Paper Citation Record · LEDGER

Learning to Generate Multiple Objects from Dense and Occluded Layouts

As of 10 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2607.03488.

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

pith.paper-citation-record.v1
2607.03488 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-12T02:09:14.783011Z

measured 27 of 27 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 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

27 of 27 outbound references displayed

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unresolved27
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ad8d662a-ab82-4948-86cb-b979f6a9e1a8 · outbound

This paper cites Seethrough3d: Occlusion aware 3d control in text-to-image generation.

Learning to Generate Multiple Objects from Dense and Occluded Layouts Seethrough3d: Occlusion aware 3d control in text-to-image generation

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c1b1e73-3785-4846-af01-11dbe46dfd98 · outbound

This paper cites Improving image generation with better captions.

Learning to Generate Multiple Objects from Dense and Occluded Layouts Improving image generation with better captions

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Observation 609b6dc2-fa71-458b-8de3-600d9bae1806 · outbound

This paper cites Make it count: Text-to-image generation with an accurate number of objects.

Learning to Generate Multiple Objects from Dense and Occluded Layouts Make it count: Text-to-image generation with an accurate number of objects

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Observation 546bdf69-a1d4-43c7-90cf-8492b2781205 · outbound

This paper cites Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer.

Learning to Generate Multiple Objects from Dense and Occluded Layouts Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Observation b3a504ab-495b-4838-a2ed-dbe46c2affe8 · outbound

This paper cites SAM 3: Segment Anything with Concepts.

Learning to Generate Multiple Objects from Dense and Occluded Layouts SAM 3: Segment Anything with Concepts

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Observation 1ad75d48-e6cb-4eeb-a44f-01b76f5a3e8a · outbound

This paper cites Attend-and-excite: Attention-based semantic guidance for text-to-image diffusion models.ACM Trans.

Learning to Generate Multiple Objects from Dense and Occluded Layouts Attend-and-excite: Attention-based semantic guidance for text-to-image diffusion models.ACM Trans

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Observation 2b72de42-dbe0-4d19-afca-bbe551e4b2cf · outbound

This paper cites Training-free layout control with cross-attention guidance.

Learning to Generate Multiple Objects from Dense and Occluded Layouts Training-free layout control with cross-attention guidance

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

Source-reported events for the cited work

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Observation bac52182-86d1-4124-a3d4-19a7fbdc2304 · outbound

This paper cites Reason out Your Layout: Evoking the Layout Master from Large Language Models for Text-to-Image Synthesis.

Learning to Generate Multiple Objects from Dense and Occluded Layouts Reason out Your Layout: Evoking the Layout Master from Large Language Models for Text-to-Image Synthesis

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Observation 1e2c5967-567b-4291-b48c-eb80a79963db · outbound

This paper cites Prompt-to-Prompt Image Editing with Cross Attention Control.

Learning to Generate Multiple Objects from Dense and Occluded Layouts Prompt-to-Prompt Image Editing with Cross Attention Control

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Observation 2891cf76-d7f7-49dc-b61c-588227f78569 · outbound

This paper cites T2i-compbench: A compre- hensive benchmark for open-world compositional text-to-image generation.Advances in Neural Information Processing Systems, 36:78723–78747, 2023.

Learning to Generate Multiple Objects from Dense and Occluded Layouts T2i-compbench: A compre- hensive benchmark for open-world compositional text-to-image generation.Advances in Neural Information Processing Systems, 36:78723–78747, 2023

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Observation f30a5ed3-2d5d-44e2-96ad-173f6abda7f6 · outbound

This paper cites Counting guidance for high fidelity text-to-image synthesis.

Learning to Generate Multiple Objects from Dense and Occluded Layouts Counting guidance for high fidelity text-to-image synthesis

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Observation ee752f6c-2d7f-4828-8124-88fb8d5e856d · outbound

This paper cites Counting Guidance for High Fidelity Text-to-Image Synthesis.

Learning to Generate Multiple Objects from Dense and Occluded Layouts Counting Guidance for High Fidelity Text-to-Image Synthesis

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Observation 8e3aea05-25e2-4707-bea1-0525d029be58 · outbound

This paper cites Omg: Occlusion-friendly personalized multi-concept generation in diffusion models.

Learning to Generate Multiple Objects from Dense and Occluded Layouts Omg: Occlusion-friendly personalized multi-concept generation in diffusion models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Observation 5d43416f-8a12-4c91-aac8-8a17abad44e5 · outbound

This paper cites Visual genome: Connecting language and vision using crowdsourced dense image annotations.International Journal of Computer Vision, 123(1):32–73, 2017.

Learning to Generate Multiple Objects from Dense and Occluded Layouts Visual genome: Connecting language and vision using crowdsourced dense image annotations.International Journal of Computer Vision, 123(1):32–73, 2017

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Observation f73cb970-9b98-4996-ae31-80138e8bc470 · outbound

This paper cites Flux.https://github.com/black-forest-labs/flux, 2024.

Learning to Generate Multiple Objects from Dense and Occluded Layouts Flux.https://github.com/black-forest-labs/flux, 2024

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Observation 69c3e2cb-3e67-411e-aeca-6af2e6782f90 · outbound

This paper cites Armand, Divyansh Srivastava, Xiaojun Shan, Zeyuan Chen, Jianwen Xie, and Zhuowen Tu.

Learning to Generate Multiple Objects from Dense and Occluded Layouts Armand, Divyansh Srivastava, Xiaojun Shan, Zeyuan Chen, Jianwen Xie, and Zhuowen Tu

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Observation 1c02271e-0704-453b-9280-97ed2c0d62d9 · outbound

This paper cites GLIGEN: Open-set grounded text-to-image generation.

Learning to Generate Multiple Objects from Dense and Occluded Layouts GLIGEN: Open-set grounded text-to-image generation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Observation 0a561043-dbf4-4c0e-82b9-b709d647e2b8 · outbound

This paper cites Microsoft COCO: Common objects in context.

Learning to Generate Multiple Objects from Dense and Occluded Layouts Microsoft COCO: Common objects in context

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Observation 1c496f91-fb53-45fa-a68c-32640b822c81 · outbound

This paper cites an unresolved cited work.

Learning to Generate Multiple Objects from Dense and Occluded Layouts Unresolved cited work

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Observation fb917b53-28ad-42c0-95a9-177487774d36 · outbound

This paper cites T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models.

Learning to Generate Multiple Objects from Dense and Occluded Layouts T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Observation 68610aff-99af-4919-99a0-790253431a02 · outbound

This paper cites Grounded text-to-image synthesis with attention refocusing.2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7932–7942, 2024.

Learning to Generate Multiple Objects from Dense and Occluded Layouts Grounded text-to-image synthesis with attention refocusing.2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7932–7942, 2024

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Observation c8be5934-5eb3-420a-b95c-8687020a0404 · outbound

This paper cites SDXL: Improving latent diffusion models for high-resolution image synthesis.

Learning to Generate Multiple Objects from Dense and Occluded Layouts SDXL: Improving latent diffusion models for high-resolution image synthesis

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Observation c910f002-b4a6-4931-8618-4922b9f6f2fe · outbound

This paper cites In- stancediffusion: Instance-level control for image generation.2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 6232–6242, 2024.

Learning to Generate Multiple Objects from Dense and Occluded Layouts In- stancediffusion: Instance-level control for image generation.2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 6232–6242, 2024

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Observation f0f2bd11-c132-492b-b0ab-4665d3e5c6ea · outbound

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

Learning to Generate Multiple Objects from Dense and Occluded Layouts Adding conditional control to text-to-image diffusion models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Observation c449535a-7ada-459c-a6f9-eae17f653397 · outbound

This paper cites Lay- outdiffusion: Controllable diffusion model for layout-to-image generation.

Learning to Generate Multiple Objects from Dense and Occluded Layouts Lay- outdiffusion: Controllable diffusion model for layout-to-image generation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Observation 80495e0e-ac8d-4d1a-adfe-6b280c9b5856 · outbound

This paper cites Migc: Multi-instance generation controller for text-to-image synthesis.

Learning to Generate Multiple Objects from Dense and Occluded Layouts Migc: Multi-instance generation controller for text-to-image synthesis

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Observation 2be1af61-4229-48f0-a74c-0e077f803af8 · outbound

This paper cites 3DIS: Depth-driven decoupled image synthesis for universal multi-instance generation.

Learning to Generate Multiple Objects from Dense and Occluded Layouts 3DIS: Depth-driven decoupled image synthesis for universal multi-instance generation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-12T02:09:14.783011Z

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Pith citing papers

No inbound Pith citation observations are available.