Paper Citation Record · LEDGER

ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2111.10265.

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

pith.paper-citation-record.v1
2111.10265 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:31:06.382571Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T10:48:02.763503Z

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 e3a77be0-34d3-4400-893f-ddb5acf4f3c4 · inbound

gen2seg: Generative Models Enable Generalizable Instance Segmentation cites this paper.

gen2seg: Generative Models Enable Generalizable Instance Segmentation ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-22T14:31:40.552717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8851ddb0-7794-4e88-be4e-17a33d7171e9 · inbound

Compositional Scene Understanding through Inverse Generative Modeling cites this paper.

Compositional Scene Understanding through Inverse Generative Modeling ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T13:31:06.382571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4eb13ec6-4d43-4f1a-ab34-36d8862a2452 · inbound

A Large-Scale Referring Remote Sensing Image Segmentation Dataset and Benchmark cites this paper.

A Large-Scale Referring Remote Sensing Image Segmentation Dataset and Benchmark ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T11:01:49.453426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ed84bdc-d3c5-4fc7-b7af-0c92c54f69ce · inbound

CObL: Toward Zero-Shot Ordinal Layering without User Prompting cites this paper.

CObL: Toward Zero-Shot Ordinal Layering without User Prompting ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T21:35:36.623403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1bf4cf7-f9b0-4423-a81f-43ac58a34529 · inbound

Formalizing the Binding Problem cites this paper.

Formalizing the Binding Problem ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation

Reference 104

Resolution: verified exact; arxiv_id, observed 2026-07-02T03:06:29.839233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d420bf98-f25d-470e-ba9d-bd4bde004430 · inbound

Dual-State Slot Attention: Decoupling Appearance and Identity for Video Object-Centric Learning cites this paper.

Dual-State Slot Attention: Decoupling Appearance and Identity for Video Object-Centric Learning ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-07-03T10:48:02.765579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.