Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-10T16:24:52.056950Z
Paper Citation Record · LEDGER
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models
As of 7 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2604.11711.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
30 of 30 outbound references displayed
- verified exact6
- verified fuzzy20
- unresolved2
- parse uncertain0
- malformed identifier1
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 27ba269e-927e-408f-ab68-45587ffd219c · outbound
This paper cites SAM 3: Segment any- thing with concepts.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models SAM 3: Segment any- thing with concepts
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T15:31:54.557133Z
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.
Observation 3de7ff46-3155-419b-96ee-438b10789d1f · outbound
This paper cites an unresolved cited work.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Unresolved cited work
Reference 2
- Resolution
- unresolved
- raw_fallback, observed 2026-05-17T15:31:54.583171Z
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.
Observation 681dfcf9-b198-4ff4-bd24-783cb71cd2cd · outbound
This paper cites SAM-Med2D.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models SAM-Med2D
Reference 3
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T08:56:01.506793Z
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.
Observation 9d522faa-e665-48e1-8a69-de6785d3a1ec · outbound
This paper cites Improved Regularization of Convolutional Neural Networks with Cutout.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Improved Regularization of Convolutional Neural Networks with Cutout
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T20:37:12.525085Z
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.
Observation ebc86eaf-f269-43f6-9728-5bbbad51bd72 · outbound
This paper cites Stable segment anything model.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Stable segment anything model
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T15:31:54.574368Z
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.
Observation b12d1eb1-d579-4a05-94b1-4825b70b1970 · outbound
This paper cites Learning to see the invisible: End-to-end trainable amodal instance segmen- tation.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Learning to see the invisible: End-to-end trainable amodal instance segmen- tation
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T15:31:54.568472Z
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.
Observation 3ccb47fa-5be3-4bd4-b02e-4ef1c15d0ad9 · outbound
This paper cites Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T08:56:01.484968Z
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.
Observation 7ba9b7df-9f5b-475c-881d-7af808383c2c · outbound
This paper cites Segment anything model for medical images?Medical Image Analysis, 92:103061.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Segment anything model for medical images?Medical Image Analysis, 92:103061
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T15:31:54.554079Z
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.
Observation 86a1f4e4-3c9b-4dda-8455-5e540d016461 · outbound
This paper cites Kvasir-instrument: Diagnostic and therapeu- tic tool segmentation dataset in gastrointestinal endoscopy.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Kvasir-instrument: Diagnostic and therapeu- tic tool segmentation dataset in gastrointestinal endoscopy
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T15:31:54.580725Z
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.
Observation cd727ae5-b5ac-4a99-a6bb-76cd883c9ba4 · outbound
This paper cites Deep occlusion- aware instance segmentation with overlapping BiLayers.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Deep occlusion- aware instance segmentation with overlapping BiLayers
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T15:31:54.551712Z
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.
Observation 63dc755b-1121-471d-939e-b7f326a0534a · outbound
This paper cites Segment any- thing.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Segment any- thing
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T15:31:54.547445Z
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.
Observation 3dbc3371-b396-4a64-9efa-1e258c5095ac · outbound
This paper cites Ultra- ecp: Ellipse-constrained and point-robust foundation model adaptation for fetal cardiac ultrasound segmentation.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Ultra- ecp: Ellipse-constrained and point-robust foundation model adaptation for fetal cardiac ultrasound segmentation
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T15:31:54.597731Z
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.
Observation 511c2192-1f85-464b-88bc-04f7967e906e · outbound
This paper cites Cao, Yifan Shen, Yi Lu, Xiang Li, Qianqian Chen, and Jintai Chen.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Cao, Yifan Shen, Yi Lu, Xiang Li, Qianqian Chen, and Jintai Chen
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T15:31:54.565559Z
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.
Observation 5be9cf29-bdad-4286-a071-3ef4d0e3a85f · outbound
This paper cites Segment anything in medical images.Nature communications, 15(1):654.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Segment anything in medical images.Nature communications, 15(1):654
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T15:31:54.592928Z
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.
Observation 4bb00c0a-10a6-49b3-ac20-ae70da91cae2 · outbound
This paper cites Segment anything model for medical image analysis: an experimental study.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Segment anything model for medical image analysis: an experimental study
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T15:31:54.585538Z
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.
Observation 12907ffe-4e53-40a0-86c9-cdaa126df7f3 · outbound
This paper cites Up2d: Uncertainty-aware progres- sive pseudo-label denoising for source-free domain adap- tive medical image segmentation.Neurocomputing, page 132659.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Up2d: Uncertainty-aware progres- sive pseudo-label denoising for source-free domain adap- tive medical image segmentation.Neurocomputing, page 132659
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T15:31:54.588378Z
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.
Observation 4bd88086-3004-4764-89c4-7b79d3d45119 · outbound
This paper cites Adaptive knowledge transferring with switching dual-student framework for semi-supervised medical image segmentation.Pattern Recognition, page 113115.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Adaptive knowledge transferring with switching dual-student framework for semi-supervised medical image segmentation.Pattern Recognition, page 113115
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T15:31:54.602561Z
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.
Observation 587a0364-37ba-49a7-8893-e6a30e04079d · outbound
This paper cites Amodal instance segmentation with KINS dataset.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Amodal instance segmentation with KINS dataset
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T15:31:54.571516Z
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.
Observation e957b1e9-3194-4bf6-84b6-e9ab6a189a66 · outbound
This paper cites SAM 2: Segment Anything in Images and Videos.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models SAM 2: Segment Anything in Images and Videos
Reference 19
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T08:56:01.517923Z
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.
Observation 091a46ce-f7f2-47a4-8f49-44e8891b7135 · outbound
This paper cites Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge.Medical image analysis, 70:101920.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge.Medical image analysis, 70:101920
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T15:31:54.577322Z
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.
Observation 92cb32de-e5f0-4412-a705-5da7fbff4e33 · outbound
This paper cites an unresolved cited work.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Unresolved cited work
Reference 21
- Resolution
- unresolved
- raw_fallback, observed 2026-05-17T15:31:54.562954Z
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.
Observation e4063464-0435-4946-b003-ac14661f3649 · outbound
This paper cites Toward embedded detection of polyps in WCE images for early diagnosis of colorectal can- cer.International journal of computer assisted radiology and surgery, 9(2):283–293.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Toward embedded detection of polyps in WCE images for early diagnosis of colorectal can- cer.International journal of computer assisted radiology and surgery, 9(2):283–293
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T15:31:54.560284Z
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.
Observation ef880a1d-a5a6-47d2-8c55-ea46ea1c282d · outbound
This paper cites Segment anything, even oc- cluded.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Segment anything, even oc- cluded
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T15:31:54.545072Z
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.
Observation 37d3e318-d456-42fe-ac1b-94f6542cbbfb · outbound
This paper cites Gurudu, and Jianming Liang.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Gurudu, and Jianming Liang
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T15:31:54.595353Z
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.
Observation cd6ddce9-133d-4ba3-9c06-aef6e61369cf · outbound
This paper cites Unsupervised multi- scale segmentation of cellular cryo-electron tomograms with stable diffusion foundation model.bioRxiv, pages 2025–06.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Unsupervised multi- scale segmentation of cellular cryo-electron tomograms with stable diffusion foundation model.bioRxiv, pages 2025–06
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T15:31:54.599957Z
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.
Observation e09bde81-910b-4bf6-81c3-1108eb8baf11 · outbound
This paper cites A benchmark for en- doluminal scene segmentation of colonoscopy images.Jour- nal of healthcare engineering, 2017(1):4037190.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models A benchmark for en- doluminal scene segmentation of colonoscopy images.Jour- nal of healthcare engineering, 2017(1):4037190
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T15:31:54.590701Z
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.
Observation 936a2b87-5096-48ce-8e4e-67b119611667 · outbound
This paper cites From specialist to generalist: Unlocking sam’s learning potential on unlabeled medical images.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models From specialist to generalist: Unlocking sam’s learning potential on unlabeled medical images
Reference 27
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T08:56:01.512873Z
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.
Observation 3af489e2-954f-4f34-834b-091aae30032d · outbound
This paper cites Describe Anything in Medical Images.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Describe Anything in Medical Images
Reference 28
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T08:56:01.501702Z
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.
Observation 887bbd4d-ebca-4aad-9fca-91cdcae56010 · outbound
This paper cites Medical SAM 2: Segment medical images as video via Segment Anything Model 2.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Medical SAM 2: Segment medical images as video via Segment Anything Model 2
Reference 29
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T08:56:01.492563Z
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.
Observation 5f1251cc-02c1-43ca-84fb-92cd921b6fd8 · outbound
This paper cites Semantic amodal segmentation.
Seeing Through the Tool: A Controlled Benchmark for Occlusion Robustness in Foundation Segmentation Models Semantic amodal segmentation
Reference 30
- Resolution
- malformed identifier
- raw_fallback, observed 2026-05-17T15:31:54.549436Z
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.
Pith citing papers
No inbound Pith citation observations are available.