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Paper Citation Record · LEDGER

SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2104.14812.

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

pith.paper-citation-record.v1
2104.14812 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 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 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T06:07:06.435893Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T13:19:50.234281Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation a3bfdefa-7765-48a2-acd8-ce81193ffa40 · inbound

UNCOVER: Unknown Class Object Detection for Autonomous Vehicles in Real-time cites this paper.

UNCOVER: Unknown Class Object Detection for Autonomous Vehicles in Real-time SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-11T21:57:53.708316Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T21:57:53.708316Z digest=sha256:40d6d1895c547a7fd4df9721247632d48d631869d7dedef8cc069e984c236074

Observation cbd3ace2-a60c-4445-b4bb-83dca9ffa77c · inbound

Benchmarking Vision Foundation Models for Input Monitoring in Autonomous Driving cites this paper.

Benchmarking Vision Foundation Models for Input Monitoring in Autonomous Driving SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-23T05:17:35.616785Z

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.

source=pdf_text observed=2026-05-23T05:16:36.255597Z digest=sha256:4fe09954a4b918eda49ae6717898e4d747a1302aa75d44fa7f7ca9a0999db99c

Observation 3810bae9-bf1d-4580-87a8-335bc390567a · inbound

Segmenting Objectiveness and Task-awareness Unknown Region for Autonomous Driving cites this paper.

Segmenting Objectiveness and Task-awareness Unknown Region for Autonomous Driving SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-16T06:07:06.435893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T06:07:06.435893Z digest=sha256:cfae0b64ed445da13c8f0198a86545a575c25aebc733831785a0f9e8707b57f5

Observation a94f2a10-2bce-4e1f-9c46-24966d57427f · inbound

MoViAD: A Modular Library for Visual Anomaly Detection cites this paper.

MoViAD: A Modular Library for Visual Anomaly Detection SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-06T16:58:30.587110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:58:30.587110Z digest=sha256:21de430aad1a47a11cda0bf3950b57388d03662e2aa1852cbb6439a7898d21f0

Observation 2e6b9ace-c3c4-4ffa-ad65-fc2ebde55db3 · inbound

An aerial color image anomaly dataset for search missions in complex forested terrain cites this paper.

An aerial color image anomaly dataset for search missions in complex forested terrain SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation

Reference 71

Resolution
unresolved
no resolver link, observed 2026-08-06T15:35:19.426084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:35:19.426084Z digest=sha256:8ff2368409ceec9956f0311605b58f39849c1ce266bc485f677290c0891ab25b

Observation ba3ca0c2-77c5-44af-94f4-4691964bf1a0 · inbound

Uncertainty-Aware Likelihood Ratio Estimation for Pixel-Wise Out-of-Distribution Detection cites this paper.

Uncertainty-Aware Likelihood Ratio Estimation for Pixel-Wise Out-of-Distribution Detection SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-06T10:09:53.329384Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T10:09:53.329384Z digest=sha256:8a857a944b8346565a7d438f295b4ebb49626a5899a8f1cb4381dc9abab58eca

Observation 32b6d210-282b-4e3b-bf8c-7aa6d0bf7806 · inbound

From Pixel to Mask: A Survey of Out-of-Distribution Segmentation cites this paper.

From Pixel to Mask: A Survey of Out-of-Distribution Segmentation SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-05T20:35:01.728444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:35:01.728444Z digest=sha256:0db37c834709d53502aa5c308e31a107139ef87ff2364684da3827a47a5f9d9a

Observation fa88a9b3-9004-49d1-b505-d79a1b827a46 · inbound

Real-World On-Vehicle Evaluation of Embedding-Based Anomaly Detection cites this paper.

Real-World On-Vehicle Evaluation of Embedding-Based Anomaly Detection SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-20T05:23:03.593189Z

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.

source=pdf_text observed=2026-05-20T05:22:43.072750Z digest=sha256:37e5a29458db0cef58b94c6fdad769a603abd6da35b62936b304da4ecebd726c

Observation f0df15e6-111e-4c04-97ab-ef453ec3a8b8 · inbound

The inattentional gap in task conditioned AI models that omit otherwise reportable safety critical signals cites this paper.

The inattentional gap in task conditioned AI models that omit otherwise reportable safety critical signals SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation

Reference 54

Resolution
verified exact
arxiv_id, observed 2026-07-04T13:19:50.235746Z

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.

source=pdf_text observed=2026-06-26T05:24:11.228672Z digest=sha256:3ad78e4af50b225aac3e98503443d7aba1991f04cac60eba735fb8a3873ea932

Observation ffc76d52-566c-4588-8c5a-5a1ecbb2ae4f · inbound

The inattentional gap in task conditioned AI models that omit otherwise reportable safety critical signals cites this paper.

The inattentional gap in task conditioned AI models that omit otherwise reportable safety critical signals SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation

Reference 54

Resolution
unresolved
no resolver link, observed 2026-07-12T11:58:52.700648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T11:58:52.700648Z digest=sha256:628ab0a0933cf45cb3d5ada633751808000b7f469c6fb145d09bd2c4fb026481

Observation 5f4f103c-327f-48ea-8803-627d99ad1667 · inbound

The inattentional gap in task conditioned AI models that omit otherwise reportable safety critical signals cites this paper.

The inattentional gap in task conditioned AI models that omit otherwise reportable safety critical signals SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-04T04:42:46.286141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T04:42:46.286141Z digest=sha256:154e9a8c898fb5729d5013363dc953235be4b5d287fc93e8d1fc5e673dd789f1

Observation 8c688c23-31ab-477d-9cec-a4ea9011dc5a · inbound

Road-Aware Anomaly Segmentation with Query-Guided Polygons and CLIP in Autonomous Driving cites this paper.

Road-Aware Anomaly Segmentation with Query-Guided Polygons and CLIP in Autonomous Driving SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation

Reference 27

Resolution
unresolved
no resolver link, observed 2026-07-11T20:13:45.453581Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-11T20:13:45.453581Z digest=sha256:826b6589e8db53e7a0b974feec96e976d89941d726b4392a99f16af23087fc8b

Observation 33fc0573-53b6-44cd-a2c8-f9a9d57d92a2 · inbound

Towards Real-Time PixOOD: Efficient Anomaly Segmentation for Autonomous Vehicles cites this paper.

Towards Real-Time PixOOD: Efficient Anomaly Segmentation for Autonomous Vehicles SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation

Reference 4

Resolution
unresolved
no resolver link, observed 2026-07-31T05:56:23.337036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T05:56:23.337036Z digest=sha256:7653527c757ee06e1304750a067a68514016d1bccb628e0def40d038543ee548

Observation ac3f34be-c1d4-4667-a3c3-1d3dff3e2fcf · inbound

ST-LoRA: Single Trajectory LoRA Ensemble for Uncertainty Aware Agricultural Segmentation cites this paper.

ST-LoRA: Single Trajectory LoRA Ensemble for Uncertainty Aware Agricultural Segmentation SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation

Reference 174

Resolution
unresolved
no resolver link, observed 2026-08-06T00:10:40.031833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T00:10:40.031833Z digest=sha256:dd04ae00e32d0a1a096856cb53ae0efde8cbd98289f3e9e142978ea29aa7ac09