Paper Citation Record · LEDGER

Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2308.16777.

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

pith.paper-citation-record.v1
2308.16777 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:56:46.733272Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T08:59:42.789854Z

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 35960a34-d1ac-4f4d-b841-7608be9e601e · inbound

Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review cites this paper.

Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Review Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models

Reference 107

Resolution: unresolved; no resolver link, observed 2026-08-15T22:56:46.733272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1139e24-fe72-445a-bf4f-f6b572461597 · inbound

GS: Generative Segmentation via Label Diffusion cites this paper.

GS: Generative Segmentation via Label Diffusion Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T16:54:06.949560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17ab4453-da85-4878-8c7a-1465cec38302 · inbound

SAM 3: Segment Anything with Concepts cites this paper.

SAM 3: Segment Anything with Concepts Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models

Reference 99

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:25:11.540542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation d091e3d8-33b3-4cc5-9775-fca2bbe34f15 · inbound

Tarot-SAM3: Training-free SAM3 for Any Referring Expression Segmentation cites this paper.

Tarot-SAM3: Training-free SAM3 for Any Referring Expression Segmentation Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models

Reference 31

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T05:21:00.958329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 10ca0460-9979-4150-b400-54897f6309df · inbound

Early Semantic Grounding in Image Editing Models for Zero-Shot Referring Image Segmentation cites this paper.

Early Semantic Grounding in Image Editing Models for Zero-Shot Referring Image Segmentation Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-14T19:47:53.617817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation dfbec891-315d-4bb3-8d94-2acc756892c3 · inbound

Vision Harnessing Agent for Open Ad-hoc Segmentation cites this paper.

Vision Harnessing Agent for Open Ad-hoc Segmentation Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-20T05:53:04.413134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 68d46feb-1245-4e7c-be43-d0b50e2cfe0b · inbound

Learning to Label: A Reinforced Self-Evolving Framework for Semi-supervised Referring Expression Segmentation cites this paper.

Learning to Label: A Reinforced Self-Evolving Framework for Semi-supervised Referring Expression Segmentation Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-29T12:53:26.687250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b8d22a44-af14-45ed-aec1-83d198025455 · inbound

Prompting Diffusion Models for Zero-Shot Instance Segmentation cites this paper.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:59:42.791441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.