Paper Citation Record · LEDGER

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention

As of 5 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2607.18112.

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

pith.paper-citation-record.v1
2607.18112 v2

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T16:07:27.138506Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+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

31 of 31 outbound references displayed

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External citation measurements

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Outbound references

Observation 494e2fa9-915c-479e-bcfe-673ffe34be2c · outbound

This paper cites Panoptic segmentation,.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Panoptic segmentation,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.004635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ce67f92-6818-4fcf-be89-4a52d02b27bd · outbound

This paper cites Panoptic feature pyramid networks,.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Panoptic feature pyramid networks,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.009878Z

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Observation f1cbd0a3-4c51-4372-803a-25320bd7b7f2 · outbound

This paper cites Panoptic-deeplab: A simple, strong, and fast baseline for bottom-up panoptic segmentation,.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Panoptic-deeplab: A simple, strong, and fast baseline for bottom-up panoptic segmentation,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.014858Z

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Observation 6ec49d82-bca1-4a5b-9737-0521dbb677d8 · outbound

This paper cites Fully convolutional networks for panoptic segmentation,.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Fully convolutional networks for panoptic segmentation,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.019630Z

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Observation ac798c56-bd50-46bc-aead-e9e79a4f59e1 · outbound

This paper cites Per-pixel classification is not all you need for semantic segmentation,.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Per-pixel classification is not all you need for semantic segmentation,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.024405Z

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Observation 504f22f8-9a1c-456e-8275-c10a6b873c90 · outbound

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

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Masked-attention mask transformer for universal image segmentation,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.029790Z

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Observation 752c9ff3-48e7-49ef-a035-41e3975fc3de · outbound

This paper cites Mask dino: Towards a unified transformer- based framework for object detection and segmentation,.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Mask dino: Towards a unified transformer- based framework for object detection and segmentation,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.034993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55bacfda-038f-4090-bb6c-e8167761aa0f · outbound

This paper cites You only segment once: Towards real-time panoptic segmentation,.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention You only segment once: Towards real-time panoptic segmentation,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.039423Z

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Unavailable: canonical work link unavailable.

Observation 8c405986-1d01-4cb6-a70f-e39a5aa9f077 · outbound

This paper cites Maskconver: Revisiting pure convolution model for panoptic segmentation,.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Maskconver: Revisiting pure convolution model for panoptic segmentation,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.043855Z

Source-reported events for the cited work

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Observation 682b9237-f2e1-481f-9e4c-24d290ff72fb · outbound

This paper cites an unresolved cited work.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Unresolved cited work

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.048143Z

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Observation c138b326-bf70-47e4-b367-bcb1f264fbfa · outbound

This paper cites Coco-olac: A benchmark for occluded panoptic segmentation and image understanding,.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Coco-olac: A benchmark for occluded panoptic segmentation and image understanding,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.052390Z

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Observation cf8c963d-7ed9-4e2d-ba4c-9f569645a016 · outbound

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

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Microsoft coco: Common objects in context,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.056799Z

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Observation c531e20b-ef45-4740-b2bd-f11e1fce68a2 · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Imagenet: A large-scale hierarchical image database,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.060965Z

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Observation debcc9df-87e9-4671-b91c-4b402206c5d7 · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding,.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention The cityscapes dataset for semantic urban scene understanding,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.065119Z

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Observation 8c9e7507-7c1d-42b8-be80-dae6877f1530 · outbound

This paper cites Amodal instance segmentation with kins dataset,.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Amodal instance segmentation with kins dataset,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.069537Z

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Observation 44ce7c52-605d-4c10-9145-6cc002366266 · outbound

This paper cites Semantic amodal segmentation,.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Semantic amodal segmentation,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.073601Z

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Observation 75eea44e-a0cc-4121-998d-4442495487cc · outbound

This paper cites MOT16: A Benchmark for Multi-Object Tracking.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention MOT16: A Benchmark for Multi-Object Tracking

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.077596Z

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Observation 4e760896-06b4-4f44-9d47-e57b8b278b75 · outbound

This paper cites Attention is all you need,.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Attention is all you need,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.082262Z

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Observation feaacbdc-f3ff-48e6-985b-1b8a28cf4101 · outbound

This paper cites Self-Attention with Relative Position Representations.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Self-Attention with Relative Position Representations

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.086550Z

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Observation dfe06377-c289-4bc6-8f0c-b43f7f931509 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.091288Z

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Observation f16b9378-4b45-4cf9-b90d-da1e438f00ca · outbound

This paper cites End-to-end object detection with transformers,.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention End-to-end object detection with transformers,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.095706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1614a84-a8e5-41fb-a921-1a0a6688bb46 · outbound

This paper cites DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.100322Z

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Unavailable: canonical work link unavailable.

Observation 9af2f7bd-7e6c-4c63-a2ca-3abe1d69edfb · outbound

This paper cites Deep occlusion-aware instance segmentation with overlapping bilayers,.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Deep occlusion-aware instance segmentation with overlapping bilayers,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.104854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4ccff36-fcea-423b-888c-1a31cad27951 · outbound

This paper cites Compositional convolutional neural networks: A robust and interpretable model for object recognition under occlusion,.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Compositional convolutional neural networks: A robust and interpretable model for object recognition under occlusion,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.109118Z

Source-reported events for the cited work

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Observation 54aa2d87-b448-43db-85cd-38644b13aa74 · outbound

This paper cites Occluded video instance segmentation: A benchmark,.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Occluded video instance segmentation: A benchmark,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.113315Z

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Unavailable: canonical work link unavailable.

Observation c2cd3287-4344-4bb9-b11a-9b111710f89f · outbound

This paper cites Mose: A new dataset for video object segmentation in complex scenes,.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Mose: A new dataset for video object segmentation in complex scenes,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.117375Z

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Observation 058e396d-566a-4603-9147-4f05c8400f51 · outbound

This paper cites Mosev2: A more challenging dataset for video object segmentation in complex scenes,.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Mosev2: A more challenging dataset for video object segmentation in complex scenes,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.121598Z

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Observation 569c2f4b-86a5-4671-a841-e907b295991d · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.125750Z

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Observation 650a3a55-8393-4e57-a776-6d1aee0966b3 · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding,.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Roformer: Enhanced transformer with rotary position embedding,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.129961Z

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Observation ace1dd1b-47b9-4090-9163-92b0e840783a · outbound

This paper cites Deep residual learning for image recognition,.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Deep residual learning for image recognition,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.134385Z

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Observation 1d256145-254f-4576-a273-ee7bee1347d7 · outbound

This paper cites Grad-cam: Visual explanations from deep networks via gradient-based localization,.

Occlusion-Aware Panoptic Segmentation with Joint Position Embedding and Occlusion-Level Attention Grad-cam: Visual explanations from deep networks via gradient-based localization,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-01T16:07:27.138506Z

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Pith citing papers

No inbound Pith citation observations are available.