Paper Citation Record · LEDGER

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation

As of 20 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2608.11681.

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

pith.paper-citation-record.v1
2608.11681 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:37:12.267555Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

verified exact1
verified fuzzy8
unresolved9
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e425046c-2cce-46fc-b70d-8ebdbc62b595 · outbound

This paper cites Neurocomputing 665, 132229.

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation Neurocomputing 665, 132229

Reference 2

Resolution: metadata mismatch; raw_fallback, observed 2026-08-16T00:37:15.132049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 17f19e31-ea58-4c82-81bd-edcb2eef2830 · outbound

This paper cites (Eds.), Computer Vision – ECCV 2022, Springer Nature Switzerland, Cham.

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation (Eds.), Computer Vision – ECCV 2022, Springer Nature Switzerland, Cham

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:37:15.829888Z

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Observation e88829f7-fb35-4b96-8827-5d1cdacd651f · outbound

This paper cites 7010–7021.

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation 7010–7021

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:37:15.787697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bd100496-35e9-4c17-a0c7-bae2b51affd3 · outbound

This paper cites Neurocomput- ing 659, 131790.

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation Neurocomput- ing 659, 131790

Reference 9

Resolution: malformed identifier; raw_fallback, observed 2026-08-16T00:37:14.499148Z

Source-reported events for the cited work

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Observation 5c85c68e-54b5-42f4-a1b4-2992669ba9bc · outbound

This paper cites Neurocomput- ing 651, 131018.

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation Neurocomput- ing 651, 131018

Reference 12

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Observation 82180302-233b-4050-9dcb-ef05035a76fa · outbound

This paper cites (Eds.), Advances in Neural Information Processing Sys- tems, Curran Associates, Inc.

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation (Eds.), Advances in Neural Information Processing Sys- tems, Curran Associates, Inc

Reference 15

Resolution: metadata mismatch; raw_fallback, observed 2026-08-16T00:37:13.775230Z

Source-reported events for the cited work

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Observation d5d7e449-de77-479f-80f6-dd6d31ff08b0 · outbound

This paper cites Neurocomputing 677, 133088.

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation Neurocomputing 677, 133088

Reference 16

Resolution: malformed identifier; raw_fallback, observed 2026-08-16T00:37:13.602515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 634dc674-1017-4637-8477-6a11fab439b6 · outbound

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

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks

Reference 18

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Observation 520b62ad-42e3-46fe-9247-ed44e3626763 · outbound

This paper cites (Eds.), Advances in Neural Information Pro- cessing Systems, Curran Associates, Inc.

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation (Eds.), Advances in Neural Information Pro- cessing Systems, Curran Associates, Inc

Reference 20

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Source-reported events for the cited work

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Observation 0f976859-3bf1-49a0-ab06-2ab413bfef11 · outbound

This paper cites 21881–21891.

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation 21881–21891

Reference 21

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Source-reported events for the cited work

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Observation f3796f8e-e81b-411d-ad50-7ff4968e1295 · outbound

This paper cites Neurocomput- ing 636, 129982.

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation Neurocomput- ing 636, 129982

Reference 22

Resolution: malformed identifier; raw_fallback, observed 2026-08-16T00:37:12.990578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c7e0a6ee-1975-4593-bd7b-1ef77eb0f354 · outbound

This paper cites (Eds.), Computer Vision – ECCV 2024, Springer Nature Switzer- land, Cham.

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation (Eds.), Computer Vision – ECCV 2024, Springer Nature Switzer- land, Cham

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:37:15.612479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 04d52dec-59f3-4882-b28c-db328ec6746a · outbound

This paper cites 14388–14397.

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation 14388–14397

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:37:15.564992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3abe94f3-4b63-46aa-a9d2-84050e39fca4 · outbound

This paper cites (Eds.), Advances in Neural Information Process- ing Systems, Curran Associates, Inc.

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation (Eds.), Advances in Neural Information Process- ing Systems, Curran Associates, Inc

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:37:15.516826Z

Source-reported events for the cited work

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Observation e66c43ea-2bc8-44bf-896d-ca4702f29d20 · outbound

This paper cites 2593–2602.

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation 2593–2602

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:37:15.473689Z

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Observation e1208927-4742-48aa-8a52-5066a482e839 · outbound

This paper cites Image Segmentation in Foundation Model Era: A Survey.

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation Image Segmentation in Foundation Model Era: A Survey

Reference 28

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Observation ed6893e1-4814-413d-bdf0-74d6f92a559c · outbound

This paper cites Exploring Discrete Diffusion Models for Image Captioning.

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation Exploring Discrete Diffusion Models for Image Captioning

Reference 29

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Observation 7ae0524b-0ef7-4d16-8432-72b08e59f5a5 · outbound

This paper cites 15116–15127.

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation 15116–15127

Reference 30

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Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation Unresolved cited work

Reference 2014

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Observation 82e0508e-adaf-4bfd-8a39-fbbd021785b8 · outbound

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Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation Unresolved cited work

Reference 2019

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Observation 40682133-2656-4871-ac1e-3d8938e59ab1 · outbound

This paper cites International journal of computer vision 128, 1956–1981.

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation International journal of computer vision 128, 1956–1981

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:37:15.734607Z

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Observation ceb692d9-b472-4424-8b7b-96e5b787569c · outbound

This paper cites (Eds.), Advances in Neural Informa- tion Processing Systems, Curran Associates, Inc.

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation (Eds.), Advances in Neural Informa- tion Processing Systems, Curran Associates, Inc

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:37:15.935580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8d5e5e23-c4f4-4758-b6aa-49560af29c41 · outbound

This paper cites 1280–1289.

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation 1280–1289

Reference 2022

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Observation 8fb4b292-c239-46b5-925b-39c813a8ab56 · outbound

This paper cites 3992–4003.

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation 3992–4003

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-16T00:37:11.866571Z

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Observation 31868b80-b1b7-42f9-b5a4-235c3aa8f2c3 · outbound

This paper cites 27948–27959.

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation 27948–27959

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-16T00:37:11.925725Z

Source-reported events for the cited work

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Observation 243bba2a-e03b-48b3-894a-25a6a4327e9f · outbound

This paper cites Neurocomput- ing 630, 129702.

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation Neurocomput- ing 630, 129702

Reference 2025

Resolution: malformed identifier; raw_fallback, observed 2026-08-16T00:37:14.687852Z

Source-reported events for the cited work

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Observation e410b405-f0e3-4c74-81c5-3ab071362add · outbound

This paper cites Neurocomputing 660, 131844.

Learning from Multimodal Pseudo-Labels for Robust Open-Vocabulary Instance and Panoptic Segmentation Neurocomputing 660, 131844

Reference 2026

Resolution: malformed identifier; raw_fallback, observed 2026-08-16T00:37:15.419016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.