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Paper Citation Record · LEDGER

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks

As of 7 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 0 inbound Pith citation observations for arXiv:2508.03566.

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

pith.paper-citation-record.v1
2508.03566 v1

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T04:28:07.373236Z

measured 60 of 60 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

60 of 60 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved60
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 613e25d0-9afa-4ec2-a8f3-2d5c8841972e · outbound

This paper cites The Common Crawl dataset.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks The Common Crawl dataset

Reference 1

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no resolver link, observed 2026-08-06T04:27:59.950420Z

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source=pdf_text observed=2026-08-06T04:27:59.950420Z digest=sha256:c00c6c5e17be43899d05ebb499c588fc5992f088fc35246f6b23f2557ff57644

Observation 5e440728-847e-41ee-80ea-f4f8d527ae58 · outbound

This paper cites The screen-to-shot project on the Github.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks The screen-to-shot project on the Github

Reference 2

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source=pdf_text observed=2026-08-06T04:28:00.017022Z digest=sha256:b50466c45595837913e4aa8be12e8a9b041e16c247d759047309ab31d70d64d4

Observation e7bd03e5-a6fc-46f4-abb3-f6438ecf3a01 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Gemini: A Family of Highly Capable Multimodal Models

Reference 3

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source=pdf_text observed=2026-08-06T04:28:00.148374Z digest=sha256:d3dde768ac156ce9bba85c96bd4fdfc757b584b06955dc1fed86441e22488ebd

Observation 904a1b57-53cd-43ba-89df-cda578e37e60 · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 4

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:28:00.275623Z digest=sha256:a1dfb55a3cc06982ca29d196f51534761c244243c8438d699bdf30478aead507

Observation a1117447-d63d-442a-9b55-9016b5d0641a · outbound

This paper cites Manmatha.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Manmatha

Reference 5

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:28:00.401597Z digest=sha256:744f9976740ccb682c199f86d1077d850aee06bc7568cb85da128ee7c8577b23

Observation 12ac9ad5-912b-450a-b605-050b8e0996d6 · outbound

This paper cites Hallucination of Multimodal Large Language Models: A Survey.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Hallucination of Multimodal Large Language Models: A Survey

Reference 6

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no resolver link, observed 2026-08-06T04:28:00.499523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:28:00.499523Z digest=sha256:a128293f4313dc13e2b07ff5a564c4da78d4bf02cf88f2e361497ba02b4e3c56

Observation 235af87d-973a-4e8d-8e8a-40ce33618365 · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 7

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T04:28:00.655962Z digest=sha256:4d82ec0d912ba924f5e9434c2de312a87649f4e147d6cce34f0533fb599f4b52

Observation edafe414-c022-436e-bdbc-8da846b89a23 · outbound

This paper cites Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell

Reference 8

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source=pdf_text observed=2026-08-06T04:28:00.776694Z digest=sha256:b31a1df76a333b18f5de0da8c7af68ac0d335be1dd4a68194882faf59294560b

Observation 0bba4b53-82b4-4acb-b10e-9c186980900a · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 9

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source=pdf_text observed=2026-08-06T04:28:00.944092Z digest=sha256:36a8153ba57cc072bad98d8a3b51f3e39bd3083cddc4029f86a66279b4aeb6e7

Observation 3e026b81-7d1b-4a26-8fef-93b5a08d73f3 · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 10

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no resolver link, observed 2026-08-06T04:28:01.151765Z

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source=pdf_text observed=2026-08-06T04:28:01.151765Z digest=sha256:a2ea38761845a7ec38a69083496221375deab36882d42ebad64e7e7c68279b3e

Observation b84e0392-3fa5-4140-8765-fd81cb4d8199 · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 11

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no resolver link, observed 2026-08-06T04:28:01.278333Z

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source=pdf_text observed=2026-08-06T04:28:01.278333Z digest=sha256:7b2fa27dec3a3e2368f9951abacab69b2474e1394707644cd7f569e02a368e54

Observation a4125290-c11e-4caf-825e-e97bdf4ada9d · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 12

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source=pdf_text observed=2026-08-06T04:28:01.445576Z digest=sha256:4da1edc358aaf93ecc107cea5ee727f4aaafb38360514e428745be4f739ae04d

Observation ceec7797-0d75-4b00-9525-124d90541a56 · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 13

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no resolver link, observed 2026-08-06T04:28:01.567399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:28:01.567399Z digest=sha256:97daa88176b7f2093d1268f040524d575d8e967f2ef721938183f38adb5f4f8b

Observation 7d90538d-eba7-4885-b975-98b9393a2fd3 · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 14

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no resolver link, observed 2026-08-06T04:28:01.698151Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:28:01.698151Z digest=sha256:5788b908bcd3387051d7311a3479ba02f20b6886f945209af1b0e1b46aba4f86

Observation 8fd64e21-ec93-489f-b1d9-1af447d7de4a · outbound

This paper cites InCoder: A Generative Model for Code Infilling and Synthesis.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks InCoder: A Generative Model for Code Infilling and Synthesis

Reference 15

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source=pdf_text observed=2026-08-06T04:28:01.851254Z digest=sha256:0a56b2328523718870a038e0a9f50c04cc49c12dee2f5ce7919fe6484886fa15

Observation 43ed78b0-fb41-4543-9ea8-97143c155a1f · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 16

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source=pdf_text observed=2026-08-06T04:28:01.993522Z digest=sha256:93145248c39093ccce2592f1cf9a43da43bd594d991a3684abf4a86f34216252

Observation 219743e8-5cd2-4c17-82cb-113826ab4827 · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 17

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no resolver link, observed 2026-08-06T04:28:02.186035Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:28:02.186035Z digest=sha256:ccd1400cfb8efddfa970ff0977682bfbfa4172b814bfb35ef7b6297ef3394eeb

Observation 47b25b3e-22c1-4de3-86a1-3c306d65124c · outbound

This paper cites DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence

Reference 18

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source=pdf_text observed=2026-08-06T04:28:02.270495Z digest=sha256:0dd1d7effff98bf0613cb647777a4795e61a0cc52f02d4cff91496ee4c685fd9

Observation aa90ee18-b647-4c95-9f7f-1af007f0ff8e · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 19

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no resolver link, observed 2026-08-06T04:28:02.403274Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:28:02.403274Z digest=sha256:3e9afa39f6b29a14914f6acd8f7992316628e1d8baa0cac01ec9e3457b3fc182

Observation 130fa9ae-0fc4-4b2f-8892-85200afbb1f7 · outbound

This paper cites Girshick.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Girshick

Reference 20

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Observation 487eab75-c3d3-4a64-80c1-68a934ee0358 · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 21

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source=pdf_text observed=2026-08-06T04:28:02.679802Z digest=sha256:55406afe0e977fa770b39cde2720ba26de48b4cc9de35c019eb7d6707f0a539d

Observation 9d337c83-6e0a-4869-a156-58e77a4f5ba3 · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 22

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:28:02.802284Z digest=sha256:d60d0e0201f8f8493060e32d28533ab6e2c3ccef2793254a6052c16bbb5492e0

Observation beebdf68-da89-4a9f-bc6d-4a0851eee541 · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 23

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source=pdf_text observed=2026-08-06T04:28:02.894306Z digest=sha256:5feb75de1427ac278f5a8b65073ae453b34a80937856cf842c903fac24f08e27

Observation 94fc235c-ca20-4561-9332-7fda0ffdc2ff · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 24

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source=pdf_text observed=2026-08-06T04:28:02.998090Z digest=sha256:f57c505db7ecac4c9b8ff9a04c40775c746fcde68775762cf7c44ebfa56d49db

Observation 9b9bd0cd-6d0e-4181-932c-ef1c036a2fae · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 25

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source=pdf_text observed=2026-08-06T04:28:03.116731Z digest=sha256:cb3d95d80fbd5721ef8f75c093381d2f6dd7ce590bc99d4183f4078a80905536

Observation 4bfe3430-938e-4c7f-9b0c-da55ad13e7d7 · outbound

This paper cites Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Dataset.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Dataset

Reference 26

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Observation c5bda58b-e7fc-4b5a-ba51-fbeedacf6184 · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 27

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no resolver link, observed 2026-08-06T04:28:03.459628Z

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source=pdf_text observed=2026-08-06T04:28:03.459628Z digest=sha256:b487058a9ce923c2e3971212d8b54cfb381b35bb2f18cba6ff473057593e5b14

Observation d46fb1fa-2356-45ca-bc2d-9768f0483152 · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 28

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:28:03.588646Z digest=sha256:c5d6d21d4b482578d68eb9b2830311afc640b35eca79e330771c029b46a5f55e

Observation 72e53f04-c5d8-46ed-b792-8508b046b0f9 · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 29

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no resolver link, observed 2026-08-06T04:28:03.695156Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:28:03.695156Z digest=sha256:b9011dd241f82cd078a5948f9086235a10c02930d9c94c10cb7f87cbf7b85a68

Observation bb45bb66-d957-44d0-9cfa-066335aacbba · outbound

This paper cites DeepSeek-VL: Towards Real-World Vision-Language Understanding.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks DeepSeek-VL: Towards Real-World Vision-Language Understanding

Reference 30

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Unavailable: canonical work link unavailable.

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Observation fc798f24-d7f8-4431-9881-68ecd81582a0 · outbound

This paper cites WizardCoder: Empowering Code Large Language Models with Evol-Instruct.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks WizardCoder: Empowering Code Large Language Models with Evol-Instruct

Reference 31

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source=pdf_text observed=2026-08-06T04:28:04.005068Z digest=sha256:0b98c01024c5261cff91df21277076b174a697b71af9e46bdcb2591f807a15a9

Observation 504907b5-2393-42bd-93e3-fbe89906d962 · outbound

This paper cites GPT-4 Technical Report.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks GPT-4 Technical Report

Reference 32

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source=pdf_text observed=2026-08-06T04:28:04.099534Z digest=sha256:6dac4962e9253a4381c14668fb221dd5287d85744232732571b8d4d1981ff4fc

Observation 58af786d-0e2c-4680-ac74-c8e7eda5fa7e · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 33

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no resolver link, observed 2026-08-06T04:28:04.230585Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:28:04.230585Z digest=sha256:31d63968617cbf2c8bdff7f4c97c91a20b6115934331c534d5fdf85d5fe5806e

Observation 049f7b19-0b42-4055-9122-f0c7068a1bc6 · outbound

This paper cites Training language models to follow instructions with human feedback.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Training language models to follow instructions with human feedback

Reference 34

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:28:04.342489Z digest=sha256:a97118ba3af0fd41656c5ccd346bde58cb2dda49759a9fb1e816eba3a55d0f34

Observation 1826bdc4-92cc-4a8f-b8f3-aced7bf7e52d · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 35

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no resolver link, observed 2026-08-06T04:28:04.472664Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:28:04.472664Z digest=sha256:063cfe82fd67cfc7d816168e2febb81b45563a12d7e5d355cbe66203e1b22f08

Observation 2df93466-44e1-4d01-8102-245eb1f0fccd · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 36

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no resolver link, observed 2026-08-06T04:28:04.654678Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T04:28:04.654678Z digest=sha256:d0ab1b5fcf6c6585ce7fb301c40a11085628122feef0b9f7cfe22c1605281a78

Observation 2cd17152-98d6-4b4a-abf5-9b050491a781 · outbound

This paper cites Sketch2code: Generating a website from a paper mockup.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Sketch2code: Generating a website from a paper mockup

Reference 37

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source=pdf_text observed=2026-08-06T04:28:04.841111Z digest=sha256:bccf63210bee2c1de9750c6451ea84dfaae1e3b3d05f49939c7bf1d3f691aa24

Observation ae28c946-58ac-4e53-a396-d6872cb0a782 · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 38

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unresolved
no resolver link, observed 2026-08-06T04:28:05.015753Z

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Observation f3da19d2-c7a2-4e1d-bbdc-3308c7a7d1d3 · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 39

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Observation 682649fe-7395-405d-a806-1951e1fe6aad · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 40

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Observation e79711a1-2485-490a-8fb8-0d5fbba66458 · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 41

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Observation 0cb9f8f9-1dde-49ff-bba6-28b2830814eb · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 42

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Observation 709d9532-bcf9-47de-87eb-a715ab074b74 · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 43

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Observation 22a1ef48-11b6-49a0-8d21-8bc30e4257a6 · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 44

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Observation 3b265c3d-e828-48f0-bb50-a8b0809b18b9 · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 45

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Observation dd16cf56-73fa-4952-b932-bf000d505dda · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 46

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Observation 9c64944f-c5fe-43c7-8eab-c8a06cc2c138 · outbound

This paper cites Automatically Generating UI Code from Screenshot: A Divide-and-Conquer-Based Approach.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Automatically Generating UI Code from Screenshot: A Divide-and-Conquer-Based Approach

Reference 47

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T04:28:06.429732Z digest=sha256:ee1b2834f920f029451efb024a1a86686a78d6f5ffa138fda52ecd2415bfaf75

Observation 0e17ba5a-d6f5-4d26-832e-f98b4dbc7730 · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 48

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source=pdf_text observed=2026-08-06T04:28:06.576324Z digest=sha256:5b68d031f19bbd50bd501cfd67fc9df46e85f1d813ac5fe6cd8e6613af0478f7

Observation bc78164d-7846-4ec3-8c73-cc77959bd0a6 · outbound

This paper cites MRWeb: An Exploration of Generating Multi-Page Resource-Aware Web Code from UI Designs.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks MRWeb: An Exploration of Generating Multi-Page Resource-Aware Web Code from UI Designs

Reference 49

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source=pdf_text observed=2026-08-06T04:28:06.134031Z digest=sha256:d026be380d2a5b97b97a6ffa2e61a89bed3ef7d15efd1a6346656b999a00037f

Observation e1342b64-8fdc-499a-82f9-37d34ccc1bb0 · outbound

This paper cites Chi, Quoc V.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Chi, Quoc V

Reference 50

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source=pdf_text observed=2026-08-06T04:28:06.774607Z digest=sha256:2ef60e4b9e866c77a24e8854702cd78cf0cf1ea3a2ad09107624799876eef380

Observation 758e9e67-e1bc-4fc8-9eda-88d08b3a2718 · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 51

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source=pdf_text observed=2026-08-06T04:28:06.876416Z digest=sha256:f70e55770f887586ae3ff0cc51ff9696c0ee086645da6a26936550be58889394

Observation acf27ba8-f03c-48d0-b68a-a4e4e15e1b10 · outbound

This paper cites Qwen2.5-Omni Technical Report.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Qwen2.5-Omni Technical Report

Reference 52

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source=pdf_text observed=2026-08-06T04:28:06.980144Z digest=sha256:7604fbd39fc02202cdcbc434500f265a8a24bb366277f4348a8ad97390dfbb52

Observation ea1b2d10-6ff3-4c27-91df-77505b1a679c · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 53

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source=pdf_text observed=2026-08-06T04:28:06.678202Z digest=sha256:b320f32da46ae8ca3fe0d1fa4b9afa0df30a5465a684e3b02b4536a8be19edf0

Observation 4716fb13-ba89-4747-b94c-eb228d464c3a · outbound

This paper cites Xing, Xiaodan Liang, and Zhiqiang Shen.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Xing, Xiaodan Liang, and Zhiqiang Shen

Reference 54

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no resolver link, observed 2026-08-06T04:28:07.167440Z

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source=pdf_text observed=2026-08-06T04:28:07.167440Z digest=sha256:7fa73ed72617e4a79d7ff70becd446230b14d50bc731de36287f71dc05d1f9d2

Observation 40ddda87-f784-436b-a208-e1f4fafcab88 · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 55

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no resolver link, observed 2026-08-06T04:28:07.254281Z

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source=pdf_text observed=2026-08-06T04:28:07.254281Z digest=sha256:340ef417d0d325abd74a37c359d76812e4736b934d79bbe219adab43fdbb8a46

Observation 0217434b-6edd-4eff-b6b9-20c5d92e826d · outbound

This paper cites an unresolved cited work.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Unresolved cited work

Reference 57

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no resolver link, observed 2026-08-06T04:28:07.078414Z

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source=pdf_text observed=2026-08-06T04:28:07.078414Z digest=sha256:4c0fdfe5549ea0c6b7ff5612407c819ac8f8db68959916490f3dced37307e446

Observation 89c1828c-df69-4e48-ab84-a0ee7a9e14ee · outbound

This paper cites Bridging Design and Development with Automated Declarative UI Code Generation.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks Bridging Design and Development with Automated Declarative UI Code Generation

Reference 60

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no resolver link, observed 2026-08-06T04:28:07.373236Z

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source=pdf_text observed=2026-08-06T04:28:07.373236Z digest=sha256:26b95f0eefc32a47fc0ac43a48f344f1f90d5469c2154c9182477ba8bb0772f9

Observation 8a48fb06-9d3c-48a0-a4ec-56cd5bcb94be · outbound

This paper cites In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Reference 2023

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no resolver link, observed 2026-08-06T04:28:03.219652Z

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source=pdf_text observed=2026-08-06T04:28:03.219652Z digest=sha256:805a097ecb7097c67ee7653b421b71644089b4ead12899213bf67bddb34939af

Observation 16bd5968-3816-48a6-8922-729aefe0dea6 · outbound

This paper cites In Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering, ASE 2024, Sacramento, CA, USA, October 27 - November 1, 2024.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks In Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering, ASE 2024, Sacramento, CA, USA, October 27 - November 1, 2024

Reference 2024

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source=pdf_text observed=2026-08-06T04:28:05.798426Z digest=sha256:7a37ac64abb728e2191ed22e20dfe6cb9220e2a02fa6e3231646b5aefa6eb3a6

Observation 1ca2ddcc-3f02-40b4-b7e8-54601dd8df69 · outbound

This paper cites In Proceedings of the International World Wide Web Conference, WWW 2025, Sydney, April 28–May 2, 2024.

SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks In Proceedings of the International World Wide Web Conference, WWW 2025, Sydney, April 28–May 2, 2024

Reference 2025

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no resolver link, observed 2026-08-06T04:28:02.100354Z

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source=pdf_text observed=2026-08-06T04:28:02.100354Z digest=sha256:728839ceafc59f2812e8128b1a40ee1db901b7492c674a0a54efaf90f9469de8

Pith citing papers

No inbound Pith citation observations are available.