Pith. sign in

Paper Citation Record · LEDGER

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

As of 12 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2607.28483.

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

pith.paper-citation-record.v1
2607.28483 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-31T05:56:24.570940Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

20 of 20 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved20
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 585b5bf8-1a17-4342-b6f2-d5a4bb6e6caa · outbound

This paper cites In: 2025 IEEE Real-Time Systems 12 L.

Towards Real-Time PixOOD: Efficient Anomaly Segmentation for Autonomous Vehicles In: 2025 IEEE Real-Time Systems 12 L

Reference 1

Resolution
unresolved
no resolver link, observed 2026-07-31T05:56:22.998095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T05:56:22.998095Z digest=sha256:d6c5027d7776871fec367eff90ee0805ce7e35ee7bde6ee03deb285747aaf71f

Observation d28e6c7b-4f55-4367-98dc-00a4117f2322 · outbound

This paper cites International Journal of Computer Vision129(11), 3119–3135 (2021).

Towards Real-Time PixOOD: Efficient Anomaly Segmentation for Autonomous Vehicles International Journal of Computer Vision129(11), 3119–3135 (2021)

Reference 2

Resolution
unresolved
no resolver link, observed 2026-07-31T05:56:23.045668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T05:56:23.045668Z digest=sha256:75e43018197e4cfeec14f3319a93d08c217d66c0cf05ce3d991313bbb930300f

Observation ead26be6-e734-4ca9-9096-5e21f471182d · outbound

This paper cites In: IEEE/CVF CVPR.

Towards Real-Time PixOOD: Efficient Anomaly Segmentation for Autonomous Vehicles In: IEEE/CVF CVPR

Reference 3

Resolution
unresolved
no resolver link, observed 2026-07-31T05:56:23.154683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T05:56:23.154683Z digest=sha256:97a4393bf1f48b386b3e2eec639621e1e18e4a2f90c2c053fe3e118af83b0e16

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

This paper cites SegmentMeIfYouCan: A Benchmark for Anomaly Segmentation.

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=sha256:6eaedf7d4ebae4e9bd620cd48657785f38e54f787c2c46a63549d958a92ac868

Observation 5aec5e5c-007c-4988-95c8-18bfc1e2627c · outbound

This paper cites In: IEEE/CVF CVPR.

Towards Real-Time PixOOD: Efficient Anomaly Segmentation for Autonomous Vehicles In: IEEE/CVF CVPR

Reference 5

Resolution
unresolved
no resolver link, observed 2026-07-31T05:56:23.381486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T05:56:23.381486Z digest=sha256:20c1504ca8e3bcbfa52b79ffd57b1e742c1d9cc47a8ea48deeb5900ed30423f6

Observation 6e7b132d-5c55-4bc9-a3cb-7fecef0299f7 · outbound

This paper cites In: IEEE/CVF ICCV.

Towards Real-Time PixOOD: Efficient Anomaly Segmentation for Autonomous Vehicles In: IEEE/CVF ICCV

Reference 6

Resolution
unresolved
no resolver link, observed 2026-07-31T05:56:23.446303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T05:56:23.446303Z digest=sha256:2303e599a95f7ffc02ea2ac57a82e01967c7cf41c1e0a101b95862a7b6bf70d2

Observation 0a2028dc-1074-4c4a-bda1-fdea003cbe25 · outbound

This paper cites In: International Conference on Architectural Support for Programming Languages and Operating Systems.

Towards Real-Time PixOOD: Efficient Anomaly Segmentation for Autonomous Vehicles In: International Conference on Architectural Support for Programming Languages and Operating Systems

Reference 7

Resolution
unresolved
no resolver link, observed 2026-07-31T05:56:23.482707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T05:56:23.482707Z digest=sha256:5c7d06383135ec2c759e79b4b5e8b751187198a066f39e47f0e61f359f9af6ff

Observation ed1d5d22-da68-44b4-bfb1-0ca7f629727b · outbound

This paper cites In: European Conference on Computer Vision (ECCV).

Towards Real-Time PixOOD: Efficient Anomaly Segmentation for Autonomous Vehicles In: European Conference on Computer Vision (ECCV)

Reference 8

Resolution
unresolved
no resolver link, observed 2026-07-31T05:56:23.594749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T05:56:23.594749Z digest=sha256:3111938beb29f29a0c7f57d464f5b8621fb7662e70284fffc76764e45c5d1d30

Observation 316445cf-b2f9-425e-95ed-b6cc6eb571fd · outbound

This paper cites In: IEEE/CVF ICCV.

Towards Real-Time PixOOD: Efficient Anomaly Segmentation for Autonomous Vehicles In: IEEE/CVF ICCV

Reference 9

Resolution
unresolved
no resolver link, observed 2026-07-31T05:56:23.736629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T05:56:23.736629Z digest=sha256:116d453188e7aa7e9db4aedeef0ffc1bb98d83f28e2dd79be2a236034dad9504

Observation 8b8d6c52-c474-477e-a8d8-ca156723a57d · outbound

This paper cites Advances in Neural Information Processing Systems33, 21464–21475 (2020).

Towards Real-Time PixOOD: Efficient Anomaly Segmentation for Autonomous Vehicles Advances in Neural Information Processing Systems33, 21464–21475 (2020)

Reference 10

Resolution
unresolved
no resolver link, observed 2026-07-31T05:56:23.806698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T05:56:23.806698Z digest=sha256:a290d894dd30af3b8234f5a21942d07152b8ec12a3209cc51516bbaa69f582d1

Observation fbce53d1-96b7-42f6-8e8c-38ecda8d4a59 · outbound

This paper cites In: IEEE/CVF ICCV.

Towards Real-Time PixOOD: Efficient Anomaly Segmentation for Autonomous Vehicles In: IEEE/CVF ICCV

Reference 11

Resolution
unresolved
no resolver link, observed 2026-07-31T05:56:23.862224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T05:56:23.862224Z digest=sha256:21c5d44426a288c9685dc6d5a53861f8d91074e9d8b5e7e4501b5e2714962560

Observation f1462ed3-3f27-4438-9ec1-db3364358f29 · outbound

This paper cites arXiv:2602.22920 (2026).

Towards Real-Time PixOOD: Efficient Anomaly Segmentation for Autonomous Vehicles arXiv:2602.22920 (2026)

Reference 12

Resolution
unresolved
no resolver link, observed 2026-07-31T05:56:23.921596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T05:56:23.921596Z digest=sha256:790b9b5e43a9b129d7f63b0b42670beb7da048abebbe754b4c15f21b90528ef6

Observation 16e54c0d-8fde-445f-8e6f-aa165711d893 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Towards Real-Time PixOOD: Efficient Anomaly Segmentation for Autonomous Vehicles DINOv2: Learning Robust Visual Features without Supervision

Reference 13

Resolution
unresolved
no resolver link, observed 2026-07-31T05:56:23.977388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T05:56:23.977388Z digest=sha256:79c7f93869c1a8f2095f3e1a299f4e29dc1883a4b4dcdb68240de3b3aa70f6b8

Observation 42e4b043-8179-43d9-9dd8-f7881bcfd389 · outbound

This paper cites In: IEEE/RSJ IROS.

Towards Real-Time PixOOD: Efficient Anomaly Segmentation for Autonomous Vehicles In: IEEE/RSJ IROS

Reference 14

Resolution
unresolved
no resolver link, observed 2026-07-31T05:56:24.049143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T05:56:24.049143Z digest=sha256:69b1bef0e6b937215bd945b247432902ca5bb7e92f455996caeaa22d8ff90dbf

Observation b82ca34f-4e36-4ad3-b9e0-7abbba1e1bbb · outbound

This paper cites In: IEEE/CVF CVPR.

Towards Real-Time PixOOD: Efficient Anomaly Segmentation for Autonomous Vehicles In: IEEE/CVF CVPR

Reference 15

Resolution
unresolved
no resolver link, observed 2026-07-31T05:56:24.176920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T05:56:24.176920Z digest=sha256:18ef23e2ec1104a707abc712a0f70580e843405990ac6e458a9f153b6389fcfb

Observation 79f1f2c8-f12c-40a9-bc5e-dd2c59a7be70 · outbound

This paper cites DINOv3.

Towards Real-Time PixOOD: Efficient Anomaly Segmentation for Autonomous Vehicles DINOv3

Reference 16

Resolution
unresolved
no resolver link, observed 2026-07-31T05:56:24.242719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T05:56:24.242719Z digest=sha256:8799989d8071b2893177dcddcf4eb61fe129fbc41c9c9389dd506373c41d0f4a

Observation f65eadb6-c80b-4234-be27-cfd622e40033 · outbound

This paper cites arXiv:2603.11441 (2026).

Towards Real-Time PixOOD: Efficient Anomaly Segmentation for Autonomous Vehicles arXiv:2603.11441 (2026)

Reference 17

Resolution
unresolved
no resolver link, observed 2026-07-31T05:56:24.308793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T05:56:24.308793Z digest=sha256:9dd764accbe174204ad331a70cf51e27ca6134e0262ae1bf6c7e825bcc357611

Observation 5315943f-8b70-4a01-8a05-bfc94b508907 · outbound

This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence (2026).

Towards Real-Time PixOOD: Efficient Anomaly Segmentation for Autonomous Vehicles IEEE Transactions on Pattern Analysis and Machine Intelligence (2026)

Reference 18

Resolution
unresolved
no resolver link, observed 2026-07-31T05:56:24.374907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T05:56:24.374907Z digest=sha256:ca230067b7f7fde0573c5ec21d2e58ae22e4608cc71153422f2c89163dea8a47

Observation 271f4d67-86a1-4ab8-991a-a58f3c328ce2 · outbound

This paper cites In: IEEE/CVF CVPR Workshops.

Towards Real-Time PixOOD: Efficient Anomaly Segmentation for Autonomous Vehicles In: IEEE/CVF CVPR Workshops

Reference 19

Resolution
unresolved
no resolver link, observed 2026-07-31T05:56:24.454282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T05:56:24.454282Z digest=sha256:4ad891b27e5497089550a5e527fe38a6700727efe07979570f73dfec01cb602e

Observation bd7f85ad-00f1-40f5-a36e-054036f1da6d · outbound

This paper cites In: IEEE/CVF CVPR.

Towards Real-Time PixOOD: Efficient Anomaly Segmentation for Autonomous Vehicles In: IEEE/CVF CVPR

Reference 20

Resolution
unresolved
no resolver link, observed 2026-07-31T05:56:24.570940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-31T05:56:24.570940Z digest=sha256:a3f05e4e499f9e19735111942baaca20b1f5613d7cc9bdf1f9d9b5d8c677e9e7

Pith citing papers

No inbound Pith citation observations are available.