mapping cites this paper.

Leveraging Image Generators to Address Data Scarcity: The Gen4Regen Dataset for Forest Regeneration Mapping FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-05-08T21:09:12.926400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 42c0edee-6a46-4ba9-b64a-27bd2a64f6f2 · inbound

Leveraging Image Generators to Address Data Scarcity: The Gen4Regen Dataset for Forest Regeneration Mapping cites this paper.

Leveraging Image Generators to Address Data Scarcity: The Gen4Regen Dataset for Forest Regeneration Mapping FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models

Reference 1309

Resolution: unresolved; no resolver link, observed 2026-08-03T02:26:10.627589Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.