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Paper Citation Record · LEDGER

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation

As of 7 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2506.06748.

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

pith.paper-citation-record.v1
2506.06748 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:53:10.443418Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

24 of 24 outbound references displayed

  • verified exact0
  • verified fuzzy15
  • unresolved9
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 19a82926-2c95-4aa3-bcea-5bd2d73fed26 · outbound

This paper cites One- shot video object segmentation.

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation One- shot video object segmentation

Reference 1

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation d4b009cf-e521-4bee-b322-0214b6f4f35e · outbound

This paper cites Xmem: Long- term video object segmentation with an atkinson-shiffrin memory model.

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation Xmem: Long- term video object segmentation with an atkinson-shiffrin memory model

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation d25ee1b0-3f5f-43ae-91e3-888e0fe39b4a · outbound

This paper cites Rethink- ing space-time networks with improved memory coverage for efficient video object segmentation.

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation Rethink- ing space-time networks with improved memory coverage for efficient video object segmentation

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation a1c78d10-fc14-446f-8dac-9c7e340d831f · outbound

This paper cites Putting the object back into video object segmentation.

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation Putting the object back into video object segmentation

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T05:53:10.379426Z digest=sha256:f4882201fd08b6c8c15cf03608358ee815920951bc0d290fb55a69531f485dc7

Observation 941f7b66-6ae6-4f07-8b74-270d4f96a4b5 · outbound

This paper cites Epic-kitchens visor benchmark: Video segmenta- tions and object relations.

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation Epic-kitchens visor benchmark: Video segmenta- tions and object relations

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T05:53:10.382571Z digest=sha256:c8e0693d80c2a22e42b74d0dc28007f91165eecf42e2fdfd7fb437724f2e4969

Observation a1cf217f-134b-4a30-a321-5aeec0587cf2 · outbound

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

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation MOSE: A new dataset for video object segmentation in complex scenes

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T05:53:10.385897Z digest=sha256:c0fc4d4c7d5e2e9fa1a370b70a16df4c24aa558713aab02a6cbf5c91b5c280a5

Observation fc2dfb4c-ce12-47bb-9e93-18b824d08f2d · outbound

This paper cites SAM2Long: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree.

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation SAM2Long: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:53:10.389194Z digest=sha256:66ea793d3ec47b58f76f33ba82462b26709a8300a7eeee8a5bcfced94f47bd64

Observation 1102344f-e822-4535-961d-45abdfe6b9f2 · outbound

This paper cites Deep learning for video object segmentation: a review.Artificial Intelligence Review, 56(1):457–531, 2023.

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation Deep learning for video object segmentation: a review.Artificial Intelligence Review, 56(1):457–531, 2023

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation c2a2926a-1205-499e-a68b-6ac7e00dc630 · outbound

This paper cites Ego-exo4d: Understanding skilled human activity from first-and third-person perspectives.

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation Ego-exo4d: Understanding skilled human activity from first-and third-person perspectives

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 43f6b820-f8e6-443c-8814-23572813fa5d · outbound

This paper cites LVOS: A Benchmark for Large-scale Long-term Video Object Segmentation.

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation LVOS: A Benchmark for Large-scale Long-term Video Object Segmentation

Reference 10

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no resolver link, observed 2026-08-07T05:53:10.398572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:53:10.398572Z digest=sha256:814a48aac689cf5e6da30f0a07b8e010c6e8e15455795415ec62c0ca64bfad2b

Observation 16d95078-7ebb-454b-a0c4-3c27e8b383aa · outbound

This paper cites Video object segmentation with adaptive feature bank and uncertain-region refinement.

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation Video object segmentation with adaptive feature bank and uncertain-region refinement

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 687cdb10-da89-4b1b-b2ee-628cc908583a · outbound

This paper cites Video object segmentation using space-time memory networks.

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation Video object segmentation using space-time memory networks

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T05:53:10.404328Z digest=sha256:ff35235b0b579295066a125f1b0c8c90e835466dd5da78cd3f4a85105fd2822e

Observation ff231224-9342-405b-a099-a12e4d4485d3 · outbound

This paper cites an unresolved cited work.

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation Unresolved cited work

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T05:53:10.406886Z digest=sha256:50a6de3208e47229646408088cc00dfcc48d94c2dd6a6d054ca592809cd2f880

Observation ad4e75bf-c6ed-4297-a0c7-36e540a42f4e · outbound

This paper cites Hd-epic: A highly-detailed egocentric video dataset.

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation Hd-epic: A highly-detailed egocentric video dataset

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation e8fba540-06d8-4cbb-bba9-fb6528151569 · outbound

This paper cites An outlook into the fu- ture of egocentric vision.IJCV, 132(11):4880–4936, 2024.

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation An outlook into the fu- ture of egocentric vision.IJCV, 132(11):4880–4936, 2024

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 037fd328-2d0d-4774-8016-4bd121ff6e34 · outbound

This paper cites The 2017 DAVIS Challenge on Video Object Segmentation.

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation The 2017 DAVIS Challenge on Video Object Segmentation

Reference 16

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:53:10.415724Z digest=sha256:83630d25db6e5b30508d8596649e0f2799f5b0f0c20564728178d2aba829ed1d

Observation f98f7708-bbaa-42eb-8627-2d312e3d8eed · outbound

This paper cites Vi- sion transformers for dense prediction.

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation Vi- sion transformers for dense prediction

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation c84fca61-5c02-477e-9a22-5b40a08f1b92 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation SAM 2: Segment Anything in Images and Videos

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation a9d641a0-7279-4079-8b35-921d99958325 · outbound

This paper cites Hi- era: A hierarchical vision transformer without the bells-and- whistles.

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation Hi- era: A hierarchical vision transformer without the bells-and- whistles

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 290196ea-093f-41f3-87bd-c9a12f9af0f8 · outbound

This paper cites A Distractor-Aware Memory for Visual Object Tracking with SAM2.

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation A Distractor-Aware Memory for Visual Object Tracking with SAM2

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:53:10.430158Z digest=sha256:433bf46a010c2b0ef5d9ff306440aa250edd265a94a7edb9c40087560b5fdfe6

Observation 70887bb4-34a3-4fe3-af64-a5ec28897730 · outbound

This paper cites Feelvos: Fast end-to-end embedding learning for video object segmenta- tion.

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation Feelvos: Fast end-to-end embedding learning for video object segmenta- tion

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

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Observation 68d8fad7-d0e9-4b5d-bb39-930d446d4197 · outbound

This paper cites YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark.

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark

Reference 22

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:53:10.437191Z digest=sha256:52d6bdf6bfcd64c397418633c2cb2d23b811de828a07695f47da7b7970c3d181

Observation 1f06af49-3be1-4c6d-b98c-c8c3f86b6ab7 · outbound

This paper cites SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory.

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory

Reference 23

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:53:10.440098Z digest=sha256:9c029ab3521adf57d20bf5f12df3c086631cdfde0aec56b956fd5236089914b3

Observation 8407f388-274e-47a0-91f8-bb5cbced8cab · outbound

This paper cites Depth Anything V2.

THU-Warwick Submission for EPIC-KITCHEN Challenge 2025: Semi-Supervised Video Object Segmentation Depth Anything V2

Reference 24

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers

No inbound Pith citation observations are available.