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Paper Citation Record · LEDGER

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

As of 7 August 2026, this Paper Citation Record lists 97 of 97 outbound references and 2 inbound Pith citation observations for arXiv:2507.02074.

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

pith.paper-citation-record.v1
2507.02074 v2

Coverage vector

measured 97 of 97 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:43:07.779736Z

measured 99 of 99 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T14:39:27.242094Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

97 of 97 outbound references displayed

  • verified exact8
  • verified fuzzy38
  • unresolved46
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch5

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 7a0a023e-1640-479c-8230-95ecd1fee14f · outbound

This paper cites Traffic monitoring and accident detection at intersections,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Traffic monitoring and accident detection at intersections,

Reference 1

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Unavailable: canonical work link unavailable.

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Observation c181588d-b6cb-4a3f-bc51-1a7a23a84639 · outbound

This paper cites A survey of vision-based trajec- tory learning and analysis for surveillance,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges A survey of vision-based trajec- tory learning and analysis for surveillance,

Reference 2

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Observation eb5f2631-df82-4955-818f-e45ad5f2f003 · outbound

This paper cites Trajectory-based anomalous event detection,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Trajectory-based anomalous event detection,

Reference 3

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Observation af03812a-3686-4184-84ee-a0e3a288fe7d · outbound

This paper cites Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections,

Reference 4

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Observation 7475dead-8473-4927-bb23-ac060d15e6e1 · outbound

This paper cites Severity of driver injury and vehicle damage in traffic crashes at intersections: a bayesian hierarchical analysis,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Severity of driver injury and vehicle damage in traffic crashes at intersections: a bayesian hierarchical analysis,

Reference 5

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Observation 2dcb33f7-1c66-470b-8d73-e91b9984e679 · outbound

This paper cites Two-stream convolutional networks for action recognition in videos,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Two-stream convolutional networks for action recognition in videos,

Reference 6

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Unavailable: canonical work link unavailable.

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Observation b9e77609-0058-4ad8-8a89-7f5eecde20c5 · outbound

This paper cites Learning spatiotemporal features with 3d convolutional networks,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Learning spatiotemporal features with 3d convolutional networks,

Reference 7

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Observation c2a74191-4297-4f9a-acf7-95cd6c5c69a5 · outbound

This paper cites Quo vadis, action recognition? a new model and the kinetics dataset,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Quo vadis, action recognition? a new model and the kinetics dataset,

Reference 8

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Observation c1e8de97-b561-499b-9a09-5acac53269e2 · outbound

This paper cites Slowfast networks for video recognition,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Slowfast networks for video recognition,

Reference 9

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Observation 8ae9d92e-0c55-4c14-b260-63172779b037 · outbound

This paper cites Real-world anomaly detection in surveillance videos,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Real-world anomaly detection in surveillance videos,

Reference 10

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Observation 762e0d57-d62c-4959-8d6e-688d5eb3f115 · outbound

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

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Future frame prediction for anomaly detection–a new baseline,

Reference 11

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Observation 45769cbc-de9b-4547-9175-371aa7ada1b0 · outbound

This paper cites Learning memory-guided nor- mality for anomaly detection,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Learning memory-guided nor- mality for anomaly detection,

Reference 12

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Observation 8a4ed46c-ec87-4d43-9dbf-1810b13cdf36 · outbound

This paper cites Uniter: Universal image-text representation learning,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Uniter: Universal image-text representation learning,

Reference 13

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Observation 0d915eb2-cc12-4d91-a601-a02dd232d72c · outbound

This paper cites Align before fuse: Vision and language representation learning with momentum distillation,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Align before fuse: Vision and language representation learning with momentum distillation,

Reference 14

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Observation cc6bc847-2d23-4df1-be3b-f5ccf41a3dda · outbound

This paper cites Less is more: Clipbert for video-and-language learning via sparse sampling,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Less is more: Clipbert for video-and-language learning via sparse sampling,

Reference 15

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Observation a7cd5b0e-f429-4709-8374-5fc24d5e9207 · outbound

This paper cites Videoclip: Contrastive pre-training for zero-shot video-text understanding,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Videoclip: Contrastive pre-training for zero-shot video-text understanding,

Reference 16

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Observation ca12865d-0f16-49f5-8ce6-5b324cb69aa9 · outbound

This paper cites GPT-4 Technical Report.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges GPT-4 Technical Report

Reference 17

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Observation 2320f2c0-64dc-4ff6-a19c-0b4923c6b4b7 · outbound

This paper cites Flamingo: a visual language model for few-shot learning,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Flamingo: a visual language model for few-shot learning,

Reference 18

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Observation ef9a3f3c-0c3d-4a13-a339-aecce4443c8a · outbound

This paper cites Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 7da91b94-5ee5-4c89-92c3-19dd261cdbed · outbound

This paper cites Anomalous video event detection using spatiotemporal context,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Anomalous video event detection using spatiotemporal context,

Reference 20

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raw_fallback, observed 2026-08-06T20:43:17.890932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 46d4f0b0-2231-4793-95df-c756a1ab9b1c · outbound

This paper cites Detecting anomalies in people’s trajectories using spectral graph analysis,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Detecting anomalies in people’s trajectories using spectral graph analysis,

Reference 21

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 314d5834-1e69-40c3-9d01-9abd50c48051 · outbound

This paper cites Using support vector machine models for crash injury severity analysis,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Using support vector machine models for crash injury severity analysis,

Reference 22

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Observation 9540b7bf-4036-4920-9371-8967656132ea · outbound

This paper cites Accident detection system using image processing and mdr,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Accident detection system using image processing and mdr,

Reference 23

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 66facb1f-0cab-46fa-ac98-36c9e68c0d40 · outbound

This paper cites Temporal segment networks: Towards good practices for deep action recognition,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Temporal segment networks: Towards good practices for deep action recognition,

Reference 24

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation c6f9de2a-893b-4ad6-a4ce-5c6cf1c02cf2 · outbound

This paper cites Multimodal machine learning: A survey and taxonomy,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Multimodal machine learning: A survey and taxonomy,

Reference 25

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raw_fallback, observed 2026-08-06T20:43:16.847350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:03.180713Z digest=sha256:619154eb6652e85170a03f28647d0213405b3c83b07fff1027111c16fad8aba6

Observation f350ee47-b512-4855-9074-cec27d8e6948 · outbound

This paper cites Multimodal deep learning,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Multimodal deep learning,

Reference 26

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raw_fallback, observed 2026-08-06T20:43:16.714894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:03.231501Z digest=sha256:594e373607d72b7376a27110eb89e5820305cf9fed1a95fdbaf5e67b4fd6fc03

Observation 545500c2-2f2f-4e0a-b70f-1704dd55d722 · outbound

This paper cites Deep multimodal learning: A survey on recent advances and trends,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Deep multimodal learning: A survey on recent advances and trends,

Reference 27

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verified fuzzy
raw_fallback, observed 2026-08-06T20:43:16.548682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:03.284396Z digest=sha256:d566175570efd699166a77b5d52c86e4a99f1f70f10d27a285868d083da7a10f

Observation 354e4939-3157-42f6-b4cd-52aaf15c6c4d · outbound

This paper cites SimVLM: Simple Visual Language Model Pretraining with Weak Supervision.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges SimVLM: Simple Visual Language Model Pretraining with Weak Supervision

Reference 28

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T20:43:03.339629Z digest=sha256:ceb48c9d383ac4d9d038495f0a361e97fc9f1d4b5c5a2c00704373e51c9e1c28

Observation ccb2bec7-8af5-441a-aa14-603706962798 · outbound

This paper cites All in one: Exploring unified video-language pre-training,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges All in one: Exploring unified video-language pre-training,

Reference 29

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Source-reported events for the cited work

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Observation 6da57f0e-1e22-43e5-89db-908caa2860bc · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges The cityscapes dataset for semantic urban scene understanding,

Reference 30

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raw_fallback, observed 2026-08-06T20:43:16.104420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 542aa650-d6ad-4e3c-8889-155d0b67985d · outbound

This paper cites nuscenes: A multimodal dataset for autonomous driving,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges nuscenes: A multimodal dataset for autonomous driving,

Reference 31

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no resolver link, observed 2026-08-06T20:43:03.452329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation ba54830f-f768-497f-bf37-d5e80076dc7e · outbound

This paper cites Are we ready for autonomous driving? the kitti vision benchmark suite,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Are we ready for autonomous driving? the kitti vision benchmark suite,

Reference 32

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raw_fallback, observed 2026-08-06T20:43:15.881441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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Observation 19ce6e3b-dbdf-4dd9-901a-bc713e0c3bdf · outbound

This paper cites Edge computing: Vision and challenges,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Edge computing: Vision and challenges,

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:03.545890Z digest=sha256:ec432e11dd0a02d2b2700c142244d66affe78eb0d70dc6b903e0a014d6ea4931

Observation e2e764c7-9eec-447c-9be3-1f8a6ff2552c · outbound

This paper cites Edge intelligence: Paving the last mile of artificial intelligence with edge computing,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Edge intelligence: Paving the last mile of artificial intelligence with edge computing,

Reference 34

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source=pdf_text observed=2026-08-06T20:43:03.594786Z digest=sha256:086d03f12be612ec3e391259d076a80efc074b014f8214a62333ee982bd46bdc

Observation 3bba2dc6-21de-4146-94df-ce35b2cb5aaa · outbound

This paper cites Videobert: A joint model for video and language representa- tion learning,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Videobert: A joint model for video and language representa- tion learning,

Reference 35

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verified fuzzy
raw_fallback, observed 2026-08-06T20:43:15.615669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:03.634399Z digest=sha256:de2148b10bc6d0aaeb0be4fc852d5568a8c38168cfc7d816acbcf8f028b226fb

Observation bd6011d1-151f-439f-a5ed-162557e87440 · outbound

This paper cites Vivit: A video vision transformer,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Vivit: A video vision transformer,

Reference 36

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verified fuzzy
raw_fallback, observed 2026-08-06T20:43:15.456053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:03.667379Z digest=sha256:4f5f356a36ab497fa8b9ffc3acac371cb68391f1519c5dad6e7f90abb5dbf739

Observation 9d5659d3-d7e4-42a9-93ba-4ad4b01dfb20 · outbound

This paper cites Videogpt: Video generation using vq-vae and transformers,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Videogpt: Video generation using vq-vae and transformers,

Reference 37

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raw_fallback, observed 2026-08-06T20:43:15.309881Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:03.731997Z digest=sha256:ec23e9fcc0939b86351f14312d08f0c8be35d0519dcb6dfa7c6e809aa8dfa7e5

Observation 7ef8defa-413b-433e-9a5a-4870e5fa5414 · outbound

This paper cites Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding

Reference 38

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no resolver link, observed 2026-08-06T20:43:03.778153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:03.778153Z digest=sha256:4cd916eef70a1274ac30f745d69cee64250b8aa9a4714dd1c3b9100015914332

Observation 288793db-74eb-4782-b296-5890ae443464 · outbound

This paper cites Improved Baselines with Visual Instruction Tuning.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Improved Baselines with Visual Instruction Tuning

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-06T20:43:03.846253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:03.846253Z digest=sha256:bf6e5839af7e82eeeae3d1d602b55c661ae455c67e811cad3358542fc9f93092

Observation d137ac91-53fc-401f-9595-d28fe3e60348 · outbound

This paper cites PaLM-E: An Embodied Multimodal Language Model.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges PaLM-E: An Embodied Multimodal Language Model

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-06T20:43:03.913715Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:03.913715Z digest=sha256:824428cd70bd7931f332fa56df724928bbfb223a82b8af845cd6c0a785aab3b0

Observation 6fad97ba-8ab2-499e-b3b6-d74d9cb07c50 · outbound

This paper cites Gpt-4v(ision) system card,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Gpt-4v(ision) system card,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:43:15.210868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:03.964175Z digest=sha256:0f397c785aa7de849438c6c2911c9358a7d2391768620e0789ea40dab275eb09

Observation 00e885d6-1256-4256-aa5c-44a0ad06ce9e · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:43:15.151435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:04.030238Z digest=sha256:ef2c1b3bc36c2ab15587326dd0735056b3bead66bb3698686d8fc2e07017c3e1

Observation b7be538e-2e9e-4368-a47b-acbe008365b6 · outbound

This paper cites Sora: Creating video from text,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Sora: Creating video from text,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:43:14.993735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:04.098209Z digest=sha256:0260f992e3c7cc3fd41b6901e1ffe756fc0d3dd95b4a0a891ca5fdf46a5a9332

Observation a12d3737-8413-4032-9198-c63c793d755e · outbound

This paper cites Crash: Crash recognition and anticipation system harnessing with context-aware and temporal focus attentions,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Crash: Crash recognition and anticipation system harnessing with context-aware and temporal focus attentions,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:43:14.782918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:04.154363Z digest=sha256:aa063b75500e00a76eaa5a728045e2b66423c5106d950529945bf65f45f781a9

Observation f0149b61-9c89-4859-b042-ebcfd31e88a7 · outbound

This paper cites When language and vision meet road safety: leveraging multimodal large language models for video-based traffic accident analysis.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges When language and vision meet road safety: leveraging multimodal large language models for video-based traffic accident analysis

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-06T20:43:04.297014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:04.297014Z digest=sha256:92b46cb5a2d0701e0cbdf2eac087bcbec61f13eb0ffab85e5cc144b3833de670

Observation 4a95e254-d30b-42cf-a757-3dc95da00af2 · outbound

This paper cites Learning transferable visual models from natural language supervision,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Learning transferable visual models from natural language supervision,

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-06T20:43:04.364558Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:04.364558Z digest=sha256:703170e4574a9fdc179ff4c1288738b17dd732ff1d7dd200ff00705ca711b473

Observation 17b3a461-14f7-47f8-a8ff-fbd05e9e0b22 · outbound

This paper cites CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-06T20:43:04.406744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:04.406744Z digest=sha256:4c07a00a3e4685e77e00269fe02b2eb1851ff7fbc8615cd5addde42ab6576e40

Observation 448b17bc-4b82-4f38-aa8e-10cba87233f4 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-06T20:43:04.442302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:04.442302Z digest=sha256:614c87afc20a1a555c0feb6b3f0424286afe9c0f7d6fea202ff686eccb3f9b41

Observation abd4cb4e-e83c-44dc-8829-7d0d6ddcc42b · outbound

This paper cites Quick and robust detection of crash events from video streams,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Quick and robust detection of crash events from video streams,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:43:14.537088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:04.505518Z digest=sha256:93d32e7f6ca21e6a44e40605bb500f7e52ff68b2f37bb3e7fe8f074ade966984

Observation 1de8b4b1-d6cf-4634-9aca-c2428ce4f0ad · outbound

This paper cites A real-time system for detection of road accidents from video streams,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges A real-time system for detection of road accidents from video streams,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:43:14.256997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:04.570143Z digest=sha256:9da7ce621edfb63cf1964641b6bf10da47c7b7cb2fabbec732becbd882c6b93d

Observation 3a3c7fb1-97ac-4f3a-987e-eb14d147d8a6 · outbound

This paper cites Video-based traffic accident detection: A survey,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Video-based traffic accident detection: A survey,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:43:13.942348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:04.622770Z digest=sha256:d79ab0c5bc70abc8b5439b7df3e58d96f1460cf2fbc522e5387c79bf2a8b3aab

Observation 87610921-fe17-4f72-9711-b5b7c8f55caf · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges LoRA: Low-Rank Adaptation of Large Language Models

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-06T20:43:04.679275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:04.679275Z digest=sha256:83c0c8a39eb29240138d11fbf3e93b0a045e92ec24e2b3dd53bf48f911f494e4

Observation 4ad41515-89e2-4ee8-aa36-f17d7c4f00e8 · outbound

This paper cites Pentagon relation and Biedenharn-Elliott identity.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Pentagon relation and Biedenharn-Elliott identity

Reference 53

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T20:43:10.153649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:04.749137Z digest=sha256:f3f2595e82d167b9b63f168ad274aaa438772a4d2c0fef53651827fce978b4f1

Observation a45733c2-3731-4332-a915-3102c9cd5cf3 · outbound

This paper cites Harnessing Neuron Stability to Improve DNN Verification.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Harnessing Neuron Stability to Improve DNN Verification

Reference 54

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T20:43:10.016965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:04.793014Z digest=sha256:927806d2bc70be722e391936fec52eec77da8842cbaf01bbec389cabaee570f6

Observation 240f0f88-0037-4bec-9b7f-087d9756e0e4 · outbound

This paper cites Partially hyperbolic diffeomorphisms that are center fixing.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Partially hyperbolic diffeomorphisms that are center fixing

Reference 55

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T20:43:09.921329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:04.825480Z digest=sha256:5a067014dcc6bae0e53ebcfa741ea5cf1ea5cabf9934c7162ece000fafd2c9a0

Observation 4956d886-5e93-4272-b982-2d98a7eb761a · outbound

This paper cites On the consistency of relative facts.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges On the consistency of relative facts

Reference 56

Resolution
unresolved
no resolver link, observed 2026-08-06T20:43:04.866116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:04.866116Z digest=sha256:6d47cd5ff1254b9c65513d24351844d80cc558a029ccb71e59710c9d8b5dca6e

Observation 645beef6-394d-46aa-8ece-d71fc68a0b65 · outbound

This paper cites Leveraging video-llms for crash detection and narrative generation: Performance analysis and challenges,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Leveraging video-llms for crash detection and narrative generation: Performance analysis and challenges,

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:43:13.654406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:04.920193Z digest=sha256:df3c75e72e47e202959e2b206d8b3a84f9188bfe4ae9488d26706f0a4b1d0d56

Observation 35443ac5-fef4-4937-b880-52f9a49d9bfa · outbound

This paper cites Joint Discriminative and Generative Learning for Person Re-identification.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Joint Discriminative and Generative Learning for Person Re-identification

Reference 58

Resolution
verified exact
local_arxiv, observed 2026-08-06T20:43:09.775500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:04.980461Z digest=sha256:df23d1336d47337cfcb095e3691148c7a481586c9507d2add3b790ec8638e982

Observation 7b496f6e-da04-4812-97ca-db92a850519c · outbound

This paper cites Trafficlens: A novel system for video-to-text conver- sion in multi-camera traffic environments,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Trafficlens: A novel system for video-to-text conver- sion in multi-camera traffic environments,

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:43:13.417130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:05.013039Z digest=sha256:78d3ea0584d574f9a9b6e15008bb001338e469d9df687222b9fa3716af257465

Observation 7d91cfaf-5882-4ede-88c1-5be018cfc9ee · outbound

This paper cites Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages

Reference 60

Resolution
verified exact
local_arxiv, observed 2026-08-06T20:43:09.652431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:05.071380Z digest=sha256:bb8438cd0eaf87cce480cf7f40bf0c3faa734082cd1929c495ee00448f63b894

Observation 3377578f-c309-4f20-8dff-a41c5b3d58d6 · outbound

This paper cites Dad: A dashcam accident dataset,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Dad: A dashcam accident dataset,

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:43:13.156123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:05.117717Z digest=sha256:418cc281db95ea99cd74fbb5f57b221f15cf0365b15746d0b1bac695f132f72b

Observation 7023c4c6-fb00-47d1-bced-8fdcceaeaa7c · outbound

This paper cites Cadp: A novel dataset for car accident detection and prediction from police reports,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Cadp: A novel dataset for car accident detection and prediction from police reports,

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:43:12.935784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:05.153296Z digest=sha256:1db751254acf7c658f1dccee86bf081a3998e644cf60a131b53a9964725ac26e

Observation ac938ad0-26ac-48b0-bb4c-abbdf1f6b792 · outbound

This paper cites BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

Reference 63

Resolution
unresolved
no resolver link, observed 2026-08-06T20:43:05.206991Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:05.206991Z digest=sha256:c005bcea16ccc9f90c138ea64148574c7d5dedcdf394b70eea0561424303239e

Observation 37a20d0b-c064-4295-a336-bf917aa640d9 · outbound

This paper cites Bulk-Boundary Correspondence in the Quantum Hall Effect.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Bulk-Boundary Correspondence in the Quantum Hall Effect

Reference 64

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T20:43:09.499953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:05.265806Z digest=sha256:be2c0fbf549100605204ca1801dc3cf56908dbd8b9dd7f05fc09a3e22402a5c0

Observation c8fcad50-7dac-412b-834f-4a7807d1b478 · outbound

This paper cites ScVLM: Enhancing Vision-Language Model for Safety-Critical Event Understanding.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges ScVLM: Enhancing Vision-Language Model for Safety-Critical Event Understanding

Reference 65

Resolution
verified exact
local_arxiv, observed 2026-08-06T20:43:09.342500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:05.322723Z digest=sha256:5ae340f880e5cc76f071da6941215445687c4c0228be7fab7f33a15c6fb68fea

Observation 0a3fc6ec-0e31-46ea-8ae6-ceae26073d34 · outbound

This paper cites TrafficVLM: A Controllable Visual Language Model for Traffic Video Captioning.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges TrafficVLM: A Controllable Visual Language Model for Traffic Video Captioning

Reference 66

Resolution
verified exact
local_arxiv, observed 2026-08-06T20:43:09.183265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:05.399630Z digest=sha256:689edda1dec6cd6c8dd68873e57f55994b221c8fc34adfc3ff27c3fe5ae3d552

Observation bd857ff8-0747-497f-9992-a83301ee16c4 · outbound

This paper cites Crash: A context-aware attention-based framework for crash anticipation,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Crash: A context-aware attention-based framework for crash anticipation,

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:43:12.649915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:05.463586Z digest=sha256:a312414c41f1982b124e2692f1080bc4f054617230596179a0594d87c3b253f5

Observation db865e62-ee75-4544-9a9b-fff300e955c1 · outbound

This paper cites MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models

Reference 68

Resolution
unresolved
no resolver link, observed 2026-08-06T20:43:05.505355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:05.505355Z digest=sha256:08516f00200fbc662c91b92495c12955d35403560c3caf563438280d8aff88c6

Observation d6bb9525-c2e1-4c40-85f6-0f802bb7578c · outbound

This paper cites Slowfast networks for video recognition,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Slowfast networks for video recognition,

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:43:12.373008Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:05.565916Z digest=sha256:6edf6111aa4e5eb7cd0265e7a28824e8addbf2fb0ac1cf55d7e110ba0096b0ab

Observation 2bceff8e-4009-4032-a3e3-a4939c79406e · outbound

This paper cites Self-supervised anomaly detection: A survey and outlook,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Self-supervised anomaly detection: A survey and outlook,

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:43:12.073096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:05.645978Z digest=sha256:d025a6f4e6cbc2ef3845fff47ceda1d6de13c13ec2f50929acf7c5a2d8c9613e

Observation eccf9c1e-c30b-4a11-8f5b-eb6dad9d780b · outbound

This paper cites Few-shot fast-adaptive anomaly detection,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Few-shot fast-adaptive anomaly detection,

Reference 71

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verified fuzzy
raw_fallback, observed 2026-08-06T20:43:11.794299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:05.691625Z digest=sha256:26480645881980083160dc8fcd3429f34f9efb57f6a62b043e7558e3da6a7d71

Observation accd368e-1106-4b7f-afb7-66e42b0905cd · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 72

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no resolver link, observed 2026-08-06T20:43:05.739969Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:05.739969Z digest=sha256:a5cb0fc044894b878c3ecc73595f2ca8da9c9b9800eaf420489479b5383fe8d7

Observation 7175c93d-b69a-401d-bf3f-7b819aa45d1c · outbound

This paper cites A Survey on Deep Learning Techniques for Video Anomaly Detection.

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

Reference 73

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local_arxiv, observed 2026-08-06T20:43:08.974237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:05.823209Z digest=sha256:99eab67fd3ffff0f50c2af2c932eda741ec7c11fd8a193870c1fa71887a26950

Observation e8ae7848-09dd-479d-af08-841db39a7e10 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 74

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no resolver link, observed 2026-08-06T20:43:05.873151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:05.873151Z digest=sha256:92d3f26687f21f754a1f8aa91fc516025cd5b8570705fb5253b5f1d62da17b02

Observation d4f5159c-5cb2-409d-821f-611f7c353325 · outbound

This paper cites Convolutional lstm network: A machine learning approach for precipitation nowcasting,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Convolutional lstm network: A machine learning approach for precipitation nowcasting,

Reference 75

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verified fuzzy
raw_fallback, observed 2026-08-06T20:43:11.532931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:05.939940Z digest=sha256:fd93f648836d0833e46a49f3f7ed3fee8388bfa04279c77f658d93b871c95beb

Observation 4d7b8a6e-051a-4240-a8bb-97af8b39e9e3 · outbound

This paper cites Video Anomaly Detection in 10 Years: A Survey and Outlook.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Video Anomaly Detection in 10 Years: A Survey and Outlook

Reference 76

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no resolver link, observed 2026-08-06T20:43:06.026238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:06.026238Z digest=sha256:b290e671af765c8e37c7914ed636f6a0933b24cfeb02dc8d35383a9431d04fdc

Observation 2c97d037-7e18-4489-90a6-291bf823bb0d · outbound

This paper cites ViViT: A Video Vision Transformer.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges ViViT: A Video Vision Transformer

Reference 77

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no resolver link, observed 2026-08-06T20:43:06.121709Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:06.121709Z digest=sha256:e786d0a30a95823df3277bca642ba62be45e6e2a64503d2adca9da0e3ba9804b

Observation 2f72261b-2234-4b57-95af-49b625d86f30 · outbound

This paper cites Bleu: a method for automatic evaluation of machine translation,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Bleu: a method for automatic evaluation of machine translation,

Reference 78

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no resolver link, observed 2026-08-06T20:43:06.207625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:06.207625Z digest=sha256:8fbafa95efc855863c2923912d9beca5612e6ed222f3d604df8a6c38436f2804

Observation a7d6492b-55f1-461d-8dc3-ba0780cc329b · outbound

This paper cites Meteor: An automatic metric for mt evaluation with improved correlation with human judgments,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Meteor: An automatic metric for mt evaluation with improved correlation with human judgments,

Reference 79

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no resolver link, observed 2026-08-06T20:43:06.264040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:06.264040Z digest=sha256:39f475f2122588cabd057056e907f738b68cf20a2cad3659ad2e59e4fb225a23

Observation 2a39a169-d8a1-46ae-b00c-51b6e5263cc6 · outbound

This paper cites Cider: Consensus-based image description evaluation,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Cider: Consensus-based image description evaluation,

Reference 80

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unresolved
no resolver link, observed 2026-08-06T20:43:06.334184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:06.334184Z digest=sha256:a16e49f762b5da89da14df46230d6619ce6f5e47224841569e368ad3ff213885

Observation c1740d64-d080-4406-84b4-848f689fc2d8 · outbound

This paper cites Sutd-trafficqa: A question answering benchmark and an efficient network for autonomous driving,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Sutd-trafficqa: A question answering benchmark and an efficient network for autonomous driving,

Reference 81

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verified fuzzy
raw_fallback, observed 2026-08-06T20:43:11.302437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:06.436310Z digest=sha256:6cc847608c24eb70f2e65e37c1b6ba060bdb2b6e834a64ea710d1dc999f3f0b4

Observation f1022a6c-eaa7-465b-98f8-9d7c89144724 · outbound

This paper cites Causallp: Visual causal question answering with knowledge graphs,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Causallp: Visual causal question answering with knowledge graphs,

Reference 82

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:43:11.171794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:06.486551Z digest=sha256:bcab7a0b6e1487bf2eb0607b63afe7841b091377c2e1e2c60f3247fbcfbac1dc

Observation f5e412ca-fdd3-4dc9-9d33-1c1f18023792 · outbound

This paper cites CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes

Reference 83

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verified exact
local_arxiv, observed 2026-08-06T20:43:08.600662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:06.555774Z digest=sha256:8ab6566b88b4b130d52ca5c1e2156d402cc29b50a0d9eee96cd845cbd9bcf47c

Observation 046aa1f3-2440-482a-965b-f55308a449ac · outbound

This paper cites Bolstering causal reasoning in video question answering with multi-event causal discovery,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Bolstering causal reasoning in video question answering with multi-event causal discovery,

Reference 84

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:43:11.013528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:06.649287Z digest=sha256:f139c46a34a9e060d9395774c8aeae4aa7f560a4821559d587a8e6e6fd7974a9

Observation 5e2b02e0-e7f2-42f7-ab20-4f32a1d7f23b · outbound

This paper cites Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM

Reference 85

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no resolver link, observed 2026-08-06T20:43:06.699602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:06.699602Z digest=sha256:c720b043d93fdd9457e75baa404bcb8be36cad0b818fb04b68769110fc06df45

Observation fa4a90d0-3923-4b4d-9ace-6a6bdbaffac6 · outbound

This paper cites Crash time matters: Hybridmamba for fine-grained temporal localization in traffic surveillance footage,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Crash time matters: Hybridmamba for fine-grained temporal localization in traffic surveillance footage,

Reference 86

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verified exact
raw_fallback, observed 2026-08-06T20:43:08.409542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:06.823875Z digest=sha256:e6e786c2e1d170db4d3bc3603f37d825e1056602c69556e0b930371885ee2ea0

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

This paper cites Deep Learning for Video Anomaly Detection: A Review.

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

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:06.875392Z digest=sha256:435ffea6b6bac61115769d18977a22b4e7dd1afc8fdbdef6339551342ae5ea61

Observation 0b092ca4-8f8d-4826-b6ab-28e387c2b98d · outbound

This paper cites Edge-Based Video Analytics: A Survey.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Edge-Based Video Analytics: A Survey

Reference 88

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verified exact
local_arxiv, observed 2026-08-06T20:43:08.050272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:06.975537Z digest=sha256:e13df0e1852ddf20b13ddaa0966214c6016d0a586ee643d28e5d05c4157076b5

Observation 717e465f-ae8d-4ec1-b57d-2bf8175191c6 · outbound

This paper cites Synthetic datasets for autonomous driving: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. XX, NO. X, MONTH YEAR 24 A survey,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Synthetic datasets for autonomous driving: IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL. XX, NO. X, MONTH YEAR 24 A survey,

Reference 89

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verified fuzzy
raw_fallback, observed 2026-08-06T20:43:10.906404Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:07.129418Z digest=sha256:e37541f21fa54faa3ceda226f5e440685fbdc0f40df75c9f17d62315be7dddbc

Observation b0c01428-eb14-473d-abfa-5624e1ff7f9c · outbound

This paper cites Domain general- ization through meta-learning: a survey,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Domain general- ization through meta-learning: a survey,

Reference 90

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verified fuzzy
raw_fallback, observed 2026-08-06T20:43:10.802463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:07.219424Z digest=sha256:b30ba4a0077d0c92119ea125002783a7cc641f2c1ec031ed9c5b154f731ef30b

Observation d82ac71d-4534-4271-872a-011aa335da6f · outbound

This paper cites Generalized Out-of-Distribution Detection: A Survey.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Generalized Out-of-Distribution Detection: A Survey

Reference 91

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no resolver link, observed 2026-08-06T20:43:07.278200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:07.278200Z digest=sha256:271d2d0a43049ab71043aadc5522d51c5d286308cb20f769336b1f70c6da239c

Observation 8303e124-e7be-4be8-b8b6-ea50d8515671 · outbound

This paper cites Carla: An open urban driving simulator,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Carla: An open urban driving simulator,

Reference 92

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no resolver link, observed 2026-08-06T20:43:07.353734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:07.353734Z digest=sha256:dcf55e53dc257e84fe2c488eadd12ec33992b5de7f574a9f76d940b2521d207d

Observation 703f5a5a-6ad8-41be-8d29-cfa24306dade · outbound

This paper cites Perception, planning, control, and coordination for autonomous vehicles,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Perception, planning, control, and coordination for autonomous vehicles,

Reference 93

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verified fuzzy
raw_fallback, observed 2026-08-06T20:43:10.701373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:07.464300Z digest=sha256:78f45ca1daf437756892ada0a5a8083c06295d8dfc615bf891cae1f2cb2980e3

Observation a8c7cc71-d1b7-48aa-930d-07d82ab268c5 · outbound

This paper cites A survey of the multi-sensor fusion object detection task in autonomous driving,.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges A survey of the multi-sensor fusion object detection task in autonomous driving,

Reference 94

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T20:43:10.573511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:07.581462Z digest=sha256:69d6a0a996f7773e57df5128600fb804a445491fe456a5580b769fe127573ff9

Observation c44dce93-6c64-4a7e-b261-b9159e25900e · outbound

This paper cites A Survey on Efficient Vision-Language Models.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges A Survey on Efficient Vision-Language Models

Reference 95

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no resolver link, observed 2026-08-06T20:43:07.662405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:07.662405Z digest=sha256:e53baaef5038c004dae5591defeb8551630b59c31cd415ac9c9a29bff5f9cf26

Observation e3e4edb3-e917-46a5-bf44-7a4e139f5ef5 · outbound

This paper cites Privacy-Preserving Video Anomaly Detection: A Survey.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges Privacy-Preserving Video Anomaly Detection: A Survey

Reference 96

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no resolver link, observed 2026-08-06T20:43:07.779736Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:43:07.779736Z digest=sha256:406a4b4a9ed5b8a4f00bf08ce2f22714b8eafebd3e55d1e56949eac951e80ace

Observation 2ff580c3-6c20-4f3b-beec-639e3d156ff0 · outbound

This paper cites CRASH: Crash Recognition and Anticipation System Harnessing with Context-Aware and Temporal Focus Attentions.

Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges CRASH: Crash Recognition and Anticipation System Harnessing with Context-Aware and Temporal Focus Attentions

Reference 2024

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metadata mismatch
local_arxiv, observed 2026-08-06T20:43:10.363170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-08-06T20:43:04.229060Z digest=sha256:7edd1c39da0c23dc6392d70b35c6cbd2f342d7cd31e60e25489b35f1de13b500

Pith citing papers

Observation 290e8f37-2e24-426b-9fd2-ef980f75c9ae · inbound

Automating Crash Diagram Generation Using Vision-Language Models: A Case Study on Multi-Lane Roundabouts cites this paper.

Automating Crash Diagram Generation Using Vision-Language Models: A Case Study on Multi-Lane Roundabouts Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges

Reference 9

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verified exact
arxiv_id, observed 2026-05-15T14:50:04.412900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-05-15T14:46:41.877762Z digest=sha256:9072dd78a46559f672593bba352fea27877f7c3a14a789b2a71707aae7af1f51

Observation eaa701fc-b87b-4e5a-b9bf-14c87c2f02c3 · inbound

TrafficRAG: A Multimodal RAG Framework for Traffic Accident Liability Determination cites this paper.

TrafficRAG: A Multimodal RAG Framework for Traffic Accident Liability Determination Large Language Models for Crash Detection in Video: A Survey of Methods, Datasets, and Challenges

Reference 3

Resolution
metadata mismatch
arxiv_id, observed 2026-07-01T23:06:20.629333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-06-28T14:39:27.242094Z digest=sha256:09883174907fccb92b5b4d7d7aefd97bc987001a3792588a828fa2185fbdcecd