Paper Citation Record · LEDGER

Prompting Diffusion Models for Zero-Shot Instance Segmentation

As of 6 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2606.22660.

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

pith.paper-citation-record.v1
2606.22660 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T10:43:15.465858Z

measured 41 of 41 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

verified exact7
verified fuzzy0
unresolved33
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malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 61a2b6b1-26ba-48c0-8e59-43aa36e62613 · outbound

This paper cites Segment anything,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Segment anything,

Reference 1

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Observation 7a0bec95-bfeb-4c58-a05f-1f886c73c3e8 · outbound

This paper cites gen2seg: Generative models enable generalizable instance segmentation,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation gen2seg: Generative models enable generalizable instance segmentation,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-26T10:43:15.465858Z

Source-reported events for the cited work

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Observation e5be4911-acd3-47e0-85f1-47d4b80de999 · outbound

This paper cites High-resolution image synthesis with latent diffusion models,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation High-resolution image synthesis with latent diffusion models,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-26T10:43:15.465858Z

Source-reported events for the cited work

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Observation 10465e86-5750-4795-8f72-52c77e182efd · outbound

This paper cites Repurposing diffusion-based image generators for monocular depth estimation,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Repurposing diffusion-based image generators for monocular depth estimation,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-26T10:43:15.465858Z

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Observation b1bf9586-789f-490b-b7a2-6c62a93af1f2 · outbound

This paper cites Geowizard: Unleashing the diffusion priors for 3d geometry estimation from a single image,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Geowizard: Unleashing the diffusion priors for 3d geometry estimation from a single image,

Reference 5

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Observation c43a3448-29bb-46ee-a76c-bef4538b41b6 · outbound

This paper cites Stablenormal: Reducing diffusion variance for stable and sharp normal,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Stablenormal: Reducing diffusion variance for stable and sharp normal,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-26T10:43:15.465858Z

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Observation 9bcebd68-28fc-48fb-ac84-0807b111b576 · outbound

This paper cites Diffpose: Toward more reliable 3d pose estimation,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Diffpose: Toward more reliable 3d pose estimation,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-26T10:43:15.465858Z

Source-reported events for the cited work

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Observation 25d070fa-158b-4f22-8126-c574bbdbfd78 · outbound

This paper cites Posediffusion: Solving pose estimation via diffusion- aided bundle adjustment,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Posediffusion: Solving pose estimation via diffusion- aided bundle adjustment,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-26T10:43:15.465858Z

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Observation 404d4c9e-2974-4498-814a-52c2ee833deb · outbound

This paper cites Flowdiffuser: Advancing optical flow estimation with diffusion models,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Flowdiffuser: Advancing optical flow estimation with diffusion models,

Reference 9

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Observation 9a13edfd-1395-4bfc-9ef3-7f64a467d600 · outbound

This paper cites LlamaSeg: Image Segmentation via Autoregressive Mask Generation.

Prompting Diffusion Models for Zero-Shot Instance Segmentation LlamaSeg: Image Segmentation via Autoregressive Mask Generation

Reference 10

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Observation 96502568-22b1-4f60-a0e6-3338ac2907c7 · outbound

This paper cites GS: Generative Segmentation via Label Diffusion.

Prompting Diffusion Models for Zero-Shot Instance Segmentation GS: Generative Segmentation via Label Diffusion

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:59:42.791681Z

Source-reported events for the cited work

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Observation 759774f1-0f87-4cb2-b955-871e7cd3f1f2 · outbound

This paper cites Generative pretraining from pixels,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Generative pretraining from pixels,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-26T10:43:15.465858Z

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Observation 7b2fa262-5786-4188-9214-54d977530cd4 · outbound

This paper cites Masked autoencoders are scalable vision learners,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Masked autoencoders are scalable vision learners,

Reference 13

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Observation 0206d51f-5858-4722-a03f-96c687c75772 · outbound

This paper cites Label-efficient semantic segmentation with diffusion models,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Label-efficient semantic segmentation with diffusion models,

Reference 14

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Source-reported events for the cited work

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Observation cd177c54-7795-4710-9cd2-2cf019e2ff4b · outbound

This paper cites Unleashing text-to-image diffusion models for visual perception,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Unleashing text-to-image diffusion models for visual perception,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-26T10:43:15.465858Z

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Observation 24bc286a-9ae3-47d4-8492-d5e9a2bfd75d · outbound

This paper cites Denoising diffusion probabilistic models,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Denoising diffusion probabilistic models,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-26T10:43:15.465858Z

Source-reported events for the cited work

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Observation 5f83245f-e155-4794-aa13-a375d7e89d13 · outbound

This paper cites Adding conditional control to text-to-image diffusion models,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Adding conditional control to text-to-image diffusion models,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-26T10:43:15.465858Z

Source-reported events for the cited work

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Observation ccc045af-550d-4f8d-8cee-72a8fbdc198a · outbound

This paper cites Exploring pre-trained text-to-video diffusion models for referring video object segmentation,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Exploring pre-trained text-to-video diffusion models for referring video object segmentation,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-26T10:43:15.465858Z

Source-reported events for the cited work

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Observation 09e37039-3a18-4bee-9a61-12398e73055f · outbound

This paper cites Dataset diffusion: Diffusion-based synthetic data generation for pixel-level semantic segmentation,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Dataset diffusion: Diffusion-based synthetic data generation for pixel-level semantic segmentation,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-26T10:43:15.465858Z

Source-reported events for the cited work

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Observation 7fab9878-d6bd-4a49-8379-089897642868 · outbound

This paper cites DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery.

Prompting Diffusion Models for Zero-Shot Instance Segmentation DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:59:42.788594Z

Source-reported events for the cited work

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Observation f7a90d7f-6818-4ac9-9f3b-7af05d00c132 · outbound

This paper cites Diffumask: Synthesizing images with pixel-level annotations for semantic segmentation using diffusion models,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Diffumask: Synthesizing images with pixel-level annotations for semantic segmentation using diffusion models,

Reference 21

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Source-reported events for the cited work

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Observation c906d2b0-c5de-4801-999d-efdcc877bc24 · outbound

This paper cites Exploring phrase-level grounding with text-to-image diffusion model,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Exploring phrase-level grounding with text-to-image diffusion model,

Reference 22

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Observation c4423563-0065-4987-b766-0b6eabad43c6 · outbound

This paper cites Vgdiffzero: Text-to-image diffusion models can be zero-shot visual grounders,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Vgdiffzero: Text-to-image diffusion models can be zero-shot visual grounders,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-26T10:43:15.465858Z

Source-reported events for the cited work

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

This paper cites Ref-Diff: Zero-shot Referring Image Segmentation with Generative Models.

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

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Observation 640a0414-edb2-4fa5-990c-278c02dff587 · outbound

This paper cites Simpleclick: Interactive image segmentation with simple vision transformers,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Simpleclick: Interactive image segmentation with simple vision transformers,

Reference 25

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Source-reported events for the cited work

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Observation a571ddfc-a4ee-4884-b5da-6d4f19f5b6f9 · outbound

This paper cites Sam 2: Segment anything in images and videos,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Sam 2: Segment anything in images and videos,

Reference 26

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Observation 2c752d28-97a8-47b6-9e99-1156c0a0069a · outbound

This paper cites Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-07-04T08:59:42.784974Z

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Observation 333d564e-3837-47ea-a626-d2ba968cdcf5 · outbound

This paper cites Sam 3: Segment anything with concepts,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Sam 3: Segment anything with concepts,

Reference 28

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Source-reported events for the cited work

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Observation 4deca140-6961-476f-adb2-29c16ea3e65c · outbound

This paper cites Image Generators are Generalist Vision Learners.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Image Generators are Generalist Vision Learners

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-07-04T08:59:42.785959Z

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Observation 38a4d654-a957-4677-8e73-a7b0097a342a · outbound

This paper cites Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-26T10:43:15.465858Z

Source-reported events for the cited work

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Observation 75ac25bb-81e3-46b0-8cff-b8dbabc210e8 · outbound

This paper cites Virtual KITTI 2.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Virtual KITTI 2

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-07-04T08:59:42.779780Z

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Observation 6734a409-4051-4291-b4d0-d93a8c584f62 · outbound

This paper cites Microsoft coco: Common objects in context,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Microsoft coco: Common objects in context,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-06-26T10:43:15.465858Z

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Observation 1d0acee3-3f5f-4f34-8799-2cd235e071a7 · outbound

This paper cites Semantic segmentation in art paintings,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Semantic segmentation in art paintings,

Reference 33

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Observation 299046b0-8922-457f-9020-a65fbbef4711 · outbound

This paper cites Fine-grained egocentric hand-object segmentation: Dataset, model, and applications,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Fine-grained egocentric hand-object segmentation: Dataset, model, and applications,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-06-26T10:43:15.465858Z

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Observation 1ee64b83-1410-42a0-9212-1dff5087b5f8 · outbound

This paper cites Pidray: A large-scale x-ray benchmark for real-world prohibited item detection,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Pidray: A large-scale x-ray benchmark for real-world prohibited item detection,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-26T10:43:15.465858Z

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Observation f3e55171-1d2d-41d6-acc9-c9acfaae6ce4 · outbound

This paper cites The pascal visual object classes (voc) challenge,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation The pascal visual object classes (voc) challenge,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-26T10:43:15.465858Z

Source-reported events for the cited work

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Observation 31ea6ba7-9299-43f2-ad36-ca7974cd314d · outbound

This paper cites Towards high-resolution salient object detection,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Towards high-resolution salient object detection,

Reference 37

Resolution: unresolved; no resolver link, observed 2026-06-26T10:43:15.465858Z

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Observation 3650190b-0c2c-4d31-b458-aed8b5b9046c · outbound

This paper cites Zerowaste dataset: Towards deformable object segmentation in cluttered scenes,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Zerowaste dataset: Towards deformable object segmentation in cluttered scenes,

Reference 38

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Observation ebfa6c59-c02d-49aa-b439-c4154d5cf993 · outbound

This paper cites Rba: Segmenting unknown regions rejected by all,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Rba: Segmenting unknown regions rejected by all,

Reference 39

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Observation 093d5ef8-9f15-41e2-bc40-45e3274c61d1 · outbound

This paper cites Masked-attention mask transformer for universal image segmentation,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Masked-attention mask transformer for universal image segmentation,

Reference 40

Resolution: unresolved; no resolver link, observed 2026-06-26T10:43:15.465858Z

Source-reported events for the cited work

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Observation ec019674-d422-446f-a7b4-a505eafc96e8 · outbound

This paper cites Detecting the unexpected via image resynthesis,.

Prompting Diffusion Models for Zero-Shot Instance Segmentation Detecting the unexpected via image resynthesis,

Reference 41

Resolution: malformed identifier; no resolver link, observed 2026-06-26T10:43:15.465858Z

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Pith citing papers

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