Paper Citation Record · LEDGER

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network

As of 20 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 1 inbound Pith citation observation for arXiv:2506.17457.

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

pith.paper-citation-record.v1
2506.17457 v1

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:13:41.893533Z

measured 66 of 66 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:06:12.683957Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T17:06:12.918342Z

Reference resolution

65 of 65 outbound references displayed

verified exact2
verified fuzzy51
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a3c97c0d-f0a1-43e0-9061-f107330028ad · outbound

This paper cites write newline.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network write newline

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T19:13:39.896683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d8373ce-d956-450e-b71e-f0031c4a113f · outbound

This paper cites Drive: Deep reinforced accident anticipation with visual explanation.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Drive: Deep reinforced accident anticipation with visual explanation

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:44.480466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8719d1ae-1d5c-440a-8766-45b6e6d2761f · outbound

This paper cites an unresolved cited work.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:13:44.467769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 20287412-f7a3-4155-b375-a0d317b5f583 · outbound

This paper cites Efficientad: Accurate visual anomaly detection at millisecond-level latencies.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Efficientad: Accurate visual anomaly detection at millisecond-level latencies

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:44.455724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b1e75ef0-9930-4583-9a52-366fce2d84f2 · outbound

This paper cites H., Vora, S., Liong, V.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network H., Vora, S., Liong, V

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T19:13:39.948157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3898a322-f2bb-437e-a306-962a99885993 · outbound

This paper cites Freeway traffic incident detection from cameras: A semi-supervised learning approach.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Freeway traffic incident detection from cameras: A semi-supervised learning approach

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:44.436052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f7232669-dc5d-4b88-b20a-de8c38b3aba5 · outbound

This paper cites Anticipating accidents in dashcam videos.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Anticipating accidents in dashcam videos

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:44.423097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2246c15e-02d5-4bde-8c06-ffbf8d8d1568 · outbound

This paper cites Adaptive discovering and merging for incremental novel class discovery.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Adaptive discovering and merging for incremental novel class discovery

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:44.407278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6197515f-3437-41fa-970a-970b23b39730 · outbound

This paper cites Automated essential concept discovery for few-shot out-of-distribution detection.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Automated essential concept discovery for few-shot out-of-distribution detection

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:44.377707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d7b64d61-0bf3-40e6-ba6a-bf89a759a06c · outbound

This paper cites Fblnet: Feedback loop network for driver attention prediction.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Fblnet: Feedback loop network for driver attention prediction

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:44.339974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 908c4a4c-477e-46ee-9c36-234c9878d547 · outbound

This paper cites an unresolved cited work.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:13:44.304503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f762cd97-9102-40a1-a7d9-c80fe9aca322 · outbound

This paper cites Gorela: Go relative for viewpoint-invariant motion forecasting.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Gorela: Go relative for viewpoint-invariant motion forecasting

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:44.284355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0940d6f0-26be-4579-87b2-c182793b9812 · outbound

This paper cites K., Winn, J., and Zisserman, A.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network K., Winn, J., and Zisserman, A

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T19:13:40.061427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9597ca15-ff88-4ef4-8c7c-33b15b1de3c2 · outbound

This paper cites Traffic accident detection via self-supervised consistency learning in driving scenarios.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Traffic accident detection via self-supervised consistency learning in driving scenarios

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:44.252546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bb49ee78-ac97-4c85-92a0-963afc5c7252 · outbound

This paper cites Vision-based traffic accident detection and anticipation: A survey.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Vision-based traffic accident detection and anticipation: A survey

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:44.232511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b3a83d5a-d6ba-41be-8223-3c128e7d640e · outbound

This paper cites Abductive ego-view accident video understanding for safe driving perception.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Abductive ego-view accident video understanding for safe driving perception

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:44.213532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 91c7d475-3965-4af4-bca9-2ca1438d9367 · outbound

This paper cites J., Conradt, J., Daniilidis, K., et al.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network J., Conradt, J., Daniilidis, K., et al

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:44.191870Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f7bd9367-37d7-47ad-ad12-20696c4da66e · outbound

This paper cites and Scaramuzza, D.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network and Scaramuzza, D

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:44.170857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6e1ebeca-bd40-4ec2-a55d-d4bac5db6f3c · outbound

This paper cites Dsec: A stereo event camera dataset for driving scenarios.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Dsec: A stereo event camera dataset for driving scenarios

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:44.151945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 71866310-2955-415e-b55b-9f8d9f8797d8 · outbound

This paper cites R., Venkatesh, S., and Hengel, A.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network R., Venkatesh, S., and Hengel, A

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:44.106992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 535347c4-5e10-4fda-b714-2bfe76d07cc3 · outbound

This paper cites and Pedraza, C.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network and Pedraza, C

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:44.023054Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 69d9bd62-aefe-418d-9349-094ded93e989 · outbound

This paper cites an unresolved cited work.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Unresolved cited work

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T19:13:40.394819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23870903-861a-43dd-8c81-704bdc81ccb1 · outbound

This paper cites A., Rehman, F.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network A., Rehman, F

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:43.991744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 36b250f2-b36a-411f-8c6a-20c55a00c16e · outbound

This paper cites K., and Davis, L.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network K., and Davis, L

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:43.968603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b361fbcf-1214-4c41-9dc3-74449cca1eb2 · outbound

This paper cites Deep residual learning for image recognition.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Deep residual learning for image recognition

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T19:13:40.557577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02acfdba-ea5a-4f8c-82e1-a69783f84609 · outbound

This paper cites Cost-sensitive semi-supervised deep learning to assess driving risk by application of naturalistic vehicle trajectories.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Cost-sensitive semi-supervised deep learning to assess driving risk by application of naturalistic vehicle trajectories

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:43.944957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c91ff335-5e74-4f72-bb14-959cbc2356ba · outbound

This paper cites v2e: From video frames to realistic dvs events.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network v2e: From video frames to realistic dvs events

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:43.899991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1e1abf48-c0fa-4034-b378-c0f52b45c859 · outbound

This paper cites The apolloscape dataset for autonomous driving.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network The apolloscape dataset for autonomous driving

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:43.707284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2796ce57-5946-4630-8a31-ad647796155c · outbound

This paper cites M., Li, Y., Qin, R., and Yin, Z.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network M., Li, Y., Qin, R., and Yin, Z

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:43.651375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1af20186-7acb-43a4-9ebe-128910b2dcd4 · outbound

This paper cites M., Yin, Z., and Qin, R.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network M., Yin, Z., and Qin, R

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:43.562283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6772c412-8777-4852-87b2-2202564b93aa · outbound

This paper cites Attention r-cnn for accident detection.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Attention r-cnn for accident detection

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:43.397596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fc35997d-2a1e-4a90-9f72-b68b8335e0c9 · outbound

This paper cites Graph-based asynchronous event processing for rapid object recognition.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Graph-based asynchronous event processing for rapid object recognition

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:43.385748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 861657da-4d2a-43e0-9c9b-29ce5ea968f2 · outbound

This paper cites A Memory-Augmented Multi-Task Collaborative Framework for Unsupervised Traffic Accident Detection in Driving Videos.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network A Memory-Augmented Multi-Task Collaborative Framework for Unsupervised Traffic Accident Detection in Driving Videos

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:13:42.114984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 23ac66a9-9590-4ab9-8915-0aa9b5b6b875 · outbound

This paper cites Text-driven traffic anomaly detection with temporal high-frequency modeling in driving videos.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Text-driven traffic anomaly detection with temporal high-frequency modeling in driving videos

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:43.302775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8601a9ae-2a54-499b-b8ae-0d44176ee97e · outbound

This paper cites an unresolved cited work.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Unresolved cited work

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-15T19:13:40.836133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1545f542-a626-46df-bd10-2b911a77ef5a · outbound

This paper cites Future frame prediction for anomaly detection--a new baseline.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Future frame prediction for anomaly detection--a new baseline

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:43.283971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dfe3de42-e1a3-4d55-9724-241652ea959a · outbound

This paper cites H., and Li, J.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network H., and Li, J

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:43.272778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 07324e4f-17f7-4edb-9e0b-efc9a3878934 · outbound

This paper cites Towards explainable artificial intelligence (xai) for early anticipation of traffic accidents.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Towards explainable artificial intelligence (xai) for early anticipation of traffic accidents

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:43.173323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation df5d0c9f-a435-4d9c-ae95-9057d196b8bc · outbound

This paper cites T., Popescu, M., Khan, F.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network T., Popescu, M., Khan, F

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:43.161439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 62dd44b6-61ba-43d0-8b20-33a7e91f277d · outbound

This paper cites Memory-augmented online video anomaly detection.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Memory-augmented online video anomaly detection

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:43.031513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9bec5771-ed51-4c0a-b3c0-da07518f413b · outbound

This paper cites K., and Fukuda, A.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network K., and Fukuda, A

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:43.018679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ee42b6d3-7b14-467b-b461-b11e1ac994ab · outbound

This paper cites K., Dogra, D.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network K., Dogra, D

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:42.965801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4b97fea5-57d3-4861-950b-ccf6049a0ee6 · outbound

This paper cites Potential risk localization via weak labeling out of blind spot.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Potential risk localization via weak labeling out of blind spot

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:42.822964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1ece9645-fd31-4f49-abfe-b6026db5a3ca · outbound

This paper cites Classification of crash and near-crash events from dashcam videos and telematics.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Classification of crash and near-crash events from dashcam videos and telematics

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:42.811912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7b945c8c-8d45-4a38-99e7-7e1a985e5b96 · outbound

This paper cites G., Huynh, M.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network G., Huynh, M

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:42.799877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c5320ed9-ca33-4515-b7c2-2e62dac3e7af · outbound

This paper cites Graph (graph): A nested graph-based framework for early accident anticipation.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Graph (graph): A nested graph-based framework for early accident anticipation

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:42.789333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6168b374-fd05-484e-a13b-05aeae74390c · outbound

This paper cites Latency-aware Road Anomaly Segmentation in Videos: A Photorealistic Dataset and New Metrics.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Latency-aware Road Anomaly Segmentation in Videos: A Photorealistic Dataset and New Metrics

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-15T19:13:41.247929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 939a568f-c0a4-48c9-94af-7e3f3ec327fa · outbound

This paper cites W., and Chanda, P.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network W., and Chanda, P

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:42.768977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e4ebba44-3cc2-4610-ae84-6a8c87b6e44d · outbound

This paper cites K., Dogra, D.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network K., Dogra, D

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:42.758337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c943dbb1-a098-46b6-a5bd-28c49580c483 · outbound

This paper cites Prophnet: Efficient agent-centric motion forecasting with anchor-informed proposals.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Prophnet: Efficient agent-centric motion forecasting with anchor-informed proposals

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:42.713094Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ce65b2b8-3e7b-44d2-a753-4ed3832ddb02 · outbound

This paper cites A new framework of vehicle collision prediction by combining svm and hmm.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network A new framework of vehicle collision prediction by combining svm and hmm

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:42.657743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation acfbc1c2-05bb-48a5-ad49-57981b2d5eca · outbound

This paper cites Classifying near-miss traffic incidents through video, sensor, and object features.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Classifying near-miss traffic incidents through video, sensor, and object features

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:42.644118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5ec181ad-d1f2-44d8-99c0-7919784d4efb · outbound

This paper cites J., and Atkins, E.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network J., and Atkins, E

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:42.603664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bcc86dc3-a119-49ab-8549-f720f4117280 · outbound

This paper cites an unresolved cited work.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Unresolved cited work

Reference 54

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:13:42.508193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 87813d15-73f6-46e1-b895-a48a40178583 · outbound

This paper cites and Han, B.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network and Han, B

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:42.496242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2e802402-98f0-4ac5-ab34-c17ad84f3be4 · outbound

This paper cites Agent-centric risk assessment: Accident anticipation and risky region localization.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Agent-centric risk assessment: Accident anticipation and risky region localization

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:42.442643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9ee0aecf-f5b7-4d89-9099-1b75b8b6857c · outbound

This paper cites Systems and methods for actor motion forecasting within a surrounding environment of an autonomous vehicle, November 2 2023.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Systems and methods for actor motion forecasting within a surrounding environment of an autonomous vehicle, November 2 2023

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:42.328307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation eba8a8c5-5484-43cf-9225-0da01cbd30f9 · outbound

This paper cites From Objects to Events: Unlocking Complex Visual Understanding in Object Detectors via LLM-guided Symbolic Reasoning.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network From Objects to Events: Unlocking Complex Visual Understanding in Object Detectors via LLM-guided Symbolic Reasoning

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:13:41.997454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7d35fc25-b499-4560-b482-eadc1819c5ef · outbound

This paper cites Anonymous model pruning for compressing deep neural networks.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Anonymous model pruning for compressing deep neural networks

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:42.316762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7948bdd2-f3bd-4532-a623-5d403572fbe8 · outbound

This paper cites Micm: Rethinking unsupervised pretraining for enhanced few-shot learning.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Micm: Rethinking unsupervised pretraining for enhanced few-shot learning

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:42.304849Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0a393c51-00d4-4df7-aecb-b0012f142445 · outbound

This paper cites Learning unknowns from unknowns: Diversified negative prototypes generator for few-shot open-set recognition.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Learning unknowns from unknowns: Diversified negative prototypes generator for few-shot open-set recognition

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:42.218074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9cd13668-6923-4e42-ba32-a64f94a4f5ae · outbound

This paper cites Spatio-temporal autoencoder for video anomaly detection.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Spatio-temporal autoencoder for video anomaly detection

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:42.202939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 50ca5a7a-cb99-4002-9d2b-4d97b6a12cfc · outbound

This paper cites Deep Event-based Object Detection in Autonomous Driving: A Survey.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Deep Event-based Object Detection in Autonomous Driving: A Survey

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-15T19:13:41.883961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ee6831f-9c1b-42a1-bd32-993f32c8051f · outbound

This paper cites Spatio-temporal feature encoding for traffic accident detection in vanet environment.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Spatio-temporal feature encoding for traffic accident detection in vanet environment

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:13:42.143244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 81082e0c-be32-41a2-ac4a-b631631ffc0c · outbound

This paper cites an unresolved cited work.

When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-15T19:13:42.127670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 2d98808f-e61b-447f-aca4-3e11789f113e · inbound

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework cites this paper.

Few-shot Human Action Anomaly Detection via a Unified Contrastive Learning Framework When Every Millisecond Counts: Real-Time Anomaly Detection via the Multimodal Asynchronous Hybrid Network

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-15T17:06:12.922774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.