Pith. sign in

Paper Citation Record · LEDGER

SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2207.14315.

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

pith.paper-citation-record.v1
2207.14315 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:30:00.340352Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T20:23:13.632857Z

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 763c8c8f-a925-49f7-9c8a-18413ae9789a · inbound

SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmark cites this paper.

SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmark SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation

Reference 46

Resolution
malformed identifier
no resolver link, observed 2026-08-06T22:30:00.340352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:30:00.340352Z digest=sha256:9a4fb00bd7807b8f1b7eaa198e2ad007bd7893dd40abb3df8d70551bb27d8f25

Observation c97311b1-63ae-4d40-bd82-d3fda87e818c · inbound

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

MoViAD: A Modular Library for Visual Anomaly Detection SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-06T16:58:31.400770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:58:31.400770Z digest=sha256:a7252bc4c67185551450bb885d71ebf4f1beda2642078921c2f143ea93c30ebd

Observation 77fb4721-b160-4fc5-b2ed-7308ccc44cca · inbound

Self-Navigated Residual Mamba for Universal Industrial Anomaly Detection cites this paper.

Self-Navigated Residual Mamba for Universal Industrial Anomaly Detection SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-06T05:36:22.672258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T05:36:22.672258Z digest=sha256:f2c6b4775fec66021ba9cef52ed0b6100c3a21b664fed99e718c0814fd7087e1

Observation 8195a55b-29ad-4336-9f35-3159ed4a13d0 · inbound

UniSpector: Towards Universal Open-set Defect Recognition via Spectral-Contrastive Visual Prompting cites this paper.

UniSpector: Towards Universal Open-set Defect Recognition via Spectral-Contrastive Visual Prompting SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-05-13T20:23:13.635404Z

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=2026-05-13T20:20:35.694280Z digest=sha256:7cbdfe4802d5f13be27931b73bb1be03d06130e25c7082bea775ff109b477c14

Observation 7f8a059e-7363-4f71-a160-c4ce36b65aa2 · inbound

Latent Anomaly Knowledge Excavation: Unveiling Sparse Sensitive Neurons in Vision-Language Models cites this paper.

Latent Anomaly Knowledge Excavation: Unveiling Sparse Sensitive Neurons in Vision-Language Models SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-05-11T06:51:16.708358Z

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=2026-05-10T17:25:34.942028Z digest=sha256:24e029f30c8ce821429010315266f31a36940125b3b662d4f5cd1d9ab8a7a6b4