Paper Citation Record · LEDGER

Deep Learning for Video Anomaly Detection: A Review

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2409.05383.

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

pith.paper-citation-record.v1
2409.05383 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:48:09.100680Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T05:51:03.987386Z

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 b6d9c613-0904-4d5a-a580-5f93e58da5bd · inbound

VAU-R1: Advancing Video Anomaly Understanding via Reinforcement Fine-Tuning cites this paper.

VAU-R1: Advancing Video Anomaly Understanding via Reinforcement Fine-Tuning Deep Learning for Video Anomaly Detection: A Review

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T12:48:09.100680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de6cdf92-6b1d-4796-9fd7-13e3091f4760 · inbound

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges cites this paper.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Deep Learning for Video Anomaly Detection: A Review

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-06T20:43:06.875392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7ee42d3-2f38-4557-b071-e817ba8dab60 · inbound

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM cites this paper.

The Evolution of Video Anomaly Detection: A Unified Framework from DNN to MLLM Deep Learning for Video Anomaly Detection: A Review

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T12:34:00.216618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation feb4a9b7-f00e-4363-8b27-fc999ea4d478 · inbound

Normality Calibration in Semi-supervised Graph Anomaly Detection cites this paper.

Normality Calibration in Semi-supervised Graph Anomaly Detection Deep Learning for Video Anomaly Detection: A Review

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-04T12:48:09.310467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38bc31c8-7759-4903-8426-2a46d0cb0355 · inbound

ESOM: Efficiently Understanding Streaming Video Anomalies with Open-world Dynamic Definitions cites this paper.

ESOM: Efficiently Understanding Streaming Video Anomalies with Open-world Dynamic Definitions Deep Learning for Video Anomaly Detection: A Review

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:51:04.086766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a6e5af97-7102-4e38-8dd0-03c44cbee2ff · inbound

Failure Identification in Imitation Learning Via Statistical and Semantic Filtering cites this paper.

Failure Identification in Imitation Learning Via Statistical and Semantic Filtering Deep Learning for Video Anomaly Detection: A Review

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-10T13:40:26.536630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.